Exploration on the innovation path of physical education teaching: The strategy of integrating personalized training and biosensor technology from the perspective of biomechanics
Year: 2025
Type: article
Abstract: Background: PE is crucial for developing lifelong fitness habits among students. Traditional methods lack personalization and real-time feedback, limiting effectiveness. Biosensor technology, which can monitor various biomechanical parameters, offers a revolutionary approach to transform PE. It enables the provision of personalized training regimens based on each student's unique biomechanical characteristics, such as muscle force exertion patterns, joint kinematics, and body movement biomechanics. Purpose: The research aims to enhance and assess a new model of PE teaching that integrates personalized training with biosensor technology with a specific focus on how it impacts and interacts with the biomechanical and physiological aspects of students' physical performance. Methods: Data collection involves capturing HR, movement patterns, key biomechanical data and exertion levels during physical activities. The collected data are preprocessed using data cleaning and normalization techniques, ensuring the accuracy and reliability of this analysis. Feature extraction uses FFT to analyze the frequency domain characteristics of the physiological signals. The study proposes an ITSO-GRNN strategy aimed at developing PE teaching and personalized training. Results: The application of individual training together with biosensor technology contributes significantly and positively in terms of students’ performance and involvement, resulting in better physical results and good evaluations. The ISTO-GRNN model outperforms all existing methods in terms of physical training (97.80%), assessing students' biomechanical and physiological states (99.62%), and efficiency of the PE teaching process (98.74%). In terms of performance metrics, it performs effectively with accuracy (98.70%), precision (96.50%), recall (90.42%), and F1-score (92.50%) showing teaching effectiveness and evaluation that are highly superior to those of the former models. Conclusion: The study highlights the potential for such innovations to not only improve physical outcomes but also promote lifelong fitness habits among students.
DOI: 10.62617/mcb756
Author: Fangshu Li
Institution: Chengdu Sport University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4407673336