意图推断的相互学习与对中风的增强视觉反
概括
相互学习改善了机器人控制,使用户能够通过视觉反来适应分类器. 这种双向方法增强了可穿戴机器人的电肌图 (EMG) 信号的意图推断.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 神经康复疗法 神经康复疗法
背景情况:
- 控制可穿戴机器人的经典意图推断方法依赖于单向生物信号输入,限制了用户的适应性.
- 现有的意图推断机器学习模型缺乏用户直接观察其内部状态.
- 有效控制辅助机器人设备需要直观的用户交互和适应.
研究的目的:
- 引入相互学习,一种新的双向范式,用于人类适应意图推断分类器.
- 通过促进用户适应机器学习模型来增强可穿戴机器人的直观控制.
- 通过自适应性意图推断,改善中风患者机器人手臂整形的性能.
主要方法:
- 开发了一种相互学习范式,涉及机器学习模型更新的代阶段和由增强视觉反引导的人类适应.
- 实现了机器人手臂整形的范式,从电肌图 (EMG) 信号中推断出意图 (打开,关闭,放松).
- 使用LED进度显示器,为用户提供对分类器预测的视觉反.
主要成果:
- 相互学习在一小部分中风受试者 (五分之二) 中显示出性能改善.
- 这种模式并没有对其他受试者的表现产生负面影响.
- 假设受试者通过相互学习学会产生更多可区分和可分离的生物信号.
结论:
- 相互学习提供了一种有前途的双向方法,以提高人类对可穿戴机器人的意图推断分类器的适应.
- 拟议的方法显示了改善辅助设备的控制的潜力,例如用于神经康复的机器人手 Orthoses.
- 需要进一步的研究来探索用户适应的机制,并优化相互学习策略.
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