Entry Date:
April 21, 2022

Chameleon

Principal Investigator Kent Larson

Project Start Date November 2021


Chameleon is a wall mounted sensor system that uses machine learning to classify building activity.

We present the system as an alternative that can make smart sensing within a building easier to scale.  The device is capable of eliminating the need for constant maintenance and heavy upfront infrastructure costs while keeping user privacy concerns at the center of its design.  Its novel architecture allows it to be used within rooms with different space layouts, ventilation systems, windows and usage patterns such as offices, classrooms and homes.

The system uses the latest version of the MIT terMITe sensors developed in the City Science group. The new hardware adds capabilities for sensing CO2 particles and movement .  These types of sensors are inherently less intrusive than cameras or other optical systems, making them a valuable alternative for privacy preserving building infrastructure.