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Bioengineering
Special Issues
Bioengineering Intelligent Systems: From Robotic Manipulation to AI-Driven Healthcare Solutions
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Bioengineering Intelligent Systems: From Robotic Manipulation to AI-Driven Healthcare Solutions

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 620

Special Issue Editor

Prof. Dr. Tae-Seong Kim

E-Mail Website
Guest Editor
Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin 17104, Korea
Interests: aI deep learning; machine learning; pattern recognition; brain engineering; biomedical imaging/signal analysis; robot intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores cutting-edge advancements in intelligent bioengineering systems, bridging robotics, artificial intelligence (AI), and healthcare technologies. It covers innovative research in robotic manipulation (e.g., soft robotics, surgical robots, and autonomous systems) and AI-driven healthcare solutions (e.g., medical imaging, diagnostics, and personalized treatment). Contributions may include novel algorithms, sensor technologies, human–machine interfaces, and real-world applications that enhance precision, efficiency, and accessibility in medicine and industry.

We welcome the submission of original research, reviews, and case studies addressing challenges and opportunities in the following areas:

  • Smart Robotics: Dexterous manipulation, bio-inspired designs, and autonomous control.
  • AI in Healthcare:
  • Time-series data analysis (e.g., EEG, IMU, and heart rate) for healthcare or disease diagnosis.
  • AI-driven medical signal and image diagnostics.
  • Federated learning and edge computing for medical applications.
  • Human-Centric Systems: Wearable devices, rehabilitation robotics, and AI-augmented decision-making.

This issue aims to foster interdisciplinary collaboration, highlighting how intelligent systems can revolutionize biomedical engineering and clinical practice.

Prof. Dr. Tae-Seong Kim
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 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

  • intelligent robotics
  • medical AI
  • robotic manipulation
  • healthcare automation
  • machine learning in biomedicine
  • surgical robotics
  • Bio-inspired systems
  • AI-driven diagnostics.

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Published Papers (1 paper)

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17 pages, 1634 KB  
Article
Non-Linear Modeling of Motor Development in Typically Developing Children and Youth Aged 5–18 Years Using Robot-Based Behavioral Assessments
by Stephan C. D. Dobri, Stephen H. Scott and T. Claire Davies
Bioengineering 2025, 12(11), 1240; https://doi.org/10.3390/bioengineering12111240 - 12 Nov 2025
Viewed by 405
Abstract
Clinical tasks are often used to differentiate the motor performance of individuals who have impaired function. However, these are not as accurate and repeatable as robotic tasks. Additionally, motor development occurs rapidly at early ages and slows as they reach adulthood, resulting in [...] Read more.
Clinical tasks are often used to differentiate the motor performance of individuals who have impaired function. However, these are not as accurate and repeatable as robotic tasks. Additionally, motor development occurs rapidly at early ages and slows as they reach adulthood, resulting in a non-linear model of performance. There is also evidence that variability in performance changes as children and youth age. Accurate normative models of performance are necessary to identify deficiencies in motor performance and to track the efficacy of therapies. This work aimed to create normative models of motor development based on robotic assessments in typically developing children and youth. Two hundred and eighty-eight participants who are typically developing (ages 5–18) completed a robotic point-to-point reaching task and an object-hitting task using the Kinarm Exoskeleton. Exponential or quadratic curves were fit to performance parameters generated by Kinarm to model typical performance. These models included a linear term to account for changing variabilities with age. Most performance parameters showed improvement with age, and none showed deterioration. Some parameters showed large changes in variability in performance with age, with up to a 74% decrease in the range of typical performance. Reduced variability occurs with age, indicating the need to account for differences in variability when developing models of typical motor performance in children and youth. The models that are used to identify deficits in motor performance should account for changing variability in data and changing repeatability with age to increase the accuracy of identification of deficits. Full article
(This article belongs to the Special Issue Bioengineering Intelligent Systems: From Robotic Manipulation to AI-Driven Healthcare Solutions)
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