Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Canine gait analysis using wearable inertial sensors is gaining attention in
veterinary clinical settings, as it provides valuable insights into a range of
mobility impairments. Neurological and orthopedic conditions cannot always be
easily distinguished even by experienced clinicians. The current study explored
and developed a deep learning approach using inertial sensor readings to assess
whether neurological and orthopedic gait could facilitate gait analysis. Our
investigation focused on optimizing both performance and generalizability in
distinguishing between these gait abnormalities. Variations in sensor
configurations, assessment protocols, and enhancements to deep learning model
architectures were further suggested. Using a dataset of 29 dogs, our proposed
approach achieved 96% accuracy in the multiclass classification task
(healthy/orthopedic/neurological) and 82% accuracy in the binary classification
task (healthy/non-healthy) when generalizing to unseen dogs. Our results
demonstrate the potential of inertial-based deep learning models to serve as a
practical and objective diagnostic and clinical aid to differentiate gait
assessment in orthopedic and neurological conditions.
