Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis

• URL: http://arxiv.org/abs/2602.22922v1
• Date: Thu, 26 Feb 2026 12:11:51 GMT
• Title: Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis
• Abstract summary: We introduce a human-in-the-loop optimization (HILO) approach to personalize a standard four- parameter prosthesis controller efficiently.<n>We show efficient convergence, robust preference elicitation, and measurable biomechanical improvements.
• Score: 3.6494031371026323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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