Abstract
BackgroundCurrently, the typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multi-fingered hand prostheses. Pattern recognition and machine learning techniques applied to sEMG can be effective for a natural control based on the residual muscles contraction of amputated people corresponding to phantom limb movements. As the researches has reached an advanced grade accuracy, these algorithms have been proved and the embedding is necessary for the realization of prosthetic devices. The aim of this work is to provide engineering tools and indications on how to choose the most suitable classifier, and its specific internal settings for an embedded control of multigrip hand prostheses.MethodsBy means of an innovative statistical analysis, we compare 4 different classifiers: Nonlinear Logistic Regression, Multi-Layer Perceptron, Support Vector Machine and Linear Discriminant Analysis, which was considered as ground truth. Experimental tests have been performed on sEMG data collected from 30 people with trans-radial amputation, in which the algorithms were evaluated for both performance and computational burden, then the statistical analysis has been based on the Wilcoxon Signed-Rank test and statistical significance was considered at p < 0.05.ResultsThe comparative analysis among NLR, MLP and SVM shows that, for either classification performance and for the number of classification parameters, SVM attains the highest values followed by MLP, and then by NLR. However, using as unique constraint to evaluate the maximum acceptable complexity of each classifier one of the typically available memory of a high performance microcontroller, the comparison pointed out that for people with trans-radial amputation the algorithm that produces the best compromise is NLR closely followed by MLP. This result was also confirmed by the comparison with LDA with time domain features, which provided not significant differences of performance and computational burden between NLR and LDA.ConclusionsThe proposed analysis would provide innovative engineering tools and indications on how to choose the most suitable classifier based on the application and the desired results for prostheses control.
Highlights
The typically adopted hand prosthesis surface electromyography control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multifingered hand prostheses
The analysis showed that, for both classification performance and number of classification parameters (Fig. 8), Support Vector Machine (SVM) attains the highest values followed by Multi-Layer Perceptron (MLP), and by Non-linear Logistic Regression (NLR)
In order to investigate the best compromise between performance and computational burden, the EOF index was presented
Summary
The typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multifingered hand prostheses. The available single degree of freedom is actuated by applying a simple threshold or a proportional amplitude method on surface electromyography (sEMG) signals recorded from antagonistic muscles (e.g., wrist flexor and wrist extensor) that can be contracted in a separate way. In the case of multi-fingered hand prosthesis with several degrees of freedom (DoFs), but still having two control signals, the switching between DoFs or predefined grasps is normally made by cocontraction, as in a finite state machine. This serial operation is slow and unnatural; in addition, it requires considerable training and cognitive effort [1]. The risk associated to the surgical re-innerving operation is the main drawback that limits the applicability of this technique to all the kinds of amputations [3, 4]
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