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Bioengineering
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Technological Advances in Neurorehabilitation
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Technological Advances in Neurorehabilitation

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2487

Special Issue Editors

Dr. Susan Duff

E-Mail Website
Guest Editor
Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Irvine, CA 92618, USA
Interests: motor control; motor learning; stroke; brachial plexus injury
Dr. Andria J. Farrens

E-Mail Website
Guest Editor
Human Analysis Lab, Research Institute of the Children's Hospital of Orange County, Orange, CA, USA
Interests: robotics; motor learning; proprioception; stroke

Special Issue Information

Dear Colleagues,

Advancements in technology have introduced a wealth of innovative tools for rehabilitation, clinical assessment, and assistive devices, offering new opportunities for persons with neurological conditions such as stroke or Parkinson’s disease, as well as for children at risk of cerebral palsy, among others. This Special Issue is dedicated to highlighting the latest breakthroughs in these areas and promoting fresh, interdisciplinary solutions to address the ongoing challenges in neurological rehabilitation.

Our goal is to stimulate rich discussion and encourage contributions from diverse research communities. By showcasing cutting-edge methodologies and experimental validations, we aim to drive forward meaningful improvements in quality of care and rehabilitation outcomes for affected individuals.

We invite original research, including experimental studies and proof of concept papers for novel technological innovations, related to, but not restricted to, the following topics:

  • Advanced Assessment Techniques
    The development and application of state-of-the-art technologies such as neuroimaging, bio-sensors, and wearable devices for precise, non-invasive assessments of neurological function.
  • Assistive and Rehabilitation Robotics
    Design, implementation, and clinical use of robotic systems aimed at motor and cognitive rehabilitation, including exoskeletons, assistive devices, and virtual reality platforms.
  • Neuro-modulation Techniques
    Exploration of non-invasive and minimally invasive therapies, such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcutaneous spinal cord stimulation (tSCS), and deep brain stimulation (DBS) to enhance neurological recovery.
  • Artificial Intelligence and Machine Learning
    Leveraging AI and machine learning to support personalized diagnosis, treatment planning, and rehabilitation outcome prediction.

We are excited to receive your contributions and look forward to fostering a dynamic exchange of ideas that will shape the future of neurological rehabilitation and assistive technologies.

Dr. Susan V. Duff
Dr. Andria J. Farrens
Guest Editors

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. 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 2700 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

  • assistive technology
  • neuroimaging
  • neurorehabilitation
  • robotics
  • wearables

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 908 KB  
Article
Computer Vision for Movement Observation and Recovery Enhancement (C-MORE): Box and Blocks Test
by Jun Min Kim, Ziqiang (Joe) Zhu, Hari Venugopalan, Vicky Chan, Matthew K. Farrens, Samuel T. King and Andria J. Farrens
Bioengineering 2026, 13(6), 602; https://doi.org/10.3390/bioengineering13060602 - 22 May 2026
Viewed by 151
Abstract
Stroke is a leading cause of chronic disability, with heterogeneous sensorimotor impairments that are not well captured by standard clinical assessments. While advanced motion capture and robotic systems provide precise measurements, they are not scalable for widespread clinical use. We developed C-MORE (Computer [...] Read more.
Stroke is a leading cause of chronic disability, with heterogeneous sensorimotor impairments that are not well captured by standard clinical assessments. While advanced motion capture and robotic systems provide precise measurements, they are not scalable for widespread clinical use. We developed C-MORE (Computer Vision for Movement Observation and Recovery Enhancement), a smartphone-based framework that uses computer vision and machine learning to automatically score the Box and Blocks Test (BBT) and extract quantitative kinematic metrics. The system combines hand tracking with a custom machine learning (ML) architecture to identify valid block transfers and segment task phases. We evaluated C-MORE in 7 individuals with chronic stroke and a cohort of 10 healthy adults. The system achieved 99.0% agreement with ground-truth scoring, with errors below clinically meaningful thresholds. Kinematic measures derived from the system were sensitive to stroke-related impairments, including reduced movement velocity and increased task duration in affected limbs. Exploratory analyses indicated that grasp-related metrics, particularly the ratio of grasp-to-transfer duration, were correlated with independent measures of proprioception. These findings demonstrate that smartphone-based computer vision can provide accurate, scalable assessment of upper-extremity function. C-MORE offers a practical approach for enhancing clinical evaluation and enabling more precise, individualized rehabilitation strategies. Full article
(This article belongs to the Special Issue Technological Advances in Neurorehabilitation)
20 pages, 12119 KB  
Article
Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders
by Andrew Gravunder, Amanda Studnicki, Julia Kline, Ahad Behboodi, Thomas C. Bulea and Diane L. Damiano
Bioengineering 2026, 13(5), 561; https://doi.org/10.3390/bioengineering13050561 - 16 May 2026
Viewed by 386
Abstract
Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain–computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present [...] Read more.
Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain–computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present a novel detection method commonly used in time-series forecasting (e.g., stock market trends), identifying crosses in fast (short) and slow (long) moving average windows to identify negative deflections in slow movement-related cortical potentials (MRCPs) or event-related desynchronization (ERD) within −400–+100 ms of movement onset. We recorded EEG data from the Cz electrode during our cued ankle dorsiflexion BCI-NFT paradigm in four adult participants, two neurotypical and two with cerebral palsy. Simulated real-time offline analyses demonstrated an 85.9% mean true positive rate and 14.1% false positive rate of detecting motor intent at a mean −182 ms from movement onset. We further evaluated whether the detection indicated a MRCP and/or ERD, with MRCP detected in 70–80% of trials in three participants, but high ERD detection (87%) instead in the other. Preliminary results indicate that this approach offers a straightforward, accurate, and well-timed method for real-time EEG detection during neurofeedback training and as a control signal for brain–computer interfaces. Full article
(This article belongs to the Special Issue Technological Advances in Neurorehabilitation)
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23 pages, 2225 KB  
Article
FDA-Listed Interactive Devices for Home Movement Rehabilitation After Stroke: A Mixed-Methods Study of Availability, User Needs, Information Gaps, and an Accompanying Dataset
by Luis Garcia-Fernandez, Juan C. Perez-Ibarra, Andria J. Farrens, Vicky Chan, Joshua J. Macopson and David J. Reinkensmeyer
Bioengineering 2026, 13(4), 387; https://doi.org/10.3390/bioengineering13040387 - 27 Mar 2026
Viewed by 1043
Abstract
Technologies for home movement rehabilitation after stroke are rapidly expanding. However, for consumers, the number and nature of available products are unclear, and the information provided by device manufacturers varies widely. To understand this landscape, we conducted a mixed-methods, descriptive study in which [...] Read more.
Technologies for home movement rehabilitation after stroke are rapidly expanding. However, for consumers, the number and nature of available products are unclear, and the information provided by device manufacturers varies widely. To understand this landscape, we conducted a mixed-methods, descriptive study in which we used the U.S. Food and Drug Administration (FDA) database to identify interactive devices for stroke rehabilitation suitable for home use. We then surveyed 13 individuals with stroke to determine what information they most wanted about home-based rehabilitation devices and contacted manufacturers to obtain those details. Thirteen FDA codes were associated with stroke rehabilitation devices, encompassing 57 devices produced by 40 companies. Nearly half were categorized under two codes: QKC (interactive rehabilitation exercise devices) and GZI (neuromuscular stimulators). Among devices for which information was available, 71% were listed after 2015, and 23% cost under $1000. The top information priorities for individuals with stroke were required usage to achieve therapeutic benefit, expected benefit, ease of use, and motivational features. Despite repeated outreach, only 45% of companies responded to our queries; among those that did, details were vague and variable. These results confirm that a large and growing number of FDA-listed devices are now available for home-based post-stroke motor rehabilitation. We further identify a need to establish industry standards for reporting ease of use, motivational effectiveness, and dose–response characteristics to help the intended consumers select appropriate technologies. The curated dataset generated in this study is provided as a resource for future work and may support the development of accurate Artificial Intelligence-based interfaces for identifying and comparing rehabilitation devices. Full article
(This article belongs to the Special Issue Technological Advances in Neurorehabilitation)
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