Machine Learning Techniques for Wi-Fi CSI-based Recognition and Sensing: A Comprehensive Review
Sai, Siva Sharma, Devansh Peelam, Mritunjay Shall Chamola, Vinay Guizani, Mohsen Niyato, Dusit
Sai, Siva
Sharma, Devansh
Peelam, Mritunjay Shall
Chamola, Vinay
Guizani, Mohsen
Niyato, Dusit
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Department
Machine Learning
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Journal article
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English
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Abstract
Wi-Fi Channel State Information (CSI) has become a widely studied modality for device-free sensing as it captures fine-grained wireless channel variations that can be mapped to human motion and presence while avoiding the explicit visual disclosure typical of vision-based systems. CSI-based pipelines have been explored for human activity and gesture recognition, fall detection, gait analysis, pose-related inference, and indoor localization. Despite strong results in controlled settings, practical deployment remains difficult due to measurement noise, sensitivity to environmental dynamics, multi-user interference, and system-level constraints in data acquisition and real-time processing. This article surveys machine learning methods forWi-Fi CSI sensing and analyzes more than 65 representative models, connecting algorithmic design choices with implementable end-to-end system design. We introduce a hierarchical taxonomy that organizes the literature into classical machine learning approaches, deep learning architectures, and hybrid strategies. Beyond modeling, we describe the full sensing pipeline- from hardware and network interface card (NIC) selection to software tools, antenna configuration, and signal conditioning- highlighting the design trade-offs that affect robustness and reproducibility. We further compare methods across major application domains and summarize open challenges in generalization to dynamic environments, multi-user separation, and resource-efficient inference. Finally, we outline research directions toward robust generalization, scalable deployment, and privacy-aware learning to support broader real-world adoption.
Citation
S. Sai, D. Sharma, M.S. Peelam, V. Chamola, M. Guizani, D. Niyato, "Machine Learning Techniques for Wi-Fi CSI-based Recognition and Sensing: A Comprehensive Review," IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1-1, 2026, https://doi.org/10.1109/jiot.2026.3657341.
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IEEE Internet of Things Journal
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Information and Computing Sciences, Data Management and Data Science, Human-Centred Computing
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IEEE