Learning Transportation Modes from Smartphone Sensors Based on Deep Neural Network - Details

author：

Fang, Shih-Hau (Fang, Shih-Hau.) ^[1] | Fei, Yu-Xaing (Fei, Yu-Xaing.) ^[2] | Xu, Zhezhuang (Xu, Zhezhuang.) ^[3] | Tsao, Yu (Tsao, Yu.) ^[4]

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Abstract：

In　recent　years,　the　importance　of　user　information　has　increased　rapidly　for　context-aware　applications.　This　paper　proposes　a　deep　learning　mechanism　to　identify　the　transportation　modes　of　smartphone　users.　The　proposed　mechanism　is　evaluated　on　a　database　that　contains　more　than　1000　h　of　accelerometer,　magnetometer,　and　gyroscope　measurements　from　five　transportation　modes,　including　still,　walk,　run,　bike,　and　vehicle.　Experimental　results　confirm　the　effectiveness　of　the　proposed　mechanism,　which　achieves　approximately　95%　classification　accuracy　and　outperforms　four　well-known　machine　learning　methods.　Meanwhile,　we　investigated　the　model　size　and　execution　time　of　different　algorithms　to　address　practical　issues.　©　2001-2012　IEEE.

Keyword：

Big data Cellular telephones Deep learning Deep neural networks Sensors Smartphones

Community：

[ 1 ] [Fang, Shih-Hau]Innovation Center for Big Data and Digital Convergence, Department of Electrical Engineering, Yuan Ze University, Taoyuan; 32003, Taiwan
[ 2 ] [Fei, Yu-Xaing]Innovation Center for Big Data and Digital Convergence, Department of Electrical Engineering, Yuan Ze University, Taoyuan; 32003, Taiwan
[ 3 ] [Xu, Zhezhuang]School of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
[ 4 ] [Tsao, Yu]Research Center for Information Technology Innovation, Academia Sinica, Taipei; 11529, Taiwan

Reprint 's Address：

[fang, shih-hau]innovation center for big data and digital convergence, department of electrical engineering, yuan ze university, taoyuan; 32003, taiwan

Email：

shfang@saturn.yzu.edu.tw

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Source ：

IEEE Sensors Journal

ISSN： 1530-437X

Year： 2017

Issue： 18

Volume： 17

Page： 6111-6118

2 . 6 1 7

JCR@2017

4 . 3 0 0

JCR@2023

ESI HC Threshold：177

JCR Journal Grade：1

CAS Journal Grade：3

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 94

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

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