Motion Sensors for Architectural decisions analysis


Architectural decisions impacts our behavior - that was the premise of that sparked this work. How can we evaluate the impact of the physical structures of buildings on users routines and what inferences can we do between architecture and well-being.
In this project we collected and analysed data in order to understand how architectural design decisions impact in human behavior. We used MIT Building E14 (Media Lab) as a case study, in this building there are two main entrances, a stair and two elevators on the lobby floor. In this case, we aimed to understand the correlation between this infrastructure and the number of people deciding to take the elevators or stairs based on which one they saw first ( data extrapolated based on which door they entered).


Data Gathering

We designed and built a sensor network - shown in the video on the right - with Passive Infra-red sensors (PIR) combined with temperature, pressure and ambient light sensors (environmental data). Connected to the sensors there was a ESP8266 Wi-Fi micro-controller, feed by 4 AAA batteries.

Whenever a person passed in front of a sensor, the data was saved in the cloud - remote server with a postgreSQL database - this information is then processed to create a map of human motion flow.

Data Visualization

In this project I worked from the hardware design, to implementation until data analysis. To analyse the data we wrote a web app using modern JavaScript libraries, like Express and React.js - more photos of the UI and data visualization will be added soon.