用户偏好优化用于控制脚外骨架,使用样本高效的积极学习
Ung Hee Lee1,2,3, Varun S Shetty1,2, Patrick W Franks3
1Department of Mechanical Engineering, University of Michigan, 2350 Hayward, Ann Arbor, MI 48109, USA.
Science robotics
|October 18, 2023
概括
这项研究开发了一种基于用户偏好的增强式外骨控制器调整的新方法. 这种方法有效地优化了外骨的辅助,以提高用户体验和舒适度.
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:
- 由于控制器调节方面的挑战,增强型外骨的广泛采用受到阻碍.
- 当前的方法往往需要大量的资源,并专注于单一的目标,忽视了诸如舒适性和稳定性等多方面的用户体验因素.
研究的目的:
- 引入一种方便的方法来调整外骨控制器参数,以最大限度地提高用户偏好.
- 为了利用佩戴者的反来平衡外骨架辅助中的多种经验因素.
主要方法:
- 一个进化算法推了控制器参数,通过使用用户偏好数据预训练的神经网络进行排名.
- 通过强制选择比较实时用户反指导了部分辅助脚外骨架的调整过程.
主要成果:
- 这种方法在与随机生成相比,在与佩戴者喜欢的控制器参数相聚时,平均达到88%的准确性.
- 用户喜欢的设置通常在43 ± 7个查询内稳定.
结论:
- 用户偏好可以有效地利用部分辅助脚外骨架的实时调整.
- 这种直观的界面表明了推进日常使用下肢可穿戴技术的潜力.
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