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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1492588
This article is part of the Research Topic Wearable Biosensing Devices View all 6 articles

Real-Time Adaptive Cancellation of TENS Feedback Artifact on sEMG for Prosthesis Closed-Loop Control

Provisionally accepted
Byungwook Lee Byungwook Lee 1Kyung-Soo Kim Kyung-Soo Kim 1Younggeol Cho Younggeol Cho 2*
  • 1 Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
  • 2 Italian Institute of Technology (IIT), Genova, Liguria, Italy

The final, formatted version of the article will be published soon.

    The prosthetic hand has been aimed to restore hand functions by estimating the user's intention via bio-signal and providing sensory feedback. Surface electromyogram (sEMG) is a widely used signal, and transcutaneous electrical nerve stimulation (TENS) is a promising method for sensory feedback. However, TENS currents can transmit through the skin and interfere as noise with the sEMG signals, referred to as 'Artifact', which degrades the performance of intention estimation.In this paper, we proposed an adaptive artifact removal method that can cancel artifacts separately across different frequencies and pulse widths of TENS. The modified least-meansquare adaptive filter uses the mean of previous artifacts as reference signals, and compensate using prior information of TENS system. Also temporal separation for artifact discrimination is applied to achieve high artifact removal efficiency. Four sEMG signals -two from flexor digitorum superficialis, flexor carpi ulnaris, extensor carpi ulnaris -was collected to validate signals both offline and online experiments.We validated the filtering performance with twelve participants performing two experiments: artifact cancellation under variable conditions and a real-time hand control simulation called the target reaching experiment (TRE). The result showed that the Signal-to-Noise Ratio (SNR) increased by an average of 10.3dB, and the performance of four TRE indices recovered to the levels similar to those without TENS. The proposed method can significantly improve signal quality via artifact removal in the context of sensory feedback through TENS in prosthetic systems.

    Keywords: Sensory feedback, TENS, artifact, adaptive filter, Prosthetic hand, surface electromyography

    Received: 07 Sep 2024; Accepted: 25 Oct 2024.

    Copyright: © 2024 Lee, Kim and Cho. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Younggeol Cho, Italian Institute of Technology (IIT), Genova, 16163, Liguria, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.