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Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control

Published on Dec 1, 2018in Journal of Neuroengineering and Rehabilitation 3.58
· DOI :10.1186/s12984-018-0361-3
Linda Resnik23
Estimated H-index: 23
(Brown University),
He Huang21
Estimated H-index: 21
(UNC: University of North Carolina at Chapel Hill)
+ 3 AuthorsNancy Wolk1
Estimated H-index: 1
(UNC: University of North Carolina at Chapel Hill)
Cite
Abstract
Background Although electromyogram (EMG) pattern recognition (PR) for multifunctional upper limb prosthesis control has been reported for decades, the clinical benefits have rarely been examined. The study purposes were to: 1) compare self-report and performance outcomes of a transradial amputee immediately after training and one week after training of direct myoelectric control and EMG pattern recognition (PR) for a two-degree-of-freedom (DOF) prosthesis, and 2) examine the change in outcomes one week after pattern recognition training and the rate of skill acquisition in two subjects with transradial amputations.
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  • References (49)
  • Citations (5)
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References49
Newest
Published on Dec 1, 2017in Journal of Neuroengineering and Rehabilitation 3.58
Niclas Nilsson1
Estimated H-index: 1
(Chalmers University of Technology),
Bo Håkansson28
Estimated H-index: 28
(Chalmers University of Technology),
Max Jair Ortiz-Catalan9
Estimated H-index: 9
(Chalmers University of Technology)
Background: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms ...
Published on Dec 1, 2017in Scientific Reports 4.01
Levi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University),
Laura A. Miller14
Estimated H-index: 14
(NU: Northwestern University)
+ 1 AuthorsTodd A. Kuiken40
Estimated H-index: 40
(NU: Northwestern University)
Recently commercialized powered prosthetic arm systems hold great potential in restoring function for people with upper-limb loss. However, effective use of such devices remains limited by conventional (direct) control methods, which rely on electromyographic signals produced from a limited set of muscles. Targeted Muscle Reinnervation (TMR) is a nerve transfer procedure that creates additional recording sites for myoelectric prosthesis control. The effects of TMR may be enhanced when paired wit...
Published on Dec 1, 2017in Journal of Neuroengineering and Rehabilitation 3.58
Adenike A. Adewuyi4
Estimated H-index: 4
(NU: Northwestern University),
Levi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University),
Todd A. Kuiken40
Estimated H-index: 40
Background The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study’s objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function.
Rami N. Khushaba17
Estimated H-index: 17
(Information Technology University),
Ali H. Al-Timemy7
Estimated H-index: 7
(UOB: University of Baghdad)
+ 1 AuthorsAdel Al-Jumaily15
Estimated H-index: 15
(Information Technology University)
The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimati...
Published on Jul 11, 2017in Frontiers in Neuroscience 3.65
Xiaolong Zhai2
Estimated H-index: 2
(CityU: City University of Hong Kong),
Beth Jelfs9
Estimated H-index: 9
(CityU: City University of Hong Kong)
+ 1 AuthorsChung Tin11
Estimated H-index: 11
(CityU: City University of Hong Kong)
Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural networ...
Published on Jun 13, 2017in Sensors 3.03
Qi Huang4
Estimated H-index: 4
,
Dapeng Yang10
Estimated H-index: 10
+ 3 AuthorsKiyoshi Kotani14
Estimated H-index: 14
Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performanc...
Marina M. C. Vidovic3
Estimated H-index: 3
(Technical University of Berlin),
Han-Jeong Hwang14
Estimated H-index: 14
(Technical University of Berlin)
+ 3 AuthorsKlaus-Robert Müller82
Estimated H-index: 82
(Technical University of Berlin)
Fundamental changes over time of surface EMG signal characteristics are a challenge for myocontrol algorithms controlling prosthetic devices. These changes are generally caused by electrode shifts after donning and doffing, sweating, additional weight or varying arm positions, which results in a change of the signal distribution-a scenario often referred to as covariate shift. A substantial decrease in classification accuracy due to these factors hinders the possibility to directly translate EMG...
Published on May 12, 2016in Frontiers in Neuroscience 3.65
Francesca Cordella5
Estimated H-index: 5
(Università Campus Bio-Medico),
Anna Lisa Ciancio6
Estimated H-index: 6
(Università Campus Bio-Medico)
+ 4 AuthorsLoredana Zollo17
Estimated H-index: 17
(Università Campus Bio-Medico)
The loss of one hand can significantly affect the level of autonomy and the capability of performing daily living, working and social activities. The current prosthetic solutions contribute in a poor way to overcome these problems due to the limitations of the interfaces adopted for controlling the prosthesis and to the absence of force or tactile feedback which limit the hand grasp capabilities. In order to provide indications for further developments in the prosthetic field to increase user sa...
Todd A. Kuiken40
Estimated H-index: 40
(Rehabilitation Institute of Chicago),
Laura A. Miller14
Estimated H-index: 14
(Rehabilitation Institute of Chicago)
+ 1 AuthorsLevi J. Hargrove29
Estimated H-index: 29
(Rehabilitation Institute of Chicago)
With existing conventional prosthesis control (direct control), individuals with a transradial amputation use two opposing muscle groups to control each prosthesis motor. As component complexity increases, subjects must switch the prosthesis into different modes to control each component in sequence. Pattern recognition control offers the ability to control multiple movements in a seamless manner without switching. In this paper, three individuals with a transradial amputation completed a home t...
Published on Sep 1, 2015in Archives of Physical Medicine and Rehabilitation 2.70
Linda Resnik23
Estimated H-index: 23
(Brown University),
Matthew Borgia10
Estimated H-index: 10
(VA: United States Department of Veterans Affairs)
Abstract Objectives To examine the internal consistency, test-retest reliability, validity, and responsiveness of the shortened version of the Disabilities of the Arm, Shoulder and Hand (QuickDASH) questionnaire in persons with upper limb amputation. Design Cross-sectional and longitudinal. Setting Three sites participating in the U.S. Department of Veterans Affairs Home Study of the DEKA Arm. Participants A convenience sample of upper limb amputees (N=44). Interventions Training with a multifun...
Cited By5
Newest
Published on Dec 1, 2019in Scientific Reports 4.01
Ananya S. Dhawan (GMU: George Mason University), Biswarup Mukherjee3
Estimated H-index: 3
(GMU: George Mason University)
+ 7 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographic control. Unlike myoelec...
Published on Jan 16, 2019in Journal of Neuroengineering and Rehabilitation 3.58
Richard B. Woodward1
Estimated H-index: 1
(NU: Northwestern University),
Levi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University)
Background Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.
Published on May 17, 2019in Health technology
Nabasmita Phukan , Nayan M. Kakoty4
Estimated H-index: 4
+ 1 AuthorsJohn Q. Gan19
Estimated H-index: 19
Developing prosthetic hands with high functionality and ease of use is the focus of current research in the area of Electromyogram (EMG) based prosthesis control. Although individuals with upper limb loss can perform grasping operations with currently available prosthetic hands, more intuitive control of finger movements is required to replicate the complex motor functions of human hands. A significant challenge is to classify the finger movements with higher recognition rates using a smaller nu...
Published on May 30, 2019in Expert Review of Medical Devices 2.21
Marcella A Kelley (Johns Hopkins University), Heather L. Benz7
Estimated H-index: 7
(CDRH: Center for Devices and Radiological Health)
+ 1 AuthorsJohn F. P. Bridges27
Estimated H-index: 27
(Johns Hopkins University)
Published on Mar 1, 2019in Current Surgery Reports
Aidan D. Roche9
Estimated H-index: 9
,
Ben Lakey (Imperial College London)+ 3 AuthorsOskar C. Aszmann24
Estimated H-index: 24
(Medical University of Vienna)
Purpose of Review This paper aims to summarise the development trends in upper limb bionics over the past 5 years.
Published on Jan 24, 2019in Sensors 3.03
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the pr...
Published on Oct 18, 2018in PLOS ONE 2.78
Linda Resnik23
Estimated H-index: 23
,
Frantzy Acluche3
Estimated H-index: 3
+ 4 AuthorsNicole Sasson4
Estimated H-index: 4
Published in bioRxiv
Ananya S. Dhawan (GMU: George Mason University), Biswarup Mukherjee3
Estimated H-index: 3
(GMU: George Mason University)
+ -3 AuthorsSiddhartha Sikdar16
Estimated H-index: 16
(GMU: George Mason University)
Technological advances in multi-articulated prosthetic hands have outpaced the methods available to amputees to intuitively control these devices. Amputees often cite difficulty of use as a key contributing factor for abandoning their prosthesis, creating a pressing need for improved control technology. A major challenge of traditional myoelectric control strategies using surface electromyography electrodes has been the difficulty in achieving intuitive and robust proportional control of multipl...