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 831

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Guest Editor
Applied Computing Department, Universidade do Vale do Itajai, Itajai, Brazil
Interests: assistive technology; human–computer interaction; robotics
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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

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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 (1 paper)

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Research

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
Viewed by 405
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
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