In both clinical and domestic environments, newborns deserve continuous attention from the medical personnel or the caring parents. In a Neonatal Intensive Care Unit (NICU), neonates affected by perinatal diseases are at risk of neonatal seizures, which are the most common sign of acute neurological dysfunctions and must be promptly and accurately recognized in order to establish timely treatments. In a domestic scenario, respiration disorders and the possible occurrence of apnoea episodes may be related with a potential risk of Sudden Infant Death Syndrome (SIDS) and should be immediately reported to a pediatrician. All these potential events may occur unexpectedly and with low rate, causing a non negligible risk of letting their initial occurrence go unnoticed with possible detriment to the health of the newborn. Continuous wide-scale newborn monitoring by caring personnel is of course unfeasible, even if we restrict our interest to a subset of the population which may exhibit a higher risk of disorders. As a consequence, there has been interest in devising automatic real-time low-cost systems, based on Information and Communication Technology (ICT), capable of continuously monitoring a newborn and prompting the attention of the caring personnel in a timely and reliable manner. Among various options, one that appears particularly convenient and appealing, both from the scientific and application viewpoints, is the use of one or multiple video cameras positioned around the cradle and framing the newborn, equipped with proper signal processing algorithms designed to detect the occurrence of possible disorders and alert the caring personnel. This tutorial will provide an overview of video signal processing methods for newborn monitoring which have been the subject of research and experimentation in recent years. As a specific case study, we focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts, such as the limbs, the chest or the abdomen. In fact, a specific category of neonatal seizures, named “clonic”, are characterized by repetitive movement patterns of some body parts, whose possible presence can be detected automatically by a video processing system. Likewise, respiration monitoring can be considered in which possible apnoeas can be detected by the temporary absence of repetitive movement patterns. After introducing the subject and providing an overview of earlier work, we shall present the principles underlying the extraction of relevant information content from video signals. We shall then present specific video-based solutions to newborn monitoring, their performance and the results of initial experiments in a real NICU environment. Finally, we shall discuss potential applications of these methods to respiration monitoring in a domestic environment employing low-cost devices, such as smartphones or tablets.
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