Bilingual physical education curriculum supported by computer vision and motion analysis algorithms

In the era of digital education, bilingual physical education courses seek to break language barriers and promote inclusivity by accommodating students with diverse linguistic backgrounds. However, the unique challenges of teaching and learning physical activities in such environments demand innovative solutions. This paper exploits the advancements in computer vision and motion analysis, facilitated by a specialized neural network designed for the precise recognition of sports training movements in video sequences. Our study proposes a novel video sampling algorithm that first dissects and analyzes input video sequences to establish an optimal sampling strategy, ensuring that the most relevant and informative frames are processed. This is crucial in mitigating the challenges of data overload and computational inefficiency. Furthermore, recognizing the paramount importance of spatial feature extraction in accurately identifying sports movements, the study introduces a sophisticated method integrating a spatial attention mechanism. This approach utilizes a residual network within a convolutional attention module, significantly improving the model’s capability to discern spatial features pivotal for accurate movement recognition. The proposed enhancements in channel attention further refine the model’s performance, addressing the prevalent issues in training convolutional attention modules. This research not only aims to enhance the learning experience but also to contribute significantly to the body of knowledge in educational technology and computer vision.