Work

Non-Intrusive Infant Body Position Detection for Sudden Infant Death Syndrome Prevention Using Pressure Mats

Year: 2025

Type: article

Abstract: Sudden Infant Death Syndrome (SIDS) is one of the leading causes of postnatal mortality, with the prone sleeping position identified as a critical risk factor. This article presents the design, implementation, and validation of a low-cost embedded system for unobtrusive, real-time monitoring of infant posture. The system acquires data from a pressure mat on which the infant rests, converting the pressure matrix into an image representing the postural imprint. A Convolutional Neural Network (CNN) has been trained to classify these images and distinguish between prone and supine positions with high accuracy. The trained model was optimized and deployed in a data acquisition and processing system (DAQ) based on the Raspberry Pi platform, enabling local and autonomous inference. To prevent false positives, the system activates a visual and audible alarm upon detection of a sustained risk position, alongside remote notifications via the MQTT protocol. The results demonstrate that the prototype is capable of reliably and continuously identifying the infant’s posture when used by people who are not technology experts. We conclude that it is feasible to develop an autonomous, accessible, and effective monitoring system that can serve as a support tool for caregivers and as a technological basis for new strategies in SIDS prevention.

Source: Technologies

DOI: 10.3390/technologies13100427

Authors: Antonio Garcia-Herraiz, Susana Nuñez-Nagy, Luis Cruz-Piris, Bernardo Alarcos-Alcazar

Institution: Universidad de Alcalá

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930