The problem PULSE AI solves

The need for an AI-powered Neonatal Vital Monitoring System stems from the critical care required in NICUs for premature and high-risk newborns. Traditional systems can be uncomfortable and may not provide real-time, comprehensive monitoring, leading to delayed detection of health risks. AI-driven solutions offer continuous, non-invasive monitoring and real-time anomaly detection, helping reduce errors and improve response times. By providing predictive insights, this system enhances neonatal care, ensuring timely interventions and better health outcomes for vulnerable infants.

The AI-Powered Neonatal Vital Monitoring System continuously tracks critical health indicators like heart rate, temperature, color of the baby, sleep cycle tracking in NICUs using AI and machine learning. Integrated with Apgar scores, it enhances newborn assessments by utilizing video including remote photoplethysmography (rPPG) for contactless monitoring. The system leverages cloud and edge computing for data storage and real-time processing, reducing human errors and enabling timely medical interventions.

Challenges we ran into

Package Error : In the Backend area i using more than 82+ Packages for that many of the packages are not supporting the python version to resolve that i created seperate environment for frontend and backend

Single Camera Access : The main challenge is i have 4 python files it needs the camera feed as an input at athe same time then only i can able to track all the features but if any one of the file accessing the camera sources rest of the files cannot able to access the camera to resolve that issue i created an file called camera.py this give act as an API to connect all the files in a single camera

Multi Threading : I want to run the 4 files parallelly for that i created a file called backend.py that used the multi threading process to run the all the python files parallelly and also don't want to run all the backend files only run the backend.py it runs the entire backend

Continuous Responce From LLM : To give the APGAR score from the LLM The data generated by the backends Continuous ly sending to the LLM model each 10 seconds the LLM calculates the APGAR SCORE based on the that score it gives the Condition of the Baby in the Paragraph Content