Intelligent Systems for Human Action Recognition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1196

Special Issue Editors


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Guest Editor
Faculty of Science and Technology, Keio University, Yokohama, Japan
Interests: information technology; computer science; data mining; image processing; deep learning; healthcare; action recognition

E-Mail Website
Guest Editor
Faculty of Science and Technology, Keio University, Yokohama, Japan
Interests: information technology; computer science; semantic communications; wireless caching networks

E-Mail Website
Guest Editor
Faculty of Science and Technology, Keio University, Yokohama, Japan
Interests: machine learning

Special Issue Information

Dear Colleagues,

Human action recognition (HAR) has become an essential area of research with significant implications for various fields, including surveillance, healthcare, sports analytics, and human–computer interaction. This Special Issue aims to collate the latest research and developments in HAR systems, highlighting innovative methodologies, applications, and interdisciplinary approaches.

The objective of this Special Issue is to provide a comprehensive platform for researchers, practitioners, and industry professionals to present their latest findings, share insights, and discuss the future directions of intelligent systems in the field of human action recognition. This Special Issue will cover theoretical contributions, practical implementations, and case studies, fostering a deeper understanding and advancing the state of the art in HAR.

We invite submissions focused on a wide range of topics related to intelligent systems for human action recognition, including, but not limited to, the following:

  • Sensor-Based HAR: Utilization of wearable sensors, IoT devices, and multimodal sensor fusion for accurate action recognition.
  • Computer Vision-Based HAR: Techniques for action recognition using video analysis, image processing, and pattern recognition.
  • Real-Time HAR Systems: Development and optimization of real-time HAR systems for various applications.
  • Human–Robot Interaction: HAR systems enabling intuitive and effective interactions between humans and robots.
  • Activity and Gesture Recognition: Techniques for recognizing specific activities and gestures in various contexts.
  • Sports and Performance Analysis: HAR for sports training, performance enhancement, and injury prevention.
  • Security and Surveillance: Implementation of HAR in public safety, anomaly detection, and security systems.
  • Multimodal HAR: Combining visual, audio, and other sensory inputs for enhanced action recognition accuracy.
  • Transfer Learning and Domain Adaptation: Applying HAR models across different domains and environments.
  • Sensor Technology for HAR: Advances in sensor technologies and their applications in HAR.
  • Healthcare Applications: HAR in monitoring and assisting elderly care, rehabilitation, and physical therapy.
  • Rehabilitation and Monitoring Applications: HAR systems for rehabilitation, patient monitoring, and healthcare assistance.
  • Behavior Analysis: Analyzing human behavior patterns for applications in psychology, social sciences, and public health.

Researchers and practitioners are invited to submit their original research articles, review papers, and case studies. All submissions will undergo a rigorous peer review process to ensure that the papers are of a high quality and relevant to the content.

Dr. Mondher Bouazizi
Dr. Yue Yin
Prof. Dr. Tomoaki Ohtsuki
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • wearable sensors
  • IoT devices
  • action recognition
  • computer vision
  • video analysis
  • image processing
  • pattern recognition
  • human–robot interaction
  • healthcare
  • biomedical sensing
  • biosignal processing
  • signal processing
  • activity detection
  • deep learning
  • AI

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Published Papers (1 paper)

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22 pages, 3735 KiB  
Article
Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning
by Chen Ye, Siyuan Xu, Zhengran He, Yue Yin, Tomoaki Ohtsuki and Guan Gui
Bioengineering 2024, 11(11), 1124; https://doi.org/10.3390/bioengineering11111124 - 7 Nov 2024
Viewed by 687
Abstract
In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the [...] Read more.
In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process. In this study, using channel state information (CSI) data, a novel cross-person activity recognition (CPAR) method is proposed by a deep learning approach with generalization capability. Combining one of the state-of-the-art deep neural networks (DNNs) used in activity recognition, i.e., attention-based bi-directional long short-term memory (ABLSTM), the snapshot ensemble is the first to be adopted to train several base-classifiers for enhancing the generalization and practicability of recognition. Second, to discriminate the extracted features, metric learning is further introduced by using the center loss, obtaining snapshot ensemble-used ABLSTM with center loss (SE-ABLSTM-C). In the experiments of CPAR, the proposed SE-ABLSTM-C method markedly improved the recognition accuracies to an application level, for seven categories of activities. Full article
(This article belongs to the Special Issue Intelligent Systems for Human Action Recognition)
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