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Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Published on Nov 2, 2017in PLOS ONE2.776
· DOI :10.1371/journal.pone.0186318
Han-Jeong Hwang16
Estimated H-index: 16
,
Janne M. Hahne9
Estimated H-index: 9
,
Klaus-Robert Müller92
Estimated H-index: 92
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Abstract
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  • References (48)
  • Citations (6)
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References48
Newest
#1Adenike A. Adewuyi (NU: Northwestern University)H-Index: 5
#2Levi J. Hargrove (NU: Northwestern University)H-Index: 33
Last. Todd A. KuikenH-Index: 43
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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.
7 CitationsSource
#1Janne M. HahneH-Index: 9
#2Marko MarkovicH-Index: 11
Last. Dario Farina (Imperial College London)H-Index: 76
view all 3 authors...
State of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer a significant increase in functionality, but their clinical impact is limited, mainly due to lack of reliability. In this study, we analyse two conceptually different machine learning approaches, focusing on their robustness and performance in a closed loop application. A classification (fi...
31 CitationsSource
#1David Hofmann (MPG: Max Planck Society)H-Index: 2
#2Ning Jiang (UW: University of Waterloo)H-Index: 29
Last. Dario Farina (GAU: University of Göttingen)H-Index: 76
view all 4 authors...
European Research Council (ERC) [DEMOVE 267888]; Federal Ministry of Education and Research (BMBF) of Germany [01GQ0811]; New Faculty Startup Grant of the University of Waterloo
19 CitationsSource
#1Gauravkumar K. Patel (DLR: German Aerospace Center)H-Index: 3
#2Strahinja Dosen (GAU: University of Göttingen)H-Index: 21
Last. Dario Farina (GAU: University of Göttingen)H-Index: 76
view all 4 authors...
Objective. Closing the loop in myoelectric prostheses by providing artificial somatosensory feedback to the user is an important need for prosthetic users. Previous studies investigated feedback strategies in combination with the control of one degree of freedom of simple grippers. Modern hands, however, are sophisticated multifunction systems. In this study, we assessed multichannel electrotactile feedback integrated with an advanced method for the simultaneous and proportional control of indiv...
9 CitationsSource
#1Marina M.-C. Vidovic (Technical University of Berlin)H-Index: 4
#2Han-Jeong Hwang (Technical University of Berlin)H-Index: 16
Last. Klaus-Robert Müller (Technical University of Berlin)H-Index: 4
view all 6 authors...
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 E...
29 CitationsSource
#1Adenike A. Adewuyi (Rehabilitation Institute of Chicago)H-Index: 5
#2Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 33
Last. Todd A. Kuiken (Rehabilitation Institute of Chicago)H-Index: 43
view all 3 authors...
Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps a...
39 CitationsSource
#1Janne M. HahneH-Index: 9
1 CitationsSource
#1Janne M. HahneH-Index: 9
#2Sven DähneH-Index: 19
Last. Lucas C. Parra (CCNY: City College of New York)H-Index: 51
view all 5 authors...
Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation o...
27 CitationsSource
#1Ali Ameri (UNB: University of New Brunswick)H-Index: 5
#2Ernest Nlandu Kamavuako (AAU: Aalborg University)H-Index: 14
Last. Philip A. Parker (UNB: University of New Brunswick)H-Index: 30
view all 5 authors...
64 CitationsSource
#1Han-Jeong Hwang (Technical University of Berlin)H-Index: 16
#2Janne M. Hahne (Technical University of Berlin)H-Index: 9
Last. Klaus-Robert MüllerH-Index: 92
view all 3 authors...
Objective. Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs). Approach. Ten able-bodied subjects and one person with congenital upper-li...
15 CitationsSource
Cited By6
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Abstract One of the most common issue in surface electromyography (sEMG) based myocontrol is to set a recurrent feature which allows to ensure a reliable multi degree of freedom (MDoF) prosthetic drive, mainly due to non-stationary behavior of signal. According to studies, electrode placement and shifts, variation in muscle contraction effort and muscle fatigue are the most common disturbance sources in sEMG recording, which traduces into a cumbersome donning and doffing recalibration. Many rele...
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#2Shyamanta M. Hazarika (IITG: Indian Institute of Technology Guwahati)H-Index: 13
view all 2 authors...
Prosthetic hands have made a significant influence on the quality of life of people with upper arm amputation. Research on prosthetic hands today is focused on replicating the functionalities of the biological hands. The present article provides a bibliometric survey on bionic hands, done through a compilation of a scientific publications database on the field of prosthetic hands spanning the last two decades. Through network-based information analysis, meaningful patterns are inferred, and seve...
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#1Andrew Y. Paek (UH: University of Houston)H-Index: 4
#2Alycia GaileyH-Index: 2
Last. Jose L. Contreras-Vidal (UH: University of Houston)H-Index: 35
view all 5 authors...
Objectivea#13; Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual's g...
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#1Carles IgualH-Index: 1
Last. Jorge IgualH-Index: 10
view all 4 authors...
State-of-the-art high-end prostheses are electro-mechanically able to provide a great variety of movements. Nevertheless, in order to functionally replace a human limb, it is essential that each movement is properly controlled. This is the goal of prosthesis control, which has become a growing research field in the last decades, with the ultimate goal of reproducing biological limb control. Therefore, exploration and development of prosthesis control are crucial to improve many aspects of an amp...
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#1Ananya S. DhawanH-Index: 2
#2Biswarup MukherjeeH-Index: 3
Last. Siddhartha SikdarH-Index: 17
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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...
#1Cosima Prahm (Medical University of Vienna)H-Index: 5
#2Alexander SchulzH-Index: 38
Last. Oskar C. Aszmann (Medical University of Vienna)H-Index: 25
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Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient’s everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, ...
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#1Alex Belyea (UNB: University of New Brunswick)H-Index: 1
#2Kevin Englehart (UNB: University of New Brunswick)H-Index: 39
Last. Erik Scheme (UNB: University of New Brunswick)H-Index: 10
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Objective: Force myography (FMG), which measures the surface pressure profile exerted by contracting muscles, has been proposed as an alternative to electromyography (EMG) for human–machine interfaces. Although FMG pattern recognition-based control systems have yielded higher offline classification accuracy, comparatively few works have examined the usability of FMG for real-time control. In this work, we conduct a comprehensive comparison of EMG- and FMG-based schemes using both classification ...
1 CitationsSource
#1Richard B. Woodward (NU: Northwestern University)H-Index: 3
#2Levi J. Hargrove (NU: Northwestern University)H-Index: 33
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.
2 CitationsSource
Jul 1, 2018 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Richard B. Woodward (NU: Northwestern University)H-Index: 3
#2Levi J. HargroveH-Index: 33
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