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Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications
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Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2667

Special Issue Editor

Dr. Brahim Brahmi

E-Mail Website
Guest Editor
Electrical Engineering Department, College Ahuntsic Montreal, Montréal, QC H2M 1Y8, Canada
Interests: rehabilitation robots; control systems; bioengineering

Special Issue Information

Dear Colleagues,

The Special Issue titled "Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications" investigates the convergence of robotics and healthcare, specifically emphasizing advanced control systems, innovative sensing technologies, and their applications in the biomedical field. The issue explores the design and implementation of robotic systems aimed at improving medical procedures, enhancing patient care, and optimizing healthcare outcomes. Contributors share insights into state-of-the-art control methodologies that refine the performance of healthcare robots and sensing technologies facilitating precise and adaptive interactions in medical environments. The applications covered include surgical robotics, rehabilitation robotics, medical imaging, and diagnostic tools.

  • Robotics in Healthcare;
  • Biomedical Robotics;
  • Control Systems;
  • Sensing Technologies;
  • Medical Applications;
  • Surgical Robotics;
  • Rehabilitation Robotics;
  • Biomedical Imaging;
  • Diagnostic Tools;
  • Healthcare Optimization.

Dr. Brahim Brahmi
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.

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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

  • robotics in healthcare
  • biomedical robotics
  • control systems
  • sensing technologies
  • medical applications
  • surgical robotics
  • rehabilitation robotics
  • biomedical imaging
  • diagnostic tools
  • healthcare optimization

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Published Papers (3 papers)

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Research

20 pages, 4945 KiB  
Article
At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System
by Juan José González-España, Lianne Sánchez-Rodríguez, Maxine Annel Pacheco-Ramírez, Jeff Feng, Kathryn Nedley, Shuo-Hsiu Chang, Gerard E. Francisco and Jose L. Contreras-Vidal
Sensors 2025, 25(5), 1322; https://doi.org/10.3390/s25051322 - 21 Feb 2025
Viewed by 621
Abstract
Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support [...] Read more.
Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. Full article
(This article belongs to the Special Issue Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications)
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11 pages, 5532 KiB  
Article
Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction
by Ibai Inziarte-Hidalgo, Estela Nieto, Diego Roldan, Gorka Sorrosal, Jesus Perez-Llano and Ekaitz Zulueta
Sensors 2024, 24(24), 8150; https://doi.org/10.3390/s24248150 - 20 Dec 2024
Viewed by 642
Abstract
In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved [...] Read more.
In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans. Additionally, medical robots are often custom-built, leading to high costs. This paper introduces a cost-effective brain retraction robot designed to perform brain retraction procedures. The robot is trained, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, using reinforcement learning techniques with a brain contact model, offering a more affordable solution for such delicate tasks. Full article
(This article belongs to the Special Issue Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications)
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27 pages, 12828 KiB  
Article
A Linear Rehabilitative Motion Planning Method with a Multi-Posture Lower-Limb Rehabilitation Robot
by Xincheng Wang, Musong Lin, Lingfeng Sang, Hongbo Wang, Yongfei Feng, Jianye Niu, Hongfei Yu and Bo Cheng
Sensors 2024, 24(23), 7506; https://doi.org/10.3390/s24237506 - 25 Nov 2024
Cited by 1 | Viewed by 867
Abstract
In rehabilitation, physicians plan lower-limb exercises via linear guidance. Ensuring efficacy and safety, they design patient-specific paths, carefully plotting smooth trajectories to minimize jerks. Replicating their precision in robotics is a major challenge. This study introduces a linear rehabilitation motion planning method designed [...] Read more.
In rehabilitation, physicians plan lower-limb exercises via linear guidance. Ensuring efficacy and safety, they design patient-specific paths, carefully plotting smooth trajectories to minimize jerks. Replicating their precision in robotics is a major challenge. This study introduces a linear rehabilitation motion planning method designed for physicians to use a multi-posture lower-limb rehabilitation robot, encompassing both path and trajectory planning. By subdividing the lower limb’s action space into four distinct training sections and classifying this space, we articulate the correlation between linear trajectories and key joint rehabilitation metrics. Building upon this foundation, a rehabilitative path generation system is developed, anchored in joint rehabilitation indicators. Subsequently, high-order polynomial curves are employed to mimic the smooth continuity of traditional rehabilitation trajectories and joint motions. Furthermore, trajectory planning is refined through the resolution of a constrained quadratic optimization problem, aiming to minimize the abrupt jerks in the trajectory. The optimized trajectories derived from our experiments are compared with randomly generated trajectories, demonstrating the suitability of trajectory optimization for real-time rehabilitation trajectory planning. Additionally, we compare trajectories generated based on the two groups of joint rehabilitation indicators, indicating that the proposed path generation system effectively assists clinicians in executing efficient and precise robot-assisted rehabilitation path planning. Full article
(This article belongs to the Special Issue Advancements in Healthcare Robotics: Control, Sensing, and Biomedical Applications)
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