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
Highlights
People are utilizing wearable devices to monitor the physical condition of their bodies during exercise and normal times
These wearable devices assist to track the number of steps, heart rate, and sleep pattern with advanced analyzing features
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
Summary
People are utilizing wearable devices to monitor the physical condition of their bodies during exercise and normal times. These wearable devices assist to track the number of steps, heart rate, and sleep pattern with advanced analyzing features. Isokinetic training with the appropriate equipment is generally utilized extensively for functional therapy and evaluation [1]. The leg muscles of an individual undergo frequent dynamic contractions, in these cases if an individual exercise excessively, their muscles may get stressed or injured [2]. Muscle fatigue is frequently assessed as a fall inside the muscle’s extreme power or strength [3]. Fatigue is usually mentioned as a reduced capability for maximal performance and the best relevant method to estimate fatigue would be straight using the highest test of presentation in the competitive occurrence of the athlete [5]
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