  Drug-induced parkinsonism affects many older adults with dementia, often
causing gait disturbances. New advances in vision-based human pose-estimation
have opened possibilities for frequent and unobtrusive analysis of gait in
residential settings. This work leverages novel spatial-temporal graph
convolutional network (ST-GCN) architectures and training procedures to predict
clinical scores of parkinsonism in gait from video of individuals with
dementia. We propose a two-stage training approach consisting of a
self-supervised pretraining stage that encourages the ST-GCN model to learn
about gait patterns before predicting clinical scores in the finetuning stage.
The proposed ST-GCN models are evaluated on joint trajectories extracted from
video and are compared against traditional (ordinal, linear, random forest)
regression models and temporal convolutional network baselines. Three 2D human
pose-estimation libraries (OpenPose, Detectron, AlphaPose) and the Microsoft
Kinect (2D and 3D) are used to extract joint trajectories of 4787 natural
walking bouts from 53 older adults with dementia. A subset of 399 walks from 14
participants is annotated with scores of parkinsonism severity on the gait
criteria of the Unified Parkinson's Disease Rating Scale (UPDRS) and the
Simpson-Angus Scale (SAS). Our results demonstrate that ST-GCN models operating
on 3D joint trajectories extracted from the Kinect consistently outperform all
other models and feature sets. Prediction of parkinsonism scores in natural
walking bouts of unseen participants remains a challenging task, with the best
models achieving macro-averaged F1-scores of 0.53 +/- 0.03 and 0.40 +/- 0.02
for UPDRS-gait and SAS-gait, respectively. Pre-trained model and demo code for
this work is available:
https://github.com/TaatiTeam/stgcn_parkinsonism_prediction.
