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Enhancing Multi-User Activity Recognition in an Indoor Environment with Augmented Wi-Fi Channel State Information and Transformer Architectures

  • 0Department of Computer Science, Nottingham Trent University, 50 Shakespeare St., Nottingham NG1 4FQ, UK.
Sensors (basel, Switzerland) +

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July 12, 2025

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July 12, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for device-free Human Activity Recognition (HAR) using Wi-Fi signals. The hybrid approach enhances accuracy in multi-user settings, even with limited data.

Area Of Science

  • Computer Science
  • Signal Processing
  • Artificial Intelligence

Background

  • Human Activity Recognition (HAR) is vital for monitoring behavior using sensor data, but traditional methods face limitations.
  • Wearable and vision-based HAR systems raise privacy concerns and struggle in dynamic environments.
  • Device-free HAR using Wi-Fi Channel State Information (CSI) offers a privacy-preserving alternative, especially for multi-user scenarios.

Purpose Of The Study

  • To address data scarcity and generalisability challenges in multi-user device-free HAR.
  • To propose a hybrid deep learning model integrating CNN and Transformer architectures.
  • To improve HAR performance in complex environments with limited labeled data.

Main Methods

  • Implemented a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Transformer models.
  • Utilized a random transformation technique for targeted data augmentation of real CSI data.
  • Employed hybrid feature extraction including statistical, spectral, and entropy-based measures.

Main Results

  • The proposed model demonstrated superior performance compared to baseline methods in both single-user and multi-user contexts.
  • Combining real and augmented CSI data significantly enhanced model generalisation.
  • Effectively addressed challenges of data scarcity and class imbalance in multi-user HAR.

Conclusions

  • The hybrid deep learning model offers a robust solution for device-free Human Activity Recognition.
  • Data augmentation and hybrid feature extraction are crucial for improving HAR in data-scarce, multi-user environments.
  • This approach advances privacy-preserving human behavior monitoring in smart environments.

Keywords: