Literature DB >> 25296406

A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees.

Sebastian Amsuess, Peter Goebel, Bernhard Graimann, Dario Farina.   

Abstract

Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem. However, the lack of robustness of the current methods impedes their routine clinical application. In this study, we propose a novel algorithm for controlling multiple degrees of freedom sequentially, inherently proportionally and with high robustness, allowing a good level of prosthetic hand function. The control algorithm is based on the spatial linear combinations of amplitude-related EMG signal features. The weighting coefficients in this combination are derived from the optimization criterion of the common spatial patterns filters which allow for maximal discriminability between movements. An important component of the study is the validation of the method which was performed on both able-bodied and amputee subjects who used physical prostheses with customized sockets and performed three standardized functional tests mimicking daily-life activities of varying difficulty. Moreover, the new method was compared in the same conditions with one clinical/industrial and one academic state-of-the-art method. The novel algorithm outperformed significantly the state-of-the-art techniques in both subject groups for tests that required the activation of more than one degree of freedom. Because of the evaluation in real time control on both able-bodied subjects and final users (amputees) wearing physical prostheses, the results obtained allow for the direct extrapolation of the benefits of the proposed method for the end users. In conclusion, the method proposed and validated in real-life use scenarios, allows the practical usability of multifunctional hand prostheses in an intuitive way, with significant advantages with respect to previous systems.

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Year:  2014        PMID: 25296406     DOI: 10.1109/TNSRE.2014.2361478

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  15 in total

1.  A Multi-User Transradial Functional-Test Socket for Validation of New Myoelectric Prosthetic Control Strategies.

Authors:  Taylor C Hansen; Abigail R Citterman; Eric S Stone; Troy N Tully; Christopher M Baschuk; Christopher C Duncan; Jacob A George
Journal:  Front Neurorobot       Date:  2022-06-17       Impact factor: 3.493

2.  A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study.

Authors:  Aidan Dominic Roche; Ivan Vujaklija; Sebastian Amsüss; Agnes Sturma; Peter Göbel; Dario Farina; Oskar C Aszmann
Journal:  J Vis Exp       Date:  2015-11-06       Impact factor: 1.355

3.  Multi-position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control.

Authors:  Robert J Beaulieu; Matthew R Masters; Joseph Betthauser; Ryan J Smith; Rahul Kaliki; Nitish V Thakor; Alcimar B Soares
Journal:  J Prosthet Orthot       Date:  2017-04

4.  Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface.

Authors:  Hai Peng Wang; Yu Xuan Zhou; He Li; Guo Dong Liu; Si Meng Yin; Peng Ju Li; Shu Yue Dong; Chao Yue Gong; Shi Yu Wang; Yun Bo Li; Tie Jun Cui
Journal:  Adv Sci (Weinh)       Date:  2022-05-07       Impact factor: 17.521

5.  Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis.

Authors:  Cristina Piazza; Matteo Rossi; Manuel G Catalano; Antonio Bicchi; Levi J Hargrove
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-08-17       Impact factor: 4.528

6.  Elective amputation and bionic substitution restore functional hand use after critical soft tissue injuries.

Authors:  Oskar C Aszmann; Ivan Vujaklija; Aidan D Roche; Stefan Salminger; Malvina Herceg; Agnes Sturma; Laura A Hruby; Anna Pittermann; Christian Hofer; Sebastian Amsuess; Dario Farina
Journal:  Sci Rep       Date:  2016-10-10       Impact factor: 4.379

7.  Proportional estimation of finger movements from high-density surface electromyography.

Authors:  Nicolò Celadon; Strahinja Došen; Iris Binder; Paolo Ariano; Dario Farina
Journal:  J Neuroeng Rehabil       Date:  2016-08-04       Impact factor: 4.262

8.  Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping.

Authors:  Marko Markovic; Meike A Schweisfurth; Leonard F Engels; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2018-09-03       Impact factor: 4.262

9.  Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.

Authors:  Yao Zhang; Yanjian Liao; Xiaoying Wu; Lin Chen; Qiliang Xiong; Zhixian Gao; Xiaolin Zheng; Guanglin Li; Wensheng Hou
Journal:  Front Neurorobot       Date:  2018-02-12       Impact factor: 2.650

10.  The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis.

Authors:  Marko Markovic; Meike A Schweisfurth; Leonard F Engels; Tashina Bentz; Daniela Wüstefeld; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2018-03-27       Impact factor: 4.262

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