Aug 2026· Journal of Advances in Engineering and Technology· 0 citations
TL;DR
This survey provides a systematic overview of cutting-edge research on Wi-Fi-enabled indoor human activity detection, classify mainstream technologies along three dimensions: signal processing pipelines, learning paradigms, and application granularity, and further dissect core challenges including environmental adaptability, data scarcity, and system scalability.
Abstract
Driven by the rising demand for privacy-friendly, unobtrusive indoor monitoring, Wi-Fi passive human sensing has developed into a widely studied technical paradigm. Compared with vision-based and wearable sensing techniques, Wi-Fi sensing exhibits unique advantages including robust illumination, privacy protection, wall penetration, device-free operation, and low hardware cost. Driven by advanced deep learning, this field has advanced substantially over the past decade, evolving from coarse-level activity classification to fine-grained tasks including 3D human skeleton estimation, dense body pose matching, and multi-user activity sensing. However, several fundamental bottlenecks remain unresolved, including severe performance degradation in cross-domain and cross-environment scenarios, insufficient spatial resolution for motion sensing, limited capability for multi-user signal disentanglement, and the lack of standardized benchmark datasets. This survey provides a systematic overview of cutting-edge research on Wi-Fi-enabled indoor human activity detection. We classify mainstream technologies along three dimensions: signal processing pipelines, learning paradigms, and application granularity, and further dissect core challenges including environmental adaptability, data scarcity, and system scalability. Finally, we highlight promising future research directions covering domain generalization, cross-modal foundation model distillation, multi-user collaborative sensing, and real-time on-device deployment.
An innovative framework that integrates WiFi-based Channel State Information with the advanced object recognition power of YOLOv8 to enable robust, contactless activity classification and employs the deep learning capabilities of YOLOv8 for precise identification of diverse indoor actions.
Hicham Boudlal, Mohammed Serrhini, Ahmed Tahiri· Multimedia tools and applica...· 0 citations
WiFi-based human sensing technology utilizing Channel State Information (CSI) has garnered significant attention due to its reduced privacy concerns and the widespread availability of existing infrastructure, demonstrating
broad development prospects in the field of intelligent computing. Deployment
on edge devices represents its most prevalent application scenario. However, the
high-complexity algorithms commonly employed to enhance sensing accuracy
face substantial challenges when deployed on devices with limited computational
resources. Furthermore, most existing studies conduct experiments only on datasets with a small number of categories. Although these approaches achieve high
accuracy, they fail to meet practical sensing requirements. Consequently, developing high-accuracy, low-complexity, and practical WiFi-based human sensing
systems remains considerably challenging. To construct an efficient and lightweight feature extraction network, we presents LipHS, a lightweight feature extraction framework capable of simultaneously capturing multi-level information
from CSI signals. To further reduce the number of model parameters, we employ
a channel pruning method based on Layer-Adaptive Magnitude-based Pruning
(LAMP) scores. LipHS achieves model lightweighting while maintaining robust
feature extraction capabilities. Experimental results demonstrate that the proposed LipHS method outperforms other baseline algorithms in sensing performance on complex multi-class gesture datasets.
ChunHao Xue· Poster Volume 0007 The 2026...· 0 citations
Human activity recognition (HAR) plays a pivotal role in ambient assisted living, particularly for monitoring the elderly and patients with chronic conditions. However, traditional approaches relying on wearable sensors or video cameras face significant challenges regarding user compliance and privacy intrusion. To mitigate these issues, this paper proposes a device-free sensing (DFS) (
https://github.com/mestrelan/MDA-CSI
) framework utilizing Wi-Fi channel state information (CSI), named . We introduce a robust Transformer-based architecture designed to capture long-range temporal dependencies in wireless signals. was validated using a comprehensive dataset from 86 volunteers, ensuring high generalization capabilities across diverse human motion patterns.
Allan Costa Nascimento dos Santos, Pamella Soares, Iandra Galdino et al.· Annals of Telecommunications· 0 citations
Wireless sensing has emerged as a promising approach for tracking and identification using commodity Internet of Things devices. However, the features derived from a single wireless modality are often fragile to variations in environmental layouts and walking trajectories. Furthermore, most existing studies are based on datasets collected in specific scenarios with limited trajectory diversity and sensing modalities, preventing a robust evaluation of system generalization. \textcolor{blue}{To address this gap, we introduce \textbf{XGait}, a multi-modality wireless sensing dataset that synchronously captures human walking using Wi-Fi and acoustic transceivers across three indoor scenarios, with vision-based measurements serving as ground truth. Specifically, XGait contains more than 22K walking samples from 27 participants, covering diverse directions and trajectories to support both indoor tracking and identity recognition. To bridge the heterogeneity of wireless sensing modalities, we propose a unified Doppler spectrogram representation that maps Wi-Fi and acoustic signals into a shared time--frequency space, along with a standardized benchmark pipeline for pre-processing, temporal alignment, and feature construction, enabling reproducible evaluation and systematic cross-modal analysis. Extensive evaluations demonstrate that Wi-Fi and acoustic sensing exhibit complementary strengths, particularly under complex trajectories and challenging propagation conditions, thereby paving the way for novel research in the field of multi-modality wireless sensing.} The dataset and code are available at https://github.com/warrior-087/XGait.
A novel lightweight cross-domain few-shot sensor-based HAR network (CFSH-Net) is proposed for cross-domain activity recognition with limited labeled samples, which demonstrates strong cross-user generalization on PAMAP2 and USC-HAD, and stable cross-dataset transfer when trained on OPPORTUNITY and evaluated on four other datasets.
Hao Zheng, Hongji Xu, Fei Gao et al.· IEEE journal of biomedical a...· 0 citations
Accidental falls among the elderly demand highly reliable detection systems; however, existing solutions based on wearables, cameras, or WiFi often suffer from environmental interference, privacy concerns, and poor performance in detecting slow-onset falls (e.g., fainting). Despite these challenges, millimeter-wave (mmWave) radar-based methods have emerged as an attractive alternative. In this article, we propose an effective and efficient fall detection method, mmFallBbox, which leverages mmWave radar to track 3-D human bounding box dynamics. By exploiting the correlation between fall states and bounding box evolution, our approach effectively distinguishes between normal activities and slow fall states, such as fainting or medical conditions, which traditional systems struggle to detect. To evaluate the performance of mmFallBbox, we collected a large-scale fall detection dataset consisting of 60 h of radar data synchronized with video annotations. This dataset will be made publicly available to the research community for further development. We achieved an $F1$ score of 0.977 on our dataset and achieved state-of-the-art performance with limited computational complexity. Moreover, extensive experimental results show significant improvements in detecting slow-onset falls and providing explainable outputs, offering a promising solution for real-world fall detection applications.
Wenxuan Li, Dongheng Zhang, Jianwen Tong et al.· IEEE Transactions on Radar S...· 0 citations
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