Classification of Sleeping Position Using Enhanced Stacking Ensemble Learning
Xi Xu1,2, Qihui Mo1, Zhibing Wang1
1School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China.
Entropy (Basel, Switzerland)
|October 25, 2024
Summary
This study introduces an enhanced stacking model for sleep position recognition using an air bag mattress. The new method improves accuracy and applicability compared to existing techniques, aiding sleep quality and disorder management.
Area of Science:
- Biomedical Engineering
- Machine Learning
- Sleep Science
Background:
- Sleep position recognition is vital for sleep quality and managing sleep disorders.
- Current non-invasive methods face limitations due to high production and computational costs.
Purpose of the Study:
- To develop a cost-effective and accurate sleep position recognition system.
- To enhance the applicability of sleep position monitoring technology.
Main Methods:
- An enhanced stacking model was developed using a specific air bag mattress.
- Hyperparameters were optimized via Bayesian optimization.
- Extreme gradient boosting (XGBoost), support vector machine (SVM), and deep neural decision tree (DNDT) were selected as base models, with logistic regression as the meta-learner.
Main Results:
- The proposed enhanced stacking model demonstrated superior classification accuracy.
- The model showed improved applicability over existing machine learning techniques.
Conclusions:
- The enhanced stacking model offers a promising solution for accurate and accessible sleep position recognition.
- This technology can contribute to better sleep management and the diagnosis of sleep-related disorders.
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