An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network's classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.
['Nasri|Nadia|N|', 'Orts-Escolano|Sergio|S|', 'Cazorla|Miguel|M|0000-0001-6805-3633']
D000465:Algorithms D001185:Artificial Intelligence D000077321:Deep Learning D004576:Electromyography D005868:Gestures D006225:Hand D006801:Humans D016571:Neural Networks, Computer D010363:Pattern Recognition, Automated D012046:Rehabilitation D018910:Video Games
rehabilitation hand gesture recognition deep learning electromyography sensor virtual reality