An IoT-based contactless neonatal respiratory monitoring system for neonatal care assistance in postpartum center

Abstract

According to previous studies, one of the major causes of 20 % to 25 % of neonatal deaths is respiratory distress syndrome (RDS). Early identification, progressive monitoring, and treatment and/or management of neonatal RDS can substantially increase the rate of survival in neonates. However, global research indicates frequent shortages and burnout among nursing staff, especially in postpartum units, contributing to the difficulty in early identification of RDS in neonates. Clinicians currently use breathing sounds and frequency as key criteria in the Neonatal Resuscitation Program (NRP) for identifying and treating RDS. In practice, the monitoring of respiratory signal abnormalities relies on sensor patches, which frequently detach from the neonates’ slippery skin, leading to potential skin injuries and unstable signal reception. This paper presents an Internet of Things (IoT)-based contactless neonatal respiratory monitoring system that integrates computer vision (CV), beamforming microphone array (BFMA), and millimeter Wave (mmWave) radar, all connected to a cloud platform. Clinical trials revealed that CV-based neonatal feature identification achieved over 96 % accuracy within 40 cm to 120 cm. The neonatal breathing sound strengthening, utilized CV and BFMA, achieved an average sound-to-noise ratio (SNR) of 5.07 dB, and CV with mmWave radar reduced chest displacement signal error from 0.66 to 0.26 BPM. Additionally, survey results showed that doctors and clinical personnel were satisfied with the system's functionality and usability. This demonstrates the system's ability to assist in monitoring respiratory signals of swaddled neonates and in the early identification of neonatal RDS, with further applications in neonatal care at postpartum centers.