Enhancing action recognition in educational settings using AI-driven information systems for public health monitoring
Bibliographic Data
| ID | 22085658 |
|---|---|
| Authors | Changchun Lu (Leshan Normal University, corresponding author), Han Ruijuan (Shihezi University) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1592228-1592228 |
| Publication date | 2025-07-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1592228 |
| PMID | 40726947 |
| OpenAlex | W4412379833 |
| Language | EN |
| References cited | 45 |
Introduction: The integration of Artificial Intelligence (AI) into educational environments is revolutionizing action recognition, offering a transformative opportunity to enhance public health monitoring. Traditional methods, which primarily rely on rule-based algorithms or handcrafted feature extraction, face significant challenges in adaptability, scalability, and real-time processing. These limitations hinder their effectiveness, particularly in detecting health-related behaviors such as sedentary patterns, social interactions, and hygiene compliance. Methods: To overcome these shortcomings, this research introduces an AI-driven information system that leverages advanced deep learning models and an Adaptive Knowledge Embedding Network (AKEN) to improve action recognition accuracy. The approach integrates AKEN with a Dynamic Personalized Learning Strategy (DPLS) to model student behaviors, predict future actions, and optimize intervention strategies by incorporating factors such as engagement levels, learning progress, and environmental conditions. Results: By utilizing reinforcement learning and explainable AI techniques, the system not only refines recognition accuracy but also ensures transparency in decision-making. Real-time engagement monitoring enhances adaptability, allowing educators and policymakers to make informed interventions. Discussion: Experimental results validate the system's superior performance over conventional approaches, demonstrating its ability to recognize complex behavioral patterns in educational settings. This innovation represents a significant step forward in AI-driven public health monitoring, fostering a safer and more responsive learning environment
Adaptability · Computer security · Human–computer interaction · Knowledge management · Machine learning · Reinforcement learning · SAFER · Scalability · Transformative learning · Computer Science · Context-Aware Activity Recognition Systems · Human Pose and Action Recognition · Intelligent Tutoring Systems and Adaptive Learning · Psychology · Artificial Intelligence
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| Highly cited | No |
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