用视频分析老人走路姿态，精准预测跌倒风险。
Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery
- 通过3D人体网格模型从视频提取步态数据
- 步态参数与传感器测量结果高度相关
- 适合社区老人跌倒风险筛查
步态评估是老年人跌倒风险和整体健康的重要临床指标。但目前临床实践主要依赖秒表测量步行速度。本文提出一个基于3D人体网格恢复（HMR）模型的分析流程，从社区中心老年人完成定时起立行走测试（TUG）的视频中提取时空步态参数，包括步态时间、坐站转换时长和步长。结果显示，视频推导的步态时间与可穿戴传感器（IMU基脚垫）测量结果显著相关。采用线性混合效应模型验证，更短且波动更大的步长、更长的坐站转换时长，均与更高的自评跌倒风险和恐跌程度相关。研究证明该流程可在社区环境中实现便捷、生态有效的步态分析。
原文摘要 · Abstract (English)
Gait assessment is a key clinical indicator of fall risk and overall health in older adults. However, standard clinical practice is largely limited to stopwatch-measured gait speed. We present a pipeline that leverages a 3D Human Mesh Recovery (HMR) model to extract gait parameters from recordings of older adults completing the Timed Up and Go (TUG) test. From videos recorded across different community centers, we extract and analyze spatiotemporal gait parameters, including step time, sit-to-stand duration, and step length. We found that video-derived step time was significantly correlated with IMU-based insole measurements. Using linear mixed effects models, we confirmed that shorter, more variable step lengths and longer sit-to-stand durations were predicted by higher self-rated fall risk and fear of falling. These findings demonstrate that our pipeline can enable accessible and ecologically valid gait analysis in community settings.
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