Activity Recognition Based on Micro-Doppler Signature with In-Home Wi-Fi

TL;DR

This paper introduces a new Wi-Fi signal-based activity recognition framework using micro-Doppler signatures, demonstrating its effectiveness in classifying daily activities for healthcare monitoring.

Contribution

The paper presents a novel in-home Wi-Fi activity recognition framework utilizing passive micro-Doppler signatures and compares its performance with traditional SVM classifiers.

Findings

Six activity signatures successfully detected and classified

Discriminative features include maximum Doppler frequency and activity duration

Sparsity induced classifier shows promising results in healthcare scenarios

Abstract

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.