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Sensors 2015, 15(12), 30270-30292; doi:10.3390/s151229797

Sensing Home: A Cost-Effective Design for Smart Home via Heterogeneous Wireless Networks

1
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3
Wuhan National Laboratory for Optoelectronics, No. 1037 Luoyu Road, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Neal N. Xiong and Xuefeng Liang
Received: 8 October 2015 / Revised: 23 November 2015 / Accepted: 25 November 2015 / Published: 3 December 2015
(This article belongs to the Special Issue Mobile Sensor Computing: Theory and Applications)
View Full-Text   |   Download PDF [7473 KB, uploaded 3 December 2015]   |  

Abstract

The aging population has inspired the marketing of advanced real time devices for home health care, more and more wearable devices and mobile applications, which have emerged in this field. However, to properly collect behavior information, accurately recognize human activities, and deploy the whole system in a real living environment is a challenging task. In this paper, we propose a feasible wireless-based solution to deploy a data collection scheme, activity recognition model, feedback control and mobile integration via heterogeneous networks. We compared and found a suitable algorithm that can be run on cost-efficient embedded devices. Specifically, we use the Super Set Transformation method to map the raw data into a sparse binary matrix. Furthermore, designed front-end devices of low power consumption gather the living data of the habitant via ZigBee to reduce the burden of wiring work. Finally, we evaluated our approach and show it can achieve a theoretical time-slice accuracy of 98%. The mapping solution we propose is compatible with more wearable devices and mobile apps. View Full-Text
Keywords: activity recognition; pervasive computing; cost-effective; mobile integration activity recognition; pervasive computing; cost-effective; mobile integration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fan, X.; Huang, H.; Qi, S.; Luo, X.; Zeng, J.; Xie, Q.; Xie, C. Sensing Home: A Cost-Effective Design for Smart Home via Heterogeneous Wireless Networks. Sensors 2015, 15, 30270-30292.

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