Self-Supervised Learning of Gait-Based Biomarkers
R. James Cotton J. D. Peiffer Kunal Shah Allison DeLillo Anthony Cimorelli Shawana Anarwala Kayan Abdou Tasos Karakostas
Markerless motion capture (MMC) is revolutionizing gait analysis in clinical
settings by making it more accessible, raising the question of how to extract
the most clinically meaningful information from gait data. In multiple fields
ranging from image processing to natural language processing, self-supervised
learning (SSL) from large amounts of unannotated data produces very effective
representations for downstream tasks. However, there has only been limited use
of SSL to learn effective representations of gait and movement, and it has not
been applied to gait analysis with MMC. One SSL objective that has not been
applied to gait is contrastive learning, which finds representations that place
similar samples closer together in the learned space. If the learned similarity
metric captures clinically meaningful differences, this could produce a useful
representation for many downstream clinical tasks. Contrastive learning can
also be combined with causal masking to predict future timesteps, which is an
appealing SSL objective given the dynamical nature of gait. We applied these
techniques to gait analyses performed with MMC in a rehabilitation hospital
from a diverse clinical population. We find that contrastive learning on
unannotated gait data learns a representation that captures clinically
meaningful information. We probe this learned representation using the
framework of biomarkers and show it holds promise as both a diagnostic and
response biomarker, by showing it can accurately classify diagnosis from gait
and is responsive to inpatient therapy, respectively. We ultimately hope these
learned representations will enable predictive and prognostic gait-based
biomarkers that can facilitate precision rehabilitation through greater use of
MMC to quantify movement in rehabilitation.