Monitoring Sucking Abilities in Newborns: Design and Validation on Adult Of a Wearable System for Non-Invasive Deglutition Detection

Nutritive Sucking (NS) is one of the earliest motor activity performed by infants, strictly related to both neurological and motor development of newborns. The main components of NS are sucking, respiration and deglutition. Despite its recognized importance, current clinical practice lacks quantitative tools for the assessment of NS. This work aims to identify a non-invasive objective method to assess deglutition. In details, we propose a new sensor fusion approach to merge both inertial and acoustic data in order to estimate deglutition time. The algorithm uses two classification criteria: one is based on signal intensity thresholding and the other on the evaluation of Waveform Dimension Trajectory (WDT). Our preliminary results on 9 healthy adult volunteers show that the sensor fusion of audio and IMU signals provides a high precision (0.93) and a good recall (0.72). Moreover, the algorithm has a good accuracy (0.84) and high specificity (0.95).