.png){kind=icon}
.png){kind=icon}
Return
Radar-Based Human Activity Recognition Using Time-Weighted Network Based on Strip Pooling
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2024.3492721.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Due to the flourishing of Internet of Things (IoT) technology, radar-based human activity recognition (HAR) technology has made significant progress and has become an indispensable research area. Many radar systems use multiple feature maps and handle them directly in image format. However, the generation of multiple forms of feature maps requires heavy computational resources, which makes it impractical for real-world applications. Additionally, many networks fail to fully extract temporal information from the time-Doppler (TD) map during the feature extraction. Therefore, a time-weighted network based on strip pooling (TWN-SP) using the TD map is proposed in this article. The TWN-SP consists of two time-weighted modules based on fire (TWMs-F) and a feature fusion module based on temporal and channel attention (FFM-TCA). Due to the application of depthwise separable convolution (DSC) and placing the feature fusion step at the forefront, the proposed network has fewer parameters. Moreover, a radar dataset named RadSet is constructed, containing TD maps of six daily activities. To validate the performance of the TWN-SP, a ten-fold cross-validation (CV) on the public dataset named Radar848 and a leave-one-subject-out (LOSO) CV on the RadSet dataset are carried out, respectively. To further validate the generalization performance of TWN-SP, a comparative analysis was conducted on another public dataset, Ci4R. The TWN-SP achieves an accuracy of 99.28% on the RadSet dataset. Experimental results indicate that the TWN-SP surpasses the current leading networks in both performance and complexity.
Keywords:
Feature extraction
Human activity recognition
Radar
Data mining
Internet of Things
Classification algorithms
Radar antennas
Doppler effect
Convolutional neural networks
Convolution
Deep learning (DL)
frequency-modulated continuous wave (FMCW) radar
human activity recognition (HAR)
micro-Doppler (mD) signatures
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W