WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities

Ahmad, Iftikhar; Ullah, Arif; Choi, Wooyeol

doi:10.1109/ojcoms.2024.3411529

IEEE Open J. Commun. Soc.

2024

DOI: 10.1109/ojcoms.2024.3411529

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WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities

Iftikhar Ahmad,

Arif Ullah,

Wooyeol Choi

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2024

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Cited by 2 publications

References 190 publications

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Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future Directions

Nguyen,

Lukito,

Liu

et al. 2024

Electronics

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Recently, with advancements in Deep Learning (DL) technology, Radio Frequency (RF) sensing has seen substantial improvements, particularly in outdoor applications. Motivated by these developments, this survey presents a comprehensive review of state-of-the-art RF sensing techniques in challenging outdoor scenarios with practical issues such as fading, interference, and environmental dynamics. We first investigate the characteristics of outdoor environments and explore potential wireless technologies. Then, we study the current trends in applying DL to RF-based systems and highlight its advantages in dealing with large-scale and dynamic outdoor environments. Furthermore, this paper provides a detailed comparison between discriminative and generative DL models in support of RF sensing, offering insights into both the theoretical underpinnings and practical applications of these technologies. Finally, we discuss the research challenges and present future directions of leveraging DL in outdoor RF sensing.

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Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future Directions

Nguyen,

Lukito,

Liu

et al. 2024

Electronics

View full text Add to dashboard Cite

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Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition

Varga

2024

Sensors

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Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset. Specifically, the benchmark fails to separate individuals between the training and test sets, leading to inflated performance metrics as models inadvertently learn individual-specific features rather than generalizable action patterns. We analyzed this issue in depth, retrained several benchmarked models using corrected data partitioning methods, and demonstrated a significant drop in accuracy when individuals were properly separated across training and testing. Our findings highlight the importance of rigorous data partitioning in CSI-based action recognition and provide recommendations for mitigating data leakage in future research. This work contributes to the development of more robust and reliable human action recognition systems using WiFi CSI.

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WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities

Cited by 2 publications

References 190 publications

Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future Directions

Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future Directions

Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition

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