Winner Taniel S, Rosenberg Michael C, Berman Gordon J, Kesar Trisha M, Ting Lena H
W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Department of Biology, Emory University, Atlanta, GA USA.
bioRxiv. 2024 May 3:2024.05.01.591976. doi: 10.1101/2024.05.01.591976.
Understanding individuals' distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that "gait signatures" describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI): 99.1-100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants' preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.
了解个体独特的运动模式对于健康、康复和运动至关重要。最近，我们开发了一个基于机器学习的框架，以表明描述健全人和中风后步态运动学的神经力学动力学的“步态特征”在不同速度下仍具有个体特异性。然而，我们仅在有限的速度范围内和有限数量的参与者中评估了步态特征，且仅使用矢状面（即二维）关节角度。在此，我们对17名健全的年轻人在从非常慢（0.3米/秒）到极快（超过步行到跑步的转换速度）的广泛速度范围内的步态特征变化进行了表征。我们进一步评估了三维运动学和/或动力学（地面反作用力、关节力矩和功率）数据是否会提高步态特征的辨别能力。我们的研究表明，步态特征在不同步行速度下仍具有个体特异性：值得注意的是，三维运动学特征在个体分类中达到了极高的准确率（99.8%，置信区间（CI）：99.1 - 100%），超过了二维运动学和三维动力学。此外，参与者在整个速度范围内的步态特征呈现出一致、可预测的线性变化。这些变化与参与者的偏好步行速度、平衡能力、步频和步长有关。这些发现支持将步态特征作为一种工具，用于表征步态中的个体差异并预测速度引起的步态动力学变化。
