The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in realworld. Therefore, in this article, we first introduce WiFi signal as a new source instead of sensor and vision for unobtrusive user behaviors analysis. Then we design BeSense, a contactless behavior analysis system leveraging signal processing and computational intelligence over WiFi channel state information (CSI). We prototype BeSense on commodity low-cost WiFi devices and evaluate its performance in realworld environments. Experimental results have verified its effectiveness in recognizing user behaviors.
In the early days, WiFi-based behavior sensing mainly uses RSS (Received Signal Strength). Sigg @cite_7 use a software radio to transmit RF signals and determine human motion based on changes in RSS. Abdelnasser leverage RSS to identify 7 different gestures @cite_3 and respiratory detection @cite_22 . We also built a similar RSS-based system PAWS to handle whole-body activities @cite_28 . Since RSS is coarse-grained and CSI can yield more detailed information, recent research mainly uses CSI for behavior sensing. Wifall @cite_8 uses CSI to implement the fall detection system. @cite_32 leverage CSI to recognize five different customer behavior states. We also built a CSI-based system MoSense to pinpoint the motions in a real-time manner @cite_16 .
@cite_22 @cite_7 @cite_8 @cite_28 @cite_32 @cite_3 @cite_16
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Background: We investigate direct use of 802.15.4 radio signal strength indication (RSSI) for human activity recognition when 1) a user carries a wireless node (device-bound) and when 2) a user moves in the wireless sensor net (WSN) without a WSN node (device-free). We investigate recognition feasibility in respect to network topology, subject and room geometry (door open, half, closed). Methods: In a 2 person office room 8 wireless nodes are installed in a 3D topology. Two subjects are outfitted with a sensor node on the hip. Acceleration and RSSI are recorded while subject performs 6 different activities or room is empty. We apply machine learning for analysis and compare our results to acceleration data. Results: 10-fold cross-validation with all nodes gives accuracies of 0.896 (device-bound), 0.894 (device-free) and 0.88 (accelerometer). Topology investigation reveals that similar accuracies may be reached with only 5 (device-bound) or 4 (device-free) selected nodes. Applying trained data from one subject to the other and vice-versa shows higher recognition difference on RSSI than on acceleration. Changing of door state has smaller effect on both systems than subject change; with least impact when door is closed. Conclusion: 802.15.4 RSSI suited for activity recognition. 3D topology is helpful in respect to type of activities. Discrimination of subjects seems possible. Practical systems must adapt no only to long-term environmental dispersion but consider typical geometric changes. Adaptable, robust recognition models must be developed. In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28 , 89.52 , and 93.05 , respectively. A radio transceiver normally provides received signal strength (RSS) quantized with 1 dB or higher step size. Currently, we know of no application which has demonstrated a need for sub-dB RSS estimates. In this paper, we demonstrate the need for, and benefits of, greater resolution in RSS for breathing rate monitoring and gesture recognition. Measuring RSS requires orders of magnitude less bandwidth than measuring OFDM channel state information (CSI) or frequency modulated carrier wave (FMCW) channel delay. We have designed a prototype with an off-the-shelf low-power transceiver and a processor to achieve an RSS estimate with a median error of 0.013 dB. We experimentally verify its performance in non-contact breathing monitoring and gesture recognition. We demonstrate that simply decreasing the step size of RSS lower than 1 dB can enable significant benefits, enabling extremely low bandwidth RF sensing systems. Results indicate that RFIC designers could enable significant gains for RF sensing applications with four more bits of RSS quantization. The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an 1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods.