Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

Abstract

Control strategies of smart hand prosthesis-based myoelectric signals in
recent years don't provide the patients with the sensation of biological
control of prostheses hand fingers. Therefore, in current work
hyperparameters optimization in machine learning algorithm and hand
gesture recognition techniques were applied to the myoelectric signal-based
on residual muscles contraction of the amputees corresponding to intact
forearm limb movement to improve their biological control. In this paper,
myoelectric signals are extracted using the MYO armband to recognize ten
gestures from ten volunteers (healthy and transradial amputation) on the
forearm, thereafter the noise of myoelectric signals using a notch filter (NF)
is removed. The proposed classification system involved two machine
learning algorithms: (1) the decision tree (DT), tri-layered neural network
(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and
ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning
classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography
(ODT) and ommatidia detecting algorithm (ODA). The experimental results
of classifiers comparison pointed out an algorithm that outperformed with
high accuracy is OEBT closely followed by OKNN achieves an accuracy of
97.8% and 97.1% for intact forearm limb, while for transradial amputation
with an accuracy of 91.9% and 91.4%, respectively.