Sports Analysis with Convolutional Neural Networks

In the realm of sports analysis, the integration of technology has always been pivotal.

Read more: Sports Analysis with Convolutional Neural Networks

A significant breakthrough in this field is presented in Jiatian Liu’s study, “Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis,” published in Frontiers in Psychology in 2021. This study introduces an innovative approach to analyzing athletes’ psychology and movements using a Convolutional Neural Network (CNN).

The Advent of Human Action Recognition (HAR) in Sports

The study focuses on Human Action Recognition (HAR), a technique designed to analyze athletes’ movements and psychological states. The core of this method is a HAR model based on CNN, which classifies current action states by analyzing movements in collected video data. This method is particularly insightful for sports like basketball, where understanding an athlete’s psychology during offensive and defensive maneuvers is crucial.

Methodology: Blending Technology with Sports Psychology

Liu’s approach is two-fold. Firstly, the HAR model classifies actions from video data. Then, the study delves into the psychology of basketball players, particularly during fake actions, by integrating sports psychological theories. This comprehensive method offers a more profound understanding of the athletes’ psychological state, predicting their next response actions.

Key Findings: Enhancing Recognition Accuracy

The study reveals that combining grayscale and red-green-blue (RGB) images can minimize image loss and significantly improve the model’s recognition accuracy. The optimized Convolutional Three-Dimensional (C3D) Network HAR model achieved an 80% recognition accuracy with an image loss of just 5.6, reducing the time complexity by 33%. These results highlight the model’s efficacy in recognizing human actions, a crucial element in sports analysis.

Implications for Sports Analysis aanda Coaching

The implications of this study are vast for sports analysis and coaching. By effectively recognizing and interpreting athletes’ movements and psychological states, coaches and analysts can gain deeper insights into performance and strategy. This technological advancement opens up new possibilities for training, strategy formulation, and overall athlete development.

Why This Research Matters

In competitive sports, understanding the subtle nuances of an athlete’s movements and psychological state can be the difference between winning and losing. Liu’s research brings a cutting-edge tool to the table, enabling a more detailed and accurate analysis of athletes’ performance. It exemplifies how technology can be leveraged to enhance our understanding of sports psychology and physical performance.

A Step Forward in Sports Technology

This study is a significant step in the fusion of technology and sports. The use of CNN for HAR in sports analysis is a testament to the evolving landscape of sports science, where digital innovation plays a crucial role in enhancing our understanding of athletic performance.


Jiatian Liu’s study on the application of CNN for human movement recognition in sports is a groundbreaking development in sports analytics. It not only offers a new perspective on analyzing athletes’ psychology and movement but also paves the way for more sophisticated, technology-driven approaches in sports analysis. For sports professionals and enthusiasts, this study heralds a new era of digital innovation in understanding and enhancing athletic performance.


Liu, J. (2021). Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis. Frontiers in Psychology, 12, 663359.

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