Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network.
Sensors (Basel)
; 23(11)2023 May 26.
Article
em En
| MEDLINE
| ID: mdl-37299830
ABSTRACT
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%.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Radar
/
Atividades Humanas
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China