Classification of forearm muscle forces for controlling assistive devices

Abstract

Force myography (FMG)-based wearable interfaces have emerged as promising solutions for non-invasive human–machine interaction (HMI) in assistive and rehabilitation applications. However, achieving reliable real-time gesture recognition using FMG is challenging due to the need for a complex sensor setup and computational algorithms. Moreover, the FMG system suffers from sensor shifting and improper force transmission. Therefore, this work proposes an affordable, multi-channel wearable FMG armband that reliably captures forces at multiple forearm muscle locations during different gestures. A real-time gesture recognition system classifies the FMG data from the armband into discrete instructions for assistive devices. An optimized four-channel sensor configuration for the armband was designed to capture distinct muscle deformation patterns associated with hand gestures. FMG signals were recorded from 10 subjects for their five different hand gestures. The recorded data underwent preprocessing, feature extraction and selection, and then machine-learning-based classification. Five classifiers, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP), were used to recognize these five gestures. The RF classifier achieved the highest offline classification accuracy of 92% and was subsequently implemented in real time on an ESP32 microcontroller unit (MCU). The system, based on an RF-trained model, depicted an accuracy of 81% in identifying real-time hand gestures. The implemented scheme was further tested to control the movement of an indigenous robotic arm using the predefined gestures. Such a simple, low-cost, and effective scheme based on FMG can help control prosthetic and robotic devices using human intentions.