Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach

TL;DR

This study develops a deep learning method using inertial sensors to accurately distinguish between orthopedic and neurological gait disorders in dogs, aiding veterinary diagnosis.

Contribution

It introduces a novel inertial sensor-based deep learning approach with optimized configurations for reliable canine gait classification.

Findings

96% accuracy in multiclass classification (healthy, orthopedic, neurological)

82% accuracy in binary classification (healthy vs. non-healthy)

Effective generalization to unseen dogs

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.