Metrological evaluation of contactless sleep position recognition using an accelerometric smart bed and machine learning

Abstract

Precise categorization of sleep postures is essential for evaluating overall physical and mental condition. A smart bed was constructed with the microelectromechanical systems (MEMS) accelerometer sensor and an STM 32-bit microcontroller board. This work applies machine learning (ML) methods to acceleration data to accurately categorize four main sleep positions: right side, left side, prone, and supine without any wearable devices. In this work, the efficiency of 9 ML methods is examined. These algorithms include Logistic Regression (LR) with one-vs-rest and multinomial logistic regression types, Linear Discriminant Analysis (LDA), K-Nearest Neighbors Classification (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machines (SVMs) with one-vs-one and one-vs-rest types, and Random Forest (RF). The best hyperparameters of each model was accomplished, based on GridSearchCV. The K-fold cross-validation with the assessing measurement stability results indicate that the LG-OvR, LDA, and RF models have the best performance, whereas LG-OvR model possesses accuracy rates of almost 99 %. Furthermore, precision, recall and F1-score are calculated with minimum value of 0.95 for all chosen models. The training and test time are also presented for the selected models. This research has important implications for healthcare, sports medicine, and ergonomics, demonstrating the potential of Artificial Intelligence (AI) approaches in improving sleep monitoring methods.