Unobtrusive sleep posture estimation using pressure sensor in home sleep

Sleep posture is associated with various physiological indicators and significantly influences sleep health and quality. Although several methods for posture estimation have been proposed, most have been evaluated using data from controlled laboratory environments. This study proposes a method for determining sleep posture in real-world settings using pressure sensor data.

The approach was developed based on data collected from 22 participants in a laboratory setting using a 7 × 14 array of force-sensitive resistors (FSR). We employed a support vector machine to classify four sleep postures-supine, left-lateral, right-lateral, and prone-based on six extracted features related to area, curvature, and row length ratio. The algorithm was subsequently evaluated using FSR data recorded from ten participants sleeping freely in their home environments.

The performance results demonstrated an accuracy of 78.1% and a Cohen's kappa of 0.71 for the laboratory data. When applied to the home-environment data, the method achieved an accuracy of 86.1% and a Cohen's kappa of 0.76 for the classification of the four sleep postures.

These findings indicate that the model trained in a laboratory setting maintained high performance in real-world conditions, supporting the feasibility of implementing sleep monitoring technologies in daily life and clinical contexts. This study contributes to the development of noninvasive, long-term sleep monitoring systems and highlights the potential for future clinical applications in embedded systems and hospital environments through the use of feature-based models with high explainability.