Analyzing Customer’s Product Preference Using Wireless Signals

TLDR: The key insight of PreFi is to extract the variance features of the fine-grained time-series CSI, which is sensitively affected by customer activity, to recognize what is the customer doing.

Abstract: Customer’s product preference provides how a customer collects products or prefers one collection over another. Understanding customer’s product preference can provide retail store owner and librarian valuable insight to adjust products and service. Current solutions offer a certain convenience over common approaches such as questionnaire and interviews. However, they either require video surveillance or need wearable sensor which are usually invasive or limited to additional device. Recently, researchers have exploited physical layer information of wireless signals for robust device-free human detection, ever since Channel State Information (CSI) was reported on commodity WiFi devices. Despite of a significant amount of progress achieved, there are few works studying customer’s product preference. In this paper, we propose a customer’s product preference analysis system, PreFi, based on Commercial Off-The-Shelf (COTS) WiFi-enabled devices. The key insight of PreFi is to extract the variance features of the fine-grained time-series CSI, which is sensitively affected by customer activity, to recognize what is the customer doing. First, we conduct Principal Component Analysis (PCA) to smooth the preprocessed CSI values since general denoising method is insufficient in removing the bursty and impulse noises. Second, a sliding window-based feature extraction method and majority voting scheme are adopted to compare the distribution of activity profiles to identify different activities. We prototype our system on COTS WiFi-enabled devices and extensively evaluate it in typical indoor scenarios. The results indicate that PreFi can recognize a few representative customer activity with satisfied accuracy and robustness.