Near-Infrared Spectroscopy Driven Human-Machine Interface based on Convolutional Neural Networks

Human-machine interfaces based on the classification of hand gestures often use electromyography to collect and interpret user intent; however, this methodology records the electrical activity produced by skeletal muscles suffering from different drawbacks that affect their performance. In this way, other approaches, such as near-infrared spectroscopy, were studied, presenting advantages over traditional techniques since it is not susceptible to electrical noise and electrode degradation. This work presents a hand gesture classification system for a human-machine interface based on near-infrared spectroscopy and a convolutional neural network that classifies five gestures. The data acquisition system shows a transparent and fluent transfer of data that represents the hemodynamics of three specific sets of muscles used with a classification model for five different hand gestures, showing comparable and promising results against traditional electromyography-based classification methods.