Real-time hand gesture recognition with EMG using machine learning

In this paper, we propose the development of a model for real-time hand gesture recognition. We use surface electromyography (EMG) and Machine Learning techniques. The recognition of gestures using EMG is not a trivial task because there are several physiological processes in the skeletal muscles underlying their generation. In the scientific literature, there are several hand gesture recognition models, but they have limitations both in the number of gestures to be recognized (i.e., classes) and in the processing time. Therefore, the primary goal of this research is to obtain a real-time hand gesture recognition model for various applications in the field of medicine and engineering with a higher recognition accuracy than the real-time models proposed in the scientific literature and a higher number of gestures to recognize (i.e. in the order of the dozens). The proposed model has five stages: acquisition of the EMG signals, preprocessing (e.g., rectification and filtering), feature extraction (e.g., time, frequency and time-frequency), classification (e.g., parametric and nonparametric) and post-processing. Generally, the main difficulties of the hand gesture recognition models with EMG using Machine Learning are: the noisy behavior of EMG signal, and the small number of gestures per person relative to the number of generated data by each gesture (e.i., curse of dimensionality). Solving these two issues could also lead to solutions for other problems such as face recognition and audio recognition, for which these two issues are a major concern.