Continuous Recognition of Multifunctional Finger and Wrist Movements in Amputee Subjects Based on sEMG and Accelerometry

Abstract

Background: While the classification of multifunctional finger and wrist movement based on surface electromyography (sEMG) signals in intact subjects can reach remarkable recognition rates, the performance obtained from amputated subjects remained low.

Methods: In this paper, we proposed and evaluated the myoelectric control scheme of upper-limb prostheses by the continuous recognition of 17 multifunctional finger and wrist movements on 5 amputated subjects. Experimental validation was applied to select optimal features and classifiers for identifying sEMG and accelerometry (ACC) modalities under the windows-based analysis scheme. The majority vote is adopted to eliminate transient jumps and produces smooth output for window-based analysis scheme. Furthermore, principle component analysis was employed to reduce the dimension of features and to eliminate redundancy for ACC signal. Then a novel metric, namely movement error rate, was also employed to evaluate the performance of the continuous recognition framework proposed herein.

Results: The average accuracy rates of classification were up to 88.7 ± 2.6% over 5 amputated subjects, which was an outstanding result in comparison with the previous literature.

Conclusion: The proposed technique was proven to be a potential candidate for intelligent prosthetic systems, which would increase quality of life for amputated subjects.

Keywords: Accelerometry; Continuous recognition; Principle component analysis; Surface electromyography.

Fig. (1)

Illustration of the window-based analysis scheme producing a decision stream.

Fig. (2)

Average classification results (a) with standard deviations and training time (b, roughly 7500 training samples) of 8 single features and 2 multi-features for 5 amputated subjects. Performance is for an SVM before and after parameter adjustment.

Fig. (3)

Comparisons of different numbers of principle components (PCs). Performance is for an SVM with MEAN feature/ACC modality over 5 amputated subjects. The error bars indicate unit standard deviation.

Fig. (4)

Comparison of classification accuracy of sEMG/MAV and sEMG+ACC/MAV+ MEAN (PCA) for four classifiers. The error bars indicate unit standard deviation.

Fig. (5)

The true and predicted labels obtained from the test sequences of the 5th subject. The predicted labels were produced by the MAV+MEAN (PCA) /sEMG+ACC for SVM classifier with 3 major vote.