Classification Of Hand Movements from EMG Signals For People With Motor Disabilities



emg signals, Assistive technology, human-machine interface, Machine Learning, text editor


People with disabilities make up about 25% of the Brazilian population. A great part of these people have physical impediments that make difficult or impossible to use computer peripherals. This article presents the development of a low-cost human-machine interface (HMI) to control an adapted text editor through the acquisition and classification of electromyographic (EMG) signals using machine learning techniques. The HMI is based on the detection of 3 different hand movements that are associated with commands for the text editor. In addition, a database with 3200 EMG signals generated by the hand movements was created, made by one user diagnosed with cerebral palsy and another user without diagnosed motor disabilities. Several tests were carried out, showing the good accuracy of the proposed system, with a success classification rate of 96% to 98%.


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