用人类反馈优化外骨骼助行控制，发现个性化效果有限。
Human-in-the-loop Optimisation in Robot-assisted Gait Training
- 用进化算法实时调整外骨骼的助力刚度以适应个体差异。
- 六名健康受试者参与实验，但优化后步态表现未显著提升。
- 揭示人机协同适应可能削弱个性化控制的实际收益，适合康复工程研究者参考。
可穿戴机器人为量化监测步态并提供系统化、自适应辅助以促进患者独立性和改善步态提供了有前景的解决方案。然而，由于行走模式存在显著的人际和个体内差异，设计能适应个体特征的机器人控制器至关重要。本文研究了人机闭环优化（HILO）在步态训练中实现个性化辅助的潜力。采用协方差矩阵自适应进化策略（CMA-ES）持续优化下肢外骨骼的按需辅助控制器。六名健康受试者参与为期两天的实验。结果表明，尽管CMA-ES似乎能为每位个体收敛到一组独特的刚度参数，但在验证试验中未观察到受试者性能的明显改善。这些发现凸显了人机协同适应与人类行为变异的影响，其效应可能超过基于规则的辅助控制器个性化所能带来的益处。本工作有助于理解当前外骨骼辅助步态康复中个性化方法的局限性，并指出了该领域有效实施人机闭环优化的关键挑战。
原文摘要 · Abstract (English)
Wearable robots offer a promising solution for quantitatively monitoring gait and providing systematic, adaptive assistance to promote patient independence and improve gait. However, due to significant interpersonal and intrapersonal variability in walking patterns, it is important to design robot controllers that can adapt to the unique characteristics of each individual. This paper investigates the potential of human-in-the-loop optimisation (HILO) to deliver personalised assistance in gait training. The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) was employed to continuously optimise an assist-as-needed controller of a lower-limb exoskeleton. Six healthy individuals participated over a two-day experiment. Our results suggest that while the CMA-ES appears to converge to a unique set of stiffnesses for each individual, no measurable impact on the subjects' performance was observed during the validation trials. These findings highlight the impact of human-robot co-adaptation and human behaviour variability, whose effect may be greater than potential benefits of personalising rule-based assistive controllers. Our work contributes to understanding the limitations of current personalisation approaches in exoskeleton-assisted gait rehabilitation and identifies key challenges for effective implementation of human-in-the-loop optimisation in this domain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。