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.
@article{arxiv.2506.11165,
title = {Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data},
author = {Almustapha A. Wakili and Babajide J. Asaju and Woosub Jung},
journal= {arXiv preprint arXiv:2506.11165},
year = {2025}
}
Comments
This Paper has been Accepted and will appear in the 23rd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2025)