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EMG-based Hand Gesture Recognition for Rehabilitation (ENHANCE)

The purpose of this research project is the development of a serious game controlled by a surface electromyography (sEMG) interface for rehabilitation purposes. The processing of the sEMG signals and the gesture recognition is performed in an end-to-end fashion by deep learning (DL) methods. Specifically, convolutional neural network (CNN) architectures are utilised for the classification of signal segments to one of the target gestures. The interface is based on a sEMG armband placed around the user’s forearm. Using sEMG sensors allows the developed system to reject unwanted compensatory movements which are difficult to filter out with typical game controllers such as cameras. The evaluation of this serious game involves patients with upper limb impairments and neurological diseases, such as multiple sclerosis. The outcomes of this research project will contribute to the development of motivational serious games for the motion improvement of physically impaired people.

More on ERT3 O3 at (in Greek)


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