Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
4.6
Journals
Bioengineering
Special Issues
Selected Papers from 1st International Workshop on Affective Computing and...
share announcement

Selected Papers from 1st International Workshop on Affective Computing and Health Care: New Research and Industrial Perspectives

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 1648

Special Issue Editors

Dr. Daniela Cardone

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: artificial intelligence methods; robotics and affective computing; human–machine interaction; processing methods and analysis of biomedical images and physiological signals; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arcangelo Merla

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared imaging; medical imaging; neuroimaging; psychophysiology; human–machine interaction
Special Issues, Collections and Topics in MDPI journals
Dr. David Perpetuini

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared thermography; functional infrared spectroscopy (fNIRS); electroencephalography (EEG); photoplethysmography (PPG); wearable sensors; affective computing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The “1st International Workshop on Affective Computing and Health Care: New Research and Industrial Perspectives”, a joint event of the IEEE International Conference on Bioinformatics and Biomedicine 2023 (BIBM) (https://www.affective-health2023.it/home) will be held in Istanbul, Turkey, on 5 December 2023. This workshop will be held online. It will enable scientists from all over the world to present their latest research in affective computing and health care, receive direct feedback, and engage in discussions with the wider scientific community. With the aid of this conference, we expect to review the state-of-the art in these subjects and to foresee and discuss the future of research in affective computing and health care.

We are honored to serve as Guest Editors of this Special Issue in Bioengineering that will contain a selection of papers both submitted and accepted at this workshop. We warmly invite researchers to submit their contributions, both original research articles and review papers, to this Special Issue. Topics include, but are not limited to, the following:

  • Artificial intelligence algorithms in affective computing;
  • Affective communication;
  • Computer vision systems for affective computing;
  • Speech recognition and natural language processing;
  • Facial expression recognition;
  • Body gesture analysis;
  • Sentiment analysis;
  • Affective odor perception and impact of odors on human wellbeing;
  • Physiological signal processing (heart rate variability, respiration, electro-dermal activity, etc.);
  • Wearable solutions for affective computing and health care;
  • Mobile solutions for affective computing;
  • Affective computing in telemedicine;
  • Human–robot interaction.

Dr. Daniela Cardone
Prof. Dr. Arcangelo Merla
Dr. David Perpetuini
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

  • affective computing
  • health care
  • human–machine interaction
  • affective communication
  • body gesture analysis
  • sentiment analysis
  • artificial intelligence algorithms in affective computing

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 1150 KiB  
Article
How the Effect of Virtual Reality on Cognitive Functioning Is Modulated by Gender Differences
by Stefania Righi, Gioele Gavazzi, Viola Benedetti, Giulia Raineri and Maria Pia Viggiano
Bioengineering 2024, 11(4), 408; https://doi.org/10.3390/bioengineering11040408 - 21 Apr 2024
Viewed by 359
Abstract
Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a [...] Read more.
Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a computer, we can concentrate, enter a state of flow, and still maintain control over our surrounding world. Differently, VR is a very immersive experience which could be a challenge for our ability to allocate divided attention to the environment to perform executive functioning tasks. This may also have a different impact on women and men since gender differences in both executive functioning and the immersivity experience have been referred to by the literature. The present study aims to investigate cognitive multitasking performance as a function of (1) virtual reality and computer administration and (2) gender differences. To explore this issue, subjects were asked to perform simultaneous tasks (span forward and backward, logical–arithmetic reasoning, and visuospatial reasoning) in virtual reality via a head-mounted display system (HDMS) and on a personal computer (PC). Our results showed in virtual reality an overall impairment of executive functioning but a better performance of women, compared to men, in visuospatial reasoning. These findings are consistent with previous studies showing a detrimental effect of virtual reality on cognitive functioning. Full article
(This article belongs to the Special Issue Selected Papers from 1st International Workshop on Affective Computing and Health Care: New Research and Industrial Perspectives)
►▼ Show Figures

Figure 1

20 pages, 3021 KiB  
Article
Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions
by Dong-Ki Jeong, Hyoung-Gook Kim and Jin-Young Kim
Bioengineering 2023, 10(9), 1040; https://doi.org/10.3390/bioengineering10091040 - 04 Sep 2023
Viewed by 1090
Abstract
To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning [...] Read more.
To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods. Full article
(This article belongs to the Special Issue Selected Papers from 1st International Workshop on Affective Computing and Health Care: New Research and Industrial Perspectives)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop