Muscle Strain Mapping Using Arduino and IoT

Muscles are an important organ in the movement of the body's skeleton to carry out sports activities. Measurement of muscle activity during the exercise process can be done using electromyography (EMG). This research uses muscle sensor (AT-04-001) which is integrated with ATMEGA 328 SMD. This project presents a wearable, real-time muscle strain mapping system using Arduino and IoT technologies. The system utilizes electromyography (EMG) sensors to detect muscle activity and strain levels. Data is transmitted wirelessly to a cloud-based platform for analysis and visualization. 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.