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

Xinyu Zhang, Qammer H. Abbasi, Francesco Fioranelli, Olivier Romain, Julien Le Kernec*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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%.

Original languageEnglish
Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
EditorsMasood Ur Rehman, Ahmed Zoha
Number of pages13
ISBN (Print)9783030955922
Publication statusPublished - 2022
Event16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online
Duration: 25 Dec 202126 Dec 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume420 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference16th EAI International Conference on Body Area Networks, BODYNETS 2021
CityVirtual, Online

Bibliographical note

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.


  • Convolutional neural network
  • Human activity recognition
  • Hybrid maps
  • Micro-Doppler
  • Radar
  • Transfer learning


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