Muscle Atrophy Monitoring Using Machine Learning and Surface Electromyography

The research project illustrates a monitoring system for muscle atrophy using electromyography and Machine Learning (ML) signals. Patients on prolonged bed rest, stroke victims, athletes, and anyone involved in car accidents are all at risk for muscle atrophy and accompanying illnesses. Many immobile patients are at risk for muscular illnesses and diseases, making the project viable since quick input is required to improve medical care and speed of recovery. The project uses a mechatronic-based Internet of Things (IoT) architecture with three layers: application, network, and sensory perception. The inventive proof of concept demonstrates how ML is applied to effectively process the sensory data to recognize coordinated muscle actions. To develop a useful and workable application for the larger healthcare industry, this machine learning technique was selected after careful consideration and justification from several data science research sources.