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http://dx.doi.org/10.9717/kmms.2022.25.3.510

Design and Implementation of a Mobile-based Sarcopenia Prediction and Monitoring System  

Kang, Hyeonmin (HCI Lab, Yonsei University)
Park, Chaieun (HCI Lab, Yonsei University)
Ju, Minina (HCI Lab, Yonsei University)
Seo, Seokkyo (Department of Obstetrics and Gynecology, Yonsei University College of medicine)
Jeon, Justin Y. (Department of Sport Industry, Yonsei University)
Kim, Jinwoo (HCI Lab, Yonsei University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.3, 2022 , pp. 510-518 More about this Journal
Abstract
This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.
Keywords
Sarcopenia; Diagnostic; Digital Bio-Marker; Healthcare; e-Health;
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