Sleep position classification with a compact pressure textile sensor mattress using convolutional neural networks

Abstract

Robust body position classification during sleep is crucial for closed-loop robotic interventions in position-dependent sleep disorders. This work investigates a compact, custom-made textile pressure sensor to automatically classify recumbent body positions. We implemented a range of traditional classification methods, including Naïve Bayes, Decision Trees, and Support Vector Machines. Furthermore, we trained different machine learning models on recordings from 19 participants, with a varying amount of personalized training data (i.e. from a generalized inter-person classifier towards fully personalized classifiers). We computed the performance metrics F1-score, precision, recall, and accuracy using multi-fold cross-validation for the four classes supine, prone, lateral left, and lateral right, as well as for the binary classes supine vs. non-supine. For the generalized classifier, we could achieve an accuracy of 82.7% for a balanced test set. The personalized models for one male and one female user showed a higher accuracy, namely 95% and 92%. For the binary classifier, the personalized classifiers (male F1-score: 0.97, female F1-score: 0.94) outperformed the generalized classifiers (F1-score: 0.91). Similar to related work, our machine-learning model demonstrated superior performance compared to the three traditional approaches we implemented. Our results show that robust body position classification is possible using small-scale unobtrusive textile bedding sensors, pathing the way for future closed-loop interventions.