Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Li, Xinyu (Beijing University of Posts and Telecommunications)
He, Yuan (Beijing University of Posts and Telecommunications)
Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems)
Jing, Xiaojun (Beijing University of Posts and Telecommunications)

2022

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.

Human activity recognition (HAR)
Radar
radar micro-Doppler (MD) effect
semisupervised learning
transfer learning (TL)

http://resolver.tudelft.nl/uuid:b6659b54-68d8-42e3-938a-cfbe7ea763ea

https://doi.org/10.1109/TGRS.2021.3090106

2022-03-30

0196-2892

IEEE Transactions on Geoscience and Remote Sensing, 60

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

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© 2022 Xinyu Li, Yuan He, F. Fioranelli, Xiaojun Jing