Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 8 references
Abstract
Wi-Fi Channel State Information (CSI) is a robust, privacy-preserving modality for Human Activity Recognition (HAR). Since raw CSI suffers from hardware desynchronizations and noise, preprocessing is vital. This study conducts an empirical ablation of CSI preprocessing using a fixed Two-Stream 2D CNN(Convolutional Neural Network) to quantify its impact on classification accuracy and latency. Results reveal that preprocessing, apart from architectural complexity, is the primary driver of the accuracy-latency trade-off. Computationally heavy methods like Hampel filtering introduce massive latency (>162 ms) without accuracy gains. In contrast, lightweight frequency-domain filtering consistently yields superior results. Specifically, dual-stream Butterworth bandpass filtering achieves 96.43% accuracy with only 49.08 ms latency. These findings demonstrate that isolating motion-relevant frequencies enables efficient, high-performance HAR suitable for real-time edge deployment.
Recently, Wi-Fi sensing has played a significant role in Human Activity Recognition (HAR), as it enables the detection of various activities using only Wi-Fi signals, ensuring privacy and remaining non-intrusive for the user. However, environmental characteristics such as reflective surfaces, hardware offsets, and other physical impairments affect recognition by the neural network, subsequently causing errors and significantly reducing model accuracy. To overcome this problem we present the WiFuse framework, a dual-stream Channel State Information (CSI) framework for human activity recognition (HAR) that pairs denoised time-domain amplitude variations with 2D-FFT-derived Delay-Doppler motion representations computed from the sanitized channel phase. The fused representation feeds a hybrid ResNet-Temporal Convolutional Network (TCN) neural architecture augmented with channel and spatio-temporal attention, where the ResNet extracts spatial-spectral features and the TCN models long-range temporal dependencies; a decoupled two-stage transfer learning strategy is employed to improve optimization stability and feature reuse. We conduct extensive experiments on two public datasets, including comparisons against state-of-the-art methods and alternative hybrid architectures, ablation studies, and cross-dataset and domain-adaptation evaluations. The proposed framework reaches an overall accuracy of up to 95.28% across the four environments of the XRF55 dataset and up to 98.20% on the multi-user Wi-MIR dataset. Overall, the results indicate that combining amplitude and Delay-Doppler representations within a dual-stream strategy, enhanced by transfer learning, improves recognition performance under conditions that typically degrade deep neural networks, such as class overlap, multipath propagation, noise, and interference.
Alison M. Fernandes, H. I. D. Monego, Bruno S. Chang et al.· 0 citations
Human Activity Recognition (HAR) based on Channel State Information (CSI) has attracted considerable attention as a privacy-preserving sensing paradigm. However, CSI-based HAR faces several challenges, including environmental noise, long-range temporal dependencies, and the anisotropic structure of CSI tensors. To address these challenges, this paper presents Wi-CSNet, a lightweight CSI-oriented framework that integrates Discrete Wavelet Transform (DWT) preprocessing, asymmetric-stride convolutions, and a Cross-Scanning State Space Duality (CS-SSD) block derived from Mamba2. DWT preprocessing compresses temporal signals while preserving motion-related trends and reducing input dimensionality. Asymmetric-stride convolutions balance feature scales across heterogeneous CSI dimensions, while the lightweight CS-SSD module captures global dependencies with only a 0.63% parameter overhead. Extensive experiments demonstrate that Wi-CSNet achieves accuracies of 97.81% on HHI, 99.92% on UT-HAR, and 100% on NTU-HAR. These results confirm the effectiveness and robustness of Wi-CSNet for fine-grained CSI-based activity recognition in complex environments.
Zhongjian Gao, Ruige Zhang, Yuwei Cai et al.· Future Internet· 0 citations
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 channel state information (CSI) can characterize wireless-channel variations induced by human activities without directly capturing identifiable visual content, providing a contactless technical approach to indoor human activity recognition (HAR). To address the difficulty of a single convolutional or recurrent network in simultaneously modeling local fluctuations, long-range temporal dependencies, and key action segments, this paper proposes CGAC, a model that integrates convolutional bidirectional gated recurrent units with temporal attention. The model first uses one-dimensional convolution and max pooling to extract and compress local temporal CSI features, then employs a BiGRU to model bidirectional contextual dependencies, and finally applies single-vector temporal attention to adaptively weight key time steps. Multi-dataset evaluations are conducted on three public datasets: UT-HAR, NTU-Fi HAR, and NTU-Fi Human-ID. CGAC achieves an accuracy of 99.70% on UT-HAR and accuracies of 97.50% and 97.81% on NTU-Fi HAR and NTU-Fi Human-ID, respectively. The results show that CGAC delivers the best performance on UT-HAR and remains competitive across different acquisition tools and CSI classification tasks.
Lili Cai· International journal of pat...· 0 citations
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.
Zengqian Song, Jingming Li· Journal of Advances in Engin...· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
