Plateau technique «Equitation et performance sportive», Institut français du cheval et de l'équitation, Avenue de l'École Nationale d'Équitation, 49411 Saumur, France.
Equipe Robotique, Biomécanique, Sport, Santé, Institut PPRIME, UPR3346 CNRS Université de Poitiers ENSMA, 86073 Poitiers, France.
Sensors (Basel). 2022 Jul 1;22(13):4981. doi: 10.3390/s22134981.
Detecting fatigue during training sessions would help riders and trainers to optimize their training. It has been shown that fatigue could affect movement patterns. Inertial measurement units (IMUs) are wearable sensors that measure linear accelerations and angular velocities, and can also provide orientation estimates. These sensors offer the possibility of a non-invasive and continuous monitoring of locomotion during training sessions. However, the indicators extracted from IMUs and their ability to show these locomotion changes are not known. The present study aims at defining which kinematic variables and indicators could highlight locomotion changes during a training session expected to be particularly demanding for the horses. Heart rate and lactatemia were measured to attest for the horse’s fatigue following the training session. Indicators derived from acceleration, angular velocities, and orientation estimates obtained from nine IMUs placed on 10 high-level dressage horses were compared before and after a training session using a non-parametric Wilcoxon paired test. These indicators were correlation coefficients (CC) and root mean square deviations (RMSD) comparing gait cycle kinematics measured before and after the training session and also movement smoothness estimates (SPARC, LDLJ). Heart rate and lactatemia measures did not attest to a significant physiological fatigue. However, the statistics show an effect of the training session (p < 0.05) on many CC and RMSD computed on the kinematic variables, indicating a change in the locomotion with the training session as well as on SPARCs indicators (p < 0.05), and revealing here a change in the movement smoothness both in canter and trot. IMUs seem then to be able to track locomotion pattern modifications due to training. Future research should be conducted to be able to fully attribute the modifications of these indicators to fatigue.
在训练期间检测疲劳有助于骑手和教练优化训练。已经表明,疲劳会影响运动模式。惯性测量单元 (IMU) 是一种可穿戴传感器,可测量线性加速度和角速度,还可以提供方向估计。这些传感器提供了在训练期间对运动进行非侵入性和连续监测的可能性。然而,从 IMU 中提取的指标及其显示这些运动变化的能力尚不清楚。本研究旨在定义哪些运动学变量和指标可以突出在训练期间可能对马特别有要求的运动变化。心率和乳酸性血症的测量证明了马匹在训练后的疲劳。使用非参数 Wilcoxon 配对检验,比较了放置在 10 匹高水平盛装舞步马身上的 9 个 IMU 获得的加速度、角速度和方向估计的指标,比较了训练前后的指标。这些指标是步态周期运动学测量前后的相关系数 (CC) 和均方根偏差 (RMSD),以及运动平滑度估计值 (SPARC、LDLJ)。心率和乳酸性血症测量并未证明存在显著的生理疲劳。然而,统计数据显示训练课程有影响(p < 0.05),对许多基于运动学变量计算的 CC 和 RMSD 以及 SPARCs 指标(p < 0.05)产生影响,这表明随着训练课程的进行,运动方式发生了变化,并且在跑步和小跑中都出现了运动平滑度的变化。因此,IMU 似乎能够跟踪由于训练而导致的运动模式变化。未来的研究应该进行,以便能够将这些指标的变化完全归因于疲劳。