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用于握力估计的表面肌电特征和测量位置的最优策略。

Optimal strategy of sEMG feature and measurement position for grasp force estimation.

机构信息

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.

School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China.

出版信息

PLoS One. 2021 Mar 30;16(3):e0247883. doi: 10.1371/journal.pone.0247883. eCollection 2021.

DOI:10.1371/journal.pone.0247883
PMID:33784334
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8009426/
Abstract

Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects' forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized.

摘要

基于表面肌电信号 (sEMG) 的握力估计对于假肢手的灵巧控制至关重要。如今，虽然增加 sEMG 测量位置的数量和提取更多特征是提高握力估计准确性的常用方法，但这会增加计算负担。本文提出了一种基于方差分析 (ANOVA) 和广义回归神经网络 (GRNN) 的方法，用于优化测量位置和特征，目的是使用更少的测量位置或特征来实现更高的估计精度。首先，我们从受试者的前臂和握力同步采集了六个通道的 sEMG。然后，从每个 sEMG 通道中提取了四种时域特征。通过结合不同的测量位置集 (MPS) 和特征集 (FS)，我们构建了 945 个数据集。这些数据集被馈送到 GRNN 中以实现握力估计。归一化均方根误差 (NRMS)、归一化平均绝对误差 (NMAE) 和估计握力与实际力之间的相关系数 (CC) 用于评估握力估计的性能。最后，引入 ANOVA 和 Tukey HSD 测试来分析握力估计结果，以获得最佳的测量位置和特征。当使用不同的 FS 时，我们获得了最佳的 MPS 用于握力估计，而当使用不同的 MPS 时，我们获得了最佳的 FS 用于握力估计。

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Sensors (Basel). 2019 Oct 22;19(20):4596. doi: 10.3390/s19204596.
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Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination.
在手臂姿势从旋前到旋后动态变化过程中对手臂弯曲力的估计。
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Sensors (Basel). 2018 Sep 25;18(10):3226. doi: 10.3390/s18103226.
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