Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform
Daniel Ovadia,
Alex Segal,
Neta Rabin
Abstract:Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This stud… Show more
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