Elderly Care - Human Activity Recognition Using Radar with an Open Dataset and Hybrid Maps

Conference paper (2022)

Authors

Xinyu Zhang University of Electronic Science and Technology of China, University of Glasgow
Qammer Abbasi University of Glasgow
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Olivier Romain Observatoire de Paris
Julien Le Kernec University of Glasgow, University of Electronic Science and Technology of China
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2022 Xinyu Zhang, Qammer H. Abbasi, F. Fioranelli, Olivier Romain, Julien Le Kernec
DOI: https://doi.org/10.1007/978-3-030-95593-9_4
Radar Convolutional neural network Transfer learning Micro-Doppler Human activity recognition Hybrid maps
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

Population ageing has become a severe problem worldwide. Human activity recognition (HAR) can play an important role to provide the elders with in-time healthcare. With the advantages of environmental insensitivity, contactless sensing and privacy protection, radar has been widely used for human activity detection. The micro-Doppler signatures (spectrograms) contain much information about human motion and are often applied in HAR. However, spectrograms only interpret magnitude information, resulting in suboptimal performances. We propose a radar-based HAR system using deep learning techniques. The data applied came from the open dataset “Radar signatures of human activities” created by the University of Glasgow. A new type of hybrid map was proposed, which concatenated the spectrograms amplitude and phase. After cropping the hybrid maps to focus on useful information, a convolutional neural network (CNN) based on LeNet-5 was designed for feature extraction and classification. In addition, the idea of transfer learning was applied for radar-based HAR to evaluate the classification performance of a pre-trained network. For this, GoogLeNet was taken and trained on the newly-produced hybrid maps. These initial results showed that the LeNet-5 CNN using only the spectrograms obtained an accuracy of 80.5%, while using the hybrid maps reached an accuracy of 84.3%, increasing by 3.8%. The classification result of transfer learning using GoogLeNet was 86.0%.