Exoskeletal Prosthetics: Recent Developments, Innovations and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 2357

Special Issue Editors


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Guest Editor
Department of Orthopaedic Surgery, Laboratory for Biomechanics and Biomaterials, Hannover Medical School, 30625 Hannover, Germany
Interests: biomechanics; prosthetics; arthroplasty; movement science

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Guest Editor
Department of Trauma Surgery, Medical School Hannover, Hannover, Germany
Interests: amputation surgery; peripheral nerve surgery; prosthetics; human–machine interfaces
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Special Issue Information

Dear Colleagues,

Research in prosthetic technologies and fitting processes have made enormous progress in recent years, triggered by the “technology boom” in the fields of microelectromechanical and biomechatronics, among other things. Microprocessor-controlled prosthetic joints and multi-articulating prosthetic hands have become part of the standard care for amputees. In particular, the current increasing interdisciplinarity in this field is leading to various approaches to promising improvements in the prosthetic care that were not previously thought possible, and is helping in addressing the remaining bottlenecks.

These include robotics, closed-loop-systems, the increased use of artificial intelligence, new methods in pattern recognition, smart materials, bionic strategies in amputation surgery, and the involvement of man–machine interfaces (e.g., osseointegrated skeletal prosthetic fittings). The goal is to create the most natural, intuitive prosthetic control possible, as well as the automated recognition and implementation of a reliable feedback mechanism of prosthetic and environmental conditions to the user at the maximum possible comfort relieving amputation-associated pain.

This Special Issue aims to provide a medium for the presentation and dissemination of current and original contributions to the field of innovations in exoskeletal prosthetic care in this interdisciplinary research field embracing medical and technological approaches representing promising advances in amputee care.

Topics of interest for this Special Issue include:

  • Bionic concepts (transcutaneous osseointegrated prosthetic systems (TOPS), targeted muscle reinnervation (TMR), targeted sensory reinnervation (TSR));
  • Prosthetics and robotics;
  • Artificial intelligence (AI);
  • Pattern recognition;
  • Closed-loop-systems;
  • Human–machine interfaces;
  • Virtual reality in prosthetics;
  • Breakthrough in rehabilitation strategies;
  • Smart materials and sockets;
  • Simulation;
  • Deep learning;
  • Supply research;
  • Feedback systems;
  • Prosthetic control.

Dr. Eike Jakubowitz
Dr. Jennifer Ernst
Guest Editors

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Published Papers (2 papers)

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Research

34 pages, 2437 KiB  
Article
Patient Reported Outcome Measures (PROMs) Amongst Lower Extremity Agonist–Antagonist Myoneural Interface (AMI) Amputees
by Rachael B. Chiao, Corey L. Sullivan, Lori Berger, Tawnee L. Sparling, Kendall Clites, Tracy Landry and Matthew J. Carty
Appl. Sci. 2023, 13(18), 10508; https://doi.org/10.3390/app131810508 - 20 Sep 2023
Viewed by 770
Abstract
(1) Background: The standard surgical approach to amputation has failed to evolve significantly over the past century. Consequently, standard amputations often fall short with regard to improving the quality of life (QoL) for patients. A modified lower extremity amputation technique incorporating agonist–antagonist myoneural [...] Read more.
(1) Background: The standard surgical approach to amputation has failed to evolve significantly over the past century. Consequently, standard amputations often fall short with regard to improving the quality of life (QoL) for patients. A modified lower extremity amputation technique incorporating agonist–antagonist myoneural interface (AMI) constructs provides patients with a novel alternative to standard amputation and, to-date, has demonstrated overall significant improvements in their physical and mental wellbeing. (2) Methods: Five PROMs surveys, (1) EQ-5D-3L, (2) Lower Extremity Functional Scale (LEFS), (3) PROMIS-57, (4) Short Form-36 (SF-36), and (5) Sickness Impact Profile (SIP), were administered to our research cohort pre-operatively (baseline) and at various timepoints post-operatively. (3) Results: The cohort’s baseline and 12-month post-operative responses were compared to determine score improvement. Significant improvements were demonstrated across all survey domains (p < 0.05). (4) Conclusions: Modified lower extremity amputation with AMI construction has the potential to provide amputees with increased quality of life when compared to the pre-operative state. However, further investigation is necessary to determine whether the patient-reported outcome measures of the AMI amputee cohort are superior to those who receive a standard amputation. Full article
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17 pages, 3779 KiB  
Article
Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study
by Eike Jakubowitz, Thekla Feist, Alina Obermeier, Carina Gempfer, Christof Hurschler, Henning Windhagen and Max-Heinrich Laves
Appl. Sci. 2023, 13(9), 5728; https://doi.org/10.3390/app13095728 - 6 May 2023
Cited by 1 | Viewed by 1201
Abstract
Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can [...] Read more.
Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can provide information prior to the actual grasping process, allowing for more intuitive prosthetic control. EEG datasets were captured from 13 healthy subjects who repeatedly performed 16 activities of daily living. Common classifiers were trained on features extracted from the waking-state frequency and total-frequency time domains. Different training scenarios were used to investigate whether classifiers can already be pre-trained by base networks for fine-tuning with data of a target person. A support vector machine algorithm with spatial covariance matrices as EEG signal descriptors based on Riemannian geometry showed the highest balanced accuracy (0.91 ± 0.05 SD) in discriminating five grasping categories according to the Cutkosky taxonomy in an interval from 1.0 s before to 0.5 s after the initial movement. Fine-tuning did not improve any classifier. No significant accuracy differences between the two frequency domains were apparent (p > 0.07). Neurofunctional representations enabled highly accurate discrimination of five different grasping movements. Our results indicate that, for upper limb prosthetics, it is possible to use them in a sufficiently timely manner and to predict the respective grasping task as a discrete category to kinematically prepare the prosthetic hand. Full article
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