The analysis of the internet of things technology for mental health of sports education students based on big data
Unlocking Student Mental Health in Sports Education with IoT & Big Data
Author: Yanbing He
This study leverages IoT and big data to address the mental health challenges faced by sports education students, developing an advanced GA-RF model for accurate psychological state perception and adaptive educational interventions.
Abstract
This study addresses the mental health challenges faced by students majoring in sports education and explores more effective strategies for mental health education. Using Internet of Things (IoT) data mining, relevant datasets are collected and categorized. A Random Forest (RF) model is then trained and optimized through a genetic algorithm, resulting in the Genetic Algorithm-Random Forest (GA-RF) psychological state perception model. The model is evaluated against multiple classification approaches. In the depression dichotomy experiment, the GA-RF model achieves superior results, with an optimized accuracy and an F1 score of 0.81, outperforming other algorithms in psychological state perception. By applying this model, routine data from students' daily activities can be analyzed to provide timely insights into their mental health. These insights support adjustments to teaching content and offer schools an evidence-based approach to improving instruction. Overall, the GA-RF model enhances data mining and prediction of students' psychological states, making it a valuable tool for advancing mental health education in sports education programs.
The Core Problem
Students majoring in sports education face intense competition and significant pressure, increasing their risk of mental health problems. Current mental health education strategies often lack adaptability, personalization, and effectiveness in timely identification and intervention.
Executive Impact at a Glance
Leveraging advanced AI, this study delivers tangible improvements for student well-being and institutional efficiency.
0.81
Optimized F1 Score (GA-RF Model)
18
Key Features Identified by GA
312s
Traditional Training Time (Transformer)
89s
GA-RF Training Time (seconds)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
20%
Increased risk of mental health problems in sports education students due to intense competition and societal pressure.
Enterprise Process Flow
Data Collection & Preprocessing
→
Algorithm Selection & Optimization
→
Model Training
→
Performance Evaluation
The study outlines a systematic approach to mental state perception, starting from raw data and culminating in a GA-RF model.
| Approach | Limitations |
|---|---|
| Social Network Analysis |
|
| Traditional Classification (Decision Tree, SVM, RF) |
|
| Sports Education Specific Studies |
|
| Deep Learning Models (CNN) |
|
| Deep Learning Models (Transformer) |
|
0.83
GA-RF Model Accuracy in Depression Dichotomy
Enterprise Process Flow
IoT Data Collection (Campus)
→
Data Preprocessing
→
Feature Engineering
→
GA Feature Selection
→
Optimal Feature Subset
→
Random Forest Model Training
→
Psychological State Prediction
→
Teaching Intervention Support
The GA-RF mental health perception system workflow.
| Model | Accuracy | Recall | F1 Score |
|---|---|---|---|
| GA-RF | 0.83 | 0.79 | 0.81 |
| Decision Tree | 0.64 | 0.58 | 0.6 |
| SVM | 0.66 | 0.63 | 0.61 |
| RF | 0.79 | 0.78 | 0.75 |
Impact on Sports Education
The GA-RF model significantly enhances the ability to identify and analyze students' depressive psychological states by leveraging network behavior data. For example, by identifying 18 key features like training duration and nighttime screen use, the model provides timely insights for personalized interventions. This forms an adaptive education pathway, bridging perception, prediction, and intervention.
9%
Accuracy drop without GA-based feature selection (0.83 to 0.76)
| Model | Feature Dimensions | Accuracy | F1-score |
|---|---|---|---|
| Full GA-RF | 18 | 0.83 | 0.81 |
| RF without GA | 50 | 0.76 | 0.74 |
| GA without RF | 18 | 0.71 | 0.69 |
| Baseline RF | 50 | 0.72 | 0.70 |
Ethical Considerations
The study identifies three primary ethical risks: continuous IoT data collection may infringe on privacy, strong association between behavioral features and psychological states may create labeling effects, and model deployment faces challenges due to disparities in campus digital infrastructure. Mitigation strategies include real-time anonymization, interpretable frameworks, and lightweight embedded modules for underdeveloped regions.
Projected ROI Calculator
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Annual Cost Savings
$0
Annual Hours Reclaimed
0
Strategic Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your organization.
Phase 1: Data Integration & GA-RF Deployment (1-3 Months)
Integrate existing campus IoT data streams (learning, training, social activity) with the GA-RF model. Configure and deploy the model for initial psychological state perception, focusing on depression dichotomy. Establish real-time anonymization mechanisms.
Phase 2: Adaptive Intervention & Feedback Loop (3-6 Months)
Develop and integrate personalized intervention strategies (e.g., training schedule adjustments, psychological counseling) based on model predictions. Implement a feedback system to refine teaching content and track intervention effectiveness. Begin developing interpretable frameworks.
Phase 3: Multimodal Data Fusion & Scalability (6-12 Months)
Expand data sources to include physiological indicators from wearables (e.g., heart rate variability) and classroom video behavior recognition. Develop a multimodal fusion architecture for comprehensive mental health profiles. Explore lightweight embedded GA-RF modules for wider campus deployment.
Phase 4: Advanced Personalization & Ethical Governance (12+ Months)
Implement advanced personalization with digital twin training systems and virtual competition exposure therapy for performance anxiety. Establish a robust algorithmic ethics framework including dynamic data anonymization and decision interpretability modules. Explore cross-institutional federated learning for model generalization.
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