eFurniture for home-based frailty detection using artificial neural networks and wireless sensors

• Published in: Medical engineering & physics Vol. 35; no. 2; pp. 263 - 268
• Main Authors: Chang, Yu-Chuan, Lin, Chung-Chih, Lin, Pei-Hsin, Chen, Chun-Chang, Lee, Ren-Guey, Huang, Jing-Siang, Tsai, Tsai-Hsuan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2013
• Subjects: Aged; Artificial neural networks; Expert Testimony; Female; Frail Elderly; Frailty; Housing; Humans; Interior Design and Furnishings; Male; Monitoring, Physiologic - instrumentation; Neural Networks (Computer); Radiology; Telemedicine - instrumentation; Wireless sensor technologies; Wireless Technology - instrumentation; Wireless sensor technologies; Frailty; Artificial neural networks
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2011.09.010

The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.