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An Infrastructure-Free Indoor Localization Algorithm for Smartphones
Open AccessArticle

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

by 1,2,3,4, 1,2,3 and 1,2,3,*
1
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
2
Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China
3
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
4
Institute of Urban Smart Transportation & Safty Maintenance, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in “Smartphones based Online Activity Recognition for Indoor Localization using Deep Convolutional Neural Network”. In Proceeding of the Ubiquitous Positioning, Indoor Navigation and Location-Based Services, Wuhan, China, 22–23 March 2018.
Sensors 2019, 19(3), 621; https://doi.org/10.3390/s19030621
Received: 11 December 2018 / Revised: 22 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Selected Papers from UPINLBS 2018)
In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research. View Full-Text
Keywords: activity recognition; indoor localization; deep learning; smartphone activity recognition; indoor localization; deep learning; smartphone
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Zhou, B.; Yang, J.; Li, Q. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors 2019, 19, 621.

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