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Sensors 2015, 15(7), 17195-17208; doi:10.3390/s150717195

Robust Indoor Human Activity Recognition Using Wireless Signals

School of Software, Dalian University of Technology, Dalian 116620, China
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 11 May 2015 / Revised: 23 June 2015 / Accepted: 8 July 2015 / Published: 15 July 2015
(This article belongs to the Section Remote Sensors)
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Abstract

Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds. View Full-Text
Keywords: wireless sensing; channel state information; action recognition wireless sensing; channel state information; action recognition
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

Wang, Y.; Jiang, X.; Cao, R.; Wang, X. Robust Indoor Human Activity Recognition Using Wireless Signals. Sensors 2015, 15, 17195-17208.

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