用用户偏好优化假肢控制，省时且更贴合真实需求。
Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis
- 基于用户偏好进行多目标贝叶斯优化，自动调节假肢参数。
- 在仿真和真实测试中均实现快速收敛与显著生物力学改善。
- 适合关注假肢个性化、人机协同设计的研究者与开发者。
为截肢者调校主动假肢耗时且依赖的指标可能无法全面反映用户需求。本文提出一种人机协同优化（HILO）方法，利用直接用户偏好高效个性化标准四参数假肢控制器。该方法采用基于偏好的多目标贝叶斯优化，结合专为偏好学习设计的先进获取函数，并包含两种算法变体：离散版（EUBO-LineCoSpar）与连续版（BPE4Prost）。在基准函数与真实应用试验中，结果表明该方法具备高效收敛性、稳健偏好获取能力及可测量的生物力学性能提升，展示了偏好驱动调优在以用户为中心的假肢控制中的潜力。
原文摘要 · Abstract (English)
Tuning active prostheses for people with amputation is time-consuming and relies on metrics that may not fully reflect user needs. We introduce a human-in-the-loop optimization (HILO) approach that leverages direct user preferences to personalize a standard four-parameter prosthesis controller efficiently. Our method employs preference-based Multiobjective Bayesian Optimization that uses a state-or-the-art acquisition function especially designed for preference learning, and includes two algorithmic variants: a discrete version (\textit{EUBO-LineCoSpar}), and a continuous version (\textit{BPE4Prost}). Simulation results on benchmark functions and real-application trials demonstrate efficient convergence, robust preference elicitation, and measurable biomechanical improvements, illustrating the potential of preference-driven tuning for user-centered prosthesis control.
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