Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focused on the development of an objective movement assessment tool to classify elite and novice athletes’ kinematic data using machine learning algorithms. Classifying elite and novice athletes can be beneficial to objectively detect differences in movement patterns between the athletes, which can then be used to provide higher quality feedback to athletes and their coaches. Currently, the method requires optical motion capture, which is expensive and time-consuming to use, creating a barrier for adoption within industry. Therefore, the purpose of this study was to assess whether machine learning could classify athletes as elite or novice using data that can be collected easily and inexpensively in the field using inertial measurement units (IMUs). A secondary purpose of this study was to refine the architecture of the tool to optimize classification rates. Motion capture data from 542 athletes performing seven dynamic screening movements were analyzed. A principal component analysis (PCA)-based pattern recognition technique and machine learning algorithms with the Euclidean norm of the segment linear accelerations and angular velocities as inputs were used to classify athletes based on skill level. Depending on the movement, using metrics achievable with IMUs and a linear discriminant analysis (LDA), 75.1–84.7% of athletes were accurately classified as elite or novice. We have provided evidence that suggests our objective, data-driven method can detect meaningful differences during a movement screening battery when using data that can be collected using IMUs, thus providing a large methodological advance as these can be collected in the field using sensors. This method offers an objective, inexpensive tool that can be easily implemented in the field to potentially enhance screening, assessment, and rehabilitation in sport and clinical settings.

中文翻译：

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使用模拟可穿戴传感器数据对新手运动员的精英进行分类

运动屏幕经常用于识别运动模式的差异，例如病理异常或运动中与技能相关的差异；然而，异常情况通常由人工评估员在视觉上检测到，从而导致可靠性较差。因此，我们之前的研究重点是开发一种客观的运动评估工具，使用机器学习算法对精英和新手运动员的运动数据进行分类。对精英运动员和新手运动员进行分类有助于客观地检测运动员之间运动模式的差异，然后可以将其用于向运动员及其教练提供更高质量的反馈。目前，该方法需要光学运动捕捉，使用起来既昂贵又耗时，这为行业内的采用创造了障碍。所以，本研究的目的是评估机器学习是否可以使用惯性测量单元 (IMU) 在现场轻松且廉价地收集的数据将运动员分类为精英或新手。本研究的第二个目的是改进工具的架构以优化分类率。分析了来自 542 名运动员执行七种动态筛选动作的动作捕捉数据。基于主成分分析 (PCA) 的模式识别技术和机器学习算法，以段线加速度和角速度的欧几里德范数为输入，用于根据技能水平对运动员进行分类。根据运动的不同，使用 IMU 可实现的指标和线性判别分析 (LDA)，75.1-84.7% 的运动员被准确分类为精英或新手。我们提供的证据表明，当使用可以使用 IMU 收集的数据时，我们的客观、数据驱动的方法可以在运动筛选过程中检测到有意义的差异，从而提供了巨大的方法论进步，因为这些可以使用传感器在现场收集。这种方法提供了一种客观、廉价的工具，可以在现场轻松实施，以潜在地增强运动和临床环境中的筛查、评估和康复。