Journal Article

Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network

Zhao, Zongmin, Liang, Song, Du, Kangning, Cao, Lin, Wang, Dongfeng, Fu, Chong

2023

Overview

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / Analysis / Artificial intelligence / attention mechanism / Cameras / Classification / Deep learning / FMCW radar sensor / Frequency modulation / Gait / Human Activities / human activity recognition / Human mechanics / Humans / Machine learning / Methods / multi-classification focus loss / multi-domain feature fusion / Neural networks / Privacy / Radar / Recognition, Psychology / Sensors / Support vector machines / Time series