Movements Analysis of Preterm Infants by Using Depth Sensor

Qualitative assessment of general movements in preterm infants is widely used in clinical practice. It can enable early detection of neurological dysfunctions and consequent neuromotor impairments in high risk infants. However, the outcome of these assessments is not standardized and it is influenced by examiner's subjective interpretation. For this reason, there is an increasing interest in the use of automated movement recognition technologies being applied in this field. In this work, we use a video-based system for preterm infant's movements assessment to provide a 3D motion analysis method able to extract some important indicators from the sequence of depth images collected by using an RGB-D sensor placed over the infant lying on the crib. The advantage of the proposed method is that it is objective, contactless, non-invasive, easy to install, affordable and suitable to be used in an indoor environment with poor lighting, as might be rooms in the Neonatal Intensive Care Unit, where these infants are taken into care. Experimental results show that the proposed method is able to derive from statistical analysis of depth data some key performance indicators, each of which describes different characteristics of the infant's spontaneous movements. Preliminary tests are conducted in the experimental phase on a preterm infant hospitalized in a women's and children's hospital. The project can be used to investigate the relationship between the characteristics of spontaneous movements and the presence of pathologies as cerebral palsy or other minor neurological dysfunctions.