Cross-Platform Gait Analysis and Fall Detection Wearable Device-Reference-Cited by-同舟云学术

Cross-Platform Gait Analysis and Fall Detection Wearable Device

Published:2023-03-04 Issue:5 Volume:13 Page:3299
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Chang Ming-Hung¹,Wu Yi-Chao²,Niu Hsi-Yu³,Chen Yi-Ting⁴,Juang Shu-Han⁵

Affiliation:

1. Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, Taiwan

2. Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin 540301, Taiwan

3. Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan

4. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan

5. Institute of Materials Science and Engineering, National Taipei University of Technology, Taipei 106344, Taiwan

Abstract

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment.

Funder

National Science and Technology Council (NSTC) of Taiwan to National Yunlin University of Science and Technology

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/5/3299/pdf

Reference25 articles.

1. Kumar, H.R., Janardhan, S., Prakash, D., and Kumar, M.K.P. (2018, January 18–19). Fall Detection System using Tri- Axial Accelerometer. Proceedings of the IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, Bangalore, India.

2. Jefiza, A., Pramunanto, E., Boedinoegroho, H., and Purnomo, M. (2017, January 19–21). Fall Detection Based on Accelerometer and Gyroscope using Back Propagation. Proceedings of the IEEE International Conference on Electrical Engineering, Computer Science and Informatics, Yogyakarta, Indonesia.

3. Zhang, Y., Zhu, Z., and Wang, S. (2018, January 22–23). Multi-Condition Constraint Adaptive Step Detection Method Based on the Characteristics of Gait. Proceedings of the IEEE International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services, Wuhan, China.

4. Laboratory Review: The Role of Gait Analysis in Sensors’ Mobility and Fall Prevention;Bridenbaug;Gerontology,2011

5. Tong, L., Chen, W., Song, Q., and Ge, Y. (2009, January 19–23). A Research on Automatic Human Fall Detection Method Based on Wearable Inertial Force Information Acquisition System. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Guilin, China.