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

TL;DR

This paper presents a human-in-the-loop Bayesian optimization method for efficiently personalizing active prosthesis controllers based on user preferences, leading to improved biomechanical outcomes.

Contribution

It introduces a preference-based multiobjective Bayesian optimization framework with novel acquisition functions and variants tailored for prosthesis tuning.

Findings

Efficient convergence in simulation and real trials

Robust preference elicitation demonstrated

Measurable biomechanical improvements achieved

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.