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- W2897764506 abstract "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 sport-specific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data pre-processing, 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 set-up, data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s)." @default.
- W2897764506 created "2018-10-26" @default.
- W2897764506 creator A5005151499 @default.
- W2897764506 creator A5010593204 @default.
- W2897764506 creator A5038219760 @default.
- W2897764506 creator A5041620219 @default.
- W2897764506 date "2018-10-11" @default.
- W2897764506 modified "2023-10-14" @default.
- W2897764506 title "Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance" @default.
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