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 and 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 (~93% F1) and recognition during transitions (~68% F1), alongside persistent challenges in cross-population generalization and cross-modal transfer. We release RevalExo to stimulate further research on these open challenges.
RevalExo contains 10.1 hours of annotated data from 27 participants across three cohorts, labeled with 11 locomotion modes.
Level Ground, Sit to Stand, Stand to Sit, Sitting, Stair Up, Stair Down, Ramp Up, Ramp Down, Grass, Uneven Ground, Carry
If you use this dataset, please cite our paper:
@misc{lamsal2026revalexo,
title={RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts},
author={Diwas Lamsal and Juha Carlon and Reinhard Claeys and Maxim Yudayev and Louis Flynn and Tom Verstraten and David Beckwée and Eva Swinnen and Mihai Bâce and Bart Vanrumste and Benjamin Filtjens},
year={2026},
eprint={2609.08090},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2609.08090},
}The RevalExo dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
You are free to share and adapt the material for non-commercial purposes, provided you give appropriate credit and indicate if changes were made.