Internet of Things in sleep monitoring: An application for posture recognition using supervised learning

TLDR

The system has broad medical applications, including sleep studies, anesthesia procedures, medical imaging, and other contexts requiring body‑posture determination on a mattress. The study proposes an IoT system for remote medical monitoring. The system uses a pressure‑sensing mattress to capture body‑pressure distribution, processes the data on a workstation with a supervised learning model trained on synthetic data, and delivers quasi‑real‑time posture recognition with low‑cost, fast computation suitable for long‑term IoT use. The posture recognition achieved a Cohen’s Kappa of 0.866, indicating promising accuracy for future work.

Abstract

In this paper, we propose an Internet of Things (IoT) system application for remote medical monitoring. The body pressure distribution is acquired through a pressure sensing mattress under the person's body, data is sent to a computer workstation for processing, and results are communicated for monitoring and diagnosis. The area of application of such system is large in the medical domain making the system convenient for clinical use such as in sleep studies, non or partial anesthetic surgical procedures, medical-imaging techniques, and other areas involving the determination of the body-posture on a mattress. In this vein, a novel method for human body posture recognition that consists in providing an optimal combination of signal acquisition, processing, and data storage to perform the recognition task in a quasi-real-time basis. A supervised learning approach was used to build a model using a robust synthetic data. The data has been generated beforehand, in a way to enhance and generalize the recognition capability while maintaining both geometrical and spatial performance. Low-cost and fast computation per sample processing along with autonomy, make the system suitable for long-term operation and IoT applications. The recognition results with a Cohen's Kappa coefficient κ = 0.866 was satisfactorily encouraging for further investigation in this field.