Project: Integrating Indoor Localization to Gaming Luis Garduño

Project Goal and Motivation The goal is to seamlessly integrate location fingerprint into games. The focus will be on the fingerprint learning phase. This learning phase can be tedious. The idea is to make users help with the learning phase with a fun activity.

Research problems What is the best approach to make wireless measurements? How to minimize user input to label the wireless fingerprints? Can QR codes help? Determine how many Wi-Fi readings are necessary to achieve a reliable localization.

Paper: Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling Philipp Bolliger, Kurt Partridge, Maurice Chu, and Marc Langheinrich. 2009. Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling. In Proceedings of the 4th International Symposium on Location and Context Awareness (LoCA '09). Springer-Verlag, Berlin, Heidelberg, 37-51.

Paper Overview and Relevance Mentions some Wi-Fi fingerprint issues and how to deal with them. Proposes an asynchronous, interval fingerprint labeling solution. Evaluates the solution and presents the results. Creators of Wi-Fi fingerprint location API which will be used in my project.

Introduction Wi-Fi location has the problem of mapping signal strength patterns with physical location. Options to overcome these issues: Calculations with detailed environment models. Collect a big data set of labeled fingerprints. Issues with collecting fingerprint measurements.

Wi-Fi measurement test

Their proposed solution PILS adaPtive Indoor Localization System. Asynchronous end-user labeling. Use of accelerometer to detect movement. Continuous measurements without user intervention. Avoid user interruption.

PILS Adds a user-provided label to: The immediately obtained fingerprint Any subsequent fingerprints while the device is stationary. Stops taking measurements when movement is detected. Use of stationary time intervals to label fingerprints at a more convenient time.

PILS - Architecture

PILS - Locator Each wireless reading is composed of pairs of BSSID and RSSI measurement. bt = (BSSIDt, RSSIt) For each location l they have A set of n readings {b1, ..., bn} Their probabilistic model for a location is

Evaluation 14 MacBook Pro’s with built-in accelerometer (or SMS). 16 Wi-Fi Access Points. 70 rooms in a total area of 1,000 m2. 5 weeks of testing. Users gained no benefit from using the application. The application would ask the users for their input when guessing their location.

Evaluation – User Interface

Results and Comparison Users labeled 31 intervals. 28 of 31 intervals labeled at the beginning. Two classifiers were compared to see the benefits from interval labeling: One learned from the immediate measurements. The other learned from all the measurements taken for each interval. A sample of 1,000 measurements was drawn to test both classifiers.

Comparison Zero likelihood (out of 1,000 scans): 924 scans for instant labeling 484 scans for interval labeling

Discussion and Conclusion Motion detection provided useful information for fingerprint labeling. Ignore meaningless locations where the user normally doesn’t stay for long. It was shown that interval labeling clearly outperforms instant labeling. The asynchronous labeling is less obtrusive to users. Did not mention what was their localization precision or average error distance.

Thank you!