Optimizing physical education schedules for long-term health benefits.

ABSTRACT:

Introduction

Physical education (PE) plays a vital role in promoting long-term health and wellness among students. Effective scheduling of PE classes is essential for maximizing fitness improvements across diverse populations. However, traditional approaches to optimizing PE schedules may not adequately account for individual differences in demographics and activity patterns.

Methods

This study proposes an efficient method for optimizing PE schedules using deep learning (DL) techniques. The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. These features are combined through a fusion layer, and a customized l

SUBMITTER: Tan L 

PROVIDER: S-EPMC12206874 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature