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International Conference on Applied Human Factors and Ergonomics

AHFE 2021: Advances in Simulation and Digital Human Modeling pp 255–262Cite as

Sensing to Learn: Deep Learning Based Wireless Sensing via Connected Digital and Physical Experiments

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 264)

Abstract

With the advancement of wireless technologies and sensing methodologies, wireless sensing empowers wireless hardware with the additional ability to learn the target location, activity, gesture, and vital signs. By analyzing the target’s influence on surrounding wireless signals, deep learning-based wireless sensing has attracted great attention due to its excellent performance in extracting discriminative sensing patterns. Nevertheless, it is labor-intensive to generate massive amounts of data for deep sensing study. Most existing efforts are focused on better exploiting the acquired sensing information, which, however, can hardly address the knowledge limitation fundamentally. In view of this, we propose an innovative learning framework by strategically integrating digital and physical experiments, alleviating data collection's intensive efforts. Specifically, one adaption module is introduced to connect the digital simulator and field tests for producing high-quality digital sensing data. This framework is expected to enable automatic, reliable, and efficient sensing data generation for future wireless sensing studies.

Keywords

  • Deep learning
  • Digital simulator
  • Experiment
  • Field test
  • Machine learning
  • Wireless sensing

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Acknowledgments

The work of Lan Zhang and Xiaoyong Yuan was supported by Michigan Technological University COE project: COVID-RESEARCH.

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Authors and Affiliations

  1. Michigan Technological University, Houghton, MI, 49931, USA

    Lan Zhang & Xiaoyong Yuan

  2. University of Florida, Gainesville, FL, 32611, USA

    Xianhao Chen

  3. Nanjing University of Information Science and Technology, Nanjing, 210094, China

    Yawei Pang

Authors
  1. Lan Zhang
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  2. Xianhao Chen
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  3. Yawei Pang
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  4. Xiaoyong Yuan
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Corresponding author

Correspondence to Lan Zhang .

Editor information

Editors and Affiliations

  1. U.S. Army Research Laboratory, Orlando, FL, USA

    Julia L. Wright

  2. Soar Tech, Orlando, FL, USA

    Daniel Barber

  3. Department of Product Development, University of Antwerp, Antwerp, Belgium

    Sofia Scataglini

  4. Biomedical Research and Environmental, NASA Lyndon Johnson Space Center, Houston, TX, USA

    Sudhakar L. Rajulu

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Cite this paper

Zhang, L., Chen, X., Pang, Y., Yuan, X. (2021). Sensing to Learn: Deep Learning Based Wireless Sensing via Connected Digital and Physical Experiments. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_31

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