arXiv subjects

Tom Verstraten

Publications and source records attributed to Tom Verstraten.

3 recordsLinked to original sources

arXiv2026

RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts

Assistive devices for people with mobility impairments, such as powered exoskeletons, rely on accurate locomotion mode recognition to adapt control strategies and provide appropriate assistance during daily activities. However, public benchmarks are typically collected from healthy adults, lack temporally precise labels necessary for detecting mode transitions, or focus on a limited set of tasks. To support development and evaluation under realistic clinical constraints and daily mobility demands, we introduce RevalExo, a functional daily-activity benchmark for inertial and visual locomotion mode recognition. RevalExo is built around a standardized, clinically and ecologically validated daily-activity protocol reflecting the cumulative everyday mobility demands in ageing and clinical populations. The benchmark includes 27 participants across three cohorts: older adults without mobility impairments, stroke survivors, and older adults with probable sarcopenia. The full cohort was recorded with lower-body IMUs, while synchronized egocentric video was collected for a clinically feasible subset of 13 participants. RevalExo provides 10.1 hours of frame-level annotations across 11 locomotion modes, including 5.1 hours of paired inertial--visual recordings. We benchmark three challenges: unimodal and multimodal locomotion mode recognition across multiple horizons, cross-population generalization from older adults without mobility impairments to clinical cohorts, and vision-guided knowledge transfer to IMU-only models. Results confirm consistent gains from fusing inertial and visual inputs but reveal a substantial gap between general recognition ($\sim$93\% F1) and recognition during transitions ($\sim$68\% F1), alongside persistent challenges in cross-population generalization and cross-modal transfer. We release RevalExo to stimulate further research on these open challenges.

cs.AI↗

arXiv2026

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

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.

cs.RO↗

arXiv2025

Dual-Variable Force Characterisation method for Human-Robot Interaction in Wearable Robotics

Understanding the physical interaction with wearable robots is essential to ensure safety and comfort. However, this interaction is complex in two key aspects: (1) the motion involved, and (2) the non-linear behaviour of soft tissues. Multiple approaches have been undertaken to better understand this interaction and to improve the quantitative metrics of physical interfaces or cuffs. As these two topics are closely interrelated, finite modelling and soft tissue characterisation offer valuable insights into pressure distribution and shear stress induced by the cuff. Nevertheless, current characterisation methods typically rely on a single fitting variable along one degree of freedom, which limits their applicability, given that interactions with wearable robots often involve multiple degrees of freedom. To address this limitation, this work introduces a dual-variable characterisation method, involving normal and tangential forces, aimed at identifying reliable material parameters and evaluating the impact of single-variable fitting on force and torque responses. This method demonstrates the importance of incorporating two variables into the characterisation process by analysing the normalized mean square error (NMSE) across different scenarios and material models, providing a foundation for simulation at the closest possible level, with a focus on the cuff and the human limb involved in the physical interaction between the user and the wearable robot.

cs.RO↗