Affectionate real-time assistant: Development of a pressure-sensitive infant holding monitoring system

This paper presents the development of the Affectionate Real-Time Assistant (A.R.A), a pressure-sensitive infant holding monitoring system designed to enhance neonatal safety. The system employs five pressure pads integrated with an Arduino Uno to capture pressure distribution data from six distinct infant holding postures. Data were collected from 33 participants, including 12 parents, 11 non-parents, and 10 neonatal medical professionals. Machine learning algorithms were applied to classify holding postures accurately. Additionally, a NodeMCU module and an MPU6050 accelerometer and gyroscope sensor were incorporated to detect excessive tilting and shaking, which can prevent Shaken Baby Syndrome (SBS) and milk aspiration. Real-time feedback and alerts are delivered through the Blynk IoT platform, enabling caregivers to monitor and adjust infant handling promptly. Survey results from 20 parents informed system requirements, emphasizing the need for user-friendly, real-time safety monitoring. Experimental evaluation demonstrates the system’s effectiveness in posture classification, tilt detection, and shaking prevention, highlighting its potential as a practical tool for improving infant care. Future work will focus on expanding sensor capabilities and refining machine learning models for broader application.