Humanized physical education teaching plan design: Utilizing biosensors to evaluate students’ movement status
Abstract
Biosensors allow the monitoring of student movement in real-time and enhance the effectiveness of personal workouts through data analysis to enhance performance. Even though there is significant potential, biosensor precision, concern about data privacy, cost, and the need for expert knowledge limit the implementation of such technologies in physical therapy. This research aims to analyze educational systems that make use of biosensors to monitor the movement of students and customize their course of ideas. In addition, it also provides more efficient tools for exercise interventions based on factual information. A Resilient Sailfish Algorithm-tuned Enriched Long Short-Term Memory (RSA-ELSTM) method is proposed to increase prediction accuracy, address data challenges, and improve motion analysis beyond current limitations. Datasets used include motion capture and sensor readings, which capture different student movement patterns. Preprocessing involves image resizing, and normalization, while VGG16-based feature extraction is used to improve model performance and accuracy. The RSA-ELSTM approach uses biosensor data and deep learning (DL) to optimize motion analysis, increasing accuracy, flexibility, and real-time analysis. The RSA-ELSTM the model obtained a 99.1% F1-score, 99.3% accuracy, 98.7% recall, and 99.2% precision. Results revealed improved accuracy in motion prediction and real-time analysis, improving personalized workouts. In conclusion, the RSA-ELSTM approach significantly enhances biosensor-based exercises, provides accurate student movement analysis, and improves individual performance management, thus making educational outcomes good.
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