Wearable Robots for Rehabilitation Engineering

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 654

Special Issue Editor


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Guest Editor
School of Engineering, University of the West of England, Bristol, UK
Interests: control engineering

Special Issue Information

Dear Colleagues,

Wearable robots and exoskeletons are important devices in medical fields, providing functionalities to assist people who are unable to move their bodies in a typical manner. Stroke is one of the most significant reasons for weakness and disability in the world and stroke patients need rehabilitation therapy to address these weaknesses. Since the early 1990s, many robotic devices for rehabilitation have been designed and developed. However, these devices cannot be used by the patients alone because they are cumbersome to use, heavily weighted, produce significant inertia, can cause joint misalignment, have static and dynamic friction, and exhibit backlash–hysteresis. Lower-limb exoskeletons can contain single or multiple joints. In a single-joint device, only one joint (e.g., pelvis, knee or ankle) is used; in a multiple-joint device, more than one hinge (e.g., ankle, knee, and hip) is actuated. The application areas of lower-limb exoskeletons include rehabilitation therapy. In recent years, many kinds of research on control approaches for knee joints have been conducted. A suitable control scheme is essential to ensure exoskeletons meet the requirements of the wearer. Dynamic system complexity, including the exoskeleton, its wearer’s accessories, and external disturbances, makes traditional controllers inappropriate. This complexity and system nonlinearity motivate researchers to suggest a variety of suitable controllers.

Dr. Mehdi Rakhtalarostami
Guest Editor

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Keywords

  • learning-based modeling and estimation for wearable robots for rehabilitation engineering
  • machine-learning-based advanced control techniques for wearable robots for rehabilitation engineering
  • digital twin technology for wearable robots for rehabilitation engineering
  • EMG-based and deep learning estimation in wearable robots
  • Next generation of wearable robots for rehabilitation engineering
  • nonlinear and robust control in wearable robots

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Published Papers (1 paper)

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24 pages, 2608 KiB  
Systematic Review
Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review
by Tala Zaim, Sara Abdel-Hadi, Rana Mahmoud, Amith Khandakar, Seyed Mehdi Rakhtala and Muhammad E. H. Chowdhury
Bioengineering 2025, 12(2), 144; https://doi.org/10.3390/bioengineering12020144 - 3 Feb 2025
Viewed by 104
Abstract
Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual’s ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor [...] Read more.
Upper limb disabilities, often caused by conditions such as stroke or neurological disorders, severely limit an individual’s ability to perform essential daily tasks, leading to a significant reduction in quality of life. The development of effective rehabilitation technologies is crucial to restoring motor function and improving patient outcomes. This systematic review examines the application of machine learning and deep learning techniques in myoelectric-controlled systems for upper limb rehabilitation, focusing on the use of electroencephalography and electromyography signals. By integrating non-invasive signal acquisition methods with advanced computational models, the review highlights how these technologies can enhance the accuracy and efficiency of rehabilitation devices. A comprehensive search of literature published between January 2015 and July 2024 led to the selection of fourteen studies that met the inclusion criteria. These studies showcase various approaches in decoding motor intentions and controlling assistive devices, with models such as Long Short-Term Memory Networks, Support Vector Machines, and Convolutional Neural Networks showing notable improvements in control precision. However, challenges remain in terms of model robustness, computational complexity, and real-time applicability. This systematic review aims to provide researchers with a deeper understanding of the current advancements and challenges in this field, guiding future research efforts to overcome these barriers and facilitate the transition of these technologies from experimental settings to practical, real-world applications. Full article
(This article belongs to the Special Issue Wearable Robots for Rehabilitation Engineering)
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