运动识别和肌肉力量估计手腕基于前臂的肌电图信号的手腕
Leiyu Zhang1, Zhenxing Jiao1, Yongzhen Li2
1Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China.
Biosensors
|April 25, 2025
概括
这项研究为手腕康复机器人引入了一种新的表面电肌图 (sEMG) 算法,将运动识别精度提高到98.7%,并使有效的自我康复训练成为可能.
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
- 神经科学是一个神经科学.
背景情况:
- 手腕损伤严重影响日常活动,需要有效的康复治疗.
- 当前的康复方法可能缺乏患者的参与和精确的反.
- 表面电肌图 (sEMG) 提供了一种非侵入性方法来监测肌肉活动.
研究的目的:
- 开发和验证基于sEMG的算法,用于识别手腕运动和估计肌肉力量.
- 提高手腕损伤患者的自我康复训练效率.
- 将sEMG数据与手腕康复机器人集成,以改善治疗.
主要方法:
- 为了特征提取,利用前臂sEMG信号的优势固有值.
- 开发了一种运动识别模型,其中包含了反向传播神经网络 (BPNN),投票决策和强化算法.
- 建立了用于静态和动态肌肉力估计的实验平台,使用平均绝对值 (MAV) 的线性拟合.
主要成果:
- 随着改进的算法,运动识别精度从90.7% (仅BPNN) 提高到98.7%.
- 静态肌肉力估计显示了较低的根平均平方误差 (RMSE):4.2N (屈曲/延伸) 和5.8N (节/半径偏差).
- 与理论值相比,动态肌肉力估计表明了类似的趋势和最小的相差,在曲运动中准确度更高.
结论:
- 拟议的sEMG算法证明了手腕运动识别和肌肉力量估计的高准确性和实用性.
- 这项技术通过为手腕康复机器人提供实时反来促进有效的自我康复培训.
- 这些发现支持了广泛采用的潜力,以提高上肢康复结果.
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