Abstract
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.
Highlights
Movement screens are widely used across many disciplines including in ergonomic, clinical, and athletic settings to identify aberrant movement patterns in hopes of decreasing risk of injury and/or improving performance (Donà et al, 2009; Kritz et al, 2009; Padua et al, 2009; Cook et al, 2014; McCall et al, 2014; McCunn et al, 2016)
Machine learning with inertial measurement units (IMUs) data as the input has been able to objectively identify children of different motor development levels during a standing long jump (Sgro et al, 2017), rugby players at a higher risk of a sport-related concussion based on a Y-balance test (Johnston et al, 2019), Australian football players at different levels of fatigue during a Y-balance test (Johnston et al, 2016), and to predict change of direction, speed, and mechanical work during cutting maneuvers (Zago et al, 2019), to name a few. These studies only looked at a single IMU placed on the low-back of the participant, these findings suggest that IMUs can be used as an inexpensive alternative to optical motion capture to characterize and classify motion
Data in the current study are simulated IMU data based on optical motion capture, this study provides proof-ofconcept that IMU-based data can provide enough information to successfully classify athletes’ movement patterns based on skill level
Summary
Movement screens are widely used across many disciplines including in ergonomic, clinical, and athletic settings to identify aberrant movement patterns in hopes of decreasing risk of injury and/or improving performance (Donà et al, 2009; Kritz et al, 2009; Padua et al, 2009; Cook et al, 2014; McCall et al, 2014; McCunn et al, 2016). The previously published technique with optical motion capture, referred to as OMAT-OPT, uses principal component analysis (PCA) (Troje, 2002; Federolf et al, 2014; Young and Reinkensmeyer, 2014) in conjunction with linear discriminant analysis (LDA) to objectively differentiate and score whole-body movement patterns between desired binary classifiers (Ross et al, 2018). OMAT-OPT provides an objective, datadriven method that can detect meaningful movement pattern differences during a movement screening battery for binary classification, it requires optical motion capture technology, which is expensive and time-consuming to set up, capture and post-process data, reducing the accessibility and feasibility of the current technique in clinical, ergonomic, and sport settings (Hadjidj et al, 2013)
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