@inproceedings{yeri_iot_2020, title = {{IoT} based Real Time Health Monitoring}, doi = {10.1109/ICIRCA48905.2020.9183194}, abstract = {Conventional sensor based diagnosis in medial field requires more number of sensors and human efforts if it is processed in a large scale. It is a difficult task due to the shortage of medical professionals and system setup. To overcome this issue an {IoT} based health care application is proposed in the research work. The proposed system consists of the web and mobile application based on continuous wireless monitoring of patients. The objective is paper is to implement a low-cost system and transmit the patient vital signs in emergency situations. Sensors are being used for measuring the patient vital signs by using the wireless network. The sensors data are collected and transmitted to the cloud for storage via Wi-Fi module connected with the controller. The data is processed in the cloud and feedback steps are taken on the analysed data which can be further analysed by a doctor remotely. Remote viewing reduces burden to doctors and provides the exact health status of patients. If the patient needs urgent attention then a message is sent to the doctor.}, eventtitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, pages = {980--984}, booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, author = {Yeri, Vani and Shubhangi, D.C.}, date = {2020-07}, keywords = {Arduino, Health, {IoT}, monitoring, patient, sensor, wireless, Cloud computing, Medical services, Monitoring, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/I2KI3UAK/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/JKZ5JC5N/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf}, } @inproceedings{chiuchisan_adopting_2014, title = {Adopting the Internet of Things technologies in health care systems}, doi = {10.1109/ICEPE.2014.6969965}, abstract = {Internet of Things based health care systems play a significant role in Information and Communication Technologies and has contribution in development of medical information systems. The developing of {IoT}-based health care systems must ensure and increase the safety of patients, the quality of life and other health care activities. The tracking, tracing and monitoring of patients and health care actors activities are challenging research directions. In this paper we propose a general architecture of a health care system for monitoring of patients at risk in smart Intensive Care Units. The system advices and alerts in real time the doctors/medical assistants about the changing of vital parameters or the movement of the patients and also about important changes in environmental parameters, in order to take preventive measures.}, eventtitle = {2014 International Conference and Exposition on Electrical and Power Engineering ({EPE})}, pages = {532--535}, booktitle = {2014 International Conference and Exposition on Electrical and Power Engineering ({EPE})}, author = {Chiuchisan, Iuliana and Costin, Hariton-Nicolae and Geman, Oana}, date = {2014-10}, keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Biomedical monitoring, health care system, internet of things, Internet of Things, Kinect, sensors, smart environment}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GC7RHLL2/6969965.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/8CZGFIAN/Chiuchisan et al. - 2014 - Adopting the Internet of Things technologies in he.pdf:application/pdf}, } @inproceedings{azimi_medical_2016, title = {Medical warning system based on Internet of Things using fog computing}, doi = {10.1109/IWBIS.2016.7872884}, abstract = {Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current {IoT}-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new {IoT}-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in {ECG} signal.}, eventtitle = {2016 International Workshop on Big Data and Information Security ({IWBIS})}, pages = {19--24}, booktitle = {2016 International Workshop on Big Data and Information Security ({IWBIS})}, author = {Azimi, Iman and Anzanpour, Arman and Rahmani, Amir M. and Liljeberg, Pasi and Salakoski, Tapio}, date = {2016-10}, keywords = {Cloud computing, Biomedical monitoring, Internet of Things, Autonomic computing, Computer architecture, Electrocardiography, Fog Comouting, Logic gates, machine learning, Patient monitoring}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/NCEXJGHU/7872884.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/B4P7DF44/Azimi et al. - 2016 - Medical warning system based on Internet of Things.pdf:application/pdf}, } @inproceedings{yeri_iot_2020-1, title = {{IoT} based Real Time Health Monitoring}, doi = {10.1109/ICIRCA48905.2020.9183194}, abstract = {Conventional sensor based diagnosis in medial field requires more number of sensors and human efforts if it is processed in a large scale. It is a difficult task due to the shortage of medical professionals and system setup. To overcome this issue an {IoT} based health care application is proposed in the research work. The proposed system consists of the web and mobile application based on continuous wireless monitoring of patients. The objective is paper is to implement a low-cost system and transmit the patient vital signs in emergency situations. Sensors are being used for measuring the patient vital signs by using the wireless network. The sensors data are collected and transmitted to the cloud for storage via Wi-Fi module connected with the controller. The data is processed in the cloud and feedback steps are taken on the analysed data which can be further analysed by a doctor remotely. Remote viewing reduces burden to doctors and provides the exact health status of patients. If the patient needs urgent attention then a message is sent to the doctor.}, eventtitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, pages = {980--984}, booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, author = {Yeri, Vani and Shubhangi, D.C.}, date = {2020-07}, keywords = {Arduino, Health, {IoT}, monitoring, patient, sensor, wireless, Cloud computing, Medical services, Monitoring, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/99VM328H/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/8JLHBZDW/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf}, } @article{anzanpour_internet_2015, title = {Internet of Things Enabled In-Home Health Monitoring System Using Early Warning Score}, volume = {"2"}, issn = {2414-1399}, url = {https://eudl.eu/doi/10.4108/eai.14-10-2015.2261616}, abstract = {Early warning score ({EWS}) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, {EWS} procedure is mostly used for in-hospital clinical cases and is performe}, pages = {174--177}, number = {8}, journaltitle = {{EAI} Endorsed Transactions on Internet of Things}, author = {Anzanpour, Arman and Rahmani, Amir-Mohammad and Liljeberg, Pasi and Tenhunen, Hannu}, urldate = {2023-04-26}, date = {2015-12-22}, file = {Full Text PDF:/home/ulinja/Zotero/storage/MUEL539U/Anzanpour et al. - 2015 - Internet of Things Enabled In-Home Health Monitori.pdf:application/pdf}, } @online{noauthor_features_nodate, title = {Features of electronic Early Warning systems which impact clinical decision making {\textbar} {IEEE} Conference Publication {\textbar} {IEEE} Xplore}, url = {https://ieeexplore.ieee.org/document/6266394}, urldate = {2023-04-26}, file = {Features of electronic Early Warning systems which impact clinical decision making | IEEE Conference Publication | IEEE Xplore:/home/ulinja/Zotero/storage/Q9BI6RWR/6266394.html:text/html;Features of electronic Early Warning systems which.pdf:/home/ulinja/Zotero/storage/SSHGFSTF/Features of electronic Early Warning systems which.pdf:application/pdf}, } @inproceedings{athira_design_2020, title = {Design and Development of {IOT} Based Multi-Parameter Patient Monitoring System}, doi = {10.1109/ICACCS48705.2020.9074293}, abstract = {Multi-parameter patient monitor captures the physiological vital signs and continuously monitors the patient condition by alerting the medical staff via alarm. The revolutionization in machine learning techniques and Internet of Things ({IOT}) in healthcare. In this paper, we have designed an {IOT} based {MPM} system where four parameter namely heart rate, respiration rate, oxygen saturation and temperature are monitored using corresponding sensors and an email is sent to patient's guardian in case of emergency. The project also focuses on improving the performance of {MPM} system using Support Vector Machine({SVM}) algorithm. The classification accuracy of 95\% has been achieved.}, eventtitle = {2020 6th International Conference on Advanced Computing and Communication Systems ({ICACCS})}, pages = {862--866}, booktitle = {2020 6th International Conference on Advanced Computing and Communication Systems ({ICACCS})}, author = {Athira, A. and Devika, T.D. and Varsha, K.R. and Bose S., Sree Sanjanaa}, date = {2020-03}, note = {{ISSN}: 2575-7288}, keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Biomedical monitoring, Heart rate, {IOT}, {MPM}, Smart Health, {SVM} classifier}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/3CAVK8H5/9074293.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Y852V2DN/Athira et al. - 2020 - Design and Development of IOT Based Multi-Paramete.pdf:application/pdf}, } @inproceedings{chowdary_efficient_2018, title = {An Efficient Wireless Health Monitoring System}, doi = {10.1109/I-SMAC.2018.8653716}, abstract = {Until now the exact method of monitoring patients in hospitals is to make the patients lay down on the bed, connect appropriate sensors for monitoring. This method of monitoring may be uncomfortable due to various reasons. To monitor the increased number of patients, a large number of trained medical professionals and doctors are required. Due to this shortage of trained medical professionals, uninterrupted patient monitoring systems have drawn significant attention during the current decade. A large number of cost-effective versions of patient monitoring systems are available, which were being utilized by authorized health care professionals. In addition to this there is a strong need for web based patient monitoring system using internet of things ({IoT}) technologies, when the patient is not in the hospital. The goal of this paper is to design and implement a low cost, portable effective patient monitoring system that can transmit the vital signs of a patient in emergency situation continuously through a wireless communication network system. Various sensors such as pulse, temperature, blood pressure and fingerprint are interfaced with the microcontroller for measuring the important physical parameters of a patient. For wireless transmission, these sensors are connected to a sensor node through {GSM} module. Raspberry-Pi is used as a sensor node as it has better features compared to the other microcontrollers. The developed system includes hardware such as Raspberry Pi 3 model B, blood pressure sensor, temperature, Pulse-oximeters and {GSM} module. The developed hardware prototype was tested successfully on four patients and satisfactory readings have been monitored on the display.}, eventtitle = {2018 2nd International Conference on 2018 2nd International Conference on I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})}, pages = {373--377}, booktitle = {2018 2nd International Conference on 2018 2nd International Conference on I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})}, author = {Chowdary, Kovuru Chandu and Lokesh Krishna, K. and Prasad, K Lalu and Thejesh, K.}, date = {2018-08}, keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Biomedical monitoring, and {IoT}, Blood flow rate, Blood pressure, {GSM}, Microcontroller, Remote monitoring, temperature Sensor node}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/AW9IDIT6/8653716.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/GCRHYVX8/Chowdary et al. - 2018 - An Efficient Wireless Health Monitoring System.pdf:application/pdf}, } @inproceedings{archip_iot_2016, title = {An {IoT} based system for remote patient monitoring}, doi = {10.1109/CarpathianCC.2016.7501056}, abstract = {Following a surgical procedure, patients are monitored in an {ICU} until physically stable, after which are discharged to a ward for further evaluation and recovery. Usually, ward evaluation does not imply continuous physiological parameters monitoring and therefore patient relapse is not uncommon. The present paper describes the steps taken to design and build a low-cost modular monitoring system prototype. This system aims to offer mobile support in order to facilitate faster and better medical interventions in emergency cases and has been developed using low-power dedicated sensor arrays for {EKG}, {SpO}2, temperature and movement. The interfaces for these sensors have been developed according to the {IoT} model: a central control unit exposes a {RESTful} based Web interface that ensures a platform agnostic behaviour and provides a flexible mechanism to integrate new components.}, eventtitle = {2016 17th International Carpathian Control Conference ({ICCC})}, pages = {1--6}, booktitle = {2016 17th International Carpathian Control Conference ({ICCC})}, author = {Archip, Alexandru and Botezatu, Nicolae and Şerban, Elena and Herghelegiu, Paul-Corneliu and Zală, Andrei}, date = {2016-05}, keywords = {Monitoring, Temperature sensors, Biomedical monitoring, Internet of Things, Electrocardiography, Logic gates, E-health, Embedded Systems, Internet of things, Prototypes, Remote patient monitoring, {RESTful} Web Services}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GR5KW752/7501056.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/SJIJNI7I/Archip et al. - 2016 - An IoT based system for remote patient monitoring.pdf:application/pdf}, } @article{gomez_patient_2016, title = {Patient Monitoring System Based on Internet of Things}, volume = {83}, issn = {1877-0509}, url = {https://www.sciencedirect.com/science/article/pii/S1877050916301260}, doi = {10.1016/j.procs.2016.04.103}, series = {The 7th International Conference on Ambient Systems, Networks and Technologies ({ANT} 2016) / The 6th International Conference on Sustainable Energy Information Technology ({SEIT}-2016) / Affiliated Workshops}, abstract = {The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits these technologies bring, thus generating a significant improvement in health care in clinical settings and out of them. Likewise, countless ordinary users are being served from the advantages of the M-Health (Mobile Health) applications and E-Health (health care supported by {ICT}) to improve, help and assist their health. Applications that have had a major refuge for these users, so intuitive environment. The Internet of things is increasingly allowing to integrate devices capable of connecting to the Internet and provide information on the state of health of patients and provide information in real time to doctors who assist. It is clear that chronic diseases such as diabetes, heart and pressure among others, are remarkable in the world economic and social level problem. The aim of this article is to develop an architecture based on an ontology capable of monitoring the health and workout routine recommendations to patients with chronic diseases.}, pages = {90--97}, journaltitle = {Procedia Computer Science}, author = {Gómez, Jorge and Oviedo, Byron and Zhuma, Emilio}, urldate = {2023-04-26}, date = {2016-01-01}, langid = {english}, keywords = {Internet of Things, Context Awareness, E-Health, Ontology}, file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/LDZVGYUT/Gómez et al. - 2016 - Patient Monitoring System Based on Internet of Thi.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/XH3BU4JZ/S1877050916301260.html:text/html}, } @inproceedings{anzanpour_context-aware_2016, location = {Cham}, title = {Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things}, isbn = {978-3-319-47063-4}, doi = {10.1007/978-3-319-47063-4_56}, series = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering}, abstract = {Early warning score ({EWS}) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing {EWS} systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.}, pages = {517--522}, booktitle = {Internet of Things. {IoT} Infrastructures}, publisher = {Springer International Publishing}, author = {Anzanpour, Arman and Rahmani, Amir-Mohammad and Liljeberg, Pasi and Tenhunen, Hannu}, editor = {Mandler, Benny and Marquez-Barja, Johann and Mitre Campista, Miguel Elias and Cagáňová, Dagmar and Chaouchi, Hakima and Zeadally, Sherali and Badra, Mohamad and Giordano, Stefano and Fazio, Maria and Somov, Andrey and Vieriu, Radu-Laurentiu}, date = {2016}, langid = {english}, keywords = {Remote patient monitoring, e-Health, Early warning score, Internet-of-Things}, } @inproceedings{de_mello_dantas_data_2020, title = {A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring}, doi = {10.1109/ISNCC49221.2020.9297315}, abstract = {The adoption of the Internet of Things ({IoT}) technologies in healthcare lead to the Healthcare 4.0 paradigm. In this paradigm, Remote Health Monitoring ({RHM}) applications emerge to provide continuous monitoring of patient's health conditions. But {RHM} applications traditionally present high rates of false alarms. This disturbance is caused by many factors, from the high sensibility of the equipments to real variations in the monitored vital signs not related to emergencies of health degradation. Hence, this work proposes a system for detection and evaluation of medical emergencies, using Wireless Body Sensor Network as its network infrastructure, able to distinguish real emergencies from other cases by considering a risk estimation from each sampled data. Experiments showed that the proposed system can reach an average accuracy rate of 93.0\% and detection rate of 87.2\%, and an energy consumption profile feasible to {WBSN} contexts.}, eventtitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})}, pages = {1--8}, booktitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})}, author = {de Mello Dantas, Hugo and Miceli de Farias, Claudio}, date = {2020-10}, keywords = {Medical services, Monitoring, Wireless communication, Biomedical monitoring, Internet of Things, Data integration, Emergency Detection, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/ZPAGY7ER/9297315.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/EIHD9N7X/de Mello Dantas and Miceli de Farias - 2020 - A data fusion algorithm for clinically relevant an.pdf:application/pdf}, } @inproceedings{b_v_review_2022, title = {Review on {IoT} based Healthcare systems}, doi = {10.1109/ICACTA54488.2022.9753547}, abstract = {The world is moving to Internet of Things ({IoT}) remote monitoring technology and quick control of objects as well more precisely. {IoT} can be beneficial in medicine as well health care facilities as it allows for long-term research of chronic diseases, vital symptom monitoring, emergency perception, diagnosis and prediction of patient level or disease. Internet of Things becomes transparent and is useful in the context of health care and venerable care, i.e., in most cases, they are activities that require the full presence of a caretaker or medical personal. The purpose of this survey is to enlighten the significance and categories of {IoT}-include adult health care programs. This paper consolidates a summary of research that report on the development and use of {IoT}-include health care adult programs. The paper covers with various available {IoT} based techniques used for health care applications.}, eventtitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})}, pages = {1--5}, booktitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})}, author = {B V, Santhosh Krishna and Sharma, Sanjeev and Swathi, Kurapati Sai and Yamini, Korapati Reddy and Kiran, Chokkam Preethi and Chandrika, Kamineni}, date = {2022-03}, keywords = {Medical services, Monitoring, Internet of Things, Electrocardiography, Diagnosis, Encryption, Heart, Internet of Things [{IoT}], Perception, Productivity, Security}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/MY7DTWBQ/9753547.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/3VPC4T36/B V et al. - 2022 - Review on IoT based Healthcare systems.pdf:application/pdf}, } @inproceedings{quraishi_internet_2021, title = {Internet of Things in Healthcare, A Literature Review}, doi = {10.1109/ICTAI53825.2021.9673369}, abstract = {Over the last few decades, extensive research has been consigned to the study of various technologies such as information technologies in order to strengthen the existing ones. One such field is the Internet of things ({IoT}). In recent times, internet of things has been a ground-breaking technology in almost all sectors of human life. {IoT} holds the tendency to make available both value-added services and mainstream services in all fields. Healthcare sector is one such area. {IoT} for healthcare tends to keep doctors and professionals more watchful and connected with the patients proactively. For the overall development of a nation, healthcare is the main service center. It is clear that technology cannot help to eradicate the issues related to health completely but it can make access to healthcare easier. In today’s scenario where healthcare has been drastically despoiled, {IoT} has completely changed the perspective of traditional healthcare methods. {IoT} tends to play a crucial role in providing better and improved services. Real-time monitoring of the data via smart devices use to transfer collected health data to a physician. Patient health observation and monitoring, emergency proceedings, remote inspection, and observation are some vital analyses that can be made with the launch of the Internet of Things in the respected sector. The standpoint of the paper is to summarize the applications of {IoT} in the field of the healthcare field.}, eventtitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})}, pages = {198--202}, booktitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})}, author = {Quraishi, Suhail Javed and Yusuf, Humra}, date = {2021-11}, keywords = {{IoT}, Medical services, Internet of Things, Bibliographies, Healthcare, Information technologies, Inspection, Real-time systems, Remote inspection, Sensors, Smart devices, Technological innovation}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/KZGMR5L4/9673369.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/KR2C82FM/Quraishi and Yusuf - 2021 - Internet of Things in Healthcare, A Literature Rev.pdf:application/pdf}, } @article{shaik_remote_2023, title = {Remote patient monitoring using artificial intelligence: Current state, applications, and challenges}, volume = {13}, issn = {1942-4795}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1485}, doi = {10.1002/widm.1485}, shorttitle = {Remote patient monitoring using artificial intelligence}, abstract = {The adoption of artificial intelligence ({AI}) in healthcare is growing rapidly. Remote patient monitoring ({RPM}) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of {RPM} systems including adopted advanced technologies, {AI} impact on {RPM}, challenges and trends in {AI}-enabled {RPM}. This review explores the benefits and challenges of patient-centric {RPM} architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of {AI} in {RPM} ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that {AI}-enabled {RPM} architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt {AI} to {RPM} systems and implementation issues. The future directions of {AI} in {RPM} applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas {\textgreater} Health Care Technologies {\textgreater} Artificial Intelligence Technologies {\textgreater} Internet of Things}, pages = {e1485}, number = {2}, journaltitle = {{WIREs} Data Mining and Knowledge Discovery}, author = {Shaik, Thanveer and Tao, Xiaohui and Higgins, Niall and Li, Lin and Gururajan, Raj and Zhou, Xujuan and Acharya, U. Rajendra}, urldate = {2023-04-26}, date = {2023}, langid = {english}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1485}, keywords = {{IoT}, artificial intelligence, noninvasive technology, remote patient monitoring}, file = {Full Text PDF:/home/ulinja/Zotero/storage/WUD6AIM4/Shaik et al. - 2023 - Remote patient monitoring using artificial intelli.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/JUM4HJDC/widm.html:text/html}, } @article{sahu_internet--things-enabled_2022, title = {Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring}, volume = {0}, issn = {0377-2063}, url = {https://doi.org/10.1080/03772063.2022.2110528}, doi = {10.1080/03772063.2022.2110528}, abstract = {Measuring the physiological parameters and their variations for 24 h provides a significantly visible pattern for patient health monitoring. The life-threatening event can be identified by determining the abnormalities in physiological parameters. These parameters play a vital role in the accurate calculation of early warning score ({EWS}). Therefore, in this paper, an automated and internet of things ({IoT})-based {EWS} calculation has been presented for timely detection and diagnosis of the abnormality associated with the physiological parameters of the patient. It will be useful in avoiding life-threatening events. At present, the {EWS} is calculated by the conventional method, where the different physiological parameters are monitored by medical staff or nurses in a particular time interval. The conventional scoring system is laborious, time-consuming, and prone to human error. Thus, the presented automatic and {IoT}-based {EWS} system for patient monitoring has been successfully implemented. The presented system can continuously calculate {EWS} by measuring and recording the physiological parameter in real-time. A laboratory prototype has been configured and validated for the {EWS} calculation and observation in the hospital and home environment.}, pages = {1--12}, number = {0}, journaltitle = {{IETE} Journal of Research}, author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar}, urldate = {2023-04-26}, date = {2022-08-15}, note = {Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/03772063.2022.2110528}, keywords = {Internet of things, Early warning score, Sensors, Automated {EWS}, In-home system, Physiological parameters}, file = {Full Text PDF:/home/ulinja/Zotero/storage/2JFXM2RX/Sahu et al. - 2022 - Internet-of-Things-Enabled Early Warning Score Sys.pdf:application/pdf}, } @incollection{kale_intelligent_2021, location = {Cham}, title = {Intelligent Healthcare}, isbn = {978-3-030-67051-1}, url = {https://doi.org/10.1007/978-3-030-67051-1_2}, series = {{EAI}/Springer Innovations in Communication and Computing}, abstract = {The Internet of Things ({IoT}) is the heart of intelligent healthcare, which is very useful because the accessibility of the internet is worldwide. This chapter begins by explaining the difference between Vital Signs Early Detection ({VSED}) and Physical Signs Early Detection ({PSED}), and it shows the relationship between {VSED} and {PSED}. This chapter concludes by outlining measuring systems of various chronic disease parameters using the latest technology – the {IoT} and {ThingSpeak}. This chapter aimed to develop a low cost system which will be easily connected with any kind of digital medical sensor and remotely provide the measured readings to medical professionals globally, thereby assisting with the early detection of chronic diseases. The various parameters are blood pressure, which is measured according to systolic and diastolic parameters, pulse rate and body temperature. The measuring system is very simple; it is plugged in and sends the measured parameter reading to the cloud, and it is accessible to the recipient who has the {ID} and password. Medical professionals will obtain the reading and analyse it properly, and on the basis of that analysis decide whether the patient requires hospitalization. This chapter outlines a new way to observe patients and their needs within the comfort of their own homes, and it indicates ways to predict chronic diseases on the basis of the early warning score ({EWS}) from the basic parameter readings. To obtain the {EWS}, readings are compared with the previous patient data and changes are used to calculate the {EWS}.}, pages = {19--31}, booktitle = {Intelligent Healthcare: Applications of {AI} in {eHealth}}, publisher = {Springer International Publishing}, author = {Kale, Yogesh S. and Rathkanthiwar, Shubhangi V. and Gawande, Prachi D.}, editor = {Bhatia, Surbhi and Dubey, Ashutosh Kumar and Chhikara, Rita and Chaudhary, Poonam and Kumar, Abhishek}, urldate = {2023-04-26}, date = {2021}, langid = {english}, doi = {10.1007/978-3-030-67051-1_2}, keywords = {Early warning score ({EWS}) and {ThingSpeak}, Intelligent healthcare, Internet of things ({IoT})}, file = {Full Text PDF:/home/ulinja/Zotero/storage/6H23NRK9/Kale et al. - 2021 - Intelligent Healthcare.pdf:application/pdf}, } @inproceedings{karvounis_hospital_2021, title = {A Hospital Healthcare Monitoring System Using Internet of Things Technologies}, doi = {10.1109/SEEDA-CECNSM53056.2021.9566252}, abstract = {In any hospital health care monitoring system, it is necessary to constantly monitor the patient's physiological parameters. Previous studies report that important parameters of any patient that have to be monitored in hospital are heart rate, respiratory rate, oxygen saturation, temperature, change in systolic blood pressure, motion, posture and its location. This work presents a monitoring system that has the capability to monitor in real-time the physiological parameters of the patient using a comfortable wearable device. A Wireless Sensor Network in collaboration with multiple wireless relay nodes, are responsible for collecting and sending the signals from the wireless sensors to the base station. The data is stored and processed using intelligent techniques in a cloud-based environment. Early warning alerts are automatically sent to the medical staff allowing them to intervene on time and earlier than when using manual vital sign observations. By grouping patients according to risk, medical experts can continuously monitor patients health status and quickly identify those that need their attention most.}, eventtitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})}, pages = {1--6}, booktitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})}, author = {Karvounis, Evaggelos and Vavva, Maria and Giannakeas, Nikolaos and Tzallas, Alexandros T. and Smanis, Ioannis and Tsipouras, Markos G.}, date = {2021-09}, keywords = {Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks, Internet of Things, Artificial Intelligence ({AI}), component, Electronic healthcare, health monitoring, Hospitals, Sensor systems, Smart healthcare, ubiquitous computing, wearable devices, Wireless Sensor Network}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/IDTXE2ZS/9566252.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/S2WR9JTE/Karvounis et al. - 2021 - A Hospital Healthcare Monitoring System Using Inte.pdf:application/pdf}, } @inproceedings{jagadish_remote_2018, location = {Singapore}, title = {Remote Continuous Health Monitoring System for Patients}, isbn = {978-981-10-8603-8}, doi = {10.1007/978-981-10-8603-8_11}, series = {Communications in Computer and Information Science}, abstract = {Recent advances in Internet of Things ({IoT}) leads to the development of several applications. Some of these applications are related to smart healthcare monitoring though there are several applications for healthcare and monitoring it does not fully address the ability to satisfy the patient’s needs, doctor convenience and the automation of system to act according to the status of the patient. In this paper, we implemented an algorithm for (a) the continuous health monitoring and frequent update to the hospital server which notifies the doctor. It also supports the mobility of patient, (b) acts according to the critical levels of patient which is safe, intermediate or emergency through cloud processing, (c) ability of the doctor to view enormous sensor record in comprehend form using the visualization and analysis and (d) Drug management system to deliver drugs to the patient on time and without the presence of the nurse using stored data in the server. Raspberry pi 2 is used to transmit the bio-sensed data of patient directly to the cloud by using the internet of smart phone or {PC} where based on the critical level sends a notification to the doctor through hospital server, caretaker and/or Ambulance service.}, pages = {129--138}, booktitle = {Data Science Analytics and Applications}, publisher = {Springer}, author = {Jagadish, D. and Priya, N. and Suganya, R.}, editor = {R, Shriram and Sharma, Mak}, date = {2018}, langid = {english}, keywords = {{IoT}, Diverse emergency situation, Health monitoring, Tele-medicine}, } @inproceedings{yeri_iot_2020-2, title = {{IoT} based Real Time Health Monitoring}, doi = {10.1109/ICIRCA48905.2020.9183194}, abstract = {Conventional sensor based diagnosis in medial field requires more number of sensors and human efforts if it is processed in a large scale. It is a difficult task due to the shortage of medical professionals and system setup. To overcome this issue an {IoT} based health care application is proposed in the research work. The proposed system consists of the web and mobile application based on continuous wireless monitoring of patients. The objective is paper is to implement a low-cost system and transmit the patient vital signs in emergency situations. Sensors are being used for measuring the patient vital signs by using the wireless network. The sensors data are collected and transmitted to the cloud for storage via Wi-Fi module connected with the controller. The data is processed in the cloud and feedback steps are taken on the analysed data which can be further analysed by a doctor remotely. Remote viewing reduces burden to doctors and provides the exact health status of patients. If the patient needs urgent attention then a message is sent to the doctor.}, eventtitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, pages = {980--984}, booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, author = {Yeri, Vani and Shubhangi, D.C.}, date = {2020-07}, keywords = {Arduino, Health, {IoT}, monitoring, patient, sensor, wireless, Cloud computing, Medical services, Monitoring, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/WR4V2DCX/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/GTWN38W9/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf}, } @inproceedings{arnil_wireless_2011, title = {Wireless sensor network-based smart room system for healthcare monitoring}, doi = {10.1109/ROBIO.2011.6181597}, abstract = {In this paper, the utilization of Zigbee as wireless sensor network ({WSN}) for medical application is demonstrated. The combination of various topologies is used to configure wireless sensors network to achieve high efficiency network architecture in medicine. The network consists of center coordinator, routers and sensor nodes. Mesh network is used for the connection between coordinator and router for range expansion. A performance of the proposed modality is tested in the normal situation. Besides, the architecture of the smart room systems is also proposed for healthcare monitoring. Physiological data and signal are transmitted using Xbee which is a wireless device operated in unlicensed radio frequency bands.}, eventtitle = {2011 {IEEE} International Conference on Robotics and Biomimetics}, pages = {2073--2076}, booktitle = {2011 {IEEE} International Conference on Robotics and Biomimetics}, author = {Arnil, Jetsada and Punsawad, Yunyong and Wongsawat, Yodchanan}, date = {2011-12}, keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks, Zigbee}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/U4YYGJI4/6181597.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/UV6QAL2S/Arnil et al. - 2011 - Wireless sensor network-based smart room system fo.pdf:application/pdf}, } @inproceedings{tiwari_iot_2021, title = {{IoT} based Smart Healthcare Monitoring Systems: A Review}, doi = {10.1109/ISPCC53510.2021.9609393}, shorttitle = {{IoT} based Smart Healthcare Monitoring Systems}, abstract = {{IoT} devices are becoming very useful in today’s world. In today’s era {IoT} devices are increasing and it has a huge impact in healthcare. It can provide early detection of health problems and can reduce the cost of medical care. The healthcare monitoring system is required for a patient who needs to be monitored 24 X 7. The {IoT} based health monitoring system can monitor the vital health parameters of a person at all times. It can help patients in the case of emergency by providing immediate health consultation from the doctor available at a distant location. Further, {IoT} based smart systems enable remote monitoring of the patient by the guardian/ family member which is considered as one of the major advantages to save the precious human life. This paper provides an overview of various {IoT} based health monitoring systems. The comparison of various healthcare devices has also been presented by taking into consideration the important healthcare parameters.}, eventtitle = {2021 6th International Conference on Signal Processing, Computing and Control ({ISPCC})}, pages = {465--469}, booktitle = {2021 6th International Conference on Signal Processing, Computing and Control ({ISPCC})}, author = {Tiwari, Divyanshu and Prasad, Devendra and Guleria, Kalpna and Ghosh, Pinaki}, date = {2021-10}, note = {{ISSN}: 2643-8615}, keywords = {{IoT}, Medical services, Monitoring, Biomedical monitoring, Remote monitoring, Health monitoring, Costs, healthcare, heart monitoring devices, medical services, Signal processing}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GTHWZ2L3/9609393.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/NANQYIU6/Tiwari et al. - 2021 - IoT based Smart Healthcare Monitoring Systems A R.pdf:application/pdf}, } @article{paganelli_conceptual_2022, title = {A conceptual {IoT}-based early-warning architecture for remote monitoring of {COVID}-19 patients in wards and at home}, volume = {18}, issn = {2542-6605}, url = {https://www.sciencedirect.com/science/article/pii/S2542660521000433}, doi = {10.1016/j.iot.2021.100399}, abstract = {Due to the {COVID}-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things ({IoT}) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining {IoT}-related technologies with early-warning scores ({EWS}) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the {NEWS}-2 has been showing remarkable results in detecting the health deterioration of {COVID}-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for {COVID}-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive {IoT}-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of {COVID}-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with {EWS}.}, pages = {100399}, journaltitle = {Internet of Things}, author = {Paganelli, Antonio Iyda and Velmovitsky, Pedro Elkind and Miranda, Pedro and Branco, Adriano and Alencar, Paulo and Cowan, Donald and Endler, Markus and Morita, Plinio Pelegrini}, urldate = {2023-04-26}, date = {2022-05-01}, langid = {english}, keywords = {{IoT}, Remote monitoring, Architecture, Consent, {COVID}-19, {NEWS}-2}, file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/GYESA337/Paganelli et al. - 2022 - A conceptual IoT-based early-warning architecture .pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/VNA4PMAC/S2542660521000433.html:text/html}, } @article{patel_can_2018, title = {Can early warning scores identify deteriorating patients in pre-hospital settings? A systematic review}, volume = {132}, issn = {0300-9572}, url = {https://www.sciencedirect.com/science/article/pii/S0300957218308190}, doi = {10.1016/j.resuscitation.2018.08.028}, shorttitle = {Can early warning scores identify deteriorating patients in pre-hospital settings?}, abstract = {Objective To evaluate the effectiveness and predictive accuracy of early warning scores ({EWS}) to predict deteriorating patients in pre-hospital settings. Methods Systematic review. Seven databases searched to August 2017. Study quality was assessed using {QUADAS}-2. A narrative synthesis is presented. Eligibility Studies that evaluated {EWS} predictive accuracy or that compared outcomes in populations that did or did not use {EWS}, in any pre-hospital setting were eligible for inclusion. {EWS} were included if they aggregated three or more physiological parameters. Results Seventeen studies (157,878 participants) of predictive accuracy were included (16 in ambulance service and 1 in nursing home). {AUCs} ranged from 0.50 ({CI} not reported) to 0.89 (95\%{CI} 0.82, 0.96). {AUCs} were generally higher ({\textgreater}0.80) for prediction of mortality within short time frames or for combination outcomes that included mortality and {ICU} admission. Few patients with low scores died at any time point. Patients with high scores were at risk of deterioration. Results were less clear for intermediate thresholds (≥4 or 5). Five studies were judged at low or unclear risk of bias, all others were judged at high risk of bias. Conclusions Very low and high {EWS} are able to discriminate between patients who are not likely and those who are likely to deteriorate in the pre-hospital setting. No study compared outcomes pre- and post-implementation of {EWS} so there is no evidence on whether patient outcomes differ between pre-hospital settings that do and do not use {EWS}. Further studies are required to address this question and to evaluate {EWS} in pre-hospital settings.}, pages = {101--111}, journaltitle = {Resuscitation}, author = {Patel, Rita and Nugawela, Manjula D. and Edwards, Hannah B. and Richards, Alison and Le Roux, Hein and Pullyblank, Anne and Whiting, Penny}, urldate = {2023-04-26}, date = {2018-11-01}, langid = {english}, keywords = {Early warning score, Critical care, Deteriorating patients, Pre hospital setting, Track and trigger system}, file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NKPJTMTR/Patel et al. - 2018 - Can early warning scores identify deteriorating pa.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/P6WFVY87/S0300957218308190.html:text/html}, } @article{azimi_self-aware_2017, title = {Self-aware early warning score system for {IoT}-based personalized healthcare}, volume = {181 {LNICST}}, issn = {1867-8211}, doi = {10.1007/978-3-319-49655-9_8}, abstract = {Early Warning Score ({EWS}) system is specified to detect and predict patient deterioration in hospitals. This is achievable via monitoring patient's vital signs continuously and is often manually done with paper and pen. However, because of the constraints in healthcare resources and the high hospital costs, the patient might not be hospitalized for the whole period of the treatments, which has lead to a demand for in-home or portable {EWS} systems. Such a personalized {EWS} system needs to monitor the patient at anytime and anywhere even when the patient is carrying out daily activities. In this paper, we propose a self-aware {EWS} system which is the reinforced version of the existing {EWS} systems by using the Internet of Things technologies and the self-awareness concept. Our self-aware approach provides (i) system adaptivity with respect to various situations and (ii) system personalization by paying attention to critical parameters. We evaluate the proposed {EWS} system using a full system demonstration. © {ICST} Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.}, pages = {49--55}, journaltitle = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, {LNICST}}, author = {Azimi, I. and Anzanpour, A. and Rahmani, A.M. and Liljeberg, P. and Tenhunen, H.}, date = {2017}, note = {{ISBN}: 9783319496542}, keywords = {Early warning score, Internet-of-Things, Personalized monitoring, Self-awareness system}, file = {Snapshot:/home/ulinja/Zotero/storage/4H8YS4KH/display.html:text/html}, } @article{paterson_prediction_2006, title = {Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit}, volume = {6}, rights = {© 2006 Royal College of Physicians}, issn = {1470-2118, 1473-4893}, url = {https://www.rcpjournals.org/content/clinmedicine/6/3/281}, doi = {10.7861/clinmedicine.6-3-281}, shorttitle = {Prediction of in-hospital mortality and length of stay using an early warning scoring system}, abstract = {{\textless}p{\textgreater}This aim of this study was to assess the impact of the introduction of a standardised early warning scoring system ({SEWS}) on physiological observations and patient outcomes in unselected acute admissions at point of entry to care. A sequential clinical audit was performed on 848 patients admitted to a combined medical and surgical assessment unit during two separate 11-day periods. Physiological parameters (respiratory rate, oxygen saturation, temperature, blood pressure, heart rate, and conscious level), in-hospital mortality, length of stay, transfer to critical care and staff satisfaction were documented. Documentation of these physiological parameters improved (P\<0.001–0.005) with the exception of oxygen saturation (P=0.069). The admission early warning score correlated both with in-hospital mortality (P\<0.001) and length of stay (P=0.001). Following the introduction of the scoring system, inpatient mortality decreased (P=0.046). Staff responding to a questionnaire indicated that the scoring system increased awareness of illness severity (80\%) and prompted earlier interventions (60\%). A standardised early warning scoring system improves documentation of physiological parameters, correlates with in-hospital mortality, and helps predict length of stay.{\textless}/p{\textgreater}}, pages = {281--284}, number = {3}, journaltitle = {Clinical Medicine}, author = {Paterson, R. and {MacLeod}, D. C. and Thetford, D. and Beattie, A. and Graham, C. and Lam, S. and Bell, D.}, urldate = {2023-04-26}, date = {2006-05-01}, langid = {english}, pmid = {16826863}, note = {Publisher: Royal College of Physicians Section: Original Papers}, file = {Full Text PDF:/home/ulinja/Zotero/storage/TNBWXWKF/Paterson et al. - 2006 - Prediction of in-hospital mortality and length of .pdf:application/pdf}, } @article{sahu_cloud-based_2022, title = {Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification}, volume = {27}, issn = {1572-8153}, url = {https://doi.org/10.1007/s11036-022-01960-4}, doi = {10.1007/s11036-022-01960-4}, abstract = {The availability, accessibility, and affordability of good healthcare services to remote, rural, and developing parts of the world is a major challenge. To resolve this dynamically growing issue of global importance, there is a need to devise an integrated and intelligent solution for the delivery of health monitoring services along with abnormality detection and alert notification. In this work, a remote patient monitoring system ({RPMS}) has been presented. Internet of things ({IoT}) and integrated cloud computing technologies are used for the implementation. The system can continuously measure different physiological parameters with the appropriate degree of accuracy required by medical standards. A mobile application has been developed for Android devices, which acts as a gateway between {RPMS} and the Cloud. The developed mobile application offers visualization and storage of physiological parameters locally as well as in Cloud along with real-time data transmission for remote monitoring and further analysis. In case of an abnormal event and emergency, the system can generate an alert notification to the local user and remote supervisor. The {RPMS} has been implemented and validated on the state-of-the-art patient monitoring system. A series of tests have been carried out to validate the system’s effectiveness and reliability for measuring different physiological parameters and its remote monitoring in real-time. In addition to this performance analysis of the cloud-based system for real-time data transmission has also been carried out.}, pages = {1894--1909}, number = {5}, journaltitle = {Mobile Netw Appl}, author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar}, urldate = {2023-04-26}, date = {2022-10-01}, langid = {english}, keywords = {Remote patient monitoring, Abnormality detection, Alert Notification, Internet of thing, Mobile Communication}, file = {Full Text PDF:/home/ulinja/Zotero/storage/BUVVMQQ9/Sahu et al. - 2022 - Cloud-Based Remote Patient Monitoring System with .pdf:application/pdf}, } @article{sahu_vital_2022, title = {Vital Sign Monitoring System for Healthcare Through {IoT} Based Personal Service Application}, volume = {122}, issn = {1572-834X}, url = {https://doi.org/10.1007/s11277-021-08892-4}, doi = {10.1007/s11277-021-08892-4}, abstract = {The most burning issues worldwide at present are the availability, accessibility, and affordability of the equitable healthcare services for all. It is getting more severe for developing countries due to increasing population and chronic diseases. The emerging technological interventions in the field of Internet of Things ({IoT})-based healthcare systems are a promising solution to meet the general public's healthcare needs. Therefore, an {IoT}-enabled vital sign monitoring system has been presented in this paper. The presented system can monitor various vital signs in real-time and store the recorded trends locally. The system can also send the data into cloud for further analysis. Abnormality detection with alert notification and automatic calculation of early warning score has been implemented. An Android application is developed to store the vital signs records on a personal server to avoid the burden and maintenance cost of the central medical server. The presented system is straightforward, compact, portable and easy to operate through personal service application. Also, the presented system is compared with the most recent work available in the field.}, pages = {129--156}, number = {1}, journaltitle = {Wireless Pers Commun}, author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar}, urldate = {2023-04-26}, date = {2022-01-01}, langid = {english}, keywords = {Healthcare, Abnormality detection, Internet of thing, Alert notification, Mobile communication, Personal service application, Real-time monitoring, Vital sign monitoring}, file = {Full Text PDF:/home/ulinja/Zotero/storage/XTBR4NVR/Sahu et al. - 2022 - Vital Sign Monitoring System for Healthcare Throug.pdf:application/pdf}, } @article{subbe_validation_2001, title = {Validation of a modified Early Warning Score in medical admissions}, volume = {94}, issn = {1460-2725}, url = {https://doi.org/10.1093/qjmed/94.10.521}, doi = {10.1093/qjmed/94.10.521}, abstract = {The Early Warning Score ({EWS}) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score ({MEWS}) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied {MEWS} to patients admitted to the 56‐bed acute Medical Admissions Unit ({MAU}) of a District General Hospital ({DGH}). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit ({ICU}) admission, high dependency unit ({HDU}) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death ({OR} 5.4, 95\%{CI} 2.8–10.7), {ICU} admission ({OR} 10.9, 95\%{CI} 2.2–55.6) and {HDU} admission ({OR} 3.3, 95\%{CI} 1.2–9.2). {MEWS} can be applied easily in a {DGH} medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the {HDU} or {ICU}. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.}, pages = {521--526}, number = {10}, journaltitle = {{QJM}: An International Journal of Medicine}, author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.}, urldate = {2023-04-26}, date = {2001-10-01}, file = {Full Text PDF:/home/ulinja/Zotero/storage/GC4BT2WU/Subbe et al. - 2001 - Validation of a modified Early Warning Score in me.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/KHAD42Z5/1558977.html:text/html}, } @inproceedings{zarabzadeh_features_2012, title = {Features of electronic Early Warning systems which impact clinical decision making}, doi = {10.1109/CBMS.2012.6266394}, abstract = {Paper-based Modified Early Warning Scorecards ({MEWS}) have been developed to help nursing staff detect hospital in-patient deterioration at an early stage. {MEWS} is based on patient vital signs where these values are transformed into a {MEWS} score. An electronic Modified Early Warning Scorecard ({eMEWS}) prototype has been designed and developed to fulfill the role of a computerized Clinical Decision Support System ({CDSS}) and to assist healthcare professionals in their decision making activities. A review of the existing electronic Early Warning Scorecards ({eEWS}) revealed they lack certain features that assist in capturing a holistic view of the patient health status for example color codes and vital sign trends. The proposed {eMEWS} prototype employs these features with the aim of assisting healthcare professionals to obtain a clear understanding of the patient status. A survey was conducted to evaluate the impact of paper-based {MEWS} and {eMEWS} as part of the decision making process. The advantages and disadvantages of {eMEWS} over the paper-based {MEWS} are presented.}, eventtitle = {2012 25th {IEEE} International Symposium on Computer-Based Medical Systems ({CBMS})}, pages = {1--4}, booktitle = {2012 25th {IEEE} International Symposium on Computer-Based Medical Systems ({CBMS})}, author = {Zarabzadeh, Atieh and O'Connell, Mervyn and O'Donoghue, John and O'Kane, Tom and Woodworth, Simon and Gallagher, Joe and O'Connor, Siobhán and Adam, Frederic}, date = {2012-06}, note = {{ISSN}: 1063-7125}, keywords = {Prototypes, Hospitals, Color, Context, Decision making, Image color analysis}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/URGX63FD/6266394.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/L2TKQQ3Y/Zarabzadeh et al. - 2012 - Features of electronic Early Warning systems which.pdf:application/pdf}, } @article{chen_qrs_2017, title = {A {QRS} Detection and R Point Recognition Method for Wearable Single-Lead {ECG} Devices}, volume = {17}, rights = {http://creativecommons.org/licenses/by/3.0/}, issn = {1424-8220}, url = {https://www.mdpi.com/1424-8220/17/9/1969}, doi = {10.3390/s17091969}, abstract = {In the new-generation wearable Electrocardiogram ({ECG}) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The {QRS} complex is a combination of three of the graphic deflection seen on a typical {ECG}. This study proposes a real-time {QRS} detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of {QRS} segments and restraining of P and T waves are carried out by the proposed {ECG} signal transformation, which also leads to the elimination of baseline wandering. In this study, the {QRS} fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four {QRS} waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the {MIT}-{BIH} Arrhythmia Database, where the {QRS} detected sensitivity (Se) and positive prediction (+P) are 99.82\% and 99.81\%, respectively. The result reveals the approach’s advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.}, pages = {1969}, number = {9}, journaltitle = {Sensors}, author = {Chen, Chieh-Li and Chuang, Chun-Te}, urldate = {2023-04-26}, date = {2017-09}, langid = {english}, note = {Number: 9 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {{IoT}, {ECG}, edge computing, heartbeat detection, mobile healthcare, {QRS} detection, wearable device}, file = {Full Text PDF:/home/ulinja/Zotero/storage/TAYBJYZT/Chen and Chuang - 2017 - A QRS Detection and R Point Recognition Method for.pdf:application/pdf}, } @online{noauthor_pm6750_nodate, title = {{PM}6750 Handheld Patient Monitor}, url = {https://www.shberrymed.com/products/handheld-pm6750}, abstract = {Make full use of modern mobile communication ({GPRS}) and the Internet (Ineternet) transmission technology on the basis of the traditional custodians of the wireless transmission of real-time data.}, titleaddon = {Berry Medical}, urldate = {2023-04-26}, file = {Snapshot:/home/ulinja/Zotero/storage/BH8GS7F6/handheld-pm6750.html:text/html}, } @article{youssef_ali_amer_vital_2020, title = {Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology}, volume = {20}, rights = {http://creativecommons.org/licenses/by/3.0/}, issn = {1424-8220}, url = {https://www.mdpi.com/1424-8220/20/22/6593}, doi = {10.3390/s20226593}, abstract = {In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores ({EWS}) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the {EWS} for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of {kNN}-{LS}-{SVM} for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of {EWS} in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of {LSTM}. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5\% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of {EWS} in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate {EWS} computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.}, pages = {6593}, number = {22}, journaltitle = {Sensors}, author = {Youssef Ali Amer, Ahmed and Wouters, Femke and Vranken, Julie and de Korte-de Boer, Dianne and Smit-Fun, Valérie and Duflot, Patrick and Beaupain, Marie-Hélène and Vandervoort, Pieter and Luca, Stijn and Aerts, Jean-Marie and Vanrumste, Bart}, urldate = {2023-04-26}, date = {2020-01}, langid = {english}, note = {Number: 22 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {early warning score, {kNN}-{LS}-{SVM}, time-series prediction, vital signs, wearable technology}, file = {Full Text PDF:/home/ulinja/Zotero/storage/FAEVF9FC/Youssef Ali Amer et al. - 2020 - Vital Signs Prediction and Early Warning Score Cal.pdf:application/pdf}, } @article{ehara_effectiveness_2019, title = {The effectiveness of a national early warning score as a triage tool for activating a rapid response system in an outpatient setting: A retrospective cohort study}, volume = {98}, url = {https://journals.lww.com/md-journal/Fulltext/2019/12270/The_effectiveness_of_a_national_early_warning.30.aspx}, doi = {10.1097/MD.0000000000018475}, shorttitle = {The effectiveness of a national early warning score as a triage tool for activating a rapid response system in an outpatient setting}, abstract = {Rapid response system ({RRS}) efficacy and national early warning score ({NEWS}) performances have largely been reported in inpatient settings, with few such reports undertaken in outpatient settings. This study aimed to investigate {NEWS} validity in predicting poor clinical outcomes among outpatients who had activated the {RRS} using single-parameter criteria. A single-center retrospective cohort study From April 1, 2014 to November 30, 2017 in an urban 350-bed referral hospital in Japan We collected patient characteristics such as activation triggers, interventions, arrival times, dispositions, final diagnoses, and patient outcomes. Poor clinical outcomes were defined as unplanned intensive care unit transfers or deaths within 24 hours. Correlations between the {NEWS} and clinical outcomes at the time of deterioration and disposition were analyzed. Among 31 outpatients, the {NEWS} value decreased significantly after a medical emergency team intervention (median, 8 vs 4, P {\textless} .001). The difference in the {NEWS} at the time of deterioration and at disposition was significantly less in patients with poor clinical outcomes (median 3 vs 1.5, P = .03). The area under the curve ({AUC}) for the {NEWS} high-risk patient group at the time of deterioration for predicting hospital admission was 0.85 (95\% confidence interval [{CI}], 0.67–1.0), while the {AUC} for the {NEWS} high-risk patient group at disposition for predicting poor clinical outcomes was 0.83 (95\% {CI}, 0.62–1.0). The difference between the {NEWS} at the time of deterioration and at disposition might usefully predict admissions and poor clinical outcomes in {RRS} outpatient settings.}, pages = {e18475}, number = {52}, journaltitle = {Medicine}, author = {Ehara, Jun and Hiraoka, Eiji and Hsu, Hsiang-Chin and Yamada, Toru and Homma, Yosuke and Fujitani, Shigeki}, urldate = {2023-04-26}, date = {2019-12}, langid = {american}, file = {Full Text:/home/ulinja/Zotero/storage/C5GPM6LE/Ehara et al. - 2019 - The effectiveness of a national early warning scor.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/PCS2IG9C/The_effectiveness_of_a_national_early_warning.30.html:text/html}, } @article{brekke_value_2019, title = {The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review}, volume = {14}, issn = {1932-6203}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210875}, doi = {10.1371/journal.pone.0210875}, shorttitle = {The value of vital sign trends in predicting and monitoring clinical deterioration}, abstract = {Background Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear. Objective To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness. Data Sources {PubMed}, Embase, Cochrane Library and {CINAHL} were searched in December 2017. Study Selection Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest. Data Extraction Performed separately by two authors using a preformed extraction sheet. Results Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated. Conclusions This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.}, pages = {e0210875}, number = {1}, journaltitle = {{PLOS} {ONE}}, author = {Brekke, Idar Johan and Puntervoll, Lars Håland and Pedersen, Peter Bank and Kellett, John and Brabrand, Mikkel}, urldate = {2023-04-26}, date = {2019-01-15}, langid = {english}, note = {Publisher: Public Library of Science}, keywords = {Heart rate, Blood pressure, Cardiac arrest, Cohort studies, Medical risk factors, Oxygen, Respiration, Systematic reviews}, file = {Full Text PDF:/home/ulinja/Zotero/storage/5VV8R3MF/Brekke et al. - 2019 - The value of vital sign trends in predicting and m.pdf:application/pdf}, } @article{buist_association_2004, title = {Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study}, volume = {62}, issn = {0300-9572}, url = {https://www.sciencedirect.com/science/article/pii/S0300957204001236}, doi = {10.1016/j.resuscitation.2004.03.005}, shorttitle = {Association between clinically abnormal observations and subsequent in-hospital mortality}, abstract = {Background: Patients with unexpected in-hospital cardiac arrest often have an abnormal clinical observation prior to the arrest. Previous studies have suggested that a medical emergency team responding to such patients may decrease in-hospital mortality from cardiac arrest, but the association between any abnormal clinical observation and subsequent increased mortality has not been studied prospectively. The aim of this study was to determine the predictive value of selected abnormal clinical observations in a ward population for subsequent in-hospital mortality. Design and setting: Prospective data collection in five general hospital ward areas at Dandenong Hospital, Victoria, Australia. Interventions: None. Results: During the study period, 6303 patients were admitted to the study areas. Of those, 564 (8.9\%) experienced 1598 pre-determined clinically abnormal events and 146 of these patients (26\%) died. The two commonest abnormal clinical events were arterial oxygen desaturation (51\% of all events), and hypotension (17.3\% of all events). Using a multiple linear logistic regression model, there were six clinical observations which were significant predictors of mortality. These were: a decrease in Glasgow Coma Score by two points, onset of coma, hypotension ({\textless}90mmHg), respiratory rate {\textless}6min−1, oxygen saturation {\textless}90\%, and bradycardia {\textgreater}30min−1. The presence of any one of the six events was associated with a 6.8-fold (95\% {CI}: 2.7–17.1) increase in the risk of mortality. Conclusions: Six abnormal clinical observations are associated with a high risk of mortality for in-hospital patients. These observations should be included as criteria for the early identification of patients at higher risk of unexpected in-hospital cardiac arrest. Sumàrio Contexto: Os doentes vı́timas de paragem cardı́aca intra-hospitalar inesperada têm frequentemente uma observação clı́nica anormal antes da paragem. Estudos prévios sugerem que se esses doentes forem socorridos por uma equipa médica de emergência pode-se diminuir a mortalidade intra-hospitalar por paragem cardı́aca, mas a associação entre alterações clı́nicas e aumento da mortalidade subsequente não foi estudada de forma prospectiva. O objectivo deste estudo foi determinar o valor preditivo, para mortalidade intra-hospitalar de alterações clı́nicas, seleccionadas, numa população de enfermaria. Desenho: Recolha prospectiva de dados em cinco áreas de enfermaria geral hospitalar no Hospital Dandenong, Victoria, Austrália. Intervenções: Nenhuma. Resultados: Durante o perı́odo de estudo, foram admitidos 6303 doentes nas áreas de estudo. Em 564 (8.9\%) ocorreram 1598 alterações clı́nicas de um grupo pré-determinado e 146 (26\%) morreram. As duas alterações clı́nicas mais frequentes foram a dessaturação de oxigénio arterial (51\% de todos os eventos), e a hipotensão (17.3\% de todos os eventos). Utilizando um modelo de regressão logı́stica linear múltipla, houve seis observações clı́nicas que significativamente preditivas de mortalidade: Deterioração do Score da Escala de Coma de Glasgow em 2 pontos, inı́cio de coma, hipotensão ({\textless}90mmHg), frequência respiratória {\textless}6min, saturação de oxigénio {\textless}90\%, e bradicardia {\textgreater}30min. A presença de qualquer um destes seis acontecimentos associou-se ao aumento de 6.8 vezes (95\% {CI}: 2.7–17.1) no risco de morte. Conclusões: Identificaram-se seis alterações clı́nicas associadas a aumento de risco de morte em doentes hospitalizados. Estas observações devem ser incluı́das como critérios para a identificação precoce dos doentes com risco mais elevado de paragem cardı́aca intra-hospitalar inesperada. Resumen Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario tienen frecuentemente hallazgos clı́nicos anormales previos al paro. Estudios previos sugieren que el equipo de emergencias médicas que responde a tales pacientes podrı́a disminuir la mortalidad intrahospitalaria, pero la asociación entre los hallazgos clı́nicos anormales y mortalidad aumentada subsiguiente no ha sido estudiada prospectivamente. El objetivo de este estudio fue determinar el valor de determinados hallazgos clı́nicos anormales para predecir mortalidad intrahospitalaria. Diseño y Ambiente: Recolección prospectiva de datos en cinco áreas de salas generales en el Hospital Dandenong, en Victoria, Australia. Intervenciones: ninguna. Resultados: Durante el perı́odo de estudio, 6303 pacientes fueron admitidos en las áreas del estudio. De aquellos, 564 (8.9\%) experimentaron 1598 eventos clı́nicamente anormales y 164 de estos pacientes (26\%) murieron. Los dos eventos clı́nicos anormales mas comunes fueron la des saturación (51\% de las alertas), y la hipotensión (17.3\% de los eventos). Se analizó usando un modelo de regresión logı́stica linear múltiple, y se encontraron seis hallazgos clı́nicos que eran predictores significativos de mortalidad. Estos fueron: una disminución en dos puntos en la escala de coma de Glasgow, instalación de coma, hipotensión ({\textless}90mmHg), frecuencia respiratoria {\textless} 6min−1, saturación de oxı́geno {\textless}90\%, y bradicardia {\textgreater}30min−1. La presencia de cualquiera de estos 6 eventos se asoció con un aumento en 6.8 veces del riesgo de mortalidad (95\% {CI}, 2.7–17.1). Conclusiones: Seis hallazgos clı́nicos anormales están asociadas con mayor riesgo de mortalidad intrahospitalaria de pacientes. Estos hallazgos clı́nicos deberı́an incluirse como criterios para la identificación temprana de pacientes en mayor riesgo de paro cardı́aco intrahospitalario inesperado.}, pages = {137--141}, number = {2}, journaltitle = {Resuscitation}, author = {Buist, Michael and Bernard, Stephen and Nguyen, Tuan V and Moore, Gaye and Anderson, Jeremy}, urldate = {2023-04-26}, date = {2004-08-01}, langid = {english}, keywords = {Cardiac arrest, Equipa de emergência médica, Equipo de emergencias médicas, Medical emergency team, Paragem cardı́aca, Paro cardı́aco}, file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/8GDBCNAU/S0300957204001236.html:text/html}, } @article{javanbakht_cost_2020, title = {Cost utility analysis of continuous and intermittent versus intermittent vital signs monitoring in patients admitted to surgical wards}, volume = {23}, issn = {1369-6998}, url = {https://doi.org/10.1080/13696998.2020.1747474}, doi = {10.1080/13696998.2020.1747474}, abstract = {Background: Complications after surgical procedures are common and can lead to a prolonged hospital stay, increased rates of postoperative hospital readmission, and increased mortality. Monitoring vital signs is an effective way to identify patients who are experiencing a deterioration in health. {SensiumVitals} is wireless system that includes a lightweight, digital patch that monitors vital signs at two minute intervals, and has shown promise in the early identification of patients at high risk of deterioration.Objective: To evaluate the cost-utility of continuous monitoring of vital signs with {SensiumVitals} in addition to intermittent monitoring compared to the usual care of patients admitted to surgical wards.Methods: A de novo decision analytic model, based on current treatment pathways, was developed to estimate the costs and outcomes. Results from randomised clinical trials and national standard sources were used to inform the model. Costs were estimated from the {NHS} and {PSS} perspective. Deterministic and probabilistic sensitivity analyses ({PSA}) were conducted to explore uncertainty surrounding input parameters.Results: Over a 30-day time horizon, intermittent monitoring in addition to continuous monitoring of vital signs with {SensiumVitals} was less costly than intermittent vital signs monitoring alone. The total cost per patient was £6,329 versus £5,863 for the comparator and intervention groups respectively and the total effectiveness per patient was 0.057 {QALYs} in each group. Results from the {PSA} showed that use of {SensiumVitals} in addition to intermittent monitoring has 73\% probability of being cost-effective at a £20,000 willingness-to-pay threshold and 73\% probability of being cost-saving compared to the comparator. Cost savings were driven by reduced costs of hospital readmissions and length of stays in hospital.Conclusions: Use of {SensiumVitals} as a postoperative intervention for patients on surgical wards is a cost-saving and cost-effective strategy, yielding improvements in recovery with decreased health resource use.Key Points for Decision {MakersSensiumVitals} has the potential to reduce the length of postoperative hospital stay, readmission rates, and associated costs in postoperative patients.In this study, {SensiumVitals} has been found to be a cost-saving (dominant) and cost-effective (dominant) intervention for monitoring the vital signs of surgical patients postoperatively.}, pages = {728--736}, number = {7}, journaltitle = {Journal of Medical Economics}, author = {Javanbakht, Mehdi and Mashayekhi, Atefeh and Trevor, Miranda and Rezaei Hemami, Mohsen and L. Downey, Candice and Branagan-Harris, Michael and Atkinson, Jowan}, urldate = {2023-04-26}, date = {2020-07-02}, pmid = {32212979}, note = {Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/13696998.2020.1747474}, keywords = {vital signs, continuous monitoring, cost-effectiveness analysis, D70, H51, {SensiumVitals}, surgical patients}, file = {Full Text PDF:/home/ulinja/Zotero/storage/ZZ7Q5R9K/Javanbakht et al. - 2020 - Cost utility analysis of continuous and intermitte.pdf:application/pdf}, } @article{van_rossum_adaptive_2022, title = {Adaptive threshold-based alarm strategies for continuous vital signs monitoring}, volume = {36}, issn = {1573-2614}, url = {https://doi.org/10.1007/s10877-021-00666-4}, doi = {10.1007/s10877-021-00666-4}, abstract = {Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.}, pages = {407--417}, number = {2}, journaltitle = {J Clin Monit Comput}, author = {van Rossum, Mathilde C. and Vlaskamp, Lyan B. and Posthuma, Linda M. and Visscher, Maarten J. and Breteler, Martine J. M. and Hermens, Hermie J. and Kalkman, Cor J. and Preckel, Benedikt}, urldate = {2023-04-26}, date = {2022-04-01}, langid = {english}, keywords = {Clinical alarms, Clinical deterioration, Physiological monitoring, Telemonitoring, Vital signs}, file = {Full Text PDF:/home/ulinja/Zotero/storage/V3VSFEIQ/van Rossum et al. - 2022 - Adaptive threshold-based alarm strategies for cont.pdf:application/pdf}, } @article{downey_patient_2018, title = {Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study}, volume = {114}, issn = {1386-5056}, url = {https://www.sciencedirect.com/science/article/pii/S1386505618302508}, doi = {10.1016/j.ijmedinf.2018.03.014}, shorttitle = {Patient attitudes towards remote continuous vital signs monitoring on general surgery wards}, abstract = {Background Vital signs monitoring is used to identify deteriorating patients in hospital. The most common tool for vital signs monitoring is an early warning score, although emerging technologies allow for remote, continuous patient monitoring. A number of reviews have examined the impact of continuous monitoring on patient outcomes, but little is known about the patient experience. This study aims to discover what patients think of monitoring in hospital, with a particular emphasis on intermittent early warning scores versus remote continuous monitoring, in order to inform future implementations of continuous monitoring technology. Methods Semi-structured interviews were undertaken with 12 surgical inpatients as part of a study testing a remote continuous monitoring device. All patients were monitored with both an early warning score and the new device. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. Findings Patients can see the value in remote, continuous monitoring, particularly overnight. However, patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care. Conclusion Early warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout.}, pages = {52--56}, journaltitle = {International Journal of Medical Informatics}, author = {Downey, C. L. and Brown, J. M. and Jayne, D. G. and Randell, R.}, urldate = {2023-04-26}, date = {2018-06-01}, langid = {english}, keywords = {Early warning scores, Interviews, Monitoring, Patient experience, Vital signs}, file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/BBCZQB5R/S1386505618302508.html:text/html;Submitted Version:/home/ulinja/Zotero/storage/AL4WYTXJ/Downey et al. - 2018 - Patient attitudes towards remote continuous vital .pdf:application/pdf}, } @article{joshi_wearable_2019, title = {Wearable sensors to improve detection of patient deterioration}, volume = {16}, issn = {1743-4440}, url = {https://doi.org/10.1080/17434440.2019.1563480}, doi = {10.1080/17434440.2019.1563480}, abstract = {Introduction: Monitoring a patient’s vital signs forms a basic component of care, enabling the identification of deteriorating patients and increasing the likelihood of improving patient outcomes. Several paper-based track and trigger warning scores have been developed to allow clinical evaluation of a patient and guidance on escalation protocols and frequency of monitoring. However, evidence suggests that patient deterioration on hospital wards is still missed, and that patients are still falling through the safety net. Wearable sensor technology is currently undergoing huge growth, and the development of new light-weight wireless wearable sensors has enabled multiple vital signs monitoring of ward patients continuously and in real time.Areas covered: In this paper, we aim to closely examine the benefits of wearable monitoring applications that measure multiple vital signs; in the context of improving healthcare and delivery. A review of the literature was performed.Expert commentary: Findings suggest that several sensor designs are available with the potential to improve patient safety for both hospital patients and those at home. Larger clinical trials are required to ensure both diagnostic accuracy and usability.}, pages = {145--154}, number = {2}, journaltitle = {Expert Review of Medical Devices}, author = {Joshi, Meera and Ashrafian, Hutan and Aufegger, Lisa and Khan, Sadia and Arora, Sonal and Cooke, Graham and Darzi, Ara}, urldate = {2023-04-26}, date = {2019-02-01}, pmid = {30580650}, note = {Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/17434440.2019.1563480}, keywords = {Continuous monitoring, hospital, patient deterioration, vital signs, ward patients, wearable sensors}, } @article{un_observational_2021, title = {Observational study on wearable biosensors and machine learning-based remote monitoring of {COVID}-19 patients}, volume = {11}, rights = {2021 The Author(s)}, issn = {2045-2322}, url = {https://www.nature.com/articles/s41598-021-82771-7}, doi = {10.1038/s41598-021-82771-7}, abstract = {Patients infected with {SARS}-{CoV}-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild {COVID}-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9\%) with mild {COVID}-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 ({NEWS}2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p {\textless} 0.0001) and oxygen saturation (r = 0.87, p {\textless} 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index ({BI}), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily {BI} was linearly associated with respiratory tract viral load (p {\textless} 0.0001) and {NEWS}2 (r = 0.75, p {\textless} 0.001). {BI} was superior to {NEWS}2 in predicting clinical worsening events (sensitivity 94.1\% and specificity 88.9\%) and prolonged hospitalization (sensitivity 66.7\% and specificity 72.7\%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.}, pages = {4388}, number = {1}, journaltitle = {Sci Rep}, author = {Un, Ka-Chun and Wong, Chun-Ka and Lau, Yuk-Ming and Lee, Jeffrey Chun-Yin and Tam, Frankie Chor-Cheung and Lai, Wing-Hon and Lau, Yee-Man and Chen, Hao and Wibowo, Sandi and Zhang, Xiaozhu and Yan, Minghao and Wu, Esther and Chan, Soon-Chee and Lee, Sze-Ming and Chow, Augustine and Tong, Raymond Cheuk-Fung and Majmudar, Maulik D. and Rajput, Kuldeep Singh and Hung, Ivan Fan-Ngai and Siu, Chung-Wah}, urldate = {2023-04-26}, date = {2021-02-23}, langid = {english}, note = {Number: 1 Publisher: Nature Publishing Group}, keywords = {Predictive medicine, Viral infection}, file = {Full Text PDF:/home/ulinja/Zotero/storage/QCVKTE57/Un et al. - 2021 - Observational study on wearable biosensors and mac.pdf:application/pdf}, } @article{gronbaek_continuous_2023, title = {Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with {COVID}-19}, volume = {67}, issn = {1399-6576}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/aas.14221}, doi = {10.1111/aas.14221}, abstract = {Background Patients admitted to the emergency care setting with {COVID}-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to {ICU} or having fatal outcome vs. those that were not. Methods Two wireless sensors continuously monitored ({CM}) respiratory rate ({RR}), heart rate ({HR}), and peripheral arterial oxygen saturation ({SpO}2). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score ({NEWS}). Results {SpO}2 {\textless} 92\% for more than 60 min was detected in 92\% of the patients with {CM} vs. 40\% with {NEWS} (p {\textless} .00001). {RR} {\textgreater} 24 breaths per minute for more than 5 min were detected in 70\% with {CM} vs. 33\% using {NEWS} (p = .0001). {HR} ≥ 111 for more than 60 min was seen in 51\% with {CM} and 22\% with {NEWS} (p = .0002). Patients admitted to {ICU} or having fatal outcome had longer durations of {RR} {\textgreater} 24 brpm (p = .01), {RR} {\textgreater} 21 brpm (p = .01), {SpO}2 {\textless} 80\% (p = .01), and {SpO}2 {\textless} 85\% (p = .02) compared to patients that were not. Conclusion Episodes of desaturation and tachypnea in hospitalized patients with {COVID}-19 infection are common and often not detected by routine measurements.}, pages = {640--648}, number = {5}, journaltitle = {Acta Anaesthesiologica Scandinavica}, author = {Grønbæk, Katja Kjær and Rasmussen, Søren Møller and Langer, Natasha Hemicke and Vincentz, Mette and Oxbøll, Anne-Britt and Søgaard, Marlene and Awada, Hussein Nasser and Jensen, Tomas O. and Jensen, Magnus Thorsten and Sørensen, Helge B. D. and Aasvang, Eske Kvanner and Meyhoff, Christian Sylvest}, urldate = {2023-04-26}, date = {2023}, langid = {english}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/aas.14221}, keywords = {continuous monitoring, {COVID}-19, deterioration, early warning score, hospital admission, patient safety}, file = {Full Text PDF:/home/ulinja/Zotero/storage/P9XWRWXW/Grønbæk et al. - 2023 - Continuous monitoring is superior to manual measur.pdf:application/pdf}, } @article{downey_strengths_2017, title = {Strengths and limitations of early warning scores: A systematic review and narrative synthesis}, volume = {76}, issn = {0020-7489}, doi = {10.1016/j.ijnurstu.2017.09.003}, shorttitle = {Strengths and limitations of early warning scores}, abstract = {Background Early warning scores are widely used to identify deteriorating patients. Whilst their ability to predict clinical outcomes has been extensively reviewed, there has been no attempt to summarise the overall strengths and limitations of these scores for patients, staff and systems. This review aims to address this gap in the literature to guide improvements for the optimization of patient safety. Methods A systematic review was conducted of {MEDLINE}®, {PubMed}, {CINAHL} and The Cochrane Library in September 2016. The citations and reference lists of selected studies were reviewed for completeness. Studies were included if they evaluated vital signs monitoring in adult human subjects. Studies regarding the paediatric population were excluded, as were studies describing the development or validation of monitoring models. A narrative synthesis of qualitative, quantitative and mixed- methods studies was undertaken. Findings 232 studies met the inclusion criteria. Twelve themes were identified from synthesis of the data: Strengths of early warning scores included their prediction value, influence on clinical outcomes, cross-specialty application, international relevance, interaction with other variables, impact on communication and opportunity for automation. Limitations included their sensitivity, the need for practitioner engagement, the need for reaction to escalation and the need for clinical judgment, and the intermittent nature of recording. Early warning scores are known to have good predictive value for patient deterioration and have been shown to improve patient outcomes across a variety of specialties and international settings. This is partly due to their facilitation of communication between healthcare workers. There is evidence that the prediction value of generic early warning scores suffers in comparison to specialty-specific scores, and that their sensitivity can be improved by the addition of other variables. They are also prone to inaccurate recording and user error, which can be partly overcome by automation. Conclusions Early warning scores provide the right language and environment for the timely escalation of patient care. They are limited by their intermittent and user-dependent nature, which can be partially overcome by automation and new continuous monitoring technologies, although clinical judgment remains paramount. © 2017 Elsevier Ltd}, pages = {106--119}, journaltitle = {International Journal of Nursing Studies}, author = {Downey, C.L. and Tahir, W. and Randell, R. and Brown, J.M. and Jayne, D.G.}, date = {2017}, keywords = {Early warning scores, Limitations, Strengths, Systematic review, Vital signs}, file = {Accepted Version:/home/ulinja/Zotero/storage/B4RXIEJI/Downey et al. - 2017 - Strengths and limitations of early warning scores.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/C4DPHSQ6/display.html:text/html}, } @article{eisenkraft_developing_2023, title = {Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor}, volume = {14}, issn = {1664-042X}, doi = {10.3389/fphys.2023.1138647}, abstract = {Background: Currently-used tools for early recognition of clinical deterioration have high sensitivity, but with low specificity and are based on infrequent measurements. We aimed to develop a pre-symptomatic and real-time detection and warning tool for potential patients’ deterioration based on multi-parameter real-time warning score ({MPRT}-{WS}). Methods: A total of more than 2 million measurements were collected, pooled, and analyzed from 521 participants, of which 361 were patients in general wards defined at high-risk for deterioration and 160 were healthy participants allocation as controls. The risk score stratification was based on cutoffs of multiple physiological parameters predefined by a panel of specialists, and included heart rate, blood oxygen saturation ({SpO}2), respiratory rate, cuffless systolic and diastolic blood pressure ({SBP} and {DBP}), body temperature, stroke volume ({SV}), cardiac output, and systemic vascular resistance ({SVR}), recorded every 5 min for a period of up to 72 h. The data was used to define the various risk levels of a real-time detection and warning tool, comparing it with the clinically-used National Early Warning Score ({NEWS}). Results: When comparing risk levels among patients using both tools, 92.6\%, 6.1\%, and 1.3\% of the readings were defined as “Low”, “Medium”, and “High” risk with {NEWS}, and 92.9\%, 6.4\%, and 0.7\%, respectively, with {MPRT}-{WS} (p = 0.863 between tools). Among the 39 patients that deteriorated, 30 patients received ‘High’ or ‘Urgent’ using the {MPRT}-{WS} (42.7 ± 49.1 h before they deteriorated), and only 6 received ‘High’ score using the {NEWS}. The main abnormal vitals for the {MPRT}-{WS} were {SpO}2, {SBP}, and {SV} for the “Urgent” risk level, {DBP}, {SVR}, and {SBP} for the “High” risk level, and {DBP}, {SpO}2, and {SVR} for the “Medium” risk level. Conclusion: As the new detection and warning tool is based on highly-frequent monitoring capabilities, it provides medical teams with timely alerts of pre-symptomatic and real-time deterioration. Copyright © 2023 Eisenkraft, Goldstein, Merin, Fons, Ishay, Nachman and Gepner.}, journaltitle = {Frontiers in Physiology}, author = {Eisenkraft, A. and Goldstein, N. and Merin, R. and Fons, M. and Ishay, A.B. and Nachman, D. and Gepner, Y.}, date = {2023}, keywords = {alarm fatigue, early warning score ({EWS}), multi-parameter monitoring, patient deterioration, pre-symptomatic detection, prevention}, file = {Full Text:/home/ulinja/Zotero/storage/VXWDWLNI/Eisenkraft et al. - 2023 - Developing a real-time detection tool and an early.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/TJCUTVMT/display.html:text/html}, } @article{lee_all-day_2020, title = {All-day mobile healthcare monitoring system based on heterogeneous stretchable sensors for medical emergency}, volume = {67}, issn = {0278-0046}, doi = {10.1109/TIE.2019.2950842}, abstract = {Epidermal and wearable electronic sensor technologies have gained extensive interest in recent years owing to deliver real-time healthcare information to the personalized smartphone. Herein, we proposed a fully integrated wearable smart patch-based sensor system with Kirigami-inspired strain-free deformable structures having temperature and humidity sensors along with a commercial acceleration sensor. The presented fully integrated wearable sensor system easily attaches to the skin to accurately determine the body information, and integrated circuit including read-out circuit and wireless communication transfer medical information (temperature, humidity, and motion) to mobile phone to assist with emergencies due to 'unpredictable' deviations and to aid in medical checkups for vulnerable patients. This article addresses the challenge of all-day continuous monitoring of human body biological signals by introducing the well-equipped breathable (water permeability 80 gm-1 h-1), excellent adhesion to the skin (peel strength {\textless} 200 gf/12 mm), biocompatible, and conformable smart patch that can absorb the moisture (sweat) generated from the skin without any harshness and allowing the users' to continuously monitor the early detection of diagnosis. Furthermore, the proposed patch-based medical device enables wireless sensing capabilities in response to rapid variation, equipped with a customized circuit design, low-power Bluetooth module, and a signal processing integrated circuit mounted on a flexible printed circuit board. Thus, a unique platform is established for multifunctional sensors to interface with hard electronics, providing emerging opportunities in the biomedical field as well as Internet-of-Things applications. © 1982-2012 {IEEE}.}, pages = {8808--8816}, number = {10}, journaltitle = {{IEEE} Transactions on Industrial Electronics}, author = {Lee, S. and Gandla, S. and Naqi, M. and Jung, U. and Youn, H. and Pyun, D. and Rhee, Y. and Kang, S. and Kwon, H.-J. and Kim, H. and Lee, M.G. and Kim, S.}, date = {2020}, keywords = {Flexible printed circuit board ({FPCB}), kirigami-serpentine heterogeneous structure, smart patch device, water permeable, wearable}, file = {Full Text:/home/ulinja/Zotero/storage/W38FIN5E/Lee et al. - 2020 - All-day mobile healthcare monitoring system based .pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/4GFD9BEF/display.html:text/html}, } @inproceedings{yeri_iot_2020-3, title = {{IoT} based Real Time Health Monitoring}, doi = {10.1109/ICIRCA48905.2020.9183194}, abstract = {Conventional sensor based diagnosis in medial field requires more number of sensors and human efforts if it is processed in a large scale. It is a difficult task due to the shortage of medical professionals and system setup. To overcome this issue an {IoT} based health care application is proposed in the research work. The proposed system consists of the web and mobile application based on continuous wireless monitoring of patients. The objective is paper is to implement a low-cost system and transmit the patient vital signs in emergency situations. Sensors are being used for measuring the patient vital signs by using the wireless network. The sensors data are collected and transmitted to the cloud for storage via Wi-Fi module connected with the controller. The data is processed in the cloud and feedback steps are taken on the analysed data which can be further analysed by a doctor remotely. Remote viewing reduces burden to doctors and provides the exact health status of patients. If the patient needs urgent attention then a message is sent to the doctor.}, eventtitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, pages = {980--984}, booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})}, author = {Yeri, Vani and Shubhangi, D.C.}, date = {2020-07}, keywords = {Arduino, Cloud computing, Health, {IoT}, Medical services, monitoring, Monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks}, file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/I86F2Q3I/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/FS73U9GZ/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf}, } @article{thippeswamy_prototype_2021, title = {Prototype development of continuous remote monitoring of {ICU} patients at home}, volume = {20}, issn = {1631-4670}, doi = {10.18280/i2m.200203}, abstract = {Vital signs are a group of essential body parameter, which provides the overall health state of a human body. They often play a pivotal role in accessing the overall physiological state of the human body. For patients requiring intense and continuous monitoring, especially those in an Intensive Care Unit, the essentiality to assess their vital signs regularly. Monitoring the health status of {ICU} patients becomes quite cost-effective when the same can be monitored within the comfort zone of their own house. The technique elaborated herein revolves around the fundamental idea of implementing a vital sign monitoring system that continuously assesses a patient and regularly updates the same to a centralized server system. In an event of a medical emergency, the relevant data is conveyed to the doctor via an efficient alert system, thereby ensuring safe and timely treatment to the patients. Also, as suggested the proposed design is characterized by {IoT} capability that allows real-time monitoring of the subject, thereby allowing, minimizing the human involvement in its operation. © 2021 Lavoisier. All rights reserved.}, pages = {79--84}, number = {2}, journaltitle = {Instrumentation Mesure Metrologie}, author = {Thippeswamy, V.S. and Shivakumaraswamy, P.M. and Chickaramanna, S.G. and Iyengar, V.M. and Das, A.P. and Sharma, A.}, date = {2021}, keywords = {{ECG}, Heart rate, {ICU}, Internet of things, Real-time monitoring, {SpO}2, Vital signs}, file = {Full Text:/home/ulinja/Zotero/storage/8XZ7QJYE/Thippeswamy et al. - 2021 - Prototype development of continuous remote monitor.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/B7RR7ZAW/display.html:text/html}, } @inproceedings{phaltankar_curaband_2021, location = {Singapore}, title = {{CuraBand}: Health Monitoring and Warning System}, isbn = {9789811551130}, doi = {10.1007/978-981-15-5113-0_86}, series = {Advances in Intelligent Systems and Computing}, shorttitle = {{CuraBand}}, abstract = {Phaltankar, {SopanTyagi}, {KirtiPrabhu}, {MeghnaJaguste}, {PranavSahu}, {ShubhamKalbande}, {DhananjayWe} have come to an era where the hospitals have increasing needs in terms of staff members. The ratio of patients to hospital staff has increased drastically over the years. One nurse cannot continuously monitor all the patients throughout the day. Hence, there is a constant desire for devices for continuous monitoring of patients that are just shifted outside the {ICU} and are still at grave risk. This paper deals with the design and development of a smart wristband—{CuraBand}, which is a compact vital parameter monitoring device. It continually monitors the temperature, heart rate, and blood oxygen saturation level and alerts the respective authorities in case of abnormalities in the readings through an android application, thus ensuring immediate action. This project aims to provide a provision for the doctors to monitor their patients from anywhere in the world.}, pages = {1017--1026}, booktitle = {International Conference on Innovative Computing and Communications}, publisher = {Springer}, author = {Phaltankar, Sopan and Tyagi, Kirti and Prabhu, Meghna and Jaguste, Pranav and Sahu, Shubham and Kalbande, Dhananjay}, editor = {Gupta, Deepak and Khanna, Ashish and Bhattacharyya, Siddhartha and Hassanien, Aboul Ella and Anand, Sameer and Jaiswal, Ajay}, date = {2021}, langid = {english}, keywords = {Alert, Android application, Cloud computing, Health care, Internet of things, {IoT}, Vital parameters, Wristband}, file = {Full Text PDF:/home/ulinja/Zotero/storage/H4IPCNUM/Phaltankar et al. - 2021 - CuraBand Health Monitoring and Warning System.pdf:application/pdf}, } @article{dagan_use_2020, title = {Use of ultra-low cost fitness trackers as clinical monitors in low resource emergency departments}, volume = {7}, issn = {2383-4625}, doi = {10.15441/ceem.19.081}, abstract = {In low resource hospitals, strained staffing ratios and lack of telemetry can put patients at risk for clinical deterioration and unexpected cardiac arrest. While traditional telemetry systems can provide real-time continuous vital signs, they are too expensive for widespread use in these set-tings. At the same time, developed countries such as the United States have been increasingly utilizing remote monitoring systems to shift patient care from hospital to home. While the con-text is dramatically different, the challenge of monitoring patients in otherwise unmonitored settings is the same. At-home monitoring solutions range from highly comprehensive and expensive systems to inexpensive fitness trackers. In the field of global health, the adoption of this technology has been somewhat limited. We believe that low cost fitness trackers present an op-portunity to address the challenge of vital sign monitoring in resource-poor settings at a fraction of the cost of existing technical solutions. © 2020 The Korean Society of Emergency Medicine.}, pages = {144--149}, number = {3}, journaltitle = {Clinical and Experimental Emergency Medicine}, author = {Dagan, A. and Mechanic, O.J.}, date = {2020}, keywords = {Fitness trackers, Global health, Internet of Things, Monitoring, physiologic, Telemedicine}, file = {Full Text:/home/ulinja/Zotero/storage/2F69NQX4/Dagan and Mechanic - 2020 - Use of ultra-low cost fitness trackers as clinical.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/EV9AC9P6/display.html:text/html}, } @inproceedings{anzanpour_internet_2015-1, title = {Internet of things enabled in-home health monitoring system using early warning score}, isbn = {978-1-63190-088-4}, doi = {10.4108/eai.14-10-2015.2261616}, abstract = {Early warning score ({EWS}) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, {EWS} procedure is mostly used for in-hospital clinical cases and is performed in a manual paper-based fashion. In this paper, we propose an automated {EWS} health monitoring system to intelligently monitor vital signs and prevent health deterioration for in-home patients using Internet-of-Things ({IoT}) technologies. {IoT} enables our solution to provide a real-Time 24/7 service for health professionals to remotely monitor inhome patients via Internet and receive notifications in case of emergency. We also demonstrate a proof-of-concept {EWS} system where continuous reading, transferring, recording, and processing of vital signs have been implemented. Copyright © 2015 {ICST}.}, eventtitle = {{MOBIHEALTH} 2015 - 5th {EAI} International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies}, author = {Anzanpour, A. and Rahmani, A.-M. and Liljeberg, P. and Tenhunen, H.}, date = {2015}, keywords = {Body Area Network, {EarlyWarning} Score, Internet of Things, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network}, file = {Full Text:/home/ulinja/Zotero/storage/37NIRLAE/Anzanpour et al. - 2015 - Internet of things enabled in-home health monitori.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/BSQHA7RC/display.html:text/html}, } @article{imtyaz_ahmed_secure_2022, title = {Secure and lightweight privacy preserving Internet of things integration for remote patient monitoring}, volume = {34}, issn = {1319-1578}, doi = {10.1016/j.jksuci.2021.07.016}, abstract = {The present article throws light on advancement in {ICTs}. It is an evident that highly intelligent and smart {IoT} based use cases are possible with the advent in {ICTs} like Internet of Things, 5G Cellular Technology and Cyber- Physical Systems ({CPS}). For an instance, people spend considerable amount of their earning towards health in the present scenario. In view of this, there is high- impact- on society use case in Healthcare as {IoT} enables Ambient Assisted Living ({AAL}), Mobile Health ({mHealth}) and Electronic Health ({eHealth}). The conventional healthcare services are prone to delay, wastage of time and money, besides causing death of people. With intelligence and prediction capabilities of {IoT}, Remote Patient Monitoring ({RPM}) on regular basis (home/office/in-hospital), for those who deliberately need it, can be exploited to overcome challenges thrown by conventional healthcare units. {IoT} based {RPM} with wearable devices, sensor network and other digital infrastructure form an early warning system for impending emergencies that lead to severe health issues and even death of patients is left untreated or even treatment is delayed. It is proposed that a secure and privacy preserving {IoT} integration with healthcare units for realizing a reliable, available and secure {RPM} system at the conclusion. The proposed system provides secure {RFID} based authentication, end-to-end secure communications and privacy protection. The system includes {MOTO} 360 watch (biosensor {\textbar} body sensor) with Android wearable {OS}, server with {REST} framework and a smart phone application to monitor and detect fall, blood pressure and heart rate. This motivating scenario is enriched with security and privacy. The empirical evaluation revealed that the proposed {RPM} has potential to help improve quality of life and healthcare services © 2021 The Authors}, pages = {6895--6908}, number = {9}, journaltitle = {Journal of King Saud University - Computer and Information Sciences}, author = {Imtyaz Ahmed, M. and Kannan, G.}, date = {2022}, keywords = {Biosensors, Healthcare, Internet of Things, Privacy, Remote patient monitoring, Security}, file = {Full Text:/home/ulinja/Zotero/storage/6ZW7G4RL/Imtyaz Ahmed and Kannan - 2022 - Secure and lightweight privacy preserving Internet.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/EFJRSHJQ/display.html:text/html}, } @article{pahlevanynejad_personalized_2023, title = {Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges}, volume = {2023}, issn = {1687-6415}, doi = {10.1155/2023/5390712}, shorttitle = {Personalized Mobile Health for Elderly Home Care}, abstract = {Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health ({PMH}) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-Text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25\% of the challenges were related to privacy, cybersecurity, and data ownership (10\%), technology (7.5\%), and implementation (7.5\%). The most frequent benefits were related to cost-saving (17.5\%), nurse engagement improvement (10\%), and caregiver stress reduction (7.5\%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use {PMH} technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits. © 2023 Shahrbanoo Pahlevanynejad et al.}, journaltitle = {International Journal of Telemedicine and Applications}, author = {Pahlevanynejad, S. and Niakan Kalhori, S.R. and Katigari, M.R. and Eshpala, R.H.}, date = {2023}, file = {Full Text:/home/ulinja/Zotero/storage/MTKHYSAJ/Pahlevanynejad et al. - 2023 - Personalized Mobile Health for Elderly Home Care .pdf:application/pdf}, }