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Sharma, A., Shrivas, N.V. and Sharma, I. (2025) An Adaptive Segmentation Scheme Based on Recurring Action Potentials for sEMG Controlled Movement Decoding. Physical and Engineering Sciences in Medicine, 48, 881-901.
https://doi.org/10.1007/s13246-025-01557-7

has been cited by the following article:

• TITLE: Design and Implementation of an Improved Exoskeleton sEMG Signal Processing System

  AUTHORS: Lijie Li, Zhe Hu, Yingshan Tian

  KEYWORDS: sEMG, Signal Processing, Motion Control, Exoskeleton Finger, Rehabilitation

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.19 No.6, June 25, 2026

  ABSTRACT: Surface Electromyography (sEMG), the bioelectric currents generated by muscle contractions on the human body surface, enables motion control of exoskeletons. sEMG signal processing combines both software and hardware components, where accuracy and timeliness directly impact the practicality of exoskeleton motion control systems. Addressing the needs of hemiplegic patients with hand function impairments, this paper designs an improved sEMG signal processing system for exoskeleton motion control and completes the structural design and physical implementation of an exoskeleton finger. Experimental results demonstrate that the exoskeleton finger exhibits excellent adaptive capability, cooperating with human fingers to grasp various objects in daily life scenarios.