ArticleSensors (Basel, Switzerland)2025
Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A large-scale Wi-Fi channel state information dataset for contactless human speech recognition.Data in brief · 2026Article
- Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments.Sensors (Basel, Switzerland) · 2026Article
- An Efficient Wi-Fi Sensing Method for Robotic Arm Motion Recognition.Sensors (Basel, Switzerland) · 2026Article
- Sensor-Driven Deep Learning for Smart Home Intelligence: Signal Analysis, Multimodal Perception, and System-Level Applications.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this study, we investigate human activity recognition (HAR) using WiFi channel state information (CSI) signals, employing a single-layer gated recurrent unit (GRU) with an attention module. To overcome the limitations of existing state-of-the-art (SOTA) models, which, despite their good performance, have substantial model sizes, we propose a lightweight model that incorporates data augmentation and pruning techniques. Our primary goal is to maintain high performance while significantly reducing model complexity. The proposed method demonstrates promising results across four different datasets, in particular achieving an accuracy of about 98.92%, outperforming an SOTA model on the ARIL dataset while reducing the model size from 252.10 M to 0.0578 M parameters. Additionally, our method achieves a reduction in computational cost from 18.06 GFLOPs to 0.01 GFLOPs for the same dataset, making it highly suitable for practical HAR applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.