Internet of Things Based Cradle Monitoring

Abstract

This cradle monitoring integrates cutting-edge technology to provide anall-inclusive solution for busy parents. This cradle is fortified with an array of sensors that regularly monitor the baby\'s welfare. The cradle gathers real-time data, and offers parents treasuredinsights into their child\'s health and well-being . Remote access is provided to the cradle\'s functionalities, which allowsthem to observe various aspects of the baby\'s backgrounds and environment. This work aims toprovide better monitoring of the infant by utilizing Machine Learning models for the classification of the emotions of the infant as well as for intruder detection. Thisseamlessintegration of the cradle with IoT technology ensures that parents receive instant alerts and notifications in case of any anomalies, fostering a heightened sense of security.

Introduction

Overview:
Childcare is a major challenge for working parents who cannot always monitor their infants due to busy schedules and security concerns with caregivers. Traditional cradles lack smart features to ensure continuous and real-time baby monitoring. To address these issues, SmartNest, an IoT-based Smart Cradle, is proposed, integrating various sensors, machine learning, and communication technologies to provide a safer and more efficient infant care solution.

Key Features and Components:

• Raspberry Pi 3 B+: Central processing unit for the system.

• Sensors and Modules:

  • DHT11: Measures temperature and humidity.

  • Sound Sensor: Detects baby cries.

  • Heartbeat Sensor: Monitors the baby’s pulse.

  • Moisture Sensor: Detects diaper wetness.

  • Speaker: Plays mother’s voice to soothe the infant.

  • DC Motor: Automatically swings the cradle.

  • Web Camera: Enables live video monitoring and captures images for emotion and intruder detection.

• Notification System: Uses Telegram to send real-time alerts to parents.

Technologies Used:

• Internet of Things (IoT): For environmental monitoring and device control.

• Machine Learning:

  • Emotion Classification using CNN with ResNet architecture.

  • Facial Recognition using LBPH (Local Binary Patterns Histogram) to detect intruders.

• Computer Vision:

  • OpenCV and Haar cascade classifier used for face detection and recognition.

Functional Highlights:

1. Real-Time Monitoring: Captures and sends sensor data (temperature, humidity, sound, heartbeat) to parents.

2. Automatic Actions: Crying triggers cradle movement and soothing audio.

3. Emotion Detection: Classifies emotions (happy, sad, scared, etc.) using webcam feed.

4. Intruder Detection: Alerts parents when an unrecognized person is detected near the baby.

Advantages Over Previous Work:

• Unlike earlier models, SmartNest integrates machine learning, real-time messaging, automated rocking, and a comprehensive sensor suite.

• Offers higher accuracy (93%) in emotion detection.

• Provides enhanced safety and parental control via Telegram alerts and live streaming.

Conclusion

We, therefore, consider our work to be a step forward in the better care of thenewer generation. Our work allows the working parents to better monitor and care for the infant whilealsobeingabletomanagetheir careers.Thiswillbeof greatassistance. Ourfeaturesofsensorsformanagingthe environment and various parameters like the wetness of the baby\'sclothesand monitoring theemotion softhebabyallow us to provide timely notifications for the parents. The live streaming feature also allows the parents to check on the baby at any time from anywhere. This allows the remote monitoring of the baby. We do, however, recognize that various other needs of the infant are too complex for our machinestohandle.Theresultsofour emotion classification model and intruder detection model hinge entirely on our trainingdata.Thequalityandquantityofthedatasetmust be adequate to ensure the right decisions are made. Regular maintenance of our system is required to ensure that it does not hinder the parents in the future. Further, we can also allowdeployingthesensor datatothecloud.Usingthecloud to storeand manage our data would be a better utilization of resources. Along with the present sensors, we can also include other sensors like PIR for movement and Methane sensorsfor smell. Wecanalsocreateamobileapplicationto display all the sensor data.

References

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Copyright

Copyright © 2025 Kavitha G L, Dr. Deepak N R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.