Abstract
Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.
Highlights
Describes the ability to stop on a target; the average number of times data appears on the target domain and disappears in each test
This review paper briefly introduces the advances of Deep learning (DL)-based surface electromyography (sEMG) pattern recognition techniques for the prosthetic hand in recent years
It could be found that gesture recognition techniques based on DL has great potential in using sEMG signal to accurately interpret amputee’s motion intention, which is of great significance to the development of intelligent prosthetic hand
Summary
The worth of hand is indisputable. The hand is the most diverse and dexterous part of the human body, which can execute various activities to interact with the environment by adopting a variety of different motion strategies (Feix et al, 2016; Sartori et al, 2016). To transform the complex and highly variable information of sEMG signal into useful control signal of prosthetic hands, advanced data analysis and pattern recognition techniques that can describe and analyze big data are needed. Parajuli et al (2019) reviewed the application of machine learning in sEMG pattern recognition of prosthetic hand It found that almost all the data used by traditional ML methods come from steady-state signals, but the sEMG signals generated by gestures in daily life are transient. Based on the investigation of related papers in recent years, this review makes a comprehensive analysis of the application of DL techniques in the field of prosthetic hand based on sEMG, including the commonly used DL model for upper limb motion intention prediction, the advantages of DL model, and the performance level for system verification. The contribution of this review is mainly in three aspects: (1) we comprehensively analyze the overall structure of DL in sEMG-based gesture recognition; (2) we comprehensively review the latest methods and technologies of sEMG based gesture recognition; (3) we raise the existing challenges and promising research prospects of sEMG based on motion intention recognition in prosthetic hand field
Sign-in/Register to view highlights & summary 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.
