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Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Jan2022, Vol. 60 Issue 1, p1-12, 12p
- Publication Year :
- 2022
-
Abstract
- Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels. Insufficient labeled data often limit the generalization of DL models. As a result, the performance of DL models will drop when being applied to a new scenario. In this sense, only labeling a small portion of data in the large-scale radar dataset is more feasible. In this article, we propose a semisupervised transfer learning (TL) algorithm, “joint domain and semantic transfer learning (JDS-TL),” for radar-based HAR, which is composed of two modules: unsupervised domain adaptation (DA) and supervised semantic transfer. By employing a sparsely labeled dataset to train the HAR model, the proposed method alleviates the need of labeling a significantly large number of radar signals. We adopt a public radar micro-Doppler spectrogram dataset including six human activities to evaluate JDS-TL. Experiments show that the proposed JDS-TL is able to recognize the six activities with an average accuracy of 87.6% when there are only 10% instances labeled in the training dataset. Ablation analysis also demonstrates the efficiency of the DA and the semantic transfer modules. [ABSTRACT FROM AUTHOR]
- Subjects :
- HUMAN activity recognition
DOPPLER radar
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 60
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 154824347
- Full Text :
- https://doi.org/10.1109/TGRS.2021.3090106