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Analyzing Customer’s Product Preference Using Wireless Signals

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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.

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Acknowledgement

This work was supported by Research of life cycle management and control system for equipment household registration, No. J770011104. We also thank the anonymous reviewers and shepherd for their valuable feedback.

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Na Pang, Dali Zhu, Wenjing Rong, Yinlong Liu & Changhai Ou

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Na Pang, Dali Zhu, Wenjing Rong, Yinlong Liu & Changhai Ou

  3. The Second High School Attached to Beijing Normal University, Beijing, China

    Kaiwen Xue

Authors
  1. Na Pang
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  2. Dali Zhu
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  3. Kaiwen Xue
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  4. Wenjing Rong
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  5. Yinlong Liu
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  6. Changhai Ou
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Corresponding author

Correspondence to Na Pang .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Pang, N., Zhu, D., Xue, K., Rong, W., Liu, Y., Ou, C. (2017). Analyzing Customer’s Product Preference Using Wireless Signals. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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