WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity Recognition

Author:

Deng Fuxiang1ORCID,Jovanov Emil2ORCID,Song Houbing3ORCID,Shi Weisong4ORCID,Zhang Yuan1ORCID,Xu Wenyao5ORCID

Affiliation:

1. College of Electronic and Information Engineering, Southwest University, Chongqing, China

2. Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA

3. Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA

4. Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA

5. Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. WiLHPE: WiFi-enabled Lightweight Channel Frequency Dynamic Convolution for HPE Tasks;2024 Tenth International Conference on Communications and Electronics (ICCE);2024-07-31

2. WiCAU: Comprehensive Partial Adaptation With Uncertainty-Aware for WiFi-Based Cross-Environment Activity Recognition;IEEE Transactions on Instrumentation and Measurement;2024

3. Machine Learning Models for Human Activity Recognition: A Comparative Study;Lecture Notes in Networks and Systems;2024

4. 3D Visualization Indoor Positioning System with Sequence Feature Enhancement Transformer Network;2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR);2023-12-08

5. On Robustness of IEEE 802.11 WLAN-based Human Activity Recognition;2023 IEEE Conference on Standards for Communications and Networking (CSCN);2023-11-06

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