Jianfei Yang

FreeDetector: Device-Free Occupancy Detection with Commodity WiFi

Occupancy detection is playing a critical role to improve the efficiency of building management system and optimize personalized thermal comfort, among many other emerging applications. Conventional occupancy detection methods, such as Passive Infra-Red (PIR) and camera, have several drawbacks including low accuracy, high intrusiveness and extra infrastructure. In this work, we propose FreeDetector, a device-free occupancy detection scheme that is able to detect human presence accurately just using existing commodity WiFi routers. We upgrade the firmware of the routers so that the channel state information (CSI) data in PHY layer can be obtained directly from them. With only two routers, FreeDetector is able to reveal the variations in CSI data caused by human presence. We leverage signal tendency index (STI) to analyze the shape similarity of adjacent time series CSI curves. The most representative subset of subcarriers is selected by greedy algorithm and we utilize machine learning algorithm to construct a detection classifier. Extensive experiments are conducted and the results demonstrate that FreeDetector is able to provide outstanding occupancy detection service in terms of both accuracy and efficiency.

Poster: WiFi-based Device-Free Human Activity Recognition via Automatic Representation Learning

Existing human activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose SmartSense, a device-free human activity recognition system based on a novel machine learning algorithm with existing commercial off-the-shelf (COTS) WiFi routers. By exploiting the prevalence of existing WiFi infrastructure in buildings, we developed a novel OpenWrt based firmware for COTS WiFi routers to collect the CSI measurements from regular data frames. To identify different human activities, an automatic kernel representation learning method, namely auto-HSRL, is established to selection informative Hilbert space patterns from time, frequency, wavelet, and shape domains. A new information fusion tool based on multi-view kernel learning is proposed to combine the representations extracted from diverse perspectives and build up a robust and comprehensive activity classifier. Extensive experiments were conducted in an office and the experimental results demonstrate that SmartSense outperforms existing methods and achieves a 98% activity recognition accuracy.