Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
PLoS One. 2024 Oct 15;19(10):e0312268. doi: 10.1371/journal.pone.0312268. eCollection 2024.
Understanding the athlete's movements and the restrictions incurred by protective equipment is crucial for improving the equipment and subsequently, the athlete's performance. The task of equipment improvement is especially challenging in sports including advanced manoeuvres such as ice hockey and requires a holistic approach guiding the researcher's attention toward the right variables. The purposes of this study were (a) to quantify the effects of protective equipment in ice hockey on player's performance and (b) to identify the restrictions incurred by it. Twenty male hockey players performed four different drills with and without protective equipment while their performance was quantified. A neural network accompanied by layer-wise relevance propagation was applied to the 3D kinematic data to identify variables and time points that were most relevant for the neural network to distinguish between the equipment and no equipment condition, and therefore presumable result from restrictions incurred by the protective equipment. The study indicated that wearing the protective equipment, significantly reduced performance. Further, using the 3D kinematics, an artificial neural network could accurately distinguish between the movements performed with and without the equipment. The variables contributing the most to distinguishing between the equipment conditions were related to the upper extremities and movements in the sagittal plane. The presented methodology consisting of artificial neural networks and layer-wise relevance propagation contributed to insights without prior knowledge of how and to which extent joint angles are affected in complex maneuvers in ice hockey in the presence of protective equipment. It was shown that changes to the equipment should support the flexion movements of the knee and hip and should allow players to keep their upper extremities closer to the torso.
了解运动员的动作和防护装备带来的限制对于改进装备，进而提高运动员的表现至关重要。在包括冰球等高级动作的运动中，改进装备的任务尤其具有挑战性，需要采用整体方法引导研究人员关注正确的变量。本研究的目的是：(a) 量化冰球运动中防护装备对运动员表现的影响；(b) 确定其带来的限制。20 名男性冰球运动员在穿着和不穿着防护装备的情况下进行了四项不同的训练，同时对其表现进行了量化。采用神经网络结合逐层相关性传播，对 3D 运动学数据进行分析，以确定对神经网络最相关的变量和时间点，从而区分装备和无装备条件，因此可假定这些变量是由防护装备带来的限制造成的。研究表明，穿着防护装备会显著降低运动员的表现。此外，利用 3D 运动学，人工神经网络可以准确区分有装备和无装备情况下的运动。对区分装备条件贡献最大的变量与上肢和矢状面运动有关。本研究提出的方法包括人工神经网络和逐层相关性传播，有助于在不了解关节角度在冰球复杂动作中受到何种影响以及影响程度的情况下，获得有关防护装备的深入见解。研究结果表明，装备的改变应支持膝关节和髋关节的弯曲运动，并应允许运动员将上肢更靠近躯干。
