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 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.
