Affordable ML Based Collaborative Approach for Baby Monitoring


There are numerous baby-monitoring devices available in the market that parents use to keep an eye on babies while they are away. The majority of them are reliant on the installation of expensive hardware, which many parents cannot afford. Another issue with these devices is that they detect high-pitched  sounds  and  frequently  give  false  alarms,  causing  both  children  and  parents  to  be disturbed. The majority of smartphone applications in the market work on sound wave and only sound an alarm when the infant start crying. In this project, we proposed the design of a mobile application to detect the status of a baby inside a crib/ on a bed. The application will alert parents when their child requires assistance, will be able to determine whether the child is sleeping in a safe or hazardous position, and will keep track of the child’s sleeping patterns. It is less reliant on hardware, making it less expensive. Here the only requirement is two paired mobile phones with the  application  installed  instead  of  expensivehardware  (IoT-based  devices).  The  application  is utilizing  the  transfer-learning  technique  on  tensor  flow  lite  Mobilenet  classification  and SSD_mobilenet_V1_coco  object  detection  models.The  accuracy  of  the  model  is  97%  for  the Mobilenet classification model and 98% for the object detection model.

Published in:

International Journal of Research in Engineering and Science (IJRES- August 2021 Volume 9-Issue 8).


Ramamani Venkatakrishna

Ravi Shukla

Sneha P. Tiwari

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