<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a proof of the concept cloud based patient monitoring and self-care platform, powered by measurements provided from various smartphone sensors. The Cloud platform provides the infrastructure and computational capacity for calculation of the navigation and motion tracking system, fall detection monitoring, as well as emergency notifications. The navigation system uses the pedometer and fusion of the accelerometer, gyroscope and magnetometer sensors. It aims to estimate precisely the patient’s movement and location. While both navigation and tracking systems can independently determine the incremental movement and indoor localization of the patients, they are fused in order to provide more accurate estimations. The fall detection monitoring is enabled by processing the raw data collected from the smartphone’s accelerometer and gyroscope. Furthermore, the cloud system provides various statistics for the physical activity of the patients, based on measurements from the pedometer. Consequently, this paper proposes a proof of the concept cloud based platform that is scalable and highly responsive, used for real-time monitoring and tracking a large number of patients. It also provides indoor navigation and other self-care features.</p></abstract>

This paper presents a proof of the concept cloud based patient monitoring and self-care platform, powered by measurements provided from various smartphone sensors. The Cloud platform provides the infrastructure and computational capacity for calculation of the navigation and motion tracking system, fall detection monitoring, as well as emergency notifications. The navigation system uses the pedometer and fusion of the accelerometer, gyroscope and magnetometer sensors. It aims to estimate precisely the patient’s movement and location. While both navigation and tracking systems can independently determine the incremental movement and indoor localization of the patients, they are fused in order to provide more accurate estimations. The fall detection monitoring is enabled by processing the raw data collected from the smartphone’s accelerometer and gyroscope. Furthermore, the cloud system provides various statistics for the physical activity of the patients, based on measurements from the pedometer. Consequently, this paper proposes a proof of the concept cloud based platform that is scalable and highly responsive, used for real-time monitoring and tracking a large number of patients. It also provides indoor navigation and other self-care features.

Cloud Based Patient Monitoring Platform Using Android Smartphone Sensors

Faculty of Computer Science and Engineering, University of Ss Cyril and Methodius, “Rugjer Boshkovikj” 16, PO Box 393, 1000 Skopje, R. of Macedonia

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Cloud Based Patient Monitoring Platform Using Android Smartphone Sensors"}

This paper presents a proof of the concept cloud based patient monitoring and self-care platform, powered by measurements provided from various smartphone...

Cloud Based Patient Monitoring Platform Using Android Smartphone Sensors

Online veröffentlicht: 19. Jan. 2016

Seitenbereich: 109 - 119

DOI: https://doi.org/10.1515/cait-2015-0094

Schlüsselwörter
Self-care, home care, patient monitoring platform, android sensor fusion, step counter, cloud computing, indoor navigation

© 2015 Vlatko Nikolovski et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Cloud Based Patient Monitoring Platform Using Android Smartphone Sensors

Online veröffentlicht: 19. Jan. 2016

Seitenbereich: 109 - 119

DOI: https://doi.org/10.1515/cait-2015-0094

SchlüsselwörterSelf-care, home care, patient monitoring platform, android sensor fusion, step counter, cloud computing, indoor navigation

© 2015 Vlatko Nikolovski et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Self-care, home care, patient monitoring platform, android sensor fusion, step counter, cloud computing, indoor navigation