Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data

Almustapha A. Wakili; Babajide J. Asaju; Woosub Jung

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arXiv · 2506.11165

Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data

Almustapha A. Wakili·Babajide J. Asaju·Woosub Jung

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Abstract

This paper compares the performance of BiLSTM and CNN+GRU deep learning models for Human Activity Recognition (HAR) on two WiFi-based Channel State Information (CSI) datasets: UT-HAR and NTU-Fi HAR. The findings indicate that the CNN+GRU model has a higher accuracy on the UT-HAR dataset (95.20%) thanks to its ability to extract spatial features. In contrast, the BiLSTM model performs better on the high-resolution NTU-Fi HAR dataset (92.05%) by extracting long-term temporal dependencies more effectively. The findings strongly emphasize the critical role of dataset characteristics and preprocessing techniques in model performance improvement. We also show the real-world applicability of such models in applications like healthcare and intelligent home systems, highlighting their potential for unobtrusive activity recognition.

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Almustapha A. Wakili, Babajide J. Asaju, Woosub Jung. 2025-06-11. Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data. https://arxiv.org/abs/2506.11165

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