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Device-Free Indoor Activity Recognition System
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 t...
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Published in: | Applied sciences 2016-11, Vol.6 (11), p.329-329 |
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container_end_page | 329 |
container_issue | 11 |
container_start_page | 329 |
container_title | Applied sciences |
container_volume | 6 |
creator | Al-qaness, Mohammed Li, Fangmin Ma, Xiaolin Zhang, Yong Liu, Guo |
description | 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. |
doi_str_mv | 10.3390/app6110329 |
format | article |
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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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app6110329</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | activity recognition Algorithms Channels Classification CSI device-free Feature extraction Laptop Line of sight Localization Perceptions Routers Sensors Stands Systems analysis WiFi wireless sensing |
title | Device-Free Indoor Activity Recognition System |
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