RevalExo：面向老年人和临床队列的惯性与视觉运动模式识别的功能性日常活动基准
RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts
- KU Leuven（鲁汶大学）
- Vrije Universiteit Brussel（布鲁塞尔自由大学）
- Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RevalExo是一个面向老年人和临床人群的日常活动基准，提供惯性和视觉数据，评估运动模式识别，发现融合多模态有增益但转换识别和跨人群泛化仍具挑战。
AI中文摘要：
对于行动不便人群的辅助设备，如动力外骨骼，依赖于准确的运动模式识别来调整控制策略并在日常活动中提供适当辅助。然而，现有的公开基准通常采集自健康成年人，缺乏检测模式转换所需的时间精确标签，或仅关注有限的任务集。为在现实临床约束和日常活动需求下支持开发与评估，我们提出了RevalExo，一个用于惯性和视觉运动模式识别的功能性日常活动基准。RevalExo基于一个标准化、经临床和生态学验证的日常活动协议构建，该协议反映了老年和临床人群累积的日常活动需求。该基准包含27名参与者，分为三个队列：无行动障碍的老年人、中风幸存者和可能患有肌少症的老年人。全体队列均使用下肢惯性测量单元（IMU）记录，同时对临床可行的13名参与者子集采集了同步的自我中心视频。RevalExo提供了10.1小时的帧级标注，涵盖11种运动模式，其中包括5.1小时的配对惯性-视觉记录。我们评估了三个挑战：跨多个时间范围的单模态和多模态运动模式识别、从无行动障碍老年人到临床队列的跨人群泛化，以及视觉引导的知识迁移至仅IMU模型。结果证实融合惯性和视觉输入带来一致的性能提升，但揭示了通用识别（约93% F1分数）与转换期间识别（约68% F1分数）之间的显著差距，同时跨人群泛化和跨模态迁移仍面临持续挑战。我们发布RevalExo以促进对这些开放挑战的进一步研究。
英文摘要：
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.
