# Verification and application of deep learning models in daily sports activities of teenagers

## TL;DR

This paper improves the accuracy of recognizing badminton movements in teenagers using a deep learning model combining VGG16, BiLSTM, and CBAM.

## Contribution

The novel contribution is integrating CBAM and BiLSTM with VGG16 for better temporal feature capture in sports activity recognition.

## Key findings

- The VGG16-BiLSTM-CBAM model achieved 0.98 recognition accuracy for badminton movements.
- The model outperformed the baseline VGG16 by 0.08 in accuracy and reached an F1 score of 0.96.
- The combination of CBAM and BiLSTM improved feature representation and temporal dependencies in action recognition.

## Abstract

With the development of smart wearable devices and deep learning (DL) technology, the monitoring and analysis of daily sports activities of teenagers face new opportunities. At present, traditional CNN (Convolutional Neural Network) models are mostly used for recognition in daily sports activities. It is difficult to capture the temporal relationship between action sequences, and the ability to express important features is weak, resulting in poor recognition accuracy. This paper took badminton as the object, based on the VGG16 (Visual Geometry Group 16) model, and adopted the advantages of the bidirectional learning time series information of the BiLSTM (Bidirectional Long Short-Term Memory) model and the channel and regional feature representation of the CBAM (Convolutional Block Attention Module) module to verify and apply the recognition of badminton movements in daily sports for teenagers. The study first built and optimized the baseline model VGG16, removed the last three fully connected layers, and used VGG16 to extract the deep features of each frame of video image and output feature maps. The CBAM module was then embedded after the last convolutional layer of the VGG16 network, and the feature maps optimized by CBAM were flattened into a time series input vector. Finally, the BiLSTM model is introduced, and the CBAM and BiLSTM are connected in a cascade manner to capture the information of the previous and next dependencies in the video frame sequence and output the action classification results of badminton. The experiment is based on the badminton training dataset in the public dataset Roboflow to explore the action recognition performance in badminton in daily sports activities of teenagers. Experimental results show that the recognition accuracy of the VGG16-BiLSTM-CBAM model has reached 0.98, which is 0.08 higher than the benchmark model VGG16, and F1 has reached 0.96. Experimental results show that combined with the DL model VGG19 and the sequential model BiLSTM, the attention CBAM module can significantly improve the performance of action recognition in youth badminton, promote the safe conduct of sports activities, and provide a good reference for incorrect postures.