Fall Risk and Gait Analysis in Community-Dwelling Older Adults using World-Spaced 3D Human Mesh Recovery

TL;DR

This paper introduces a pipeline using 3D Human Mesh Recovery to analyze gait in older adults from videos, correlating it with fall risk indicators and demonstrating its potential for accessible community-based assessment.

Contribution

The study presents a novel video-based gait analysis pipeline for older adults that correlates with traditional measures and predicts fall risk.

Findings

Video-derived step time correlates with IMU measurements.

Shorter, variable step lengths linked to higher fall risk.

Pipeline enables ecological gait analysis in community settings.

Abstract

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.