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

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.

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.

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.

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