  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.
