基于人类下肢表面电肌图谱的关节角度预测
Hongyu Zhao1,2, Zhibo Qiu1,2, Daoyong Peng3
1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究表明,电肌图 (EMG) 信号可以预测下肢关节角度,用于可穿戴外骨控制. 与其他算法相比,Cuckoo Search优化随机森林 (CS-RF) 模型实现了更高的预测准确性.
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
- 人机界面 人机界面
背景情况:
- 可穿戴的外骨架可以增强残疾人的移动性.
- 电肌图 (EMG) 信号先于运动,为预测用户意图提供了潜在的可能性.
- 准确预测关节角度对于有效的外骨控制至关重要.
研究的目的:
- 开发和评估使用表面电肌图 (sEMG) 信号的下肢关节角度的预测模型.
- 为了比较Cuckoo Search优化随机森林 (CS-RF) 算法的性能与其他机器学习模型进行这个预测任务.
- 评估模型在不同运动活动中的有效性:步行,爬楼梯和爬坡.
主要方法:
- 使用OpenSim软件识别了下肢关键肌肉部位.
- 在各种机动任务中收集sEMG和惯性数据.
- 应用了一个完整的集体实证模式分解与自适应噪声 (CEEMDAN) 算法来减少sEMG噪声.
- 从处理的sEMG信号中提取时间域特征.
- 使用四边形和坐标转换计算联合角度.
- 开发了一个使用Cuckoo Search优化随机森林 (CS-RF) 回归算法的预测模型.
主要成果:
- 与标准的随机森林 (RF),支持矢量机器 (SVM) 和背向传播 (BP) 神经网络相比,CS-RF模型显示了膝关节和关节角度的优异预测性能.
- 通过CS-RF模型在步行,爬楼梯和上坡步行场景中实现了最佳的评估指标 (RMSE,MAE,R2).
- 该模型有效地将sEMG信号转化为准确的关节角度预测.
结论:
- CS-RF算法提供了一个非常准确和强大的方法,用于从sEMG信号中预测下肢关节角度.
- 这种方法具有显著的潜力,可以改善可穿戴外骨在康复中的控制和响应能力.
- 这些发现表明,通过先进的信号处理和机器学习技术,增强人类外骨交互的途径是有希望的.
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