Research on real-time collection and analysis of student health and physical fitness data using biosensors
Abstract
Biosensors have emerged as efficient devices for monitoring personal fitness levels and health profiles as an important part of this technological development. With growing concern about students’ health and bodily fitness, educational and health experts as well as lawmakers have increasingly emphasized their importance. The goal of the study is to explore a real-time system for collecting and analyzing data on students’ physical fitness and health utilizing biosensors and advanced algorithms. The study proposed a novel Efficient Osprey Optimized Adjustable Random Forest (EOO-ARF) to predict the student health and physical fitness level. The student health and physical fitness data was gathered from a Kaggle source. To gather information using wearable biosensors to constantly monitor crucial health parameters such as blood oxygen levels, body temperature, heart rate, and physical activity. The data was pre-processed using the Z-score normalization to enhance the quality of the data. The Principal Component Analysis (PCA) was used to extract the features from pre-processed data. This model takes the indices of students’ physical health as the input parameters and produces an overall health score. EOO is used for optimization, and the process aims at selecting the most appropriate features to identify the health metrics most relevant to influencing students’ general fitness levels. ARF is applied to predict the health and fitness levels of students. The performance of the suggested approach is evaluated in terms of F1-score (98.13%), recall (98.2%), and accuracy (98.44%). The integration of biosensors with innovative analytic methods could transform the monitoring and improvement of the physical fitness and health of students take place in real-time.
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