Principal Investigators: Tanzeem Choudhury and Alex Pentland
The first successful experiment in automatically learning the communication patterns
within a community and the interaction dynamics using wearable sensing devices
MIT PhD Thesis
Shortcuts project, we are developing methods to automatically and unobtrusively learn the social network structure that arises within a group based on
data collected using the sociometer. The questions we are exploring are:
- How does information diffuse within the community?
- A group of 23 people within a community agree to wear sociometers - 6 hours daily.
- We collect information about their interactions over an extended period of time (2weeks).
- Learn group structure and dynamics using statistical pattern recognition techniques.
- Learn how interaction dynamics influence the social network relationships
The users agree to have the device on them for the duration that they are on MIT campus. We collected the following data from each individual -
- his/her neighborhood information, i.e. the people nearby using IR
We have collected 1518 hours of interaction data from 23 individuals over two-weeks.
We use the hoarder board with the multi-sensor board extension for data collection in this project. The hoarder board is a microcontroller-based data acquisition platform suitable for user sensing applications. Developed by Vadim Gerasimov, Rich Devaul and Josh Weaver, the general-purpose hoarder board is customized for particular applications through specially designed daughter boards, which provide the sensing hardware.
Special thanks to Brian Clarkson for the effort he put into the design of the shoulder mount.
The use of human subjects for this project has been approved by COUHES (application # 2889)