Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sportspecific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data preprocessing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental setup , data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).
Three-dimensional motion capture systems such as Vicon have been used to validate commercial electronic performance and tracking systems. However, three-dimensional motion capture cannot be used for large capture areas such as a full football pitch due to the need for many fragile cameras to be placed around the capture volume and a lack of suitable depth of field of those cameras. There is a need, therefore, for a hybrid testing solution for commercial electronic performance and tracking systems using highly precise three-dimensional motion capture in a small test area and a computer vision system in other areas to test for full-pitch coverage by the commercial systems. This study aimed to establish the validity of VisionKit computer vision system against three-dimensional motion capture in a stadium environment. Ten participants undertook a series of football-specific movement tasks, including a circuit, small-sided games and a 20 m sprint. There was strong agreement between VisionKit and three-dimensional motion capture across each activity undertaken. The root mean square difference for speed was 0.04 m·s−1 and for position was 0.18 m. VisionKit had strong agreement with the criterion three-dimensional motion capture system three-dimensional motion capture for football-related movements tested in stadium environments. VisionKit can thus be used to establish the concurrent validity of other electronic performance and tracking systems in circumstances where three-dimensional motion capture cannot be used.
Cust, EE, Elsworthy, N, and Robertson, S. Analysis of training loads in elite under 18 Australian rule football players. J Strength Cond Res 32(9): 2521-2528, 2017-Differences in training loads (TLs) between under 18 (U18) Australian rules football (AF) state academy-selected and state academy-nonselected players were investigated. Players were categorized relating to their highest representative level: state academy-selected (n = 9) and TAC cup-level players (n = 38). Data were obtained from an online training-monitoring tool implemented to collect player training and match information across a 20-week period during the regular season. Parameters modeled included AF skills, strength, and other sport training sessions. Descriptive statistics (mean ± SD) and between-group comparisons (Cohen's d) were computed. A J48 decision tree modeled which TL variables could predict selection level. Pooled data showed 60% of weekly training duration consisted of AF training sessions. Similar AF TL were reported between state academy and TAC cup players (1,578 ± 1,264 arbitrary units (AU) vs. 1,368 ± 872 AU; d = 0.05). Although higher TLs were reported for state-selected players comparative with TAC cup in total training (d = 0.20), core stability (d = 0.36), flexibility (d = 0.44), on-feet conditioning (d = 0.26), and off-feet conditioning (d = 0.26). Decision tree analysis showed core stability duration and flexibility TL, the most influential parameters in classifying group selection (97.7% accuracy TAC cup level; 35.8% accuracy state academy level). Insights of U18 AF players' weekly training structures, loads, and characteristics of higher achieving players are provided. This study supports the application of training diaries and session rating of perceived exertion for TL monitoring in junior athletes.
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