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Human-Centric Sensing Technology and Systems: 2nd Edition

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 999

Special Issue Editors


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Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao, China
Interests: smart sensing; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: mobile computing and sensing; cyber security and privacy; Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
Interests: mobile and pervasive computing and embedded systems; cyber security and privacy; wireless networks and sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human-centric sensing plays a crucial role in the domains of smart home and office environments, including safety protection, well-being monitoring/management, healthcare, and smart appliance interaction. Various physical or physiological sensors are interconnected and designed to sense a broad spectrum of contexts for human beings, laying the foundation of pervasive computing. However, sensor technologies have several limitations relating to their deployment cost, usability, and adherence, which render them unacceptable for practical application.

Consequently, the pursuit of convenience in human perception necessitates a wireless, sensorless, and contactless sensing paradigm. Recent novel technologies have shown the potential of reusing wireless signals (such as WiFi, mmWave, RFID, LoRa, acoustic, light, and radiofrequency) or environmental infrastructures originally designed for lighting or data transmission to sense human activities. Such studies thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, and gesture recognition to the monitoring of vital signs and user identification, involving even finer-grained motion sensing. Relevant human-centric sensing technologies and solutions are still in their preliminary stages.

The Guest Editors encourage the submission of papers addressing physical models, technologies, and applications of human-centric sensing. Original research contributions, tutorials, case studies, and review papers are also welcome. Manuscripts should provide content that is accessible to general audiences working in the field of sensing systems.

Topics of interest for this Special Issue include (but are not limited to) the following:

  • Human-centric wireless signal analytic model;
  • Intrusion detection;
  • Identity recognition;
  • Activity recognition;
  • Gesture detection and recognition;
  • Vital signs monitoring;
  • Localization and tracking;
  • Learning algorithms and models for human behavior perception;
  • Applications and deployment experiences.

Prof. Dr. Feng Hong
Dr. Jiadi Yu
Dr. Yan Wang
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.

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Published Papers (1 paper)

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Research

25 pages, 17437 KiB  
Article
ACD-Net: An Abnormal Crew Detection Network for Complex Ship Scenarios
by Zhengbao Li, Heng Zhang, Ding Gao, Zewei Wu, Zheng Zhang and Libin Du
Sensors 2024, 24(22), 7288; https://doi.org/10.3390/s24227288 - 14 Nov 2024
Viewed by 339
Abstract
Abnormal behavior of crew members is an important cause of frequent ship safety accidents. The existing abnormal crew recognition algorithms are affected by complex ship environments and have low performance in real and open shipborne environments. This paper proposes an abnormal crew detection [...] Read more.
Abnormal behavior of crew members is an important cause of frequent ship safety accidents. The existing abnormal crew recognition algorithms are affected by complex ship environments and have low performance in real and open shipborne environments. This paper proposes an abnormal crew detection network for complex ship scenarios (ACD-Net), which uses a two-stage algorithm to detect and identify abnormal crew members in real-time. An improved YOLOv5s model based on a transformer and CBAM mechanism (YOLO-TRCA) is proposed with a C3-TransformerBlock module to enhance the feature extraction ability of crew members in complex scenes. The CBAM attention mechanism is introduced to reduce the interference of background features and improve the accuracy of real-time detection of crew abnormal behavior. The crew identification algorithm (CFA) tracks and detects abnormal crew members’ faces in real-time in an open environment (CenterFace), continuously conducts face quality assessment (Filter), and selects high-quality facial images for identity recognition (ArcFace). The CFA effectively reduces system computational overhead and improves the success rate of identity recognition. Experimental results indicate that ACD-Net achieves 92.3% accuracy in detecting abnormal behavior and a 69.6% matching rate for identity recognition, with a processing time of under 39.5 ms per frame at a 1080P resolution. Full article
(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems: 2nd Edition)
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