Device-Free Indoor Activity Recognition System
| Autor: | Yong Zhang, Fangmin Li, Guo Liu, Mohammed A. A. Al-qaness, Xiaolin Ma |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Router
Engineering business.product_category Feature extraction Real-time computing 02 engineering and technology lcsh:Technology Activity recognition lcsh:Chemistry Non-line-of-sight propagation CSI 0202 electrical engineering electronic engineering information engineering General Materials Science Point (geometry) activity recognition Instrumentation wireless sensing lcsh:QH301-705.5 Simulation Fluid Flow and Transfer Processes device-free business.industry lcsh:T Process Chemistry and Technology WiFi General Engineering 020206 networking & telecommunications lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 Channel state information lcsh:TA1-2040 Laptop 020201 artificial intelligence & image processing business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences; Volume 6; Issue 11; Pages: 329 Applied Sciences, Vol 6, Iss 11, p 329 (2016) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app6110329 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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