IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning

Abstract

Child care is necessary for parents, but nowadays taking care of a child has become a lot more challenging, especially for working mothers. It has become increasingly difficult for parents to continuously monitor their baby's condition. Thus, a smart baby monitoring system based on IoT and machine learning is implemented to overcome the monitoring issues and provide intimation to parents in real-time. In the proposed system, the necessary monitoring features like room temperature and humidity, cry detection, and face detection were monitored by exploiting different sensors. The sensor data is transferred to the Blynk server via controllers with an Internet connection. The system is also capable of detecting the facial emotions of the registered babies by using a machine learning model. Parents can monitor the live activities and emotions of their child through the external web camera and can swing the baby cradle remotely upon cry detection using their mobile application. They can also check the real-time room temperature and humidity level. In case an abnormal action is detected, a notification is sent to the parent's mobile application to take action thus, making the baby monitoring system a relief for all working parents to manage their time efficiently while taking care of their babies simultaneously.