DCA-based multimodal fusion for robust recognition of adolescent sports and abnormal health behaviors

Abstract

Dynamic monitoring of health behaviors in adolescent sports is crucial for promoting healthy growth. Traditional monitoring methods, however, suffer from limitations such as incomplete data acquisition, low analysis efficiency, and poor sustainability. This study addresses these issues by proposing a dynamic monitoring system for adolescent sports and health behaviors. The proposed system utilizes multi-source sensor data and image extraction algorithms. A novel multimodal feature fusion algorithm based on depth motion images (DMIs) was developed for efficient sports action recognition and anomaly analysis. Depth data was converted into DMIs, and inertial data into signal images. Both image types were processed using local ternary pattern (LTP) to generate multimodal input data. Convolutional neural networks (CNNs) were used to extract features from each modality. Feature-level fusion was then performed using discriminant correlation analysis (DCA) to maximize feature correlation between modalities and minimize feature correlation within modalities and between categories. Finally, fused features were classified using a multi-class support vector machine (SVM) to achieve efficient motion behavior recognition and analysis. The experimental results demonstrate that the proposed system achieved a recognition accuracy exceeding 90% in classifying five adolescent sports behaviors. This performance surpassed that of other comparative algorithms. Furthermore, the method achieved competitive recognition accuracy across the evaluated action categories, demonstrating its applicability and robustness in complex scenarios. The findings of this study offer new insights and methodologies for monitoring and intervening in adolescent sports health behaviors. The proposed system and algorithms provide technical support for the sustainable design of dynamic monitoring systems in this field.