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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

TL;DR

This work introduces a human-in-the-loop optimization (HILO) approach that leverages direct user preferences to personalize a standard four-parameter prosthesis controller efficiently.

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.

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

TL;DR

This work introduces a human-in-the-loop optimization (HILO) approach that leverages direct user preferences to personalize a standard four-parameter prosthesis controller efficiently.

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.
Paper Structure (21 sections, 11 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 21 sections, 11 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Bayesian Preference Elicitation (BPE)
  3. Problem Definition
  4. Probabilistic Modeling
  5. Preference elicitation strategy
  6. Algorithms
  7. EUBO-LineCoSpar
  8. Bayesian Preference Elicitation for Prosthesis Optimization: BPE4Prost
  9. Simulation evaluation
  10. Simulation Setup
  11. Simulation Results
  12. Human subject experiment
  13. Tunable Control Framework
  14. Protocol
  15. Human subject results
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Mean $\log_{10}$ simple regret over 11 runs for all benchmark functions. Each algorithm was run for 50 iterations. Shaded regions indicate the standard error. Across both the 2D problem (Branin) and the 4D problems (Ackley, Alpine1, Hartmann), BPE4Prost consistently achieves lower regret compared to the other methods.
  • Figure 2: Runtime comparison of the three algorithms on Ackley (4D) and Branin (2D). BPE4Prost scales approximately linearly with the number of queries, while EUBO-LineCoSpar remains near-constant due to its line-restricted evaluations.
  • Figure 3: [A] Active prosthetic knee system used in this study. [B] High-level control framework based on a finite state machine (FSM) with three states: Stance (S), Swing Flexion (SF), and Swing Extension (SE). Threshold rules between states are indicated with tunable parameters written in red. [C] Mid-level control framework. A position controller is used in S, while impedance controllers are employed in SF ans SE. Tunable parameters are indicated in red.
  • Figure 4: Comparison of knee angle trajectories for preferred solutions and their corresponding challengers. Each subplot displays the mean $\pm$ std of the knee angle over the normalized gait cycle (0–100%) across steps. The number of gait cycles used to compute each mean trajectory is indicated.
  • Figure 5: Evolution of the estimated optimal configuration for TF2, $\hat{x} = \arg\max_x \mu_{N}(x)$, for the three trials, shown for each dimensions. The final estimate for each trial is highlighted. Trials 1 and 2 converge rapidly in most of the dimension, whereas Trial 3 exhibits increased variability, likely due to noisy user feedback.