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Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Li, Xinyu ; He, Yuan ; Fioranelli, Francesco ; Jing, Xiaojun

IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-12 [Periódico revisado por pares]

New York: IEEE

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Título:
Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures
Autor: Li, Xinyu ; He, Yuan ; Fioranelli, Francesco ; Jing, Xiaojun
Assuntos: Ablation ; Activity recognition ; Algorithms ; Data models ; Datasets ; Deep learning ; Domains ; Doppler sonar ; Feature extraction ; Health care ; Human activity recognition ; Human activity recognition (HAR) ; Labelling ; Labels ; Machine learning ; Modules ; Moving object recognition ; Radar ; radar micro-Doppler (MD) effect ; Radar signatures ; Semantics ; semisupervised learning ; Spectrogram ; Training ; Transfer learning ; transfer learning (TL)
É parte de: IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-12
Descrição: 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.
Editor: New York: IEEE
Idioma: Inglês

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Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Li, Xinyu ; He, Yuan ; Fioranelli, Francesco ; Jing, Xiaojun

New York: IEEE

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