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Machines
Special Issues
Intelligent Medical Robotics：Design, Control and Applications of Medical Robotics
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Intelligent Medical Robotics：Design, Control and Applications of Medical Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 2222

Special Issue Editor

Prof. Dr. Allon Guez

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA
Interests: control systems; robotics; artificial intelligence (A.I.); automated decision making; medical robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on intelligent medical robotics—the intersection of AI and medical robotics, which has already been proven to be a viable, fruitful domain on its own. This Special Issue aims to collect recent research on all of the below-listed topics. Review papers are also welcome. Topics of interest include (but are not limited to) the use of AI in the following areas: brain–computer interfaces; autonomous robots; mobile robots; service robots; robotic implants; nano-, micro-, and milli-robots; educational (interactive) robots; modular robots; collaborative robots; medical military robots; surgical robots; rehabilitation robots; biorobots; telepresence robots; pharmacy/lab automation; companion robots; disinfection robots; machine intelligence; neural networks; deep learning; medical big data; medical predictive analytics; and medical robotics.

Prof. Dr. Allon Guez
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 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. Machines 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 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

  • brain–computer interfaces
  • autonomous robots
  • mobile robots
  • service robots
  • robotic implants
  • nano-, micro-, and milli-robots
  • educational (interactive) robots
  • modular robots
  • collaborative robots
  • medical military robots
  • surgical robots
  • rehabilitation robots
  • biorobots
  • telepresence robots
  • pharmacy/lab automation
  • companion robots
  • disinfection robots
  • machine intelligence
  • neural networks
  • deep learning
  • medical big data
  • medical predictive analytics
  • and medical robotics

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Published Papers (1 paper)

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22 pages, 3596 KiB  
Article
Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques
by Laura Orona-Trujillo, Isaac Chairez and Mariel Alfaro-Ponce
Machines 2024, 12(1), 49; https://doi.org/10.3390/machines12010049 - 10 Jan 2024
Viewed by 1772
Abstract
Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, [...] Read more.
Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient’s therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information. Full article
(This article belongs to the Special Issue Intelligent Medical Robotics：Design, Control and Applications of Medical Robotics)
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