Mask and Health Monitoring System using Edge Computing
September 2021
Purpose
During COVID pandemic there were no automated solutions for automating health parameters like heart/pulse rate, mask recognition and temperature. After the pandemic most of the offices will start for on-site work and schools will start, during this time we also need to take care if any employee or student is having correct body temperature and is wearing mask. Instead of hiring someone and risk their life to check temperature and mask for so much employees and students is tough task.
So to solve this problem we want to create an integrated 3D printed device which monitors their heart rate, temperature and checking are they wearing a mask using Edge Impulse model on AWS IoT Edukit and Embedded Camera.
Team
-
Guide: Dr. Sonal Gore
- Aniket Dhole
- Mohit Gandhi
- Shrishail Kumbhar
- Harsh Singhal
Working
All the Computer Vision are completely processed over ESP32 Cam using Deep Learning model. The Mask Detection model is trained on Edge Impulse and custom dataset and the generated library is used in ESP32 CAM.
Edge Computing: Means performing all computations directly at source of data instead of sending data to external server or cloud for processing.
Flow:
- The IR Distance sensor will check when user is standing in front of Device and start camera and heart sensor and temperature sensor
- Read the Image and inference it on Mask Model.
- Check whether Employee/Person is wearing Mask using Mask Classification Model trained on Edge Impulse and using MobileNet V1 Architecture
- Send Predictions to M5Core2 using UART communication.
- Then Check the Pulse Rate using Pulse Sensor and send to M5Core2 to display on screen.
- The Temperature sensor will calculate body temperature and send to M5Core2 to display on screen.
- If results are showing that Person is not healthy then that case will be reported to admin using AWS IoT and SMTP via SES.