Abstract
The construction of motion decoder based on surface Electromyography (sEMG) signals is an important part in the clinical trial of rehabilitation robot. Its performance directly determines the success of clinical trial. However, feature extraction is essential in motion decoder. A single feature such as time domain and frequency domain can achieve good classification results, but it is only suitable for a single posture. Hence, for the lying and sitting postures, different feature analysis and their combination are used in this study to improve the sEMG-based lower limb motion classification performance. Nine participants in the clinical trial performed four different movements respectively. Through feature extraction and pattern recognition of sEMG, the trained motion decoders were obtained. The control commands are sent to the robot through labels retrieval to drive the lower limbs for corresponding rehabilitation training. The effectiveness of the based on sEMG signals control method is verified through real-time experimental analysis.
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.
