  In this paper, we investigated whether we can 1) detect participants with
ataxia-specific gait characteristics (risk-prediction), and 2) assess severity
of ataxia from gait (severity-assessment) using computer vision. We created a
dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with
(or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task
of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical
sites located in 8 different states across the United States. We develop a
computer vision pipeline to detect, track, and separate out the participants
from their surroundings and construct several features from their body pose
coordinates to capture gait characteristics like step width, step length,
swing, stability, speed, etc. Our risk-prediction model achieves 83.06%
accuracy and an 80.23% F1 score. Similarly, our severity-assessment model
achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's
correlation coefficient score of 0.7268. Our models still performed
competitively when evaluated on data from sites not used during training.
Furthermore, through feature importance analysis, we found that our models
associate wider steps, decreased walking speed, and increased instability with
greater ataxia severity, which is consistent with previously established
clinical knowledge. Our models create possibilities for remote ataxia
assessment in non-clinical settings in the future, which could significantly
improve accessibility of ataxia care. Furthermore, our underlying dataset was
assembled from a geographically diverse cohort, highlighting its potential to
further increase equity. The code used in this study is open to the public, and
the anonymized body pose landmark dataset is also available upon request.
