a Institute for Health and Sport (IHES) , Victoria University , Melbourne , Australia.
b Western Bulldogs Football Club , Melbourne , Australia.
J Sports Sci. 2019 Mar;37(5):568-600. doi: 10.1080/02640414.2018.1521769. Epub 2018 Oct 11.
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).
客观评估运动员的表现对精英运动至关重要，有助于进行详细分析。自动化检测和识别特定于运动的动作克服了与手动性能分析方法相关的局限性。本研究的目的是系统地回顾使用惯性测量单元 (IMU) 和/或计算机视觉数据输入进行特定于运动的动作识别的机器和深度学习文献。进行了多次数据库搜索。纳入的研究必须针对特定于运动的动作进行调查，并通过机器或深度学习方法进行分析以开发模型。共有 52 项研究符合纳入和排除标准。数据预处理、处理、模型开发和评估方法在研究之间存在差异。运动识别模型的开发主要使用监督分类方法。支持向量机算法的核形式在 53%的 IMU 和 50%的基于视觉的研究中使用。有 12 项研究使用深度学习方法作为卷积神经网络算法的一种形式，还有一项研究在其模型中采用了长短时记忆架构。实验设置、数据预处理和模型开发方法的适应性最好根据目标运动的特点来考虑。
