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)

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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