SENSOR BASED MONITORING OF SOCIAL NETWORKS

• US 20100023300A1
• Filed: 08/20/2009
• Published: 01/28/2010
• Est. Priority Date: 07/28/2008
• Status: Abandoned Application

• Current Assignee: Charles River Analytics, Inc., Massachusetts Institute of Technology
• Original Assignee: Charles River Analytics, Inc.
• Inventors: Pfautz, Jonathan, Farry, Michael, Madden, Samuel
• Application Number:
  US12/510,299
• Publication Number:
  US 20100023300A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A method and system for detecting and monitoring discrete interactions within a physical social network is provided. The system and method detects attributes associated with human communication activities and distinguishes them from other concurrently detected activities in order to identify discrete interactions that are in turn transmitted across a network having a limited data rate. Generally, in its simplest form a plurality of wireless sensors are deployed across a cross-section of people of interest. Once deployed, the wireless sensors establish an ad-hoc network that transmits a small amount of data relating to the each of the discrete individuals bearing a sensor. The collected data is analyzed through the application of rules set forth in a Bayesian belief network to make a determination regarding the probability that an actual interaction between two individuals bearing sensors in fact occurred.

Claims

1. A system for reliably predicting interactions among a plurality of people of interest, comprising:

a plurality of wireless sensor nodes deployed among said plurality of people of interest, said wireless sensor nodes each including data collecting sensors thereon, said wireless sensor nodes forming an ad hoc network;

a data polling system that operates across said ad hoc network to gather data collected by said sensors; and

an analysis algorithm that operates on said gathered data, said algorithm first comparing data collected from each of said wireless sensor nodes to determine the relative similarity in collected data from each sensor node and second applying a predictive calculation on similar sensor nodes to determine the probability that an interaction occurred.

2. The system of claim 1, wherein said sensors collect data selected from the group consisting of:

light, temperature, proximity, x-axis acceleration, y-axis acceleration and sound.

3. The system of claim 1, wherein the data collected by said sensors is a limited subset of the full data range that said sensor is capable of.

4. The system of claim 3, wherein the limited subset includes data readings of high, medium and low.

5. The system of claim 1, wherein said determination of relative similarity in collected data is done periodically using temporal periods of limited duration.

6. The system of claim 1, the relative similarity in collected data from each sensor node is determined by application of similarity metrics to compare signals from the same sensor modalities on different sensor nodes to determine that a high degree of similarity between sensor signal values.

7. The system of claim 6, wherein said determination of relative similarity in collected data is done periodically using temporal periods of limited duration.

8. The system of claim 1, wherein analysis algorithm is located on a computer processor, said computer processor being a node on said ad hoc network.

9. The system of claim 1, wherein said application of a predictive calculation is application of a Bayesian Belief Network.

10. The system of claim 9, wherein the Bayesian Belief Network compares the relative similarity of the data collected from each of the sensors on each of the sensor node pairs to produce a belief about whether an interaction in the current timeframe.

11. The system of claim 10, wherein said belief is a true or false value and a probability value.

12. The system of claim 10, wherein said Bayesian Belief Network produces said belief based on sequential comparisons performed periodically using temporal periods of limited duration.

13. A system for reliably predicting interactions among a plurality of people of interest, comprising:

a plurality of wireless sensor nodes deployed among said plurality of people of interest, said wireless sensor nodes each including data collecting sensors thereon, said wireless sensor nodes forming an ad hoc network;

a computer system connected as a node on said ad hoc network, said computer system including a process and a data storage device;

a data polling system that operates on said processor and across said ad hoc network to gather data collected by said sensors and place said data onto said storage device; and

an analysis algorithm on said computer processor that operates on said gathered data, said algorithm first comparing data collected from each of said wireless sensor nodes to determine the relative similarity in collected data from each sensor node and second applying a predictive calculation on similar sensor nodes to determine the probability that an interaction occurred.

14. The system of claim 13, wherein said sensors collect data selected from the group consisting of:

light, temperature, proximity, x-axis acceleration, y-axis acceleration and sound.

15. The system of claim 13, wherein the data collected by said sensors is a limited subset of the full data range that said sensor is capable of.

16. The system of claim 15, wherein the limited subset includes data readings of high, medium and low.

17. The system of claim 13, wherein said determination of relative similarity in collected data is done periodically using temporal periods of limited duration.

18. The system of claim 13, the relative similarity in collected data from each sensor node is determined by application of similarity metrics to compare signals from the same sensor modalities on different sensor nodes to determine that a high degree of similarity between sensor signal values.

19. The system of claim 18, wherein said determination of relative similarity in collected data is done periodically using temporal periods of limited duration.

20. The system of claim 13, wherein said application of a predictive calculation is application of a Bayesian Belief Network.

21. The system of claim 20, wherein the Bayesian Belief Network compares the relative similarity of the data collected from each of the sensors on each of the sensor node pairs to produce a belief about whether an interaction in the current timeframe.

22. The system of claim 21, wherein said belief is a true or false value and a probability value.

23. The system of claim 21, wherein said Bayesian Belief Network produces said belief based on sequential comparisons performed periodically using temporal periods of limited duration.