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Research on Collaborative Quality Assessment Model of Elbow Muscles based on MC-MMG and DRSN

Published: 22 December 2021 Publication History

Abstract

The purpose of our study was to investigate the individual muscle contribution to generated force under four representative of elbow multi-muscle contraction tasks: flexion, extension, pronation, and supination. In this paper, we proposed a collaborative quality assessment model of muscles to elbow generated force based on a multi-channel mechanomyogram (MC-MMG) to explore the relationship between the elbow generated force and the individual muscles under different contraction tasks. Based on the analysis of elbow anatomy, MMG signals of brachial biceps (BB), brachial (BR), triceps (TR), brachioradialis (BRD) were collected by using MC-MMG collection platform. The Kernel Principal Component Analysis (KPCA) algorithm was used to reduce the dimension of the original MMG signal. Then, the Mean Average Value (MAV) feature of the signals was extracted as the input of the Deep Residual Shrinkage Network (DRSN), which is a new deep learning algorithm to establish the relationship between MC-MMG and generated force. Mean Impact Value (MIV) index was used to assess the contribution level of different muscles groups for estimating the generated force. The experimental results show that the single muscle with the highest MIV value can track the change of generated force better than multiple muscles under different contraction tasks. This result can provide effective guidance for estimating generated force and can be further applied to the recognition of motion intention.

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  1. Research on Collaborative Quality Assessment Model of Elbow Muscles based on MC-MMG and DRSN

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        cover image ACM Other conferences
        ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
        October 2021
        593 pages
        ISBN:9781450395588
        DOI:10.1145/3500931
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 22 December 2021

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        Author Tags

        1. Deep Residual shrinkage network
        2. Kernel principal component analysis
        3. Mean impact value
        4. Mechanomyogram

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