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%.
Original language | English |
---|---|
Title of host publication | Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings |
Editors | Masood Ur Rehman, Ahmed Zoha |
Publisher | Springer |
Pages | 39-51 |
Number of pages | 13 |
ISBN (Print) | 9783030955922 |
DOIs | |
Publication status | Published - 2022 |
Event | 16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online Duration: 25 Dec 2021 → 26 Dec 2021 |
Publication series
Name | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST |
---|---|
Volume | 420 LNICST |
ISSN (Print) | 1867-8211 |
ISSN (Electronic) | 1867-822X |
Conference
Conference | 16th EAI International Conference on Body Area Networks, BODYNETS 2021 |
---|---|
City | Virtual, Online |
Period | 25/12/21 → 26/12/21 |
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-careOtherwise 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.
Keywords
- Convolutional neural network
- Human activity recognition
- Hybrid maps
- Micro-Doppler
- Radar
- Transfer learning