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- Publisher Website: 10.1007/978-3-030-79763-8_31
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Conference Paper: Sensing to Learn: Deep Learning Based Wireless Sensing via Connected Digital and Physical Experiments
Title | Sensing to Learn: Deep Learning Based Wireless Sensing via Connected Digital and Physical Experiments |
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Authors | |
Keywords | Deep learning Digital simulator Experiment Field test Machine learning Wireless sensing |
Issue Date | 2021 |
Publisher | Springer |
Citation | 12th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2021), July 25-29 2021. In Wright, JL, Barber, D, Scataglini, S, et al. (Eds.), Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 25-29, 2021, USA, p. 255-262. Cham: Springer, 2021 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/316595 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.171 |
Series/Report no. | Lecture Notes in Networks and Systems ; 264 |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Lan | - |
dc.contributor.author | Chen, Xianhao | - |
dc.contributor.author | Pang, Yawei | - |
dc.contributor.author | Yuan, Xiaoyong | - |
dc.date.accessioned | 2022-09-14T11:40:50Z | - |
dc.date.available | 2022-09-14T11:40:50Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 12th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2021), July 25-29 2021. In Wright, JL, Barber, D, Scataglini, S, et al. (Eds.), Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 25-29, 2021, USA, p. 255-262. Cham: Springer, 2021 | - |
dc.identifier.isbn | 9783030797621 | - |
dc.identifier.issn | 2367-3370 | - |
dc.identifier.uri | http://hdl.handle.net/10722/316595 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Springer | - |
dc.relation.ispartof | Advances in Simulation and Digital Human Modeling: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors and Simulation, and Digital Human Modeling and Applied Optimization, July 25-29, 2021, USA | - |
dc.relation.ispartofseries | Lecture Notes in Networks and Systems ; 264 | - |
dc.subject | Deep learning | - |
dc.subject | Digital simulator | - |
dc.subject | Experiment | - |
dc.subject | Field test | - |
dc.subject | Machine learning | - |
dc.subject | Wireless sensing | - |
dc.title | Sensing to Learn: Deep Learning Based Wireless Sensing via Connected Digital and Physical Experiments | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-79763-8_31 | - |
dc.identifier.scopus | eid_2-s2.0-85112010798 | - |
dc.identifier.spage | 255 | - |
dc.identifier.epage | 262 | - |
dc.identifier.eissn | 2367-3389 | - |
dc.publisher.place | Cham | - |