Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia

TL;DR

This paper introduces Pose2Gait, a neural network that extracts 3D gait features from monocular videos of older adults with dementia, enabling remote health monitoring and early detection of gait changes.

Contribution

It presents a novel deep learning approach tailored for clinical populations to estimate gait features from monocular video data.

Findings

Velocity and step length correlated with depth camera data (r=0.83, 0.60)

Model successfully predicts key gait features from monocular video

Potential for early detection of health changes in dementia patients

Abstract

Video-based ambient monitoring of gait for older adults with dementia has the potential to detect negative changes in health and allow clinicians and caregivers to intervene early to prevent falls or hospitalizations. Computer vision-based pose tracking models can process video data automatically and extract joint locations; however, publicly available models are not optimized for gait analysis on older adults or clinical populations. In this work we train a deep neural network to map from a two dimensional pose sequence, extracted from a video of an individual walking down a hallway toward a wall-mounted camera, to a set of three-dimensional spatiotemporal gait features averaged over the walking sequence. The data of individuals with dementia used in this work was captured at two sites using a wall-mounted system to collect the video and depth information used to train and evaluate our model. Our Pose2Gait model is able to extract velocity and step length values from the video that are correlated with the features from the depth camera, with Spearman's correlation coefficients of .83 and .60 respectively, showing that three dimensional spatiotemporal features can be predicted from monocular video. Future work remains to improve the accuracy of other features, such as step time and step width, and test the utility of the predicted values for detecting meaningful changes in gait during longitudinal ambient monitoring.