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Applied Sciences
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Future Human-Technology Interactions and Their Intelligent Applications
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Future Human-Technology Interactions and Their Intelligent Applications

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 18518

Special Issue Editor

Dr. Diego R. Faria

E-Mail Website
Guest Editor
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK
Interests: robotics; pattern recognition; brain-computer interfaces; applied machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The domain of human–technology interaction stands at the forefront of innovation, driving progress in fields such as affective intelligence, applied machine learning, assistive technology, and robotics. This synergy between human and machine holds immense promise for enhancing various aspects of human life, spanning healthcare, education, smart cities, and beyond. Within this dynamic landscape, the exploration of interdisciplinary topics becomes imperative, paving the way for groundbreaking research and transformative applications.

This Special Issue (SI) endeavors to delve into the multifaceted dimensions of human–technology interaction, with a focus on advancing understanding and fostering innovation across diverse domains. Potential themes for original research contributions include, but are not limited to, the following:

  • Applied machine learning applications in healthcare, education, and smart cities;
  • Human–robot interaction (HRI) and its implications for enhancing human capabilities and experiences;
  • Affective intelligence encompassing affective robotics, affective computing, and emotion recognition;
  • Behavior analysis and its role in understanding human behavior and guiding technological interventions;
  • Social robotics and its potential to augment social interactions and address societal challenges;
  • Assistive technology innovations aimed at improving accessibility and quality of life for individuals with diverse needs;
  • The integration of affective intelligence and machine learning for personalized and adaptive systems;
  • Human–machine interaction modalities, including brain–computer interfaces, speech recognition, and biometrics;
  • Ethical considerations and societal implications of advancing human–technology interaction.

We invite researchers from academia, industry, and beyond to contribute their original research, reviews, and perspectives to this SI, facilitating interdisciplinary discourse and catalyzing impactful advancements at the interface between humans and technology.

Dr. Diego R. Faria
Guest Editor

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

  • advancements in affective intelligence
  • applied machine learning
  • assistive technology
  • robotics for health, education, and smart cities

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Published Papers (5 papers)

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Research

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20 pages, 2884 KiB  
Article
Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
by Ayşen Yalman, Mehmet Arif Arık, Mehmet Kayakuş, Murad Karaduman, Sibel Karaduman, Fatma Yiğit Açıkgöz, Tuba Livberber and Fahrettin Kayan
Appl. Sci. 2025, 15(3), 1157; https://doi.org/10.3390/app15031157 - 24 Jan 2025
Viewed by 1149
Abstract
Phubbing, defined as the disruption of social relationships and interactions due to excessive cell phone use, is becoming an increasing concern in modern society. Since one of the primary motivations for cell phone use is internet access, it is crucial to assess the [...] Read more.
Phubbing, defined as the disruption of social relationships and interactions due to excessive cell phone use, is becoming an increasing concern in modern society. Since one of the primary motivations for cell phone use is internet access, it is crucial to assess the time that individuals spend online to measure the prevalence of phubbing as a social behaviour disorder. This study aimed to better understand and evaluate the phubbing phenomenon by predicting future trends in internet usage using machine learning techniques. Four machine learning models—an artificial neural network (ANN), support vector regression (SVR), random forest (RF) regression, and time series—were employed to predict the average internet usage. Data from 2014 to 2024 were obtained from the World Bank, and cross-validation was used to enhance the reliability and accuracy of the models. All four models were successful in predicting internet usage, with the ANN showing the highest accuracy, followed by SVR, RF, and the time series. According to the data, the average daily time spent online increased from 277 min in 2014 to 417 min in 2024. Projections based on these machine learning models estimate that this figure will rise to 507 min by 2030 and 603 min by 2035. These findings provide valuable insights into the potential risks of increased phubbing behaviours on social interactions and offer a foundation for the exploration of the long-term health implications of excessive internet use. Future research could further examine the effects of phubbing on mental health and develop strategies to mitigate this social behaviour disorder. Full article
(This article belongs to the Special Issue Future Human-Technology Interactions and Their Intelligent Applications)
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20 pages, 1263 KiB  
Article
ExamVoice: Innovative Solutions for Improving Exam Accessibility for Blind and Visually Impaired Students in Saudi Arabia
by Walla Al-Eidarous, Aeshah Alsiyami, Malak Aljabri, Sara Alqethami and Badriah Almutanni
Appl. Sci. 2024, 14(19), 8813; https://doi.org/10.3390/app14198813 - 30 Sep 2024
Viewed by 1851
Abstract
Ensuring equitable educational opportunities for blind and visually impaired (BVI) students in Saudi Arabian higher education remains challenging despite legislative and institutional efforts. This paper proposes a system to assist BVI students independently during exams, aiming to create an inclusive exam environment that [...] Read more.
Ensuring equitable educational opportunities for blind and visually impaired (BVI) students in Saudi Arabian higher education remains challenging despite legislative and institutional efforts. This paper proposes a system to assist BVI students independently during exams, aiming to create an inclusive exam environment that minimizes distractions and facilitates their participation alongside peers. Key contributions include the development of a secure platform, ensuring exam confidentiality, and fostering confidence and active engagement among BVI individuals. The study highlights the importance of integrating innovative assistive technologies and implementing rigorous policies to overcome systemic barriers and improve support mechanisms for BVI students in educational settings. Full article
(This article belongs to the Special Issue Future Human-Technology Interactions and Their Intelligent Applications)
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29 pages, 6331 KiB  
Article
Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning
by Diego Resende Faria, Abraham Itzhak Weinberg and Pedro Paulo Ayrosa
Appl. Sci. 2024, 14(15), 6631; https://doi.org/10.3390/app14156631 - 29 Jul 2024
Cited by 4 | Viewed by 2871
Abstract
Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning [...] Read more.
Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human–robot interaction, and cross-cultural communication. Full article
(This article belongs to the Special Issue Future Human-Technology Interactions and Their Intelligent Applications)
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24 pages, 6484 KiB  
Article
The Effectiveness of UWB-Based Indoor Positioning Systems for the Navigation of Visually Impaired Individuals
by Maria Rosiak, Mateusz Kawulok and Michał Maćkowski
Appl. Sci. 2024, 14(13), 5646; https://doi.org/10.3390/app14135646 - 28 Jun 2024
Cited by 4 | Viewed by 3303
Abstract
UWB has been in existence for several years, but it was only a few years ago that it transitioned from a specialized niche to more mainstream applications. Recent market data indicate a rapid increase in the popularity of UWB in consumer products, such [...] Read more.
UWB has been in existence for several years, but it was only a few years ago that it transitioned from a specialized niche to more mainstream applications. Recent market data indicate a rapid increase in the popularity of UWB in consumer products, such as smartphones and smart home devices, as well as automotive and industrial real-time location systems. The challenge of achieving accurate positioning in indoor environments arises from various factors such as distance, location, beacon density, dynamic surroundings, and the density and type of obstacles. This research used MFi-certified UWB beacon chipsets and integrated them with a mobile application dedicated to iOS by implementing the near interaction accessory protocol. The analysis covers both static and dynamic cases. Thanks to the acquisition of measurements, two main candidates for indoor localization infrastructure were analyzed and compared in terms of accuracy, namely UWB and LIDAR, with the latter used as a reference system. The problem of achieving accurate positioning in various applications and environments was analyzed, and future solutions were proposed. The results show that the achieved accuracy is sufficient for tracking individuals and may serve as guidelines for achievable accuracy or may provide a basis for further research into a complex sensor fusion-based navigation system. This research provides several findings. Firstly, in dynamic conditions, LIDAR measurements showed higher accuracy than UWB beacons. Secondly, integrating data from multiple sensors could enhance localization accuracy in non-line-of-sight scenarios. Lastly, advancements in UWB technology may expand the availability of competitive hardware, facilitating a thorough evaluation of its accuracy and effectiveness in practical systems. These insights may be particularly useful in designing navigation systems for blind individuals in buildings. Full article
(This article belongs to the Special Issue Future Human-Technology Interactions and Their Intelligent Applications)
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Review

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34 pages, 2829 KiB  
Review
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
by Adrianos Pavlopoulos, Theodoros Rachiotis and Ilias Maglogiannis
Appl. Sci. 2024, 14(19), 9068; https://doi.org/10.3390/app14199068 - 8 Oct 2024
Cited by 6 | Viewed by 8465
Abstract
This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A [...] Read more.
This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings. Full article
(This article belongs to the Special Issue Future Human-Technology Interactions and Their Intelligent Applications)
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