Studying the control of non invasive prosthetic hands over large time spans
This addresses the repeatability issue for non-invasive prosthetic hand users, but it is incremental as it applies an existing method to a new data scenario.
The study tackled the problem of classifying 17 hand postures from EMG signals over multiple days to assess repeatability in prosthetic control, finding that SVM classification correctly identified over 10 postures consistently across the time span.
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.