Papers for

rehabilitation technology teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Bayesian optimization improves control of exoskeletons for comfort and energy use

Multi-Objective Human-in-the-Loop Bayesian Optimization of a Lower-Limb Exoskeleton

Abstract: Human-in-the-loop optimization (HILO) is a common approach for optimizing the control of assistive devices to account for the wearer's unique biomechanics and subjective preferences. However, despite research suggesting that a person may have a different prioritization of objectives depending on time-varying factors such as the environment, their mood, or energy levels, existing HILO approaches only consider a single objective or enforce a fixed weighting on a set of objectives. Neither approach is capable of representing an individual's preferences over objectives. In this work, we propose Multi-Objective Human-in-the-loop Bayesian Optimization (MO-HILBO), which builds on explicit multi-objective Bayesian optimization to efficiently infer a personalized set of Pareto-optimal controllers. We compare our approach with an existing multi-objective HILO method and experimentally demonstrate MO-HILBO on a lower-limb exoskeleton across two objectives: metabolic cost (efficiency) and ordinal human feedback (comfort). We find that MO-HILBO (1) discovers Pareto-optimal controllers, and (2) that the pairwise ordering of points on the Pareto front itself is consistent with validation trials. Lastly, we open-source mohilo, a Python package for running both HILO and MO-HILBO on wearable devices: https://dynamicmobility.github.io/mohilo/.

Fri 25 SeptRoboticsHuman-Computer Interaction
The gist
Assistive devices like exoskeletons need to work well for each individual, since everyone moves differently and prefers different things. The authors show a new way to tune such devices using feedback from the person wearing it, balancing multiple goals like comfort and energy efficiency. Their method finds many good settings that suit different preferences rather than just one fixed balance. They tested this on a lower-limb exoskeleton and showed their approach finds solutions people prefer. They also provide open-source software to help others try it on wearable devices.
Open → 2609.30695v1

RevalExo dataset supports better recognition of walking modes in older and clinical adults

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

Abstract: 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.

Tue 8 SeptArtificial IntelligenceComputer Vision and Pattern Recognition
The gist
People using assistive devices like robotic exoskeletons need those devices to understand how they are moving to provide the right help. Existing datasets usually come from healthy adults and don’t include enough detailed information about changes in movement. The authors created RevalExo, a new dataset collected from older adults and people with mobility challenges, including videos and sensor data, to better recognize different walking and movement types during daily activities. They tested methods using this data and found that combining video and sensor data helps, but recognizing transitions between movements and generalizing across different groups remain hard problems.
Open → 2609.08090v1