通过sEMG和手指运动数据协助手势分类和康复评估
Xiuxiu Yang1, Lingfeng Zhang1,2,3, Fukui Wu1,4
1College of Intelligent Science and Engineering, Hubei Minzu University, Enshi, Hubei, China.
Frontiers in bioengineering and biotechnology
|January 26, 2026
概括
一个新的可穿戴设备捕获手部运动和肌肉信号,用于评估运动障碍. 该系统在识别手势方面达到90%以上的准确性,有助于康复和神经肌肉研究.
科学领域:
- 生物医学工程 生物医学工程
- 康复技术 康复技术 康复技术
- 可穿戴式传感器 穿戴式传感器
背景情况:
- 现有的手功能评估工具缺乏整合性和临床适用性.
- 需要先进的系统来捕获动力学和肌电数据,以进行全面的手部功能分析.
研究的目的:
- 开发和验证可穿戴的运动捕捉系统,同时记录手指曲率和表面电肌图 (sEMG) 信号.
- 评估系统在对健康个体和运动障碍患者的手势分类方面的表现.
主要方法:
- 一个六通道的sEMG运动捕捉手套被用来收集15名参与者的900个手势测量.
- 数据采集是在医院环境中进行的,以确保临床相关性.
- 信号处理需要Savitzky-Golay过和短时间里埃变换 (STFT) 来生成光谱图.
- 机器学习模型 (SVM,LightGBM,MLP) 用于手势分类.
主要成果:
- 开发的系统表现出强大的手势分类性能,在交叉验证和测试集上都达到90%以上的精度.
- 在不同的手势类型和受试者条件中保持了高的识别精度,包括那些严重运动障碍的人.
结论:
- 拟议的可穿戴系统有效用于临床环境中的手部功能评估和手势识别.
- 收集的数据集对于推动手势识别,康复评估和神经肌肉信号分析方面的研究非常有价值.
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