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Special Issue "Human Activity Detection and Recognition"

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

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Dr. Mario Martínez-Zarzuela
Website
Guest Editor
Universidad de Valladolid, Valladolid, Spain
Interests: computer vision; human activity detection and recognition; machine learning; deep learning; GPU computing; physical and cognitive rehabilitation applications; human body tracking; consumer depth cameras; IMUs for motion acquisition; virtual and augmented reality
Dr. David González Ortega
Website
Guest Editor
Universidad de Valladolid, Valladolid, Spain
Interests: computer vision; machine learning; driving simulation; human body detection and tracking; consumer depth cameras; neural networks; deep learning

Special Issue Information

Dear Colleagues,

Systems for human activity detection and recognition are becoming more and more sophisticated everyday. On the one hand, many different kinds of sensors for human action acquisition and tracking are available, ranging from conventional vision-based solutions (RGB, depth, and multi-view cameras) to wearable devices (smart watches and phones or more specific comercial IMU-based sensors for movement tracking) and including many other kinds of IoT devices. On the other hand, the number of proposed techniques for data processing and analysis is increasing everyday. In particular, newer approaches using deep learning and classical machine learning techniques are gaining significant interest. It is also worth noticing that the new situation derived from the Covid-19 pandemic has raised the necessity of physical telerehabilitation systems or tools to control social distance. This Special Issue is intended to present a collection of scientific papers that represent the current state of the art of methods and sensors for the detection, tracking, and recognition of all forms of human activity.

Dr. Mario Martínez-Zarzuela
Dr. David González Ortega
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

  • Multisensor human activity detection, tracking, and recognition
  • Deep and machine learning techniques for human activity analysis
  • Human activity detection and recognition (i.e., pose estimation, activity classification)
  • Human activity in health apps (i.e., physical and cognitive rehabilitation, remote motion tracking)
  • Human activity in safety apps (i.e., driver monitoring, ergonomics)
  • Human activity monitoring and surveillance (i.e., Covid-19, social distance control, risk alert, terrorism)
  • Human activity for computer interaction (i.e., natural-based interfaces, virtual and augmented reality)
  • Human activity recording, simulation and generation (i.e., database acquisition, synthethic data)

Published Papers (1 paper)

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Research

Open AccessArticle
Low-Asymmetry Interface for Multiuser VR Experiences with Both HMD and Non-HMD Users
Sensors 2021, 21(2), 397; https://doi.org/10.3390/s21020397 - 08 Jan 2021
Abstract
We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the [...] Read more.
We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment. Full article
(This article belongs to the Special Issue Human Activity Detection and Recognition)
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