Securing the Open Softphone

Undergraduate Student: Avi Klausner, Matt Schoen

Advisors: Ari Trachtenberg, David Starobinski

Recognizing Gait

We investigate whether smartphones can be used to distinguish different users based on their gait, the rhythmical body movements of human beings as they walk. To this end, we propose, describe, and experimentally evaluate a system that classifies peoples' gait patterns using the tri-axial accelerometer of the Motorola Droid phone. The system employs the wavelet transform to extract features from raw acceleration data and the k Nearest Neighbors (kNN) algorithm to perform the classification. Preliminary experimental results show that the system achieves high classification rates (i.e. above 90%) when users walk at approximately constant speeds regardless of variations in environment. Our results show promise toward using gait as a means of user recognition.

-- JiaxiJin - 05 Oct 2010