Performance Analysis of Classification Algorithms for Activity Recognition using Micro-Doppler Feature

Lin, Y. and Le Kernec, J. (2018) Performance Analysis of Classification Algorithms for Activity Recognition using Micro-Doppler Feature. In: The 13th International Conference on Computational Intelligence and Security (CIS 2017), Hong Kong, China, 15-18 Dec 2017, ISBN 0769563414 (doi:10.1109/CIS.2017.00111)

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Abstract

Classification of different human activities using micro-Doppler data and features is considered in this study, focusing on the distinction between walking and running. 240 recordings from 2 different human subjects were collected in a series of simulations performed in the real motion data from the Carnegie Mellon University Motion Capture Database. The maximum the micro-Doppler frequency shift and the period duration are utilized as two classification criterions. Numerical results are compared against several classification techniques including the Linear Discriminant Analysis (LDA), Naïve Bayes (NB), K-nearest neighbors (KNN), Support Vector Machine(SVM) algorithms. The performance of different classifiers is discussed aiming at identifying the most appropriate features for the walking and running classification.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Le Kernec, Dr Julien
Authors: Lin, Y., and Le Kernec, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:0769563414
Copyright Holders:Copyright © 2017 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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