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以偏好为基础的辅助优化,用于用软背外套提升和降低.

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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
  • 生物力学 生物力学

背景情况:

  • 穿戴式机器人设备,像外衣一样,在职业和康复环境中提供了潜在的潜力.
  • 广泛采用受限于可用性问题:舒适性,限制运动和不清楚的功能益处.
  • 用户的感知对于开发有效和可接受的辅助技术至关重要.

研究的目的:

  • 探索以偏好为基础的控制器优化,用于背部外套,旨在协助举重任务.
  • 研究用户感知如何影响可穿戴机器人辅助的设计和有效性.
  • 通过以用户为中心的控制策略,提高背部外衣的可用性和采用性.

主要方法:

  • 采用强制选择贝叶斯优化方法,以实现高效的控制器参数采样.
  • 利用仅仅是明显差异的领域知识来改进辅助设置.
  • 优化了背部外套的两个控制参数,收集参与者的偏好.

主要成果:

  • 确定了一致但参与者独特的首选外套辅助设置.
  • 发现了对不对称参数配置的普遍偏好,偏好升降辅助而不是降低辅助.
  • 发现用户人体识别显著影响了首选的辅助设置.

结论:

  • 感知引导辅助优化对可穿戴机器人设备具有重大潜力.
  • 根据用户反和物理特征量身定制外衣辅助是提高可用性的关键.
  • 这种方法标志着背部外衣在现实世界职业和康复应用中更好地整合的一步.