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Open AccessEditor’s ChoiceArticle

The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study

1
Department of Architecture, College of Architecture, Texas A&M University, College Station, TX 77843-3137, USA
2
Department of Construction Science, College of Architecture, Texas A&M University, College Station, TX 77843-3137, USA
3
Nokia Bell Labs, Murray Hill, NJ 07974-0636, USA
4
Department of Architecture and Architectural Engineering, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 983; https://doi.org/10.3390/s19050983
Received: 18 January 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments. View Full-Text
Keywords: smart home; occupant activity recognition; channel state information (CSI); Wi-Fi; housing environment smart home; occupant activity recognition; channel state information (CSI); Wi-Fi; housing environment
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Lee, H.; Ahn, C.R.; Choi, N.; Kim, T.; Lee, H. The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study. Sensors 2019, 19, 983.

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