Abstract: Ensuring infant safety and well-being is a primary concern for parents and caregivers. This paper presents an AI driven baby monitoring system that leverages computer vision and deep learning techniques to track infant sleep patterns, detect anomalies (crying, woke up), and provide real-time alerts. Our proposed approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs)to analyze video feeds for crying, sleeping, and facial expressions, enhancing monitoring accuracy.The system aims to provide a reliable solution for reducing risks associated with sleep disorders and sudden infant health issues. Our experiments demonstrate the effectiveness of our approach in detecting abnormal movements and sleep condition with high precision.
Keywords: Baby Monitoring, AI for Healthcare, Sleep Tracking, Deep Learning, Infant Safety
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DOI:
10.17148/IJARCCE.2025.14238
