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

2025-07-08

AI Summary

In veterinary clinical practice, gait abnormalities in dogs caused by neurological versus orthopedic disorders exhibit substantial symptomatic overlap, rendering subjective differentiation challenging and necessitating objective, automated diagnostic support. This study proposes a wearable inertial sensor–based gait analysis framework integrated with deep learning: it optimizes multi-site sensor placement and introduces a dual-task deep neural network designed for cross-individual generalization—simultaneously supporting multi-class classification (healthy, orthopedic, neurological) and binary classification (abnormal vs. normal). Evaluated on a clinically collected dataset of 29 dogs, the model achieves 96% accuracy in multi-class classification and 82% cross-individual binary classification accuracy. To our knowledge, this is the first approach demonstrating robust, real-world veterinary deployment for automated canine gait abnormality discrimination. It establishes a novel paradigm for standardized and intelligent clinical diagnosis in veterinary medicine.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Distinguish neurological and orthopedic gait disorders in dogs
• Optimize deep learning for gait analysis generalization
• Improve diagnostic accuracy using inertial sensor data

Innovation

• Deep learning analyzes canine gait with inertial sensors
• Optimized model distinguishes neurological and orthopedic disorders
• Achieves 96% accuracy in multiclass gait classification