Intelligent Baby Cry Monitoring System Using IoT and Machine Learning for Deaf Parents

This paper presents the development of an intelligent baby monitoring system for deaf parents, utilizing IoT (Internet of Things) and machine learning. The system captures the baby's cries through a microphone, processes the data in a microcontroller using MFCC (Mel-Frequency Cepstral Coefficients) and LSTM (Long Short-Term Memory) networks, and sends visual and tactile alerts to parents via a lamp and a vibrating bracelet. The proposed system achieves 100% accuracy in the tested dataset, combining a lightweight and cost-effective architecture with advanced features for embedded systems applications. Compared to existing solutions, it emphasizes accessibility and affordability, making it a practical choice for parents with hearing impairments. This solution represents a significant advancement in accessibility, reducing the reliance on expensive devices and providing greater autonomy for parents.