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Enhancing Rehabilitation and Assistance through Human–Robot Interaction: Current Trends and Future Directions

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 1060

Special Issue Editors


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Guest Editor
Biomedical Engineering Program, Gannon University, Erie, PA 16541, USA
Interests: integration of biomechanics and motor control; experimental setups for multi-sensor fusion; analytical and numerical modeling of complex systems; rehabilitation and sport performance

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Guest Editor
Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico
Interests: vision-based control of hybrid systems holonomic/non-holonomic; multisensory integration (lasers, ultrasound, force and moment sensors, computer vision) using Kalman techniques for controlling mobile robots

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Guest Editor
Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico
Interests: robotics; dynamic system control; biomechanics

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Guest Editor
Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Querétaro 76130, Mexico
Interests: biomechanics; biomechatronics; human–machine interaction; robotics

Special Issue Information

Dear Colleagues,

In the realm of healthcare, the integration of robotics and sensor technologies is revolutionizing the landscape of rehabilitation and assistance. This Special Issue aims to delve into the dynamic field of Human–Robot Interaction (HRI) in the context of rehabilitation and assistance, shedding light on novel developments, challenges, and potential breakthroughs. By fostering a deeper understanding of the interplay between humans and robots in therapeutic and caregiving settings, this Special Issue seeks to pave the way for more effective and personalized approaches to patient care.

Highlighting the role of sensors in this paradigm, we explore how these technologies enable seamless interaction, data acquisition, and feedback mechanisms between humans and robots. Via interdisciplinary research, this Special Issue will reveal innovative sensor-based solutions that enhance the adaptability and responsiveness of robotic systems, ensuring safer and more efficient interventions.

By addressing the convergence of human–robot interaction, rehabilitation, and assistance, this Special Issue emphasizes the pivotal role of sensors in enabling real-time communication, data collection, and analysis. The integration of sensors into robotic systems for rehabilitation and assistance aligns seamlessly with the journal's objective of advancing sensor technologies and their multifaceted applications. Researchers contributing to this Special Issue will present sensor-driven methodologies, algorithms, and systems that contribute to the ongoing evolution of healthcare robotics and human-centered care.

Prof. Dr. Davide Piovesan
Prof. Dr. Antonio Cardenas
Prof. Dr. Mauro Eduardo Maya
Dr. Alejandro González
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

  • human–robot interaction
  • rehabilitation
  • assistance
  • sensor technologies
  • healthcare robotics
  • user-centered design
  • assistive devices
  • physical therapy
  • sensor integration

Published Papers (1 paper)

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Research

12 pages, 5362 KiB  
Article
Assessment of Surgeons’ Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study
by Kristóf Takács, Eszter Lukács, Renáta Levendovics, Damján Pekli, Attila Szijártó and Tamás Haidegger
Sensors 2024, 24(9), 2915; https://doi.org/10.3390/s24092915 - 2 May 2024
Viewed by 766
Abstract
Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci [...] Read more.
Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing. Full article
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