结合手腕和手势,增加肌电控制课程的数量
概括
结合手和手腕运动,为假肢控制创造了新的类别. 这种方法提高了对真实数据的分类准确性,但对复杂的多关节运动的合成数据存在局限性.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 人与计算机的交互
背景情况:
- 控制多关节假肢是复杂的,因为许多自由度 (DoFs).
- 现有的肌电假肢控制方法难以复制自然,协调的运动.
- 合并不同的手势类提供了扩大控制能力的潜在策略.
研究的目的:
- 研究用于增强肌电假肢控制的组合手势类的形成和分类.
- 评估合并的手和手腕运动的分离性和分类准确性.
- 评估使用收集与人工合成电肌图 (EMG) 数据对分类性能的影响.
主要方法:
- 从9名从事单独手和手腕运动的受试者那里收集了表面EMG数据.
- 双手势课 (手势和手腕运动) 合并形成"组合课".
- 巴塔卡里亚距离 (BD) 用于测量特征空间分离性;线性差异分析 (LDA) 用于分类.
主要成果:
- 组合类在特征空间中形成了独特的,可分离的区域.
- 使用收集的EMG数据实现了97%的高分类成功率.
- 当使用人工合成数据时,观察到分类性能下降17%.
结论:
- 合并高度可分离的手和手腕运动是创建多DoF假肢控制新类的可行策略.
- 这些发现强调了使用合成数据计算复制复杂的多DoF动态的挑战.
- 需要进一步的研究来提高合成EMG数据的准确性,用于假肢控制应用.
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