Virtual rehabilitation system for fine motor skills using artificial neural networks

Abstract

This work presents a rehabilitation system for upper limbs performed by the acquisition of electromyographic signals (EMG) of a patient with movement limitation after stroke. To determine the physical gestures of the arm, the MYO Armband is used, and to increase the default number of movements (4 gestures), a recognition algorithm is performed using artificial neural networks. MATLAB has been used for the extraction of a vector containing the characteristics through probabilistic signals treatment and to determinate the angles of rotation. In addition, an immersive environment is developed in Unity 3D software allowing the user to experience a more motivating therapy. As a peripheral output, HTC VIVE glasses and binaural hearing aids were chosen. To validate this proposal, five users with an age range of 49 to 69 years have made 200 tests in total with the proposed physical gestures (7). In addition, the qualitative validation of the system is done through the SUS usability test and as a result it is obtained (55.25 ± 0.18), demonstrating good acceptance.