Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control

Mojisola Grace Asogbon; Oluwarotimi Williams Samuel; Ejay Nsugbe; Yongcheng Li; Frank Kulwa; Deogratias Mzurikwao; Shixiong Chen; Guanglin Li

doi:10.3389/fnins.2023.1018037

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control

Front Neurosci. 2023 Feb 22:17:1018037. doi: 10.3389/fnins.2023.1018037. eCollection 2023.

Authors

Mojisola Grace Asogbon¹, Oluwarotimi Williams Samuel¹, Ejay Nsugbe², Yongcheng Li¹, Frank Kulwa¹, Deogratias Mzurikwao³, Shixiong Chen¹, Guanglin Li¹

Affiliations

¹ CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China.
² Nsugbe Research Labs, Swindon, United Kingdom.
³ Unit of Biomedical Engineering, Department of Physiology, School of Engineering, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.

Abstract

Introduction: Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question.

Method: This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder.

Result: The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.

Keywords: electromyogram (EMG); finger gestures; pattern recognition (PR); prostheses; signal recording duration (SRD).

Grants and funding

This work was supported by the National Natural Science Foundation of China Grants (#U1913601, #62150410439, and #82050410452), Ministry of Science and Technology, Shenzhen (#QN2022032013L), Shenzhen Governmental Basic Research Grant (#JCYJ20180507182508857), and Shenzhen Governmental Collaborative Innovation Program (#SGLH20180625142402055), and National Key R&D Program of China (#2022YFE0197500).