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Technologies
Special Issues
Advanced Technologies for Enhancing Safety, Health, and Well-Being
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Advanced Technologies for Enhancing Safety, Health, and Well-Being

A Special Issue of Technologies (ISSN 2227-7080) belonging to the section "Assistive Technologies".

Deadline for manuscript submissions: closed (30 December 2025) | Viewed by 6812

Editors

Dr. Siu Shing Man

E-Mail Website
Guest Editor
School of Design, South China University of Technology, Panyu District, Guangzhou 510006, China
Interests: safety and health; human factors and ergonomics; engineering management; industrial design
Special Issues, Collections and Topics in MDPI journals
Dr. Alan H. S. Chan

E-Mail Website
Guest Editor
Department of Systems Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
Interests: human factors and ergonomics; safety and health
Special Issues, Collections and Topics in MDPI journals
Dr. Fangrong Chang

E-Mail Website
Guest Editor
School of Resource and Safety Engineering, Central South University, Changsha 410083, China
Interests: transportation safety analysis and proactive risk assessment; intelligent traffic signal optimization; CAV–pedestrian interaction policy and behavioural safety; trajectory planning and motion control for autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Li Liu

E-Mail Website
Guest Editor
Department of Marine Engineering, Dalian Maritime University, Dalian 116026, China
Interests: human factors and ergonomics; human-robot interaction

Special Issue Information

Dear Colleagues,

The Special Issue aims to present cutting-edge research and comprehensive reviews exploring the latest technological innovations designed to improve human safety, health, and well-being. It seeks to highlight interdisciplinary efforts involving artificial intelligence, machine learning, wearable technologies, robotics, virtual and augmented reality, IoT systems, big data analytics, and advanced materials. Contributions will discuss theoretical and practical aspects, including system design, implementation, efficacy evaluations, ethical considerations, and regulatory implications.

Key topics of interest include but are not limited to predictive analytics for hazard prevention, smart wearable sensors for real-time health monitoring, autonomous systems for high-risk tasks, immersive technologies for training and behavioral change, technological advancements in protective gear, and ergonomic solutions to enhance human–technology interactions. The Special Issue will provide a valuable academic platform for scholars, industry experts, and policymakers to exchange knowledge, identify research gaps, and foster collaborations, ultimately driving forward innovative solutions that meaningfully improve safety and health across diverse environments.

Dr. Siu Shing Man
Dr. Alan H. S. Chan
Dr. Fangrong Chang
Dr. Li Liu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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 1800 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

  • occupational health and safety
  • road safety
  • mental health
  • rehabilitation
  • artificial intelligence
  • machine learning
  • wearable technology
  • Internet of Things
  • robotics and automation
  • virtual/augmented reality
  • ergonomics and human factors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

24 pages, 1010 KB  
Article
Beyond Short-Frame Acoustic Features: Capturing Long-Term Speech Patterns for Depression Detection
by Shizuku Fushimi, Mohammad Aiman Azani, Mizuto Chiba and Yoshifumi Okada
Technologies 2026, 14(4), 198; https://doi.org/10.3390/technologies14040198 - 25 Mar 2026
Viewed by 1638
Abstract
Speech-based depression detection is promising for objective mental health assessment. However, conventional methods relying on short-frame acoustic features often fail to capture long-term temporal and behavioral characteristics of speech essential for modeling depression-specific speaking patterns. Herein, four novel acoustic feature sets extracted from [...] Read more.
Speech-based depression detection is promising for objective mental health assessment. However, conventional methods relying on short-frame acoustic features often fail to capture long-term temporal and behavioral characteristics of speech essential for modeling depression-specific speaking patterns. Herein, four novel acoustic feature sets extracted from long-term speech are proposed: utterance interval feature set (UIFS), pause interval feature set (PIFS), response interval feature set (RIFS), and speech density (SD). These features explicitly characterize temporal structures and session-level speech behaviors beyond short-frame analysis. These features are combined with conventional acoustic features, including standard features extracted using openSMILE and voice level features, and evaluated using support vector machines under subject-independent conditions for the binary classification of depressed and nondepressed speakers. Incorporating the proposed features improves classification performance compared with baseline features (accuracy: 0.54 for openSMILE and 0.52 for openSMILE + voice level features). The configuration integrating all four proposed feature sets achieves an accuracy of 0.58, a precision of 0.56, a recall of 0.58, and a specificity of 0.58, indicating consistent performance gains under subject-independent and strictly controlled evaluation conditions. Thus, depression-related speech patterns can be captured by explicitly modeling temporal and behavioral speech characteristics across entire dialog sessions. This study contributes to advancing acoustic feature design for speech-based depression detection and developing clinically supportive screening and monitoring technologies. Full article
(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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19 pages, 748 KB  
Article
Patient Experiences of Remote Patient Monitoring: Implications for Health Literacy and Therapeutic Relationships
by Josephine Stevens, Amir Hossein Ghapanchi, Afrooz Purarjomandlangrudi and Stephanie Bruce
Technologies 2025, 13(10), 464; https://doi.org/10.3390/technologies13100464 - 13 Oct 2025
Cited by 4 | Viewed by 2649
Abstract
This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: [...] Read more.
This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: “knowing” and “relationship.” The “knowing” theme encompassed data-driven awareness and contextualized education that empowered patients in their health management. The “relationship” theme highlighted the importance of interpersonal connections with healthcare providers and the sense of security from clinical oversight. Technology served as a communication platform supporting patient-clinician interactions rather than replacing them. The findings demonstrate that remote monitoring programs enhance chronic disease self-management through two interconnected mechanisms: the development of ‘situated health literacy’ through real-time, personalized data interpretation, and strengthened therapeutic relationships enabled by technology-mediated clinical oversight. Rather than replacing human interaction, technology serves as a platform for meaningful patient-provider communication that supports both immediate health management and long-term self-management capability development. These exploratory findings suggest potential design considerations for patient-centered telehealth services that integrate health literacy enhancement with relationship-centered care. Full article
(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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21 pages, 5787 KB  
Article
Design and Validation of a Walking Exoskeleton for Gait Rehabilitation Using a Dual Eight-Bar Mechanism
by Fidel Chávez, Juan A. Cabrera, Alex Bataller and Javier Pérez
Technologies 2025, 13(10), 463; https://doi.org/10.3390/technologies13100463 - 13 Oct 2025
Cited by 2 | Viewed by 1726
Abstract
Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility [...] Read more.
Improvements in exoskeletons and robotic systems are gaining increasing attention because of their potential to improve neuromuscular rehabilitation and assist people in their daily activities, significantly improving their quality of life. However, the high cost and complexity of current devices limit their accessibility to many patients and rehabilitation centers. This work presents the design and development of a low-cost walking exoskeleton, conceived to offer an affordable and simple alternative. The system uses a compact eight-bar mechanism with only one degree of freedom per leg, drastically simplifying motorization and control. The exoskeleton is customized for each patient using a synthesis process based on evolutionary algorithms to replicate a predefined gait. Despite the reduced number of degrees of freedom, the resulting mechanism perfectly matches the desired ankle and knee trajectories. The device is designed to be lightweight and affordable, with components fabricated using 3D printing, standard aluminum bars, and one actuator per leg. A working prototype was fabricated, and its functionality and gait accuracy were confirmed. Although limited to a predefined gait pattern and requiring crutches for balance and steering, this exoskeleton represents a promising solution for rehabilitation centers with limited resources, offering accessible and effective gait assistance to a wider population. Full article
(This article belongs to the Special Issue Advanced Technologies for Enhancing Safety, Health, and Well-Being)
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