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Lower Limb Rehabilitation Robots

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 2317

Special Issue Editors


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Guest Editor
Neural and Cognitive Engineering group, Consejo Superior de Investigaciones Científicas Center for Automation and Robotics (CSIC), Ctra. Campo Real, km 0.200 La Poveda - Arganda del Rey, 28500 Madrid, Spain
Interests: rehabilitation robotics; neurophysiology; tremor; cerebral palsy; neuroprosthetics

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Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: posture control; bipedal robots; robots for posture rehabilitation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Centre for Research & Technology Hellas (CERTH), 6th km Harilaou, 57001 Thermi, Thessaloniki, Greece
Interests: physiology simulations; analysis of movement in sports and rehabilitation

Special Issue Information

Dear Colleagues,

The scientific and medical community is becoming more and more interested in Rehabilitation Robotics. This special issue is structured around the most important scientific aspects of  man-machine interaction. It is, particularly, focused on the lower limb rehabilitation. The topic, starting with passive devices that sustained the patient and forced him to perform gait exercises, is now strongly intertwined with biped robotics, and biometric signal processing.

In this field, the interaction with humans increased from a pure exchange of information (in teleoperation tasks) and service robotics to the involvement of physical and cognitive modalities.  This interaction has a twofold scenario, namely, first, a cognitive interaction by means of which the human is able to control the robot while the robot transmits feedback to the human; secondly, a biomechanical interaction leading to the application of controlled forces between both actors. Compliance, reinforcement of balance and multi-joints voluntary control of the exoskeleton by the patient are the frontier of this technology.

In this Special Issue we plan to include studies dealing with the cognitive aspects of this interaction. Interaction based on bioelectrical and biomechanical activity as well as novel approaches based on interfacing both the Peripheral and Central Nervous Systems (PNS, CNS) with the robot are welcome. Physical aspects of this interaction should also be addressed, in this regard; signal processing and artificial intelligence are expected to play an important role in interpreting the available data, for training, and for the real time control. Links between exoskeletons and Functional Electrical Stimulation (FES) are also considered.

Theoretical contributions and experiences in the aforementioned fields are invited.

This special issue is addressed to experts and practitioners on control, robotics, signal processing, bioengineering, neuroscience, physiatry, and rehabilitation.

Dr. Eduardo Rocon
Prof. Dr. Giuseppe Menga
Dr. Alexandros Alexopoulos
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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

  • lower limb exoskeleton
  • biped robotics
  • human gait analysis
  • biometric data (EMG, EEG) processing
  • artificial intelligence

Published Papers (1 paper)

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Research

18 pages, 5403 KiB  
Article
Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons
by Muhammad Tallal Saeed, Jahan Zeb Gul, Zareena Kausar, Asif Mahmood Mughal, Zia Mohy Ud Din and Shiyin Qin
Appl. Sci. 2022, 12(8), 3973; https://doi.org/10.3390/app12083973 - 14 Apr 2022
Cited by 2 | Viewed by 1925
Abstract
Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents [...] Read more.
Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results. Full article
(This article belongs to the Special Issue Lower Limb Rehabilitation Robots)
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