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表面肌电 (sEMG) 系统在握力监测中的应用。

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.

机构信息

Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Sensors (Basel). 2024 Jun 13;24(12):3818. doi: 10.3390/s24123818.

DOI:10.3390/s24123818
PMID:38931601
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11207591/
Abstract

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

摘要

肌肉在人类生活中起着不可或缺的作用。表面肌电图（sEMG）作为一种非侵入性方法，对于监测肌肉状态至关重要。它具有实时、便携的特点，广泛应用于运动和康复科学领域。本研究提出了一种基于多通道 sEMG 的无线采集系统，用于客观监测握力。该系统由一个包含四个离散通道的 sEMG 采集模块和一个主机接收模块组成，采用蓝牙无线传输。该系统具有便携、可穿戴、低成本和易于操作的特点。利用该系统，设计了一个用于握力预测的实验，采用秃鹰搜索（BES）算法增强随机森林（RF）算法。该方法建立了一个基于双通道 sEMG 信号的握力预测模型。经测试，采集终端的性能如下：增益高达 1125 倍，在 sEMG 信号带宽范围内共模抑制比（CMRR）保持较高（100Hz 时为 96.94dB，500Hz 时为 84.12dB），而握力预测算法的性能 R 为 0.9215，MAE 为 1.0637，MSE 为 1.7479。所提出的系统在实时信号采集和握力预测方面表现出优异的性能，是康复、训练、疾病监测和科学健身应用中一种有效的肌肉状态监测工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/8f4e96221609/sensors-24-03818-g013a.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/8f4e96221609/sensors-24-03818-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/8835aa9b9a05/sensors-24-03818-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/7d5d1c96832c/sensors-24-03818-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/44fcb156765d/sensors-24-03818-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/b9ab353ed88c/sensors-24-03818-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/27d632a40ca5/sensors-24-03818-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/b5f6c9980b7c/sensors-24-03818-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/0761791c95e2/sensors-24-03818-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/6324b5af48d8/sensors-24-03818-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/3edfada74aa2/sensors-24-03818-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/eb5e4b1b2cc0/sensors-24-03818-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/cf7bdd6f6e99/sensors-24-03818-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/feb7/11207591/8f4e96221609/sensors-24-03818-g013a.jpg

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