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arXiv · 2602.22922

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

Sophia Taddei·Wouter Koppen·Eligia Alfio·Stefano Nuzzo·Louis Flynn·Maria Alejandra Diaz·Sebastian Rojas Gonzalez·Tom Dhaene·Kevin De Pauw·Ivo Couckuyt·Tom Verstraten
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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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Sophia Taddei, Wouter Koppen, Eligia Alfio, Stefano Nuzzo, Louis Flynn, Maria Alejandra Diaz, Sebastian Rojas Gonzalez, Tom Dhaene, Kevin De Pauw, Ivo Couckuyt, Tom Verstraten. 2026-02-26. Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis. https://arxiv.org/abs/2602.22922

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