Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification

Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification

To address the issues of accuracy and generalization in action recognition within complex tennis training scenarios, this study proposes an Adaptive Semantic-Enhanced Convolutional Neural Network (ASE-CNN) model. The model optimizes multimodal data integration and complex action classification perfo...

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Bibliographic Details
Main Author: Weichao Yang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Tennis action recognition
convolutional neural network architecture
multimodal data
feature extraction
ASE-CNN
Online Access:https://ieeexplore.ieee.org/document/10870269/
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Summary:To address the issues of accuracy and generalization in action recognition within complex tennis training scenarios, this study proposes an Adaptive Semantic-Enhanced Convolutional Neural Network (ASE-CNN) model. The model optimizes multimodal data integration and complex action classification performance, enabling precise analysis of key action features in tennis training. Experimental results demonstrate that ASE-CNN excels in multimodal feature extraction, achieving a Receiver Operating Characteristic-Area Under Curve (ROC-AUC) value of 0.85–0.95 and a Feature Distribution Uniformity of 0.75–0.85, validating its accuracy and stability in feature classification. The fused weighted Precision-Recall Area Under Curve (PR-AUC) value ranges from 0.80 to 0.92, highlighting its capability for synergistic optimization of multimodal data. For key action recognition, the classification accuracy reaches up to 0.98, with Euclidean distances between key actions and ideal action templates as low as 0.05–0.15, demonstrating exceptional fine-detail discrimination. Compared to state-of-the-art models, ASE-CNN exhibits significant advantages in per-frame processing time and resource utilization efficiency, offering potential for efficient real-time feedback in resource-constrained environments. This study provides scientific evidence and technical support for optimizing actions and improving strategies in professional training.
ISSN:2169-3536

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