Next Article in Journal
The Effect of Fuel Mass Fraction on the Combustion and Fluid Flow in a Sulfur Recovery Unit Thermal Reactor
Next Article in Special Issue
A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition
Previous Article in Journal
Strategy and Evaluation of Vehicle Collision Avoidance Control via Hardware-in-the-Loop Platform
Previous Article in Special Issue
Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(11), 329; doi:10.3390/app6110329

Device-Free Indoor Activity Recognition System

1
Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
3
Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China
*
Author to whom correspondence should be addressed.
Academic Editors: Plamen Angelov and José Antonio Iglesias Martínez
Received: 11 September 2016 / Revised: 23 October 2016 / Accepted: 28 October 2016 / Published: 1 November 2016
(This article belongs to the Special Issue Human Activity Recognition)
View Full-Text   |   Download PDF [2065 KB, uploaded 1 November 2016]   |  

Abstract

In this paper, we explore the properties of the Channel State Information (CSI) of WiFi signals and present a device-free indoor activity recognition system. Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices. The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie. We have built the prototype with an effective feature extraction method and a fast classification algorithm. The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios, and the results validate the performance of the proposed system. View Full-Text
Keywords: activity recognition; device-free; CSI; wireless sensing; WiFi activity recognition; device-free; CSI; wireless sensing; WiFi
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Al-qaness, M.A.A.; Li, F.; Ma, X.; Zhang, Y.; Liu, G. Device-Free Indoor Activity Recognition System. Appl. Sci. 2016, 6, 329.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top