Classification of Hand Movements Based on EMG Signals using Topological Features

Abstract

Hand movement classification based on Electromyography (EMG) signals has been extensively investigated in the past decades as a promising approach used for controlling upper prosthetics or robotics. Topological data analysis is a relatively new and increasingly popular tool in data science that uses mathematical techniques from topology to analyze and understand complex data sets. This paper proposes a method for classifying hand movements based on EMG signals using topological features crafted with the tools of TDA. The main findings of this work on hand movement EMG classification are as follows: (1) topological features are effective in classifying EMG signals and outperform other time domain features tested in the experiments; (2) the 0-th Betti numbers are more effective than the 1-st Betti numbers; (3) Betti amplitude is a more stable and powerful feature than other topological features discussed in this paper. Additionally, Betti curves were used to visualize topological patterns for hand movement EMG.