Department of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary.
PLoS One. 2022 Mar 9;17(3):e0264299. doi: 10.1371/journal.pone.0264299. eCollection 2022.
Dog harnesses are becoming more popular, with their large variety stemming from the idea that different dogs and scenarios require different types of harnesses. While their benefits over collars are self-explanatory, there is a lack of research on their effect on gait, and even the existing studies examine only a limited set of parameters. The goal of present study was to establish a method capable of quantifying canine gait in detail. Based on 3D motion capture, the developed method allows for the examination of 18 joint angles and 35 spatio-temporal parameters throughout multiple gait cycles, and can be used to analyze canine movement in detail in any kind of scenario (e.g. comparing healthy and lame dogs, or measuring the effect of training). The method is presented through the measurement of how different harnesses affect walking kinematics compared to free (unleashed) movements. Four dogs with varying body sizes and breeds and multiple types of harnesses were included. Marker data was filtered using a zero-lag 6th order Butterworth-filter with a cutoff frequency of 20 Hz. The normality of the spatio-temporal and joint range of motion parameters was tested using the Anderson-Darling test (p = 0.05), with most parameters passing in 60+% of test cases. Swing time and range of motion of the sagittal aspect of spinal angle at T1 vertebrae failed more regularly, both resulting from the measurement setup rather than the actual parameters being not normally distributed. Two-sample Kolmogorov-Smirnov tests (p = 0.05) were used to compare each parameter's distribution between cases, showing that most parameters are significantly altered by the harnesses in about 2/3rd of the cases. Based on the results, there's no absolute superior harness, however, it is possible to select the best fit for a specific dog and application, justifying their large variety.
狗背带越来越受欢迎，它们的种类繁多，源于这样一种理念，即不同的狗和场景需要不同类型的背带。虽然它们相对于项圈的好处不言而喻，但关于它们对步态的影响的研究还很缺乏，甚至现有的研究也只检查了有限的参数。本研究的目的是建立一种能够详细量化犬步态的方法。该方法基于 3D 运动捕捉，允许检查 18 个关节角度和 35 个时空参数，在多个步态周期内，并可用于在任何场景下详细分析犬类运动（例如，比较健康和跛行犬，或测量训练效果）。该方法是通过测量不同背带相对于自由（未系绳）运动如何影响行走运动学来展示的。研究纳入了 4 只体型和品种不同的狗和多种类型的背带。使用截止频率为 20 Hz 的零滞后 6 阶巴特沃斯滤波器对标记数据进行滤波。使用安德森-达林检验（p = 0.05）检验时空和关节运动范围参数的正态性，大多数参数在 60%以上的测试案例中通过。摆动时间和 T1 椎骨矢状面脊柱角度的运动范围更频繁地失败，这两者都源于测量设置，而不是实际参数的非正态分布。双样本 Kolmogorov-Smirnov 检验（p = 0.05）用于比较病例之间每个参数的分布，结果表明，在大约 2/3 的病例中，大多数参数都因背带而发生显著变化。根据结果，没有绝对优越的背带，但是，可以为特定的狗和应用选择最合适的背带，这也证明了它们种类繁多的合理性。
