Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584CM, Utrecht, The Netherlands.
Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden.
Sci Rep. 2020 Oct 20;10(1):17785. doi: 10.1038/s41598-020-73215-9.
For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.
几个世纪以来，人类一直着迷于马在运动中的自然之美及其不同的步态。步态分类（GC）通常通过视觉评估进行，因此需要可靠的、自动化的实时客观 GC 方法。在这项研究中，我们使用了一个全身无线网络的无线、高采样率传感器，结合机器学习，实现了完全自动的步态分类。我们使用来自四个不同家马品种的 120 匹马的数据，配备了七个运动传感器，包括来自八种不同步态的 7576 步。GC 使用几种机器学习方法进行训练，包括从特征提取数据和原始传感器数据。我们最好的 GC 模型达到了 97%的准确率。我们的技术实现了准确的 GC，能够进行深入的生物力学研究，并允许对遗传研究和育种中的步态进行高度精确的表型分析。我们的方法可以应用于其他四足动物，而无需开发步态/动物特定的算法。
