This contactless system combines embedded ML and sensors to improve elevator safetyArduino Team — January 29th, 2022 As an entry into the 5th IEEE National Level Project Competition, Anway Pimpalkar and his team wanted to design a system that could help improve safety and usability within elevators by detecting if a humanAs an entry into the 5th IEEE National Level Project Competition, Anway Pimpalkar and his team wanted to design a system that could help improve safety and usability within elevators by detecting if a human is present, the floor they wish to travel towards, and automatically go to the ground floor in the event of a fire.
For determining when a person is standing within the elevator’s cabin, Pimpalkar used a Nano 33 BLE Sense and an OV7675 camera module that take advantage of embedded machine learning for facial detection. From there, the Nano will notify the user via a blinking LED that it is ready to accept a verbal command for the floor number and will transport the user when processed. Perhaps most importantly, an MQ-2 smoke sensor and LM-35 temperature sensor were added to the custom PCB. These two pieces of hardware are responsible for sensing if there is a fire nearby and subsequently activating an alarm and then moving the cabin to the ground floor if needed.
Altogether, this project is a great showcase of how powerful tinyML can be when it comes to both safety and accessibility. To read more about the system, you can check out Pimpalkar’s GitHub repository here.
https://www.civilengineering.ai/this-contactless-system-combines-embedded-ml-and-sensors-to-improve-elevator-safety/
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