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Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity

Tayyaba Parveen¹, Rehan Khan¹, Umer Saeed²

¹Department of Electrical Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.

Sensors (Basel, Switzerland)

|June 26, 2026

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This summary is machine-generated.

This study benchmarks deep learning models for WiFi sensing, finding CNNs efficient for structured activities and GRUs for dynamic ones. Results highlight challenges in distinguishing low-motion states and offer a reproducible framework for human activity recognition research.

Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

WiFi Channel State Information (CSI) is a promising technology for contactless human activity recognition.
Inconsistent datasets and methodologies hinder reproducible comparisons of deep learning models in CSI sensing.

Purpose of the Study:

To establish a unified baseline evaluation of four deep learning architectures (MLP, CNN, GRU, CNN-GRU) for human activity recognition using CSI.
To provide a standardized preprocessing and training pipeline for reproducible CSI-based sensing research.

Main Methods:

Harmonized multiple public CSI datasets and implemented a standardized pipeline for preprocessing and training.
Evaluated MLP, CNN, GRU, and CNN-GRU models on diverse sensing tasks: single-person activity recognition, fall-risk estimation, multi-person occupancy classification, and localization-aware activity recognition.

Keywords:

WiFi sensing channel state information contactless sensing convolutional neural networks deep learning gated recurrent unit human activity recognition indoor localization multilayer perceptron radio-frequency sensing

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Main Results:

CNNs offer a good accuracy-complexity trade-off in structured activities; GRUs excel in temporal dynamics but incur higher costs.
MLPs generally underperform due to limitations in capturing spatial-temporal dependencies.
Dynamic and low-motion states remain difficult to differentiate, emphasizing the need for robust temporal modeling.

Conclusions:

The study provides a reproducible framework and benchmark results for CSI-based human activity recognition.
Future research directions include cross-dataset generalization, advanced hybrid model designs, and lightweight deployment strategies for WiFi sensing.