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Machines
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Advanced Human–Machine Interaction and Assistive Robotics for Rehabilitation
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Advanced Human–Machine Interaction and Assistive Robotics for Rehabilitation

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2674

Special Issue Editor

Dr. Alejandro Ramirez

E-Mail Website
Guest Editor
Applied Computing Department, Universidade do Vale do Itajai, Itajai, Brazil
Interests: assistive technology; human–computer interaction; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Assistive robotics has gained significant traction as a field dedicated to enhancing the quality of life for individuals with disabilities. Recent advancements in artificial intelligence (AI) and robotics have expanded the capabilities of assistive robots, enabling them to support rehabilitation, social interaction, and daily living activities. From wearable robots to autonomous companions, these systems are revolutionizing the way we approach neurological rehabilitation and assistive care.

This Special Issue of Machines seeks to highlight the latest advancements in human–machine interactions and assistive robotics, with a focus on their application in neurorehabilitation.We invite submissions of original research articles, reviews, and case studies that explore the intersection of human–machine interactions and assistive robotics in the context of neurorehabilitation. Topics of interest include, but are not limited to, the following:

  • Assistive Robotics: Design, development, and evaluation of robotic systems for rehabilitation and daily assistance.
  • Social Robots: Robots designed to facilitate social interaction and emotional support for individuals with neurological disorders.
  • Wearable Robots: Exoskeletons and other wearable devices for motor rehabilitation and mobility enhancement.
  • Rehabilitation Robots: Robotic systems tailored for cognitive and physical rehabilitation post-stroke or other neurological conditions.
  • Human–Machine Interaction: Advanced interfaces for neurorehabilitation, including brain–computer interfaces (BCI) and eye-tracking systems.
  • Robotic Manipulators: Assistive robotic arms and manipulators for enhancing independence in daily tasks.
  • Autonomous Robot Companions: AI-driven robots for long-term care and companionship.
  • AI and Machine Learning in Robotics: Applications of AI for adaptive and personalized rehabilitation strategies.
  • Sensor Integration and Control Systems: Innovations in sensors and control algorithms for assistive and rehabilitation robots.

We look forward to your submissions.

Dr. Alejandro Ramirez
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines 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 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

  • neurological disorders
  • human–computer interfaces (HCI)
  • rehabilitation
  • assistive robotics
  • social robots
  • wearable robots
  • rehabilitation robots

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Published Papers (3 papers)

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Research

21 pages, 5104 KB  
Article
Trust Isn’t Binary: Analysis of User Sentiment for Assistive Human–Robot Interaction
by Randyll Pandohie, Edgard M. Maboudou-Tchao, Nihad Habizada, Morris Beato and Aman Behal
Machines 2026, 14(5), 488; https://doi.org/10.3390/machines14050488 - 27 Apr 2026
Viewed by 372
Abstract
Understanding how users perceive assistive robotic systems is critical for their successful adoption, particularly in rehabilitation settings where both patients and clinicians influence decision-making. While prior work has focused on technical performance and overall usability, affective responses such as trust, control, and perceived [...] Read more.
Understanding how users perceive assistive robotic systems is critical for their successful adoption, particularly in rehabilitation settings where both patients and clinicians influence decision-making. While prior work has focused on technical performance and overall usability, affective responses such as trust, control, and perceived independence are often captured using coarse, single-score measures that overlook important nuances. This study analyzes focus group discussions with individuals with spinal cord injury to examine how users evaluate different aspects of assistive robot design. A hybrid aspect-based sentiment analysis approach is applied, combining lexicon-based and transformer-based methods to capture both interpretable and context-sensitive sentiment. The analysis separates sentiment across key dimensions, including independence, functionality, safety, control, cost, and data sharing. Participants expressed consistently positive views toward independence and functional support, while responses related to safety, control, and data sharing were more conditional. In particular, trust emerged as something that depends on transparency, user control, and the ability to override system behavior, rather than a fixed attitude toward the technology. These findings suggest that successful assistive robotic systems must balance autonomy with user authority and provide clear, adaptable mechanisms for control and data governance. Full article
(This article belongs to the Special Issue Advanced Human–Machine Interaction and Assistive Robotics for Rehabilitation)
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25 pages, 2162 KB  
Article
Effective Compensations for Disability: Results from a Usability Evaluation of an Assistive Robot Among Spinal-Cord-Injured Users
by Eva L. Parkhurst, Fernando Montalvo, Zhangchi Ding, Janan A. Smither and Aman Behal
Machines 2026, 14(2), 174; https://doi.org/10.3390/machines14020174 - 3 Feb 2026
Viewed by 621
Abstract
Wheelchair-mounted robotic arms (WMRAs) are assistive manipulators designed to increase the functional independence of individuals with limitations in the upper and lower extremities. While previous research has identified several visual, cognitive, and physical abilities that facilitate optimal operation of such a device, these [...] Read more.
Wheelchair-mounted robotic arms (WMRAs) are assistive manipulators designed to increase the functional independence of individuals with limitations in the upper and lower extremities. While previous research has identified several visual, cognitive, and physical abilities that facilitate optimal operation of such a device, these have yet to be accounted for when designing the human–robot interface. This study investigates whether interface-level compensations can improve usability and support more personalized control across users with different abilities. Five interface compensations were implemented and evaluated: object highlighting, move suggestions, slowing near objects, a one-click approach, and a level indicator. A user study was conducted among individuals with spinal cord injury as well as able-bodied younger and older adults serving as comparison groups. Evaluations of task performance metrics, perceived WMRA usability, and user experience were conducted. The results indicate that younger adults achieved the highest task efficiency, followed closely by participants with spinal cord injury, with both groups reporting good-to-excellent usability, whereas older adults showed lower efficiency and rated the system as having fair usability. The one-click automated object approach feature was identified as the most useful compensation among all participant groups. Overall, participant feedback indicated that spatial visualization and spatial orientation were the most important individual differences affecting the operation of the arm. Full article
(This article belongs to the Special Issue Advanced Human–Machine Interaction and Assistive Robotics for Rehabilitation)
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23 pages, 4357 KB  
Article
Mechanism Design of Lower-Limb Rehabilitation Robot Based on Chord Angle Descriptor Method
by Liuxian Zhu, Wei Wei, Li Li and Shan Gong
Machines 2025, 13(11), 1059; https://doi.org/10.3390/machines13111059 - 17 Nov 2025
Cited by 1 | Viewed by 907
Abstract
The rapid aging of the global population and the escalating prevalence of conditions such as stroke and spinal cord injuries are driving an urgent demand for effective lower-limb rehabilitation. This necessitates the development of robotic devices capable of providing intensive, repetitive, and consistent [...] Read more.
The rapid aging of the global population and the escalating prevalence of conditions such as stroke and spinal cord injuries are driving an urgent demand for effective lower-limb rehabilitation. This necessitates the development of robotic devices capable of providing intensive, repetitive, and consistent movement therapy, with superior stability and safety being paramount. This paper presents a comprehensive design and modeling framework for a novel lower-limb rehabilitation robot, addressing the critical gap between computational complexity and model fidelity in gear-linkage mechanisms. A single-degree-of-freedom gear-five-bar mechanism synthesized via a Chord Angle Descriptor method is proposed that enables efficient, normalized-trajectory generation for standing motions. Subsequently, a hybrid dynamic modeling framework is developed, which explicitly incorporates time-varying gear mesh stiffness—a factor typically oversimplified in traditional models. By decomposing stiffness via a Finite Element Method-based potential energy analysis, characterizing it with a Fourier series, and integrating it into a Lagrangian model, our approach accurately captures complex dynamics with minimal degrees of freedom. Computational validation confirms exceptional stability, with a maximum gear angular amplitude below 0.003145 radians for a 250 kg user. This study provides both a robust theoretical foundation and a practical design paradigm for high-performance rehabilitation robots. Full article
(This article belongs to the Special Issue Advanced Human–Machine Interaction and Assistive Robotics for Rehabilitation)
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