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.
Highlights
Human activity recognition (HAR) has increasingly attracted intense academic and industrial interest due to its various applications in real life such as elderly monitoring
We present a device-free human activity recognition system which leverages the fluctuation of channel state information (CSI) at the receiver end of the wireless system
We present a device-free activity recognition system which enables us to classify several human activities in both LOS and NLOS scenarios
Summary
Human activity recognition (HAR) has increasingly attracted intense academic and industrial interest due to its various applications in real life such as elderly monitoring. HAR systems have been considered as device-based approaches such as vision-based [1], body-worn sensors [2], and smartphone interior sensors [3]. Both vision-based and sensor-based activity recognition systems have certain drawbacks. Vision-based systems cannot work through walls and require good lighting conditions. Sensor-based approaches are inappropriate for the user to carry a device that is sometimes easy to forget or inconvenient in some conditions
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