Development of personalized physical education teaching plan: Research on evaluating students’ physical fitness and sports adaptability using biosensors
Abstract
Physical education plays an essential role in the growth of students’ overall health, fitness, and well-being. Wearable biosensors revolutionize physical performance monitoring by providing real-time data on physiological parameters, providing valuable insights into students’ fitness and flexibility during physical activities. The research aims to develop an approach for assessing students’ athletic adaptation and physical fitness using biosensors. Traditional monitoring systems have complexity in managing the huge volumes of data collected from several sensors because of noise and ambiguity. These research difficulties are addressed with the help of a deep learning (DL) based assessment model, which monitors students’ fitness using biosensor data. This research proposed a novel dynamic Bumblebee mating refined deep neural networks (DBBM-RDNN) to forecast student physical fitness and sports adaptability levels using biosensors. The biosensor dataset provides different data types that capture various aspects of physical activity and fitness. The data was preprocessed using low-pass filters to remove noise from the achieved data. Principal Component Analysis (PCA) is developed to extract the features from preprocessed data. DBBM is utilized to optimize the features in sensor data and RDNN to classify or predict fitness and adaptability levels in students based on data from sensors in real time. In a comparative analysis, the research assessed various performance metrics, such as accuracy (98.05%), precision (90.9%), recall (90.1%), F1-score (88.55%), MAE (1.915) and RMSE (2.505). Experimental results indicate the proposed model achieved superior performance in predicting student physical fitness compared to other conventional algorithms. The research highlights the integration of biosensor technology with DL, which provides an accurate and dependable system for tracking students’ physical performance.
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