Human Muscle State Machine using Electromyography Classification with Machine Learning

This research aims at the creation of a tool that can recognize the state of the human skeletal muscle from surface Electromyography signals. The goal of this muscle state machine is for the use in functional rehabilitation of people suffering from Spinal cord injury and for stroke survivors who have lost mobility in their upper body limbs. The use of machine learning techniques for the classification of these muscle states brought forth the need for database creation to train the generated ML model. For the data collection process, an experimental protocol was proposed and tests were conducted in healthy individuals with a Nexus MKII medical device. Following the data collection, a signal analysis procedure was performed to extract features from the sEMG signals that directly relate to the muscle state. In addition to the signal analysis, a Machine Learning classification model was created to recognize and classify the sEMG signals in the different states of the muscle. This classification had a high enough accuracy of producing the correct result, given that the training and sampling size of the database was considerably small provided that in similar cases of ML classifying models the size of the Databases includes way more samples than the one in this research. The future steps for this research are the creation of a more extensive and diverse database and the use of this model in real-time situations.