NDLI: An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers

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An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers

Content Provider	ACM Digital Library
Author	Muaaz, Muhammad Mayrhofer, René
Abstract	Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.
Starting Page	293
Ending Page	300
Page Count	8
File Format	PDF
ISBN	9781450321068
DOI	10.1145/2536853.2536895
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-12-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Biometrics Authentication Machine learning Accelerometer Gait recognition
Content Type	Text
Resource Type	Article

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