Functional benchmark improves recognizing movement in older adults and patients
RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts
Artificial IntelligenceComputer Vision and Pattern Recognition
Summary
Devices like powered exoskeletons help people who have trouble moving, but they need to know what kind of movement a person is doing to work well. The authors created RevalExo, a detailed set of real-life movement data from older adults and clinical patients, including videos and sensor data. Their work shows combining video and motion sensors helps recognize movements better, but detecting quick changes and applying models across different groups remains challenging. The new benchmark aims to help improve assistive technology by providing realistic test data and highlighting these difficulties.
powered exoskeletonslocomotion mode recognitioninertial measurement units (IMUs)egocentric videoclinical cohortsdaily-activity protocolmultimodal data fusioncross-population generalizationmode transitionsknowledge transfer
Authors
Diwas Lamsal, Juha Carlon, Reinhard Claeys, Maxim Yudayev, Louis Flynn, Tom Verstraten, David Beckwée, Eva Swinnen, Mihai Bâce, Bart Vanrumste, Benjamin Filtjens
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.