Smartphone based classification system for indoor navigation

Al Jeroudi, Yazan and Legowo, Ari and Sulaeman, Erwin (2015) Smartphone based classification system for indoor navigation. Applied Mechanics and Materials, 775. pp. 436-440. ISSN 1660-9336

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Abstract

This paper introduces a smartphone-based classification system for an indoor environment of a walking person. The system relies only on smartphone inertial data and it can be considered as a smartphone-based aiding system for an indoor navigation. In addition, it does not need pre-installing of wireless network in the environment or heavily tuning process before the navigation run. Therefore, this system can be used as an aiding block where the person wants to localize himself in an indoor environment starting from known navigations point. This system categorizes person navigation in indoor environment into three types of classes: walking straight, turning right, and turning left. There is an ELM (Extreme Learning Machine)-Based neural network for deciding the class of the current navigation action. The evaluation measure shows that the best performance is obtained with the Radial Basis Function (RBF) as the activation function of the neural network. Also, the obtained accuracy rates up to 95%.

Item Type:	Article (Journal)
Additional Information:	5334/46560
Uncontrolled Keywords:	Classification, GPS-denied navigation, Navigation aiding block, Indoor navigation, Smartphone based navigation, Artificial Neural Network (ANN), On line Extreme Learning Machine (ELM)
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ181 Mechanical movements
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Dr Erwin Sulaeman
Date Deposited:	29 Dec 2015 14:50
Last Modified:	29 Nov 2016 09:30
URI:	http://irep.iium.edu.my/id/eprint/46560

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