Muscle fatigue detection and treatment system driven by internet of things

Author:

Ma Bin,Li Chunxiao,Wu Zhaolong,Huang Yulong,van der Zijp-Tan Ada Chaeli,Tan Shaobo,Li Dongqi,Fong Ada,Basetty Chandan,Borchert Glen M.,Benton Ryan,Wu Bin,Huang Jingshan

Abstract

Abstract Background Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. Methods To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. Results With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. Discussion Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. Conclusions Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Reference23 articles.

1. Liu Z, Shu M, Kong X, Shan K, Zhao K. Design and implementation of bluetooth ecg acquisition terminal. Autom Instrum. 2008;39(08):80–3.

2. Chooruang K, Mangkalakeeree P. Wireless heart rate monitoring system using MQTT. Procedia Comput Sci. 2016;86:160–3.

3. Liu Y, Zhang L, Wang Y. Wireless control design of intelligent window. Intell Factory. 2016;11:88–90.

4. Qichuan D, Anbin X, Xingang Z, Jianda H. Review and application of motion intent recognition methods based on sEMG. Acta Automat Sin. 2016;42(01):13–25.

5. Todd G, Gandevia SC, Taylor JL. Change in manipulation with muscle fatigue. Eur J Neurosci. 2010;32(10):1686–94.

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