Commercial Application: Physical Education and Training Movement Detection Based on Biotechnology

Baifu Guo
Guangxi College for Preschool Education, Nanning, 530023, Guangxi, China
Chenhua Huang
Guangxi College for Preschool Education, Nanning, 530023, Guangxi, China

DOI:https://doi.org/10.5912/jcb2479

Abstract:

This study explores the integration of biotechnology enhanced motion analysis into commercial sports training systems, maintaining- the core deep convolutional neural network (DCNN) architecture for error detection in physical education. By incorporating biomechanical sensor arrays and physiological feedback mechanisms, the framework extends conventional DCNN-based action recognition to interpret biological motion signatures. The network processes synchronized datasets comprising visual motion capture and wearable biosensor streams, utilizing convolutional layers to extract spatiotemporal features while preserving the original batch normalization approach. Commercial viability is demonstrated through prototype testing with athletic training equipment manufacturers, where the system achieved consistency with professional coaches' assessments in identifying high-risk movement patterns. This - biotechnology-integrated DCNN framework offers a commercially scalable solution for precision sports training, bridging advanced motion analytics with physiological performance optimization.