Deep Augmentation for Electrode Shift Compensation in Transient High-density sEMG: Towards Application in Neurorobotics

Tianyun Sun, Jacqueline Libby, John Ross Rizzo, S. Farokh Atashzar

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Scopus citations

    Abstract

    Going beyond the traditional sparse multi-channel peripheral human-machine interface that has been used widely in neurorobotics, high-density surface electromyography (HD-sEMG) has shown significant potential for decoding upper-limb motor control. We have recently proposed heterogeneous temporal dilation of LSTM in a deep neural network architecture for a large number of gestures (>60), securing spatial resolution and fast convergence. However, several fundamental questions remain unanswered. One problem targeted explicitly in this paper is the issue of 'electrode shift,' which can happen specifically for high-density systems and during doffing and donning the sensor grid. Another real-world problem is the question of transient versus plateau classification, which connects to the temporal resolution of neural interfaces and seamless control. In this paper, for the first time, we implement gesture prediction on the transient phase of HD-sEMG data while robustifying the human-machine interface decoder to electrode shift. For this, we propose the concept of deep data augmentation for transient HD-sEMG. We show that without using the proposed augmentation, a slight shift of 10mm may drop the decoder's performance to as low as 20%. Combining the proposed data augmentation with a 3D Convolutional Neural Network (CNN), we recovered the performance to 84.6% while securing a high spatiotemporal resolution, robustifying to the electrode shift, and getting closer to large-scale adoption by the end-users, enhancing resiliency.

    Original languageEnglish
    Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
    Pages6148-6153
    Number of pages6
    ISBN (Electronic)9781665479271
    DOIs
    StatePublished - 2022
    Event2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan
    Duration: 23 Oct 202227 Oct 2022

    Publication series

    NameIEEE International Conference on Intelligent Robots and Systems
    Volume2022-October
    ISSN (Print)2153-0858
    ISSN (Electronic)2153-0866

    Conference

    Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
    Country/TerritoryJapan
    CityKyoto
    Period23/10/2227/10/22

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