Convolution Neutral Network Enhanced Binary Sensor Network for Human Activity Recognition

Liu, Guocheng; Liang, Jinhao; Lan, Gongjin; Hao, Qi; Chen, Mei

10.1109/ICSENS.2016.7808519

2016

21689229

978-1-4799-8288-2

2016 IEEE SENSORS

0

1-3

30 Oct.-3 Nov. 2016

Orlando, FL, United states

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper presents binary sensor network for human activity recognition, specifically, whose performance is enhanced by using convolution neutral networks (CNNs). The goal of this research is to develop a sensing system based on CNNs that can recognize various human daily activities with minimum data acquisition. The whole system consists of pyroelectric infrared (PIR) sensor arrays, feature extractor, and a classifier. All the sensory data are converted into binary numbers to preserve the geometry and motion information of targets. In this work, we compare the feature selection and classification methods of CNNs, which can extract feature automatically, with another method. The experiment results have demonstrated that the proposed system can recognize human activity recognition only with a few bits of sensory data. Besides, the CNNs can achieve the best recognition performance despite their high computational complexity.

activity recognition binary sensor networks convolution neutral networks

第一 ; 通讯

英语

CPCI-S ; EI

Southern University of Science and Technology University Research Committee[FRG-SUSTC1501A-29]

Engineering ; Remote Sensing

Engineering, Electrical & Electronic ; Remote Sensing

WOS:000399395700112

20170503313428

Convolution ; Data acquisition ; Pattern recognition ; Sensor networks

Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2

Web of Science

Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China

Liu, Guocheng,Liang, Jinhao,Lan, Gongjin,et al. Convolution Neutral Network Enhanced Binary Sensor Network for Human Activity Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2016:1-3.