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Updated: Jan 15, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

Tools and Methods for Achieving Wi-Fi Sensing in Embedded Devices.

Jesus A Armenta-Garcia¹, Felix F Gonzalez-Navarro¹, Jesus Caro-Gutierrez¹

¹Engineering Institute, Universidad Autonoma de Baja California, Calle de la Normal S/N Col. Insurgentes Este, Mexicali 21100, Mexico.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This study introduces an efficient embedded Wi-Fi sensing system for Human Activity Recognition (HAR). It enables accurate, privacy-preserving HAR on edge devices using microcontrollers, overcoming hardware and cloud limitations.

Keywords:

HAR Wi-Fi sensing data augmentation deep learning

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Last Updated: Jan 15, 2026

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Area of Science:

Computer Science
Electrical Engineering
Signal Processing

Background:

Wi-Fi sensing, using Channel State Information (CSI), is a key technology for Human Activity Recognition (HAR).
Existing methods often require specialized hardware and resource-intensive deep learning models, limiting edge deployment.
These limitations hinder the scalability and accessibility of Wi-Fi sensing for privacy-preserving HAR.

Purpose of the Study:

To develop a novel, low-cost, embedded solution for Wi-Fi sensing-based HAR.
To address the challenges of hardware dependency and cloud-based inference in current HAR systems.
To create a privacy-preserving HAR system deployable on resource-constrained edge devices.

Main Methods:

Developed a novel CSI collection tool for low-cost microcontrollers, optimizing packet rate efficiency.
Created an optimized DenseNet-based HAR model for deployment on edge devices.
Introduced an Empirical Mode Decomposition (EMD)-based data augmentation technique to address limited training data.
Presented a new HAR dataset for evaluating embedded Wi-Fi sensing solutions.

Main Results:

The EMD-based data augmentation significantly improved model accuracy from 59.91% to 97.55%.
A compact DenseNet variant achieved 92.43% accuracy with 232 ms inference latency on an ESP32-S3 microcontroller.
The proposed model requires minimal memory (127 kB), demonstrating efficient edge deployment.

Conclusions:

The proposed embedded solution offers a scalable, low-cost, and privacy-preserving approach to Wi-Fi sensing for HAR.
This system overcomes the limitations of existing methods by enabling on-device inference without cloud dependency.
The research demonstrates the feasibility of high-accuracy HAR on resource-constrained edge devices.