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Prosthesis
Special Issues
Innovations in the Control and Assessment of Prosthetic Arms
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Innovations in the Control and Assessment of Prosthetic Arms

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

Deadline for manuscript submissions: closed (10 December 2023) | Viewed by 6554

Special Issue Editors

Prof. Dr. Peter Kyberd

E-Mail Website
Guest Editor
Department of Ortho and MSK Science, University College London, London WC1E 6BT, UK
Interests: upper limb prosthetics; assessment; sensors
Dr. Alix Chadwell

E-Mail Website
Guest Editor
School of Healthcare Enterprise and Innovation, University of Southampton, Southampton SO16 7NS, UK
Interests: prostheses; biomechanics; sensors

Special Issue Information

Dear Colleagues,

In recent years, innovations in the design and application of technology for upper limb prosthetics have produced a wider range of solutions and techniques than we have ever seen before. The increasing pace of change, greater levels of large-scale integration in electronics, and innovation of materials have combined with new attitudes to difference and created a range of new opportunities. Changes in attitude over appearance have opened up possibilities for novel non-anthropomorphic hand designs.

We are pleased to invite you to contribute to a Special Edition of Prosthesis that captures the latest innovations in the design, control, and application of prosthetic arms and hands.

This Topic aims to capture areas of recent innovation in the application of prosthetic arms in the clinical setting.

In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel sensors for reading user commands;
  • Sensors for feedback to the controller or wearer;
  • Assessing the impact of new control paradigms in prosthesis use; 
  • Novel terminal device designs.

We look forward to receiving your contributions.

Prof. Dr. Peter Kyberd
Dr. Alix Chadwell
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

  • upper limb prosthesis
  • artificial arm
  • hand prosthesis
  • control
  • sensory
  • human–machine interface
  • feedback
  • assessment
  • outcome measure

Published Papers (4 papers)

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23 pages, 4468 KiB  
Article
Non-Invasive Sensory Input Results in Changes in Non-Painful and Painful Sensations in Two Upper-Limb Amputees
by Eugen Romulus Lontis, Ken Yoshida and Winnie Jensen
Prosthesis 2024, 6(1), 1-23; https://doi.org/10.3390/prosthesis6010001 - 19 Dec 2023
Viewed by 1010
Abstract
Designs of active prostheses attempt to compensate for various functional losses following amputation. Integration of sensory feedback with the functional control re-enables sensory interaction with the environment through the prosthetic. Besides the functional and sensory loss, amputation induces anatomical and physiological changes of [...] Read more.
Designs of active prostheses attempt to compensate for various functional losses following amputation. Integration of sensory feedback with the functional control re-enables sensory interaction with the environment through the prosthetic. Besides the functional and sensory loss, amputation induces anatomical and physiological changes of the sensory neural pathways, both peripherally and centrally, which can lead to phantom limb pain (PLP). Additionally, referred sensation areas (RSAs) likely originating from peripheral nerve sprouting, regeneration, and sensory reinnervation may develop. RSAs might provide a non-invasive access point to sensory neural pathways that project to the lost limb. This paper aims to report on the sensory input features, elicited using non-invasive electrical stimulation of RSAs that over time alleviated PLP in two upper-limb amputees. The distinct features of RSAs and sensation evoked using mechanical and electrical stimuli were characterized for the two participants over a period of 7 and 9 weeks, respectively. Both participants received transradial and transhumeral amputation following traumatic injuries. In one participant, a relatively low but stable number of RSAs provided a large variety of types of evoked phantom hand (PH) sensations. These included non-painful touch, vibration, tingling, stabbing, pressure, warmth/cold as well as the perception of various positions and movements of the phantom hand upon stimulation. Discomforting and painful sensations were induced with both mechanical and electrical stimuli. The other participant had a relatively large number of RSAs which varied over time. Stimulation of the RSAs provided mostly non-painful sensations of touch in the phantom hand. Temporary PLP alleviation and a change in the perception of the phantom hand from a tight to a more open fist were reported by both participants. The specificity of RSAs, dynamics in perception of the sensory input, and the associated alleviation of PLP could be effectively exploited by designs of future active prostheses. As such, techniques for the modulation of the sensory input associated with paradigms from interaction with the environment may add another dimension of protheses towards integrating personalized therapy for PLP. Full article
(This article belongs to the Special Issue Innovations in the Control and Assessment of Prosthetic Arms)
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14 pages, 4495 KiB  
Article
Myo Transformer Signal Classification for an Anthropomorphic Robotic Hand
by Bolivar Núñez Montoya, Edwin Valarezo Añazco, Sara Guerrero, Mauricio Valarezo-Añazco, Daniela Espin-Ramos and Carlos Jiménez Farfán
Prosthesis 2023, 5(4), 1287-1300; https://doi.org/10.3390/prosthesis5040088 - 28 Nov 2023
Viewed by 1120
Abstract
The evolution of anthropomorphic robotic hands (ARH) in recent years has been sizable, employing control techniques based on machine learning classifiers for myoelectric signal processing. This work introduces an innovative multi-channel bio-signal transformer (MuCBiT) for surface electromyography (EMG) signal recognition and classification. The [...] Read more.
The evolution of anthropomorphic robotic hands (ARH) in recent years has been sizable, employing control techniques based on machine learning classifiers for myoelectric signal processing. This work introduces an innovative multi-channel bio-signal transformer (MuCBiT) for surface electromyography (EMG) signal recognition and classification. The proposed MuCBiT is an artificial neural network based on fully connected layers and transformer architecture. The MuCBiT recognizes and classifies EMG signals sensed from electrodes patched over the arm’s surface. The MuCBiT classifier was trained and validated using a collected dataset of four hand gestures across ten users. Despite the smaller size of the dataset, the MuCBiT achieved a prediction accuracy of 86.25%, outperforming traditional machine learning models and other transformer-based classifiers for EMG signal classification. This integrative transformer-based gesture recognition promises notable advancements for ARH development, underscoring prospective improvements in prosthetics and human–robot interaction. Full article
(This article belongs to the Special Issue Innovations in the Control and Assessment of Prosthetic Arms)
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26 pages, 16399 KiB  
Technical Note
Controlling a Below-the-Elbow Prosthetic Arm Using the Infinity Foot Controller
by Peter L. Bishay, Jack Wilgus, RunRun Chen, Diego Valenzuela, Victor Medina, Calvin Tan, Taylor Ittner, Miguel Caldera, Cristina Rubalcava, Shaghik Safarian, Gerbert Funes Alfaro, Alfredo Gonzalez-Martinez, Matthew Gosparini, Jose Fuentes-Perez, Andy Lima, Jonnathan Villalobos and Abrahan Solis
Prosthesis 2023, 5(4), 1206-1231; https://doi.org/10.3390/prosthesis5040084 - 20 Nov 2023
Viewed by 1543
Abstract
Nowadays there are various prosthetic arm designs in the literature, the market, and CAD design websites, with different shapes, sizes, and degrees of freedom. Only limited options are available for controlling such prostheses. Prosthetic arm users reported muscle fatigue and unreliability when using [...] Read more.
Nowadays there are various prosthetic arm designs in the literature, the market, and CAD design websites, with different shapes, sizes, and degrees of freedom. Only limited options are available for controlling such prostheses. Prosthetic arm users reported muscle fatigue and unreliability when using the market-dominated myoelectric sensors. This work presents the “Infinity Foot Controller” as a new approach to control a five-finger below-the-elbow prosthetic arm with wrist rotation and bending capabilities. This foot control system receives user input from a custom insole and a sensor-controller unit placed alongside the user’s shoe to perform various hand grips, gestures, and/or rotations. To demonstrate the new foot controller, a design of a 3D-printed below-the-elbow prosthetic arm, called the “Infinity Arm”, is presented. This arm is suitable for gripping relatively lightweight objects and making hand gestures. It includes a wrist actuation system that permits 120° wrist rotation and 70° wrist extension and flexion. It also includes a haptic feedback system that utilizes fingertip force sensors to relay a vibratory response in an armband placed on the user’s arm, giving the user a sense of touch. A proof-of-concept model was built to demonstrate the system and a testing procedure was proposed. Full article
(This article belongs to the Special Issue Innovations in the Control and Assessment of Prosthetic Arms)
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22 pages, 3023 KiB  
Perspective
A Perspective on Prosthetic Hands Control: From the Brain to the Hand
by Cosimo Gentile and Emanuele Gruppioni
Prosthesis 2023, 5(4), 1184-1205; https://doi.org/10.3390/prosthesis5040083 - 16 Nov 2023
Viewed by 2046
Abstract
The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging [...] Read more.
The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand. Full article
(This article belongs to the Special Issue Innovations in the Control and Assessment of Prosthetic Arms)
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