WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features

• URL: http://arxiv.org/abs/2608.00642v1
• Date: Sat, 01 Aug 2026 12:54:09 GMT
• Title: WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features
• Abstract summary: WiFuse is a dual-stream Channel State Information framework for human activity recognition.<n>It pairs amplitude and Delay-Doppler representations within a dual-stream strategy, enhanced by transfer learning.<n>It 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.
• Score: 1.0554048699217669
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention [55.570606479386434]
  WiFi-based human pose estimation (HPE) enables the detection and interpretation of human body positions and movements without the need for wearable devices.<n>This paper introduces a novel framework, WiLHPE, for lightweight and efficient human pose estimation using WiFi CSI signals.
  arXiv  Detail & Related papers  (2026-07-03T11:04:05Z)
• Variational Dual-path Attention Network for CSI-Based Gesture Recognition [0.0]
  Wi-Fi gesture recognition based on Channel State Information (CSI) is challenged by high-dimensional noise and resource constraints on edge devices.<n>This paper proposes a lightweight feature preprocessing module--the Variational Dual-path Attention Network (VDAN)<n>It performs structured feature refinement through frequency-domain filtering and temporal detection.
  arXiv  Detail & Related papers  (2026-01-20T09:02:02Z)
• WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism [61.79272554643873]
  We propose a gesture recognition network that integrates a multi-semantic attention mechanism with a self-attention-based channel mechanism.<n>The results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%.
  arXiv  Detail & Related papers  (2025-12-04T07:09:13Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition [8.028748052177146]
  We propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)<n>Specifically, we propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR)
  arXiv  Detail & Related papers  (2025-06-13T09:38:57Z)
• Multiresolution Analysis and Statistical Thresholding on Dynamic Networks [49.09073800467438]
  ANIE (Adaptive Network Intensity Estimation) is a multi-resolution framework designed to automatically identify the time scales at which network structure evolves.<n>We show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise.
  arXiv  Detail & Related papers  (2025-06-01T22:55:55Z)
• STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection [5.4610555622532475]
  We present STeInFormer, a spatial-temporal interaction Transformer architecture for multi-temporal feature extraction.<n>We also propose a parameter-free multi-frequency token mixer to integrate frequency-domain features that provide spectral information for RSCD.
  arXiv  Detail & Related papers  (2024-12-23T03:40:04Z)
• Multi-channel Time Series Decomposition Network For Generalizable Sensor-Based Activity Recognition [2.024925013349319]
  This paper proposes a new method, Multi-channel Time Series Decomposition Network (MTSDNet) It decomposes the original signal into a combination of multiple components and trigonometric functions by the trainable parameterized temporal decomposition. It shows the advantages in predicting accuracy and stability of our method compared with other competing strategies.
  arXiv  Detail & Related papers  (2024-03-28T12:54:06Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• Harvesting Ambient RF for Presence Detection Through Deep Learning [12.535149305258171]
  This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection.
  arXiv  Detail & Related papers  (2020-02-13T20:35:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.