Inspiration
Inspired by the drastic impact and high volume presence of poor hand hygiene practices in the industry. Simply awareness and implementation of standard precautions, particularly effective hand hygiene practices at the bedside, can increase patient safety and save lives lost to healthcare-associated infections.
What it does
This device uses image processing coupled with machine learning to notify health care personnel' of their hand hygiene compliance level. In cases of low compliance level, the device will advise the staffs to wash their hands.
How we built it
We divided the project into two aspects, image capturing and image processing. The image capturing involved programming the Arduino 101 to illuminate a light when a certain color is detected through the camera still. The image processing utilized OpenCV to process still images under a black light and differentiate between dirty and non-dirty areas through black and white highlighting. With these two elements in conjunction, they create a product that identifies effective hand hygiene.
Challenges we ran into
Arduino 101 firmware was incorrect, leading to bad I/O.
Accomplishments that we're proud of
Finishing
What we learned
We learned about the capabilities of OpenCV and the cv2 functions within Python that help with image processing.
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