Upper Arm Motion High-Density SEMG Recognition Optimization Based on Spatial and Time-Frequency Domain Features

Overview

Journal J Healthc Eng

Specialty Biomedical Engineering

Date 2019 May 14

PMID 31080576

Citations 6

Authors

Dianchun Bai

Shutian Chen

Junyou Yang

Affiliations

Soon will be listed here.

Abstract

Background: Spatial characteristics of sEMG signals are obtained by high-density matrix sEMG electrodes for further complex upper arm movement classification. Multiple electrode channels of the high-density sEMG acquisition device aggravate the burden of the microprocessor and deteriorate control system's real-time performance at the same time. A shoulder motion recognition optimization method based on the maximizing mutual information from multiclass CSP selected spatial feature channels and wavelet packet features extraction is proposed in this study.

Results: The relationship between the number of channels and recognition rate is obtained by the recognition optimization method. The original 64 electrodes channels are reduced to only 4-5 active signal channels with the accuracy over 92%.

Conclusion: The shoulder motion recognition optimization method is combined with the spatial-domain and time-frequency-domain features. In addition, the spatial feature channel selection is independent of feature extraction and classification algorithm. Therefore, it is more convenient to use less channels to achieve the desired classification accuracy.

Citing Articles

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Dai Q, Wong Y, Kankanhali M, Li X, Geng W Bioengineering (Basel). 2023; 10(9).

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Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals.

Montazerin M, Rahimian E, Naderkhani F, Atashzar S, Yanushkevich S, Mohammadi A Sci Rep. 2023; 13(1):11000.

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People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations.

Arvanitidis M, Jimenez-Grande D, Haouidji-Javaux N, Falla D, Martinez-Valdes E Sci Rep. 2022; 12(1):15178.

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Guo B, Ma Y, Yang J, Wang Z, Zhang X Comput Intell Neurosci. 2021; 2020:8846021.

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