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Advancing Human–Machine Interaction Through Intelligent Sensors and Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1078

Special Issue Editors


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Guest Editor
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile
Interests: information and communication technology; information technology; information technologies; digital signal processing; human–computer interaction

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Guest Editor
1. Research on Economics, Management and Information Technologies, REMIT, Portucalense University, 4200-072 Porto, Portugal
2. Institute of Eletronics and Informatics Engineering of Aveiro, IEETA, Aveiro University, Aveiro, Portugal
Interests: digital transformation; technology in higher education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world continues to evolve, with the integration of intelligent sensors and advanced technologies becoming a crucial aspect of enhancing human–machine interaction. Artificial intelligence and machine learning have made significant strides in recent years, enabling machines to perform tasks with increasing efficiency and autonomy.

Advancing human–machine interaction (HMIs) through intelligent sensors and technology is at the forefront of innovation, transforming the way we interact with devices and systems. This field brings together advancements in sensor technology, machine learning, artificial intelligence, and user experience design to create interfaces that are not only more intuitive and responsive but also adaptive to individual needs and environments.

This Special Issue will publish high-quality, original research papers in the overlapping fields of

  • Multimodal sensing: Combining multiple types of sensors (e.g., visual, auditory, tactile) and enabling devices to gather richer data about human intentions and environmental conditions.
  • Wearable technology: Sensors that monitor physiological signals, such as heart rate, galvanic skin response, and even brain activity (EEG), enabling systems to gauge emotional states, stress levels or cognitive load.
  • Adaptative user interfaces: Intelligent systems can dynamically adjust user interfaces based on sensor data, or sensors that continuously monitor user preferences and behavior patterns, enabling machines to learn and anticipate needs, offering personalized content or interfaces adjustments.
  • Safety monitoring: Wearable sensors can track worker movements, posture, and environment exposure to hazardous conditions. Real-time monitoring can alert users or supervisors to potential risks, reducing workplace accidents.
  • Health monitoring: From monitoring elderly patients to detecting early signs of health issues, wearable sensors can offer continuous health tracking and send alerts to healthcare providers.
  • Assistive technologies: Sensors in assistive devices can enable individuals with disabilities to interact with machines through customized inputs, including eye tracking, voice commands, or touchless gestures.
  • Adaptive input mechanisms: Intelligent sensors can modify control schemes based on user ability, making systems more inclusive.
  • Biometric authentication: Intelligent sensors can enhance security by using biometric data for authentication.

Prof. Dr. Sandra Cano
Prof. Dr. Fernando Moreira
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. Applied Sciences 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 2400 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

  • intelligent sensors
  • voice recognition
  • gesture recognition
  • human–machine interaction
  • natural language processing
  • emotion recognition
  • sentiment analysis
  • wearable technology
  • haptic feedback
  • artificial intelligence
  • user experience design (UX)
  • physiological monitoring
  • biometric authentication
  • personalized interaction
  • adaptive user interfaces
  • virtual reality (VR)
  • augmented reality (AR)

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Published Papers (1 paper)

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Research

18 pages, 1844 KiB  
Article
Coordination of Speaking Opportunities in Virtual Reality: Analyzing Interaction Dynamics and Context-Aware Strategies
by Jiadong Chen, Chenghao Gu, Jiayi Zhang, Zhankun Liu, Boxuan Ma and Shin‘ichi Konomi
Appl. Sci. 2024, 14(24), 12071; https://doi.org/10.3390/app142412071 - 23 Dec 2024
Viewed by 835
Abstract
This study explores the factors influencing turn-taking coordination in virtual reality (VR) environments, with a focus on identifying key interaction dynamics that affect the ease of gaining speaking opportunities. By analyzing VR interaction data through logistic regression and clustering, we identify significant variables [...] Read more.
This study explores the factors influencing turn-taking coordination in virtual reality (VR) environments, with a focus on identifying key interaction dynamics that affect the ease of gaining speaking opportunities. By analyzing VR interaction data through logistic regression and clustering, we identify significant variables impacting turn-taking success and categorize typical interaction states that present unique coordination challenges. The findings reveal that features related to interaction proactivity, individual status, and communication quality significantly impact turn-taking outcomes. Furthermore, clustering analysis identifies five primary interaction contexts: high competition, intense interaction, prolonged single turn, high-status role, and low activity, each with unique turn-taking coordination requirements. This work provides insights into enhancing turn-taking support systems in VR, emphasizing contextually adaptive feedback to reduce speaking overlap and turn-taking failures, thereby improving overall interaction flow in immersive environments. Full article
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