An Efficient Edge Deep Learning Computer Vision System to Prevent Sudden Infant Death Syndrome

Abstract

Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors, also called an "outside stressor," that is responsible for Sudden Infant Death Syndrome (SIDS), is the sleeping position of the baby. According to past research, the risk of SIDS increases when the baby sleeps facedown on the stomach. We propose a Convolutional Neural Network (CNN) based computer-vision system that estimates the sleeping pose of the baby and alerts caregivers on their mobile phones within a few seconds of the baby moving to the hazardous facedown sleeping position. The system has a low computational load and a low memory footprint. This characteristic allows the system to be embedded in low power edge devices such as certain baby monitors. Processing at the edge also alleviates privacy concerns with regards to sending images into the network. We experimented with various numbers of convolutional processing units and dense layers as well as the number of convolutional kernels to arrive at the optimal production configuration. We observed a consistently high accuracy of detection of infant sleeping position changes from turning to facedown positions with a potential towards even higher accuracies with caregiver feedback for model retraining. Therefore, this system is a viable candidate for consideration as a non-intrusive solution to assist in preventing SIDS.