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.
Highlights
Limb amputation is a major cause of disability in the world [1]
Principle component analysis was employed to reduce the dimension of features and to eliminate redundancy for ACC signal
Fig. (2) shows that the Mean Absolute Value (MAV) and Wavelet packet transform (WPT) perform best for single features (75.85±2.58% and 75.12±3.22% respectively), regardless of the training time consumed for WPT
Summary
The key issues of accomplishing practical upperlimb prosthesis are functionality, controllability and cosmetics [2] In recent decades, these prostheses controlled by the surface electromyography (sEMG) containing rich information of neuromuscular activity have been applied to replace those controlled by the original motor commands in a noninvasive way [2, 3]. These prostheses controlled by the surface electromyography (sEMG) containing rich information of neuromuscular activity have been applied to replace those controlled by the original motor commands in a noninvasive way [2, 3] These approaches have developed from control of simple functional prosthesis, such as wrist flexion and extension, to multifunctional prostheses [3]. 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
