用惯性传感器+深度学习区分狗的骨科与神经科步态异常
Canine Clinical Gait Analysis for Orthopedic and Neurological Disorders: An Inertial Deep-Learning Approach
- 基于可穿戴传感器数据，设计深层网络模型识别步态差异
- 多分类准确率达96%，跨狗泛化下二分类仍达82%
- 适合兽医临床辅助诊断，尤其难以区分的神经/骨科疾病
利用可穿戴惯性传感器进行犬类步态分析在兽医临床中日益受到关注，能为多种运动功能障碍提供重要信息。神经科与骨科疾病常难以仅凭经验区分。本研究探索并开发了一种基于惯性传感器读数的深度学习方法，以辅助区分两类步态异常。重点优化了模型性能与泛化能力，考察了传感器配置、评估协议及深度学习模型架构的改进。基于29只犬的数据集，所提方法在多类别分类任务（健康/骨科/神经科）中达到96%准确率，在跨狗泛化下的二分类任务（健康/非健康）准确率为82%。结果表明，基于惯性的深度学习模型具备作为客观、实用的诊断辅助工具潜力，可用于区分骨科与神经科步态异常。
原文摘要 · Abstract (English)
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.
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