Deep learning to promote health through sports and physical training
BackgroundPhysical activity plays a crucial role in maintaining health and preventing chronic diseases. However, accurately assessing the impact of sports and physical training on health improvement remains a challenge. Recent advancements in deep learning and time-series analysis offer an opportuni...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1583581/full |
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| Summary: | BackgroundPhysical activity plays a crucial role in maintaining health and preventing chronic diseases. However, accurately assessing the impact of sports and physical training on health improvement remains a challenge. Recent advancements in deep learning and time-series analysis offer an opportunity to develop more personalized and accurate predictive models for assessing health improvement trends.MethodsThis study proposes a Health Improvement Score (HIS) prediction model based on a sequence-to-sequence deep learning architecture with Long Short-Term Memory (LSTM) networks and an attention mechanism. The model integrates heterogeneous time-series data, including physiological parameters (heart rate, blood oxygen levels, respiration rate), activity metrics (steps, distance, calories burned), sleep patterns, and body measurements. A dataset comprising 384 participants over a 32-day period was used to train and evaluate the model.ResultsThe experimental results demonstrate that the proposed HIS prediction model outperforms traditional and machine learning-based models. It achieves 22.8% lower Mean Absolute Error (MAE), 19.3% lower Root Mean Squared Error (RMSE), 6.5% higher R2, and 7.9% higher Explained Variance Score (EVS) compared to competitive models.ConclusionThe proposed HIS prediction model effectively captures complex temporal dependencies and improves the accuracy of health improvement predictions. |
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| ISSN: | 2296-2565 |