scholarly journals Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study (Preprint)

2020 ◽  
Author(s):  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Plinio Pelegrini Morita
Keyword(s):  
Data Collection ◽  
Health Monitoring ◽  
Smart Home ◽  
Patient Monitoring ◽  
Health Agency ◽  
Motion Sensors ◽  
Remote Patient Monitoring ◽  
The Public ◽  
Remote Patient ◽  
Smart Thermostat

BACKGROUND Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. OBJECTIVE The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. METHODS We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. RESULTS The results showed a significant Spearman rank correlation coefficient of 0.8 (<i>P&lt;</i>.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. CONCLUSIONS The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home. CLINICALTRIAL

scholarly journals Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study

10.2196/21016 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21016
Author(s):  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Plinio Pelegrini Morita
Keyword(s):  
Data Collection ◽  
Health Monitoring ◽  
Smart Home ◽  
Patient Monitoring ◽  
Health Agency ◽  
Motion Sensors ◽  
Remote Patient Monitoring ◽  
The Public ◽  
Remote Patient ◽  
Smart Thermostat

Background Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. Objective The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual’s movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. Methods We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. Results The results showed a significant Spearman rank correlation coefficient of 0.8 (P<.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. Conclusions The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home.

Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Rationale of Trial Design (Preprint)

2021 ◽  
Author(s):  
Ankit Bhatia ◽  
Gregory Ewald ◽  
Thomas Maddox
Keyword(s):  
Heart Failure ◽  
Data Collection ◽  
Patient Monitoring ◽  
Patient Adherence ◽  
Performance Status ◽  
Prospective Trial ◽  
Emergency Department Visits ◽  
Remote Patient Monitoring ◽  
Home Based ◽  
Remote Patient

UNSTRUCTURED Heart Failure (HF) remains a leading cause of mortality, and a major driver of healthcare utilization. Effective outpatient management requires the ability to identify and manage impending HF decompensation. Remote patient monitoring (RPM) aims to further address this current need in HF care. To date, RPM approaches employing noninvasive, home-based patient sensors have failed to demonstrate clinical efficacy. The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure Trial (Nov-RPM-HF) aims to address current noninvasive RPM limitations. Nov-RPM-HF will evaluate a clinician-codesigned RPM platform employing emerging data collection and presentation tools. These tools include: (1) a ballistocardiograph to monitor nocturnal patient biometrics, such as heart and respiratory rate, (2) clinical alerts for abnormal biometrics, and (3) longitudinal data presentation for clinician review. Nov-RPM-HF is a 100-patient single-center prospective trial, evaluating patients over 6 months. Outcomes will include: (1) patient adherence to data collection, (2) patient/clinician-perceived utility of the RPM platform, (3) medication changes- including the titration of guideline-directed medical therapy to target doses, (4) HF symptoms/performance status, and (5) unplanned HF hospitalizations or emergency department visits. The results will help to inform the role of noninvasive RPM as a viable clinical management strategy in HF care.

Remote Elderly Health Monitoring System Using Cloud-Based WBANs

2018 ◽  
pp. 265-288
Author(s):  
D. Najumnissa Jamal ◽  
S. Rajkumar ◽  
Nabeena Ameen
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Telecommunication Infrastructure ◽  
Wireless Telecommunication ◽  
Elderly Health ◽  
Patient Monitoring System ◽  
Remote Patient ◽  
Vital Parameters

Monitoring the physical condition of patients is a major errand for specialists. The development of wireless remote elderly patient monitoring system has been intensive in the past. RPM (remote patient monitoring) is reliant on the person's inspiration to deal with their wellbeing. The flow of patient data requires a group of medicinal services suppliers to deal with the information. RPM sending is reliant on a wireless telecommunication infrastructure, which may not be accessible/practical in provincial territories. Patients' data are shared as service on cloud in hospitals. Therefore, in the current research, a new approach of cloud-based wireless remote patient monitoring system during emergency is proposed as a model to monitor the critical health data. The vital parameters are measured and transmitted. In this chapter, the authors present an extensive review of the significant technologies associated with wireless patient monitoring using wireless sensor networks and cloud.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Leadless and Wireless Cardiac Devices: The Next Frontier in Remote Patient Monitoring

2021 ◽  
Vol 46 (5) ◽  
pp. 100800
Author(s):  
Abdulaziz Joury ◽  
Tamunoinemi Bob-Manuel ◽  
Alexandra Sanchez ◽  
Fnu Srinithya ◽  
Amber Sleem ◽  
...  
Keyword(s):  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Cardiac Devices ◽  
Remote Patient
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