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Non-nutritive sucking (NNS) is defined as the sucking action that occurs whena finger, pacifier, or other object is placed in the baby's mouth, but there isno nutrient delivered. In addition to providing a sense of safety, NNS even canbe regarded as an indicator of infant's central nervous system development. Therich data, such as sucking frequency, the number of cycles, and their amplitudeduring baby's non-nutritive sucking is important clue for judging the braindevelopment of infants or preterm infants. Nowadays most researchers arecollecting NNS data by using some contact devices such as pressure transducers.However, such invasive contact will have a direct impact on the baby's naturalsucking behavior, resulting in significant distortion in the collected data.Therefore, we propose a novel contact-less NNS data acquisition andquantification scheme, which leverages the facial landmarks tracking technologyto extract the movement signals of baby's jaw from recorded baby's suckingvideo. Since completion of the sucking action requires a large amount ofsynchronous coordination and neural integration of the facial muscles and thecranial nerves, the facial muscle movement signals accompanying baby's suckingpacifier can indirectly replace the NNS signal. We have evaluated our method onvideos collected from several infants during their NNS behaviors and we haveachieved the quantified NNS patterns closely comparable to results from visualinspection as well as contact-based sensor readings.

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