Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis

Xiaofei Huang, Alaina Martens, Emily Zimmerman, Sarah Ostadabbas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 39-45

Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the babyOs mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as an indicator of infantOs central nervous system development. The rich data, such as sucking frequency, the number of cycles, and their amplitude during babyOs non-nutritive sucking is important clue for judging the brain development of infants or preterm infants. Nowadays most researchers are collecting NNS data by using some contact devices such as pressure transducers. However, such invasive contact will have a direct impact on the babyOs natural sucking behavior, resulting in significant distortion in the collected data. Therefore, we propose a novel contact-less NNS data acquisition and quantification scheme, which leverages the facial landmarks tracking technology to extract the movement signals of babyOs jaw from recorded babyOs sucking video. Since completion of the sucking action requires a large amount of synchronous coordination and neural integration of the facial muscles and the cranial nerves, the facial muscle movement signals accompanying babyOs sucking pacifier can indirectly replace the NNS signal. We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.