EgoInertia-MI：用于运动障碍评估的多模态自我中心视觉与惯性测量单元基准测试
EgoInertia-MI: A Multimodal Egocentric Vision and IMU Benchmark for Motor Impairment Assessment
AI总结：
研究针对运动障碍评估，结合自我中心视觉与惯性测量单元信号创建EgoInertia-MI数据集，设动作识别等任务并评估基线，发现多模态融合性能最佳，凸显两者结合用于运动评估的潜力。
摘要：
运动障碍在多种疾病中常见，传统评估有局限。可穿戴IMU和第三人视角视频有问题，自我中心视觉是更优选择。本文介绍EgoInertia-MI数据集，含健康志愿者模拟不同程度运动障碍的活动。建立两项基准任务并评估基线，结果表明自我中心视频对评估有重要线索，多模态融合性能最佳。
英文摘要：
Motor impairments, including tremor, bradykinesia, gait abnormalities, and postural instability, are common across many neurological and movement-related conditions. Conventional clinical assessments are often intermittent and may fail to capture subtle temporal variations in motor behavior. While wearable IMUs and third-person video have shown promise for objective motor assessment, third-person recordings raise privacy concerns and require constrained acquisition setups. In contrast, egocentric vision provides a more naturalistic and privacyaware alternative. In this work, we introduce EgoInertia-MI, a multimodal benchmark dataset combining synchronized egocentric video and wearable IMU signals for motor impairment analysis. The dataset contains 19 upper- and lower-body activities performed by healthy volunteers simulating varying levels of motor impairment severity levels: no impairment, mild impairment, and severe impairment. We establish two benchmark tasks: action recognition and motor impairment severity estimation, and evaluate multiple unimodal and multimodal baselines. Experimental results show that egocentric video provides strong cues for motor impairment assessment, while multimodal fusion achieves the best overall performance, reaching 0.78 Macro-F1 for severity estimation and 0.93 Macro-F1 for action recognition. These findings highlight the potential of combining egocentric vision and wearable sensing for ecologically valid and privacy-aware motor assessment. Code and data are available at:https://fatemah-alh.github.io/EgoInertia-MI-Page/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
