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Radar and Multimodal Sensing for Ambient Assisted Living
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Radar and Multimodal Sensing for Ambient Assisted Living

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 398

Special Issue Editors

Dr. Haobo Li

E-Mail Website
Guest Editor
School of Science and Engineering, University of Dundee, Dundee DD1 4HN , UK
Interests: Radar; photonic sensing; radar sensing; multimodal sensing; information fusion; human activity recognition
Dr. Ilya Starshynov

E-Mail Website
Guest Editor
Advanced Research Centre, University of Glasgow, Glasgow G11 6EW, UK
Interests: quantum optics; disordered media; light scattering; machine learning; signal processing; multimodal sensing

Special Issue Information

Dear Colleagues,

The Special Issue on "Radar and Multimodal Sensing for Ambient Assisted Living" focuses on the latest advancements in and innovative applications of radar technology, radar information and multimodal sensing in monitoring human activities in real-world scenarios such as smart homes and ambient assisted living environments.

This collection of research articles highlights the integration of radar systems with advanced data processing techniques to enhance the accuracy, reliability, and usability of these systems, such as the use of micro-Doppler analysis to monitor the movements of humans and animals, providing detailed information on their activities and behaviors, as well as the use of radar data to facilitate advanced activity recognition, enabling precise identification of various actions and states. By combining data from various radar domains, distributed radar systems, and integrating radar with multimodal sensing and information fusion, deep learning-based techniques can be employed to improve classification and regression tasks across different sensing modalities, ensuring the reliable performances, robustness and accuracy of sensing systems.

Overall, this Special Issue aims to inspire the future development of radar technologies and applications for use in smart, ambient assisted living environments.

Dr. Haobo Li
Dr. Ilya Starshynov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar sensing
  • activity recognition
  • vital signs monitoring
  • radar data processing
  • ambient assisted living
  • multimodal sensing
  • information fusion
  • deep learning

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Published Papers (1 paper)

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Research

24 pages, 2169 KiB  
Article
Human Multi-Activities Classification Using mmWave Radar: Feature Fusion in Time-Domain and PCANet
by Yier Lin, Haobo Li and Daniele Faccio
Sensors 2024, 24(16), 5450; https://doi.org/10.3390/s24165450 - 22 Aug 2024
Viewed by 206
Abstract
This study introduces an innovative approach by incorporating statistical offset features, range profiles, time–frequency analyses, and azimuth–range–time characteristics to effectively identify various human daily activities. Our technique utilizes nine feature vectors consisting of six statistical offset features and three principal component analysis network [...] Read more.
This study introduces an innovative approach by incorporating statistical offset features, range profiles, time–frequency analyses, and azimuth–range–time characteristics to effectively identify various human daily activities. Our technique utilizes nine feature vectors consisting of six statistical offset features and three principal component analysis network (PCANet) fusion attributes. These statistical offset features are derived from combined elevation and azimuth data, considering their spatial angle relationships. The fusion attributes are generated through concurrent 1D networks using CNN-BiLSTM. The process begins with the temporal fusion of 3D range–azimuth–time data, followed by PCANet integration. Subsequently, a conventional classification model is employed to categorize a range of actions. Our methodology was tested with 21,000 samples across fourteen categories of human daily activities, demonstrating the effectiveness of our proposed solution. The experimental outcomes highlight the superior robustness of our method, particularly when using the Margenau–Hill Spectrogram for time–frequency analysis. When employing a random forest classifier, our approach outperformed other classifiers in terms of classification efficacy, achieving an average sensitivity, precision, F1, specificity, and accuracy of 98.25%, 98.25%, 98.25%, 99.87%, and 99.75%, respectively. Full article
(This article belongs to the Special Issue Radar and Multimodal Sensing for Ambient Assisted Living)
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