对于辅助机器人虚拟平台的肌电控制模式的比较
Cristina Polo-Hortigüela1,2, Miriam Maximo2, Carlos A Jara3
1Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, 03202 Elche, Spain.
Bioengineering (Basel, Switzerland)
|May 25, 2024
概括
这项研究证明了使用表面电肌图 (sEMG) 来控制虚拟机器人手臂用于假肢和辅助机器人训练的可行性. 简单的控制算法为上肢受损的用户提供了自然而强大的交互方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 人与计算机的交互
背景情况:
- 辅助机器人和假肢需要直观的控制方法.
- 虚拟环境为培训提供了一个安全和可访问的平台.
- 表面电肌图 (sEMG) 显示了对非侵入性神经控制的潜力.
研究的目的:
- 证明由sEMG控制的虚拟环境的可行性,用于训练上肢损伤的个人.
- 评估简单的控制算法与复杂的机器学习方法进行假肢控制.
- 探索智能辅助机器人掌握任务的好处.
主要方法:
- 创建了一个虚拟的厨房环境,用于对象抓取和存储任务.
- 通过基于sEMG信号的不同肌电控制模式来控制一个虚拟机器人臂.
- 参与者使用各种控制策略执行任务,包括简单的算法和智能辅助.
主要成果:
- 在虚拟环境中,所有参与者都实现了高性能.
- 参与者报告了关于测试的控制模式的积极经验和相似意见.
- 简单的控制算法展示了强度和自然相互作用.
结论:
- 通过sEMG控制的虚拟环境可用于训练假肢和辅助设备的用户.
- 与复杂的ML方法相比,简单的控制算法为辅助机器人提供了更自然和更强大的交互.
- 对智能辅助的进一步研究可以增强辅助机器人的掌握活动.
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