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Special Issue "Multi-Sensor for Human Activity Recognition"

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

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Georgios Meditskos
E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki 57001, Greece
Interests: knowledge representation; semantic web; context-based multisensor seasoning and fusion; semantic dialogue management; knowledge-driven decision making
Dr. Stefanos Vrochidis
E-Mail Website
Guest Editor
Centre for Research and Technology Hellas (CERTH), Information Technologies Institure (ITI), Thessaloniki, Greece
Interests: multimedia analysis; artificial intelligence; web and social media mining; semantics; information retrieval; multimodal analytics; decision support; security; health and environmental applications
Dr. Ioannis Yiannis Kompatsiaris
E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: semantic multimedia analysis; indexing and retrieval; social media and big data analysis; knowledge structures; reasoning and personalization for multimedia applications; e-health and environmental applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) is a research topic that has recently made significant progress, attracting growing attention in a number of disciplines and application domains. However, whether it is video-based or sensor-based, data-driven or knowledge-driven, HAR still faces many issues and challenges that motivate the development of new activity recognition techniques to improve accuracy under more realistic conditions. Key challenges in the domain include, among others: difficulty in feature extraction, data annotation scarcity, data heterogeneity, recognition of concurrent, overlapped and multioccupant activities, increased computational cost, temporal imperfections, noise, context-based and high-level interpretability, and non-invasive activity sensing and privacy.

This Special Issue focuses on the current state-of-the-art of HAR approaches, with a special emphasis on multisensor environments, where information is typically collected from multiple sources and complementary modalities, such as from multimedia streams (e.g., using video analysis and speech recognition), lifestyle, and environmental sensors. The main objective is to stimulate original, unpublished research addressing the challenges above through the concurrent use of multiple sensors and innovative fusion schemes, frameworks, algorithms, and platforms. Surveys are very welcomed, too.

Authors are invited to submit original contributions or survey papers for publication in the open access Sensors journal. Topics of interest include (but are not limited to) the following:

  • Modeling and analysis of multisensors for human activity recognition;
  • Knowledge-driven multisensor fusion frameworks for human activity recognition;
  • Data-driven and machine-learning-driven multisensor fusion frameworks for human activity recognition;
  • Distributed sensor networks and IoT for human activity recognition;
  • Interoperability frameworks and semantic situational awareness for high-level human activity recognition and decision making;
  • Multisensor human activity recognition under uncertainty, noise, and incomplete data;
  • Multisensor human activity recognition in healthcare;
  • Multisensor human activity recognition in security and surveillance applications;
  • Multisensor human activity recognition in ambient assisted living; 
  • Multisensor human activity recognition to assist human–computer interaction;
  • Multisensor human activity recognition in augmented/virtual reality applications;
  • Security and privacy issues in multisensor human activity recognition.

Dr. Georgios Meditskos
Dr. Stefanos Vrochidis
Dr. Ioannis Kompatsiaris
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 papers will be 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 2200 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

  • Multi-sensor human activity recognition
  • Multi-sensor fusion and interpretation
  • IoT networks and interoperability
  • Data- and knowledge-driven multi-sensor human activity recognition
  • Security and privacy in multi-sensor monitoring
  • Surveys on multi-sensor human activity recognition

Published Papers (1 paper)

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Research

Article
Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction
Sensors 2021, 21(11), 3906; https://doi.org/10.3390/s21113906 - 05 Jun 2021
Viewed by 678
Abstract
Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in [...] Read more.
Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system. Full article
(This article belongs to the Special Issue Multi-Sensor for Human Activity Recognition)
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