Transformations of Gaussian Process priors for user matching

Feng, S. and Murray-Smith, R. (2016) Transformations of Gaussian Process priors for user matching. International Journal of Human-Computer Studies, 86, pp. 32-47. (doi:10.1016/j.ijhcs.2015.09.001)

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Abstract

We describe the use of transformations of Gaussian Process (GP) priors to improve the context sensing capability of a system composed of a Kinect sensor and mobile inertial sensors. The Bayesian nonparametric model provides a principled mechanism for incorporating the low-sampling-rate position measurements and the high-sampling-rate derivatives in multi-rate sensor fusion which takes account of the uncertainty of each sensor type. The complementary properties of these sensors enable the GP model to calculate the likelihood of the observed Kinect skeletons and inertial data to identify individual users. We conducted three experiments to test the performance of the proposed GP model: (1) subtle hand movements, (2) walking with a mobile device in the trouser pocket, and (3) walking with a mobile device held in the hand. We compared the GP with the direct acceleration comparison method. Experimental results show that the GP approach can achieve successful matches (with mean accuracy μ>90%) in all 3 contexts, including when there are only subtle hand movements, where the acceleration comparison method performs poorly (μ<20%).

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Feng, Mr Shimin
Authors: Feng, S., and Murray-Smith, R.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:International Journal of Human-Computer Studies
Publisher:Elsevier
ISSN:1071-5819
ISSN (Online):1095-9300
Published Online:11 September 2015

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