Intelligent health monitoring system based on smart clothing
In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2018-08-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718794318 |
| _version_ | 1797711923397001216 |
|---|---|
| author | Chung-Chih Lin Chih-Yu Yang Zhuhuang Zhou Shuicai Wu |
| author_facet | Chung-Chih Lin Chih-Yu Yang Zhuhuang Zhou Shuicai Wu |
| author_sort | Chung-Chih Lin |
| collection | DOAJ |
| description | In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R -peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring. |
| first_indexed | 2024-03-12T07:14:09Z |
| format | Article |
| id | doaj.art-62ed6af134874706bcf7f7bf0067f606 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:14:09Z |
| publishDate | 2018-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-62ed6af134874706bcf7f7bf0067f6062023-09-02T22:53:51ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772018-08-011410.1177/1550147718794318Intelligent health monitoring system based on smart clothingChung-Chih Lin0Chih-Yu Yang1Zhuhuang Zhou2Shuicai Wu3Division of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital and College of Medicine, Chang Gung University, Taoyuan, TaiwanDepartment of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan, TaiwanFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaCollege of Life Science and Bioengineering, Beijing University of Technology, Beijing, ChinaIn this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R -peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.https://doi.org/10.1177/1550147718794318 |
| spellingShingle | Chung-Chih Lin Chih-Yu Yang Zhuhuang Zhou Shuicai Wu Intelligent health monitoring system based on smart clothing International Journal of Distributed Sensor Networks |
| title | Intelligent health monitoring system based on smart clothing |
| title_full | Intelligent health monitoring system based on smart clothing |
| title_fullStr | Intelligent health monitoring system based on smart clothing |
| title_full_unstemmed | Intelligent health monitoring system based on smart clothing |
| title_short | Intelligent health monitoring system based on smart clothing |
| title_sort | intelligent health monitoring system based on smart clothing |
| url | https://doi.org/10.1177/1550147718794318 |
| work_keys_str_mv | AT chungchihlin intelligenthealthmonitoringsystembasedonsmartclothing AT chihyuyang intelligenthealthmonitoringsystembasedonsmartclothing AT zhuhuangzhou intelligenthealthmonitoringsystembasedonsmartclothing AT shuicaiwu intelligenthealthmonitoringsystembasedonsmartclothing |