TY  - JOUR
TI  - Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices
ID  - uninimx669
AV  - public
UR  - http://doi.org/10.32604/cmc.2022.023861
EP  - 1015
JF  - Computers, Materials & Continua
A1  - Gehlot, Anita
A1  - Singh, Rajesh
A1  - Siwach, Sweety
A1  - Vaseem Akram, Shaik
A1  - Alsubhi, Khalid
A1  - Singh, Aman
A1  - Delgado Noya, Irene
A1  - Choudhury, Sushabhan
SP  - 999
VL  - 72
SN  - 1546-2226
KW  - LabVIEW; muscle fatigue; sEMG; wearable sensor; IoT; cloud server
Y1  - 2022/02//
N2  - 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.
IS  - 1
ER  -