Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors responsible for the syndrome, called an ‘outside stressor’, is the sleeping position of the baby. When the baby sleeps on the stomach with face down, the risk of SIDS occurring is very high. We propose a Convolutional Neural Network (CNN) based computer vision system that can alert caregivers on their mobile phones within a few seconds of the baby moving to a hazardous face-down sleeping position. The model processes real-time image feeds with a single efficient forward pass. It has a low computational load and a low memory footprint. This would allow it to be embedded in low power edge devices such as crib cameras. Processing at the edge would also alleviate privacy concerns in sending images into the network. The CNN architecture is composed of multiple sets of processing units, each unit containing a 2D convolutional layer with the Rectified Linear Unit activation function followed by a Max Pooling layer. The final layer in the architecture is a fully connected dense layer with the Sigmoid activation function and outputs three classes of sleeping position indicators. The seed corpus for the training dataset was generated from realistic baby dolls with diverse racial mix in three sleeping positions (face-up, turning, face-down). These seed images were used to generate additional images by applying various image transformations. 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 sleeping position changes to turning and face-down positions with a trend towards even higher accuracies with caregiver feedback. Therefore, this system is a viable candidate for consideration as a non-intrusive technology to assist in preventing the Sudden Infant Death Syndrome.
An Efficient Convolutional Neural Network Computer Vision System to Prevent Sudden Infant Death Syndrome
