Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

Article Subjects > Engineering Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Abierto Inglés metadata Gehlot, Anita and Singh, Rajesh and Siwach, Sweety and Vaseem Akram, Shaik and Alsubhi, Khalid and Singh, Aman and Delgado Noya, Irene and Choudhury, Sushabhan mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, aman.singh@uneatlantico.es, irene.delgado@uneatlantico.es, UNSPECIFIED (2022) Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices. Computers, Materials & Continua, 72 (1). pp. 999-1015. ISSN 1546-2226

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Abstract

Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino controller, Wi-Fi module, and EMG sensor are utilized in developing the wearable device. The Time-frequency domain spectrum technique is employed for classifying the three muscle fatigue conditions including mean RMS, mean frequency, etc. A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data. The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as >2 V: Extensive); 1–2 V: Moderate, and <1 V: relaxed. The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue. Moreover, the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices. The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.

Item Type: Article
Uncontrolled Keywords: LabVIEW; muscle fatigue; sEMG; wearable sensor; IoT; cloud server
Subjects: Subjects > Engineering
Divisions: Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Date Deposited: 13 May 2022 23:55
Last Modified: 13 May 2022 23:55
URI: https://repositorio.uneatlantico.es/id/eprint/669

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