Design and optimization of an aerobics movement recognition system based on high-dimensional biotechnological data using neural networks

This study presents the design and optimization of an aerobics movement recognition system utilizing high-dimensional biotechnological data. Deep learning techniques are employed to achieve accurate classification and recognition of movement actions. Biosensing technology and wearable devices are used to collect real-time, multidimensional physiological signal data from key anatomical regions of athletes. The system is constructed using convolutional neural networks (CNNs) and Long Short-Term Memory. Model performance is optimized through parameter selection and strategies such as Xavier initialization, the cross-entropy loss function, and the Adam optimizer. The results indicate that Model C achieves an accuracy of 0.987, significantly outperforming the standalone CNNs (accuracy 0.975) and the recurrent neural network models (accuracy 0.965). Furthermore, it demonstrates notable efficiency in practical applications, with considerably reduced execution times of 10 s for data processing, 25 s for feature extraction, and 20 s for classification. This aerobics recognition system excels in performance and efficiency, supporting precise movement classification.