发表机构
University of Alabama（阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对髋关节外骨骼辅助行走，提出融合七项生物力学指标的个性化复合平衡代价，结合贝叶斯模型选择最优辅助条件，缩小候选集以提升实验效率。
AI 中文摘要
髋关节外骨骼可能改善从意外步态扰动中的恢复，但由于平衡是多维的，且人在回路实验样本量小且噪声大，个性化辅助仍然困难。我们提出了一种参与者特定的复合平衡代价，该代价整合了七个生物力学子指标，涵盖稳定裕度、质心动力学和全身角动量。这些子指标被转换为方向对齐的无量纲代价特征，并在单纯形上学习非负融合权重。结合经验贝叶斯分层模型，学习到的复合选择器估计每个测试条件为最佳的后验概率P(best)，以及大小为$K_{0.8}$的高概率候选集。该框架在46种髋关节辅助条件下，以1.1 m/s速度行走并施加单侧皮带滑动扰动，对三名参与者进行了评估。在全预算分析（B=每种条件4次重复）中，选择器将80%的后验概率集中在46种条件中的1至5种内，而等权融合为2至12种，主成分分析融合为4至37种。这一更小的候选集可缩短个性化实验，并限制参与者未来研究中重复扰动的暴露。所选条件下的试验显示，观察到的复合代价低于无扭矩试验，P2和P3的名义p<0.05。留一重复重拟合对所有参与者产生了正的平均留出秩相关，且学习到的权重和候选集具有中等稳定性。这些概念验证结果支持在基于扰动的人在回路实验中，使用参与者特定的复合平衡评估进行候选选择。
英文摘要
Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidimensional and human-in-the-loop experiments are small-sample and noisy. We present a participant-specific composite balance cost that integrates seven biomechanical sub-metrics spanning margin of stability, center-of-mass dynamics, and whole-body angular momentum. The sub-metrics are converted to direction-aligned, dimensionless cost features, and nonnegative fusion weights are learned on the simplex. Coupled with an empirical-Bayes hierarchical model, the learned-composite selector estimates each tested condition's posterior probability of being best, P(best), and a high-probability candidate set with size $K_{0.8}$. The framework was evaluated with three participants walking at 1.1 m/s during unilateral belt-slip perturbations across 46 hip-assistance conditions. In the full-budget analysis (B = 4 repeats per condition), the selector concentrated 80% of the posterior probability within 1 to 5 of 46 conditions, compared with 2 to 12 for equal-weight fusion and 4 to 37 for principal component analysis fusion. This smaller candidate set could shorten personalization experiments and limit participants' exposure to repeated perturbations in future studies. Selected-condition trials showed lower observed composite costs than no-torque trials, with nominal p < 0.05 for P2 and P3. Leave-one-repeat-out refits yielded positive mean held-out rank correlations for all participants and moderate stability of the learned weights and candidate sets. These proof-of-concept results support participant-specific composite balance evaluation for candidate selection in perturbation-based human-in-the-loop experiments.
Comments8 pages, 6 figures. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
