Special Issue "Bioengineering for Physical Rehabilitation"

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: 30 November 2022 | Viewed by 2683

Special Issue Editor

Dr. Aleksandar Vakanski
E-Mail Website
Guest Editor
Industrial Technology, University of Idaho, Moscow, ID 83844, USA
Interests: biomedical informatics; machine learning and artificial intelligence; rehabilitation assessment; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioengineering applications in physical rehabilitation present unique opportunities for assisting patients recovering from stroke, surgery, or musculoskeletal trauma. Worldwide, millions of patients are enrolled in various physical rehabilitation programs, and the related costs impose enormous burdens on patients and healthcare systems. Subsequently, the urgent requirement for new tools to support physical rehabilitation has been recognized in numerous reports and publications. The development of innovative and cross-disciplinary bioengineering approaches, devices, and solutions can address the lack of such tools, as well as reduce the time taken to reach functional recovery and healthcare costs. For example, the recent progress in machine learning offers great potential for mining rehabilitation data, assessing performance, detecting compensatory movements, and tracking patient progress. Similarly, the advancements in sensors and wearable devices, vision cameras, non-intrusive motion tracking technology, and serious games can be applied to improve patient outcomes in home-based rehabilitation. Furthermore, intelligent robots and smart mechatronic devices empowered with AI approaches can complement clinical support and reduce the duration of rehabilitation programs.  

This Special Issue on “Bioengineering for Physical Rehabilitation” will present original research and comprehensive review papers that introduce novel theoretical bioengineering approaches and/or applications of technologies in physical rehabilitation.  

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Computational approaches in rehabilitation;
  • Rehabilitation robotics;
  • Assistive devices for rehabilitation;
  • Machine learning and AI-based methods;
  • Automated screening/assessment;
  • Sensors for rehabilitation;
  • Biomechanics;
  • Serious games for rehabilitation;
  • Physical prosthetics;
  • Brain–computer interfaces;
  • Virtual and augmented reality in rehabilitation.

Dr. Aleksandar Vakanski
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.

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. Bioengineering 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 1700 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

  • Rehabilitation robotics;
  • Rehabilitation assessment;
  • Assistive devices;
  • Sensors for rehabilitation;
  • Physical prosthetics.

Published Papers (4 papers)

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Research

Article
Bilateral Sensorimotor Cortical Communication Modulated by Multiple Hand Training in Stroke Participants: A Single Training Session Pilot Study
Bioengineering 2022, 9(12), 727; https://doi.org/10.3390/bioengineering9120727 - 24 Nov 2022
Viewed by 152
Abstract
Bi-manual therapy (BT), mirror therapy (MT), and robot-assisted rehabilitation have been conducted in hand training in a wide range of stages in stroke patients; however, the mechanisms of action during training remain unclear. In the present study, participants performed hand tasks under different [...] Read more.
Bi-manual therapy (BT), mirror therapy (MT), and robot-assisted rehabilitation have been conducted in hand training in a wide range of stages in stroke patients; however, the mechanisms of action during training remain unclear. In the present study, participants performed hand tasks under different intervention conditions to study bilateral sensorimotor cortical communication, and EEG was recorded. A multifactorial design of the experiment was used with the factors of manipulating objects (O), robot-assisted bimanual training (RT), and MT. The sum of spectral coherence was applied to analyze the C3 and C4 signals to measure the level of bilateral corticocortical communication. We included stroke patients with onset <6 months (n = 6), between 6 months and 1 year (n = 14), and onset >1 year (n = 20), and their Brunnstrom recovery stage ranged from 2 to 4. The results showed that stroke duration might influence the effects of hand rehabilitation in bilateral cortical corticocortical communication with significant main effects under different conditions in the alpha and beta bands. Therefore, stroke duration may influence the effects of hand rehabilitation on interhemispheric coherence. Full article
(This article belongs to the Special Issue Bioengineering for Physical Rehabilitation)
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Article
Two-Dof Upper Limb Rehabilitation Robot Driven by Straight Fibers Pneumatic Muscles
Bioengineering 2022, 9(8), 377; https://doi.org/10.3390/bioengineering9080377 - 09 Aug 2022
Cited by 1 | Viewed by 508
Abstract
In this paper, the design of a 2-dof (degrees of freedom) rehabilitation robot for upper limbs driven by pneumatic muscle actuators is presented. This paper includes the different aspects of the mechanical design and the control system and the results of the first [...] Read more.
In this paper, the design of a 2-dof (degrees of freedom) rehabilitation robot for upper limbs driven by pneumatic muscle actuators is presented. This paper includes the different aspects of the mechanical design and the control system and the results of the first experimental tests. The robot prototype is constructed and at this preliminary step a position and trajectory control by fuzzy logic is implemented. The pneumatic muscle actuators used in this arm are designed and constructed by the authors’ research group. Full article
(This article belongs to the Special Issue Bioengineering for Physical Rehabilitation)
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Article
Actuator and Contact Force Modeling of an Active Soft Brace for Scoliosis
Bioengineering 2022, 9(7), 303; https://doi.org/10.3390/bioengineering9070303 - 11 Jul 2022
Viewed by 781
Abstract
Scoliosis is an abnormality of the spinal curvature that severely affects the musculoskeletal, respiratory, and nervous systems. Conventionally, it is treated using rigid spinal braces. These braces are static, rigid, and passive in nature, and they (largely) limit the mobility of the spine, [...] Read more.
Scoliosis is an abnormality of the spinal curvature that severely affects the musculoskeletal, respiratory, and nervous systems. Conventionally, it is treated using rigid spinal braces. These braces are static, rigid, and passive in nature, and they (largely) limit the mobility of the spine, resulting in other spinal complexities. Moreover, these braces do not have precise control over how much force is being applied by them. Over-exertion of force may deteriorate the spinal condition. This article presents a novel active soft brace that allows mobility to the spine while applying controlled corrective forces that are regulated by varying the tensions in elastic bands using low-power light weight twisted string actuators (TSAs). This article focuses on the actuator and contact force modeling of the active soft brace (ASB). The actuator modeling is required to translate the twisting of string in terms of contraction of the string’s length, whereas the contact force modeling helps in estimating the net resultant force exerted by the band on the body using single point pressure/force sensors. The actuators (TSAs) are modeled as helix geometry and validated using a laser position sensor. The results showed that the model effectively tracked the position (contraction in length) with root mean square error (RMSE) of 1.7386 mm. The contact force is modeled using the belt and pulley contact model and validated by building a custom testbed. The actuator module is able to regulate the pressure in the range 0–6 Kpa, which is comparable to 0–8 Kpa pressure regulated in rigid braces. This makes it possible to verify and demonstrate the working principle of the proposed active soft brace. Full article
(This article belongs to the Special Issue Bioengineering for Physical Rehabilitation)
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Article
Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach
Bioengineering 2022, 9(7), 288; https://doi.org/10.3390/bioengineering9070288 - 29 Jun 2022
Viewed by 751
Abstract
Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were [...] Read more.
Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were included in the study, two thirds of which needed total or partial revision for prosthetic loosening. The analysis is based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing for proper standardization, images were analyzed through a convolutional neural network (the DenseNet169 network), aiming to predict prosthesis failure. The entire dataset was divided in three subsets: training, validation, and test. These contained transfer learning and fine-tuning algorithms, based on the training dataset, and were implemented to adapt the DenseNet169 network to the specific data and clinical problem. Results: After the training procedures, in the test set, the classification accuracy was 0.97, the sensitivity 0.97, the specificity 0.97, and the ROC AUC was 0.99. Only five images were incorrectly classified. Seventy-four images were classified as failed, and eighty as non-failed with a probability >0.999. Conclusion: The proposed deep learning procedure can detect the loosening of the hip prosthesis with a very high degree of precision. Full article
(This article belongs to the Special Issue Bioengineering for Physical Rehabilitation)
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