Human sleep position classification using a lightweight model and acceleration data

作     者：Vu, Hoang-Dieu Tran, Duc-Nghia Pham, Huy-Hieu Pham, Dinh-Dat Can, Khanh-Ly Dao, To-Hieu Tran, Duc-Tan 

作者机构：Phenikaa Univ Fac Elect & Elect Engn Hanoi 12116 Vietnam VAST Inst Informat Technol Hanoi Vietnam VinUniv Coll Engn & Comp Sci Hanoi Vietnam GUST VAST Hanoi Vietnam 

出 版 物：《SLEEP AND BREATHING》 (Sleep Breathing)

年 卷 期：2025年第29卷第1期

页      面：1-12页

核心收录：

学科分类：1002[医学-临床医学] 10[医学] 

基　　金：Vingroup Innovation Foundation (VINIF) [VINIF.2023.TS.021] NAFOSTED [NCUD.02-2024.11] 

主　　题：Sleep position monitoring Positional therapy Accelerometer-based wearable Sleep posture Deep learning classification GERD 

摘      要：PurposeThis exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux disease (GERD) by tracking sleep postures, promoting healthier habits, and improving both reflux symptoms and sleep quality without requiring hospital-based *** study developed AnpoNet, a lightweight deep learning model combining 1D-CNN and LSTM, optimized with BN and Dropout. The 1D-CNN captures short-term movement features, while the LSTM identifies long-term temporal dependencies. Experiments were conducted on data from 15 participants performing twelve sleep positions, with each position recorded for one minute at a sampling frequency of 50 Hz. The model was evaluated using 5-Fold cross-validation and unseen participant data to assess *** achieved a classification accuracy of 94.67% +/- 0.80% and an F1-score of 92.94% +/- 1.35%, outperforming baseline models. Accuracy was computed as the mean of accuracies obtained for three participants in the test set, averaged over five independent random seeds. This evaluation approach ensures robustness by accounting for variability in both individual participant performance and model initialization, underscoring its potential for real-world, home-based *** study provides a foundation for a portable system enabling continuous, non-invasive sleep posture monitoring at home. By addressing the needs of GERD patients, the device holds promise for improving sleep quality and supporting positional therapy. Future research will focus on larger cohorts, extended monitoring durations, and user-friendly interfaces for broader adoption.