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Prosthesis
Special Issues
Sensorimotor Prostheses and Powered Exoskeletons for Movement Control
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Sensorimotor Prostheses and Powered Exoskeletons for Movement Control

A special issue of Prosthesis (ISSN 2673-1592).

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 3357

Special Issue Editors

Dr. Monzurul Alam

E-Mail Website
Guest Editor
NeuroRecovery Research Hub, University of New South Wales, Sydney, NSW 2052, Australia
Interests: sensorimotor prostheses; robotic exoskeleton; neuromodulation; neurorehabilitation
Dr. M. S. Wong

E-Mail Website
Guest Editor
Department of Biomedical Engineering, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong
Interests: scoliosis; spinal orthotics; prevention of fragility fractures; gait and posture analysis; CAD/CAM in prosthetics and orthotics; prosthetics and orthotics outcome evaluation

Special Issue Information

Dear Colleagues,

Prosthesis technology has changed significantly from the age of ‘Captain Hook’ to the US Defense Advanced Research Projects Agency’s ‘Revolutionizing Prosthetics’; however, there is still a way to go toward completely replicating the natural function of human limbs due to the lack of sensorimotor integration. Similarly, recent developments in power exoskeletons lack sensory integration for precise motor control. The sensory circuitry in healthy nervous systems silently monitors the synergic operation of our body and provides essential feedback (or feedforward?) to the control centers.

The aim of this Special Issue is to showcase new and exciting strategies of sensory integration for prosthetic and/or exoskeleton control.

In this Issue, we like to invite new submissions on novel strategies of sensorimotor integration for better and safer functional movements of prostheses and exoskeletons. Furthermore, 3D technologies are rapidly changing the prosthetic and exoskeleton industry, and we would like to invite new submissions on this aspect as well.

Dr. Monzurul Alam
Dr. M. S. Wong
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. Prosthesis 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 1600 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

  • sensorimotor prostheses
  • robotic exoskeleton
  • control algorithm
  • sensory integration
  • functional movement
  • 3D printing
  • artificial intelligence
  • clinical interface
  • patient education and training
  • outcome measure

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Published Papers (1 paper)

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Research

19 pages, 23806 KiB  
Article
Human Walking Gait Classification Utilizing an Artificial Neural Network for the Ergonomics Study of Lower Limb Prosthetics
by Farika Tono Putri, Wahyu Caesarendra, Grzegorz Królczyk, Adam Glowacz, Hartanto Prawibowo, Rifky Ismail and Ragil Tri Indrawati
Prosthesis 2023, 5(3), 647-665; https://doi.org/10.3390/prosthesis5030046 - 12 Jul 2023
Cited by 10 | Viewed by 2523
Abstract
Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due [...] Read more.
Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due to certain factors. One of the factors is vascular-related disease, which leads to amputation. Prosthetic usage can increase an amputee’s quality of life. Therefore, studies of the ergonomic design of prosthetics are important. The ergonomic factor in design delivers prosthetic products that are comfortable for daily use. One way to incorporate the ergonomic design of prosthetics is by studying the human walking gait. This paper presents a multiclassification of human walking gait based on electromyography (EMG) signals using a machine learning method. An EMG sensor was attached to the bicep femoris longus and gastrocnemius lateral head to acquire the EMG signal. The experiment was conducted by volunteers during normal walking activity at various speeds and the movements were segmented as initial contact, which was labeled as initial gait; loading response to the terminal stance, which was labeled as mid-gait; and pre-swing to terminal swing, which was labeled as final gait. The EMG signal was then characterized using an artificial neural network (ANN) and compared to six training accuracy methods, i.e., the Levenberg–Marquardt backpropagation training algorithm, quasi-Newton training method, Bayesian regulation backpropagation training method, gradient descent backpropagation, gradient descent with adaptive learning rate backpropagation, and one-step secant backpropagation. The machine learning study performed well in the classification of three classes of human walking gait with an overall accuracy (training, testing, and validation) of 96% for Levenberg–Marquardt backpropagation. The gait data will be used to explore the design of lower limb prosthetics in future research. Full article
(This article belongs to the Special Issue Sensorimotor Prostheses and Powered Exoskeletons for Movement Control)
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