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Applications of Sensors-Assisted Computational Intelligence in Human–Computer Interaction and Image...
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Applications of Sensors-Assisted Computational Intelligence in Human–Computer Interaction and Image Processing

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 7634

Special Issue Editors

Prof. Dr. Cui Tao

E-Mail Website
Guest Editor
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Interests: clinical and public health informatics; ontology-based HCI
Dr. Jun Kong

E-Mail Website
Guest Editor
Department of Computer Science, North Dakota State University, Fargo, ND 58105, USA
Interests: HCI

Special Issue Information

Dear Colleagues,

Nowadays, sensors become pervasive in a human’s daily life. The fast growth of sensors transits human–computer interaction (HCI) from traditional keyboard/mouse-based input to sensor-assisted multimodal input. The integration of ubiquitous sensing, artificial intelligence and HCI enables the development of smart systems that can adapt their behavior to fit the interaction context. Those smart systems find a wide variety of application domains, such as health care, smart homes, etc. With a variety of sensors, such as a camera, microphone, accelerometer, etc., artificial intelligence is essential to analyze sensing information from different sources, infer a user’s intention, and translate it to an interaction command. In summary, the aim of this Special Issue is to highlight technologies that sense and analyze context information and their applications to develop smart interaction.

Prof. Dr. Cui Tao
Dr. Jun Kong
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

  • developing novel AI methods on improving data and image processing involved in HCI
  • smart applications (smartphones, tablets and wearable devices in different fields
  • applying conversational agents in health-related fields
  • data transparency, security, and management of smart apps
  • applying semantic web and internet of things in HCI
  • gestures detection in virtual reality
  • image processing and its application in HCI
  • smart apps
  • AI
  • HCI
  • internet of things

Published Papers (1 paper)

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Review

34 pages, 1865 KiB  
Review
Mobile Phone Data: A Survey of Techniques, Features, and Applications
by Mohammed Okmi, Lip Yee Por, Tan Fong Ang and Chin Soon Ku
Sensors 2023, 23(2), 908; https://doi.org/10.3390/s23020908 - 12 Jan 2023
Cited by 7 | Viewed by 7231
Abstract
Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various [...] Read more.
Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people’s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people’s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected. Full article
(This article belongs to the Special Issue Applications of Sensors-Assisted Computational Intelligence in Human–Computer Interaction and Image Processing)
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