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Electronics
Special Issues
Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives
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Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2025) | Viewed by 1457

Special Issue Editors

Dr. Wujing Cao

E-Mail Website
Guest Editor
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518005, China
Interests: lower limb exoskeletons; intelligent prosthesis; rehabilitation robotics; human–machine interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiaoling Meng

E-Mail Website
Guest Editor
Institute of Intelligent Rehabilitation Engineering of USST, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: soft exoskeletons; rehabilitation robotics; intelligent prosthesis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Li

E-Mail Website
Guest Editor
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: medical rehabilitation robot; elderly care robot; home service robot; allosteric unmanned system
Prof. Dr. Chi Zhu

E-Mail Website
Guest Editor
Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
Interests: wheelchair mobile robot; automatic cruise and handling robot; wearable exoskeleton robot; brain-computer interface; humanoid robot

Special Issue Information

Dear Colleagues,

We are excited to announce a new Special Issue entitled "Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives". This Special Issue aims to gather innovative research regarding the rapid evolution of assistive robotics, a field that could transform the lives of individuals with disabilities or special needs.

We welcome submissions from researchers, engineers, and practitioners across various disciplines, including robotics, artificial intelligence, biotechnology, and healthcare. We are particularly interested in articles that explore the latest technological advancements in assistive robotics, such as exoskeletons, wearable devices, and autonomous assistants, as well as their potential applications in daily living, rehabilitation, and elderly care.

In addition to showcasing groundbreaking research, we encourage contributors to critically analyze the current challenges faced by the field, including technical limitations, ethical considerations, and societal barriers. We also welcome discussions concerning innovative solutions that could overcome these challenges and promote the development of more inclusive and accessible robotics.

Dr. Wujing Cao
Prof. Dr. Qiaoling Meng
Prof. Dr. Jian Li
Prof. Dr. Chi Zhu
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 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 robotics
  • wearable sensing systems
  • rehabilitation robotics
  • exoskeleton robots
  • intelligent prosthesis
  • human–robot interaction

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Published Papers (2 papers)

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Research

15 pages, 1639 KB  
Article
Quantifying Effects of Dataset Size, Data Variability, and Data Curvature on Modelling of Simulated Age-Related Motor Development Data
by Stephan C. D. Dobri, Stephen H. Scott and T. Claire Davies
Electronics 2025, 14(16), 3271; https://doi.org/10.3390/electronics14163271 - 18 Aug 2025
Viewed by 452
Abstract
Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, past models have not accounted for these differences. The [...] Read more.
Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, past models have not accounted for these differences. The objective of this research was to create an algorithm that accounts for variability in sample size and age, modifying the curvature and variability of the data to test the accuracy and repeatability. While increased sample size improves the model, data collection is often limited by funding or population interest in participation. The simulations provide models of variability relative to sample size of the typically developing population from 5 to 18 years. The algorithm was evaluated with a sample of two-hundred and eighty-eight children. Using these models based on varying sample size, one can have greater confidence when identifying motor deficits from outlying data. Future researchers can use the model accuracy and repeatability information from this work to assess confidence in their own models based on the dataset size, data variability, and data curvature. Full article
(This article belongs to the Special Issue Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives)
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16 pages, 8051 KB  
Article
Design and Joint Dynamics of Human Recumbent Rehabilitation Training Devices
by Qiulong Wu, Chaoyue Sun, Yi Liu, Sikai Wang, Jian Li and Peng Su
Electronics 2025, 14(9), 1724; https://doi.org/10.3390/electronics14091724 - 23 Apr 2025
Viewed by 570
Abstract
(1) Background: Patients bedridden due to accidental injuries, diseases, or age-related functional impairments require accelerated recovery of autonomous limb movement. A prone-position rehabilitation training device was developed to provide training intensity tailored to patients’ motor capabilities. (2) Methods: Based on principles of human [...] Read more.
(1) Background: Patients bedridden due to accidental injuries, diseases, or age-related functional impairments require accelerated recovery of autonomous limb movement. A prone-position rehabilitation training device was developed to provide training intensity tailored to patients’ motor capabilities. (2) Methods: Based on principles of human prone limb motion mechanics and torque balance, this study analyzed joint torque during limb movements using optical motion capture and six-dimensional force plate data. Joint torque curves during prone-position training were simulated, and a prototype device was developed. Prototype assembly and experimental validation of device–human synergy was conducted. (3) Results: Comparative analysis of joint torques between healthy individuals and patients revealed that joint torque increases as limbs contract inward. The maximum torque for upper limb joints was approximately 3.5 Nm, while the knee joint torque reached around 40 Nm. (4) Conclusions: Prototype testing confirmed the device’s design rationality, meeting human–machine synergy and rehabilitation training intensity requirements. This study provides a reference for the design of prone-position rehabilitation training devices. Full article
(This article belongs to the Special Issue Emerging Technologies for Assistive Robotics: Current Challenges and Future Perspectives)
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