一个早期的力预测控制方案,使用多式传感电肌图和数字力信号的多式传感
Salman Mohd Khan1, Abid Ali Khan1,2, Omar Farooq3,2
1Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India.
Heliyon
|April 17, 2024
概括
这项研究开发了一种早期的预测控制方案,使用电肌学 (EMG) 和数字力信号来精确确定握力. 该系统实现了90%的准确性,使生物体设备能够在提升前进行力调整.
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:
- 通过电肌图 (EMG) 测量前臂肌肉活动,随着抓握手势和抓握力而变化.
- 电磁图形信号被用于上肢生物设备的手势选择,但力量操纵,特别是精确抓取,是不太探索的.
- 现有的研究主要集中在使用模式识别的手势分类上,对基于EMG的力控制的注意力有限.
研究的目的:
- 设计一个早期的预测控制方案,以使用EMG和数字力信号有效地确定抓地力.
- 研究使用EMG信号,数字力信号或两者的组合的最佳模式识别 (PR) 控制方案.
- 为了实现对握力水平的早期预测和高分类准确性,以增强仿生设备控制.
主要方法:
- 从EMG信号中提取特征,包括斜率信号变化,威利森振幅,自动回归系数和波形长度.
- 采用了诸如随机森林,梯度提升,线性差异分析,支向量机器,k-最近邻居和决策树等分类器.
- 评估了三个输入组合:只有EMG信号,只有数字力信号,以及EMG和数字力信号的组合.
主要成果:
- 随机森林分类器使用EMG和数字力信号作为输入,实现了最高的分类精度 (90%).
- 该系统能够从抓开始就在1000毫秒内预测抓力水平.
- 这种预测能力允许在提升物体之前确定力级.
结论:
- 使用EMG和数字力信号与随机森林的早期预测控制方案是有效的抓地力级别分类.
- 开发的方法有助于早期预测和准确确定握力,这对于生物模拟调节至关重要.
- 这项技术具有显著的潜力,可以改善生物体设备的精确抓取控制,特别是对于视力受损的人来说.
相关概念视频
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