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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 2100

Special Issue Editor

Dr. Brahim Brahmi

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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

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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 295
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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Figure 1

Figure 1
<p>Timeline and phases of the early-feasibility testing for the NeuroExo BMI-exoskeleton system.</p> Full article ">Figure 2
<p>Graphics user interface (GUI) depicting the visual feedback provided to the user during the positioning of the NeuroExo device on the head. The impedance of EEG electrodes—scalp and EOG sensors—face are color-coded from low impedance (white) to high impedance (black) values. The correct positioning of the headset leads to lower impedance values.</p> Full article ">Figure 3
<p>An example of a participant fitted with the NeuroExo device and upper-limb exoskeleton while performing a trial at home. The tablet allowed the participant to set up the system and receive visual feedback (reproduced with permission from [<a href="#B12-sensors-25-01322" class="html-bibr">12</a>]).</p> Full article ">Figure 4
<p>Characterizations of the performance of the NeuroExo system in terms of users’ compliance, perceived BCI performance, and electrode signal quality. (<b>a</b>). For each of the five participants with chronic stroke, the age, sex, impaired side, and home state are provided. Each graph depicts the level of electrode impedance [0, <math display="inline"><semantics> <mrow> <mn>100</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">k</mi> <mo>Ω</mo> </mrow> </semantics></math>] (five symbols are used to code for electrode location along the frontocentral scalp in the 10–20 system). Users’ compliance is denoted as the number of blocks performed by the users per week in a counterclockwise direction (shading). The percentage of adequate impedance values (&lt;=<math display="inline"><semantics> <mrow> <mn>100</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">k</mi> <mo>Ω</mo> </mrow> </semantics></math>) per week is shown in parenthesis. Note that participant S3 conducted NeuroExo therapy over 18 weeks due to therapy interruptions caused by extensive travel. Perceived BCI performance is color-coded by week on each graph. (<b>b</b>). The signal quality distribution is shown; the majority of the percentages for adequate impedances are located in upper buckets. Key: * indicates that these participants did not receive any assistance from family or friends during therapy.</p> Full article ">Figure 5
<p>Trial block duration decreased with training. Boxplots display the distribution of impedance data across weeks and participants, with a linear fit overlaid to highlight trends in trial block duration (min) over time. Red + symbol indicates the outliers in the data. The fit was performed using MATLAB’s polynomial curve fitting function. Key: * indicates that these participants did not receive assistance from family members or friends during the trial.</p> Full article ">Figure 6
<p>MRCP amplitude in early versus late sessions. Early MRCPs in blue represent the MRCP across a block of trials at the beginning of this longitudinal study and green MRCPs represent the last block of trials at the end of the longitudinal study. The annotation of the impedance values is provided to assess signal quality. Key: * indicates that these participants were not assisted by family members/friends.</p> Full article ">Figure 7
<p>AUC amplitude in FC1, FCz, and FC2 in early versus late sessions. Each graph shows the early versus late Area Under the Curve (AUC) computed from the first and last two blocks in this longitudinal study for every participant by channel location. Red + symbol indicate the outliers in the data.</p> Full article ">Figure 8
<p>Newest NeuroExo headset version. Based on user feedback, some of the joints were reinforced, the micro-USB was replaced with USB-C, and the positioning of the EEG electrodes was more stable and easy to adjust.</p> Full article ">Figure A1
<p>System assessment survey. Survey taken by participants after every session. It includes five prompts to assess usability, comfort, and perceived BCI performance of system.</p> Full article ">
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 479
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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Figure 1

Figure 1
<p>Brain retraction.</p> Full article ">Figure 2
<p>Contact model retraction example.</p> Full article ">Figure 3
<p>Real test environment.</p> Full article ">Figure 4
<p>Simulation architecture.</p> Full article ">Figure 5
<p>Reinforcement learning-based controller.</p> Full article ">Figure 6
<p>The most effective brain retractions.</p> Full article ">Figure 7
<p>Retraction operation in the real validation environment.</p> Full article ">Figure 8
<p>A 30 mm retraction.</p> Full article ">
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
Viewed by 747
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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Figure 1

Figure 1
<p>Prototype of the MHLRR. MHLRR: Multistage Hemiplegic Lower-Limb Rehabilitation Robot. Multiple training postures for patients in all stages of recovery. Multiple training sides for patients with hemiplegia.</p> Full article ">Figure 2
<p>Overall structure of the MHLRR.</p> Full article ">Figure 3
<p>The kinematic model.</p> Full article ">Figure 4
<p>Definition and nomenclature of the action space. This figure represents a sitting position, with the patient’s joint ranges of motion set to <span class="html-italic">α</span><sub>max</sub> = 75°, <span class="html-italic">α</span><sub>min</sub> = 10°, <span class="html-italic">β</span><sub>max</sub> = −5°, and <span class="html-italic">β</span><sub>min</sub> = −95°.</p> Full article ">Figure 5
<p>Section division of the action space.</p> Full article ">Figure 6
<p>The positioning of points P34, Q3, and Q4 within the bottom region of the action space: (<b>a</b>) <span class="html-italic">x<sub>Q</sub></span><sub>4</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>3</sub> &lt; <span class="html-italic">x<sub>P</sub></span><sub>34</sub> in lying position; (<b>b</b>) <span class="html-italic">x<sub>Q</sub></span><sub>4</sub> &lt; <span class="html-italic">x<sub>P</sub></span><sub>34</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>3</sub> in sitting position; (<b>c</b>) <span class="html-italic">x<sub>Q</sub></span><sub>4</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>3</sub> ≤ <span class="html-italic">x<sub>P</sub></span><sub>34</sub> in sitting position.</p> Full article ">Figure 7
<p>The positioning of points <span class="html-italic">P</span><sub>14</sub> and <span class="html-italic">P</span><sub>23</sub> within the middle region of the action space: (<b>a</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> &gt; <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in lying position; (<b>b</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> &lt; <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in lying position; (<b>c</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> = <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in lying position; (<b>d</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> &gt; <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in sitting position; (<b>e</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> &lt; <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in sitting position; (<b>f</b>) <span class="html-italic">y<sub>P</sub></span><sub>14</sub> = <span class="html-italic">y<sub>P</sub></span><sub>23</sub> in sitting position.</p> Full article ">Figure 8
<p>The positioning of points <span class="html-italic">P</span><sub>12</sub>, <span class="html-italic">Q</span><sub>1</sub>, and <span class="html-italic">Q</span><sub>2</sub> within the top region of the action space: (<b>a</b>) <span class="html-italic">x<sub>Q</sub></span><sub>1</sub> ≤ <span class="html-italic">x<sub>P</sub></span><sub>12</sub> and <span class="html-italic">x<sub>Q</sub></span><sub>2</sub> ≤ <span class="html-italic">x<sub>P</sub></span><sub>12</sub> in a lying position; (<b>b</b>) <span class="html-italic">x<sub>Q</sub></span><sub>1</sub> ≤ <span class="html-italic">x<sub>P</sub></span><sub>12</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>2</sub> in a lying position; (<b>c</b>) <span class="html-italic">x<sub>P</sub></span><sub>12</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>1</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>2</sub> in a lying position; (<b>d</b>) <span class="html-italic">x<sub>Q</sub></span><sub>2</sub> &lt; <span class="html-italic">x<sub>Q</sub></span><sub>1</sub> &lt; <span class="html-italic">x<sub>P</sub></span><sub>12</sub> in sitting position.</p> Full article ">Figure 9
<p>The positioning of the origin point <span class="html-italic">M</span> and the terminus point <span class="html-italic">N</span> of the linear trajectory: (<b>a</b>) <span class="html-italic">M</span> and <span class="html-italic">N</span> are on different arcs <span class="html-italic">C<sub>i</sub>C<sub>j</sub></span> (with <span class="html-italic">i</span> ≠ <span class="html-italic">j</span>); (<b>b</b>) <span class="html-italic">M</span> and <span class="html-italic">N</span> are on the same arc <span class="html-italic">C</span><sub>3</sub><span class="html-italic">C</span><sub>3</sub>; (<b>c</b>) <span class="html-italic">M</span> and <span class="html-italic">N</span> are on the same arc <span class="html-italic">C</span><sub>2</sub><span class="html-italic">C</span><sub>2</sub>.</p> Full article ">Figure 10
<p>The positions and the ultimate joint angles of the linear trajectories within the action space of Type 10: (<b>a</b>) the nine linear trajectories (<span class="html-italic">l</span><sub>1</sub>~<span class="html-italic">l</span><sub>9</sub>) from bottom to top; (<b>b</b>) the ultimate angles of joints corresponding to the position of the linear trajectories.</p> Full article ">Figure 11
<p>The five key metrics to evaluate joint rehabilitation of linear trajectories: (<b>a</b>) the key metrics of joint rehabilitation of <span class="html-italic">l</span><sub>1</sub>~<span class="html-italic">l</span><sub>5</sub>; (<b>b</b>) the key metrics of joint rehabilitation of <span class="html-italic">l</span><sub>5</sub>~<span class="html-italic">l</span><sub>9</sub>.</p> Full article ">Figure 12
<p>Operating system for generating linear rehabilitation paths based on joint rehabilitation indicators.</p> Full article ">Figure 13
<p>The trajectory composed of multiple segments.</p> Full article ">Figure 14
<p>Hardware system and experimental platform for trajectory tracking.</p> Full article ">Figure 15
<p>Main technology parameters of the encoder.</p> Full article ">Figure 16
<p>The computational time of trajectory generation: (<b>a</b>) line chart; (<b>b</b>) box chart.</p> Full article ">Figure 17
<p>The motion data of the robot’s end effector in Cartesian space: (<b>a</b>) positions; (<b>b</b>) velocities; (<b>c</b>) accelerations; (<b>d</b>) jerks.</p> Full article ">Figure 17 Cont.
<p>The motion data of the robot’s end effector in Cartesian space: (<b>a</b>) positions; (<b>b</b>) velocities; (<b>c</b>) accelerations; (<b>d</b>) jerks.</p> Full article ">Figure 18
<p>The motion data of the robot’s joints for the optimized trajectories and random trajectories: (<b>a</b>) displacements of hip; (<b>b</b>) displacements of knee; (<b>c</b>) velocities of hip; (<b>d</b>) velocities of knee; (<b>e</b>) accelerations of hip; (<b>f</b>) accelerations of knee; (<b>g</b>) jerks of hip; (<b>h</b>) jerks of knee.</p> Full article ">Figure 18 Cont.
<p>The motion data of the robot’s joints for the optimized trajectories and random trajectories: (<b>a</b>) displacements of hip; (<b>b</b>) displacements of knee; (<b>c</b>) velocities of hip; (<b>d</b>) velocities of knee; (<b>e</b>) accelerations of hip; (<b>f</b>) accelerations of knee; (<b>g</b>) jerks of hip; (<b>h</b>) jerks of knee.</p> Full article ">
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