Studying the control of non invasive prosthetic hands over large time spans

TL;DR

This study investigates the long-term consistency of classifying 17 hand postures using EMG signals for non-invasive prosthetic control, demonstrating promising results over multiple days with SVM classifiers.

Contribution

It is the first to examine the repeatability of EMG-based hand posture classification over several days for non-invasive prosthetic control.

Findings

SVM achieved high classification accuracy for over 10 postures across days

EMG signals showed sufficient stability for reliable classification over time

Pilot data supports potential for long-term prosthetic control using EMG

Abstract

The electromyography (EMG) signal is the electrical manifestation of a neuromuscular activation that provides access to physiological processes which cause the muscle to generate force and produce movement. Non invasive prostheses use such signals detected by the electrodes placed on the user's stump, as input to generate hand posture movements according to the intentions of the prosthesis wearer. The aim of this pilot study is to explore the repeatability issue, i.e. the ability to classify 17 different hand postures, represented by EMG signal, across a time span of days by a control algorithm. Data collection experiments lasted four days and signals were collected from the forearm of a single subject. We find that Support Vector Machine (SVM) classification results are high enough to guarantee a correct classification of more than 10 postures in each moment of the considered time span.