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EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability

Published on Jan 1, 2019in IEEE Journal of Translational Engineering in Health and Medicine
· DOI :10.1109/jtehm.2018.2883943
Linda Resnik23
Estimated H-index: 23
(Providence VA Medical Center),
Frantzy Acluche3
Estimated H-index: 3
(Providence VA Medical Center)
+ 2 AuthorsSam L. Phillips5
Estimated H-index: 5
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Abstract
The DEKA Arm has multiple degrees of freedom which historically have been operated primarily by inertial measurement units (IMUs). However, the IMUs are not appropriate for all potential users; new control methods are needed. The purposes of this study were: 1) to describe usability and satisfaction of two controls methods—IMU and myoelectric pattern recognition (EMG-PR) controls—and 2) to compare ratings by control and amputation level. A total of 36 subjects with transradial (TR) or transhumeral (TH) amputation participated in the study. The subjects included 11 EMG-PR users (82% TR) and 25 IMU users (68% TR). The study consisted of in-laboratory training (Part A) and home use (Part B). The subjects were administered the Trinity Amputation and Prosthesis Experience satisfaction scale and other usability and satisfaction measures. Wilcoxon rank-sum tests compared the differences by control type. The differences were compared for those who did and did not want a DEKA Arm. The preferences for features of the DEKA Arm were compared by control type. The comparisons revealed poorer ratings of skill, comfort, and weight among EMG-PR users. The TR amputees using IMUs rated usability more favorably. TH amputees rated usability similarly. The TR amputees using EMG-PR were less satisfied with weight, pinch grip, and wrist display, whereas the TH amputees were less satisfied with the full system, wires/cables, and battery. Usability and satisfaction declined after Part B for EMG-PR users. Overall, we found that the IMU users rated the DEKA Arm and the controls more favorably than the EMG-PR users. The findings indicate that the EMG-PR system we tested was less well accepted than the IMUs for control of the DEKA Arm.
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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
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Published on Dec 1, 2014in Prosthetics and Orthotics International 1.48
Linda Resnik23
Estimated H-index: 23
(Brown University),
Shana Lieberman Klinger6
Estimated H-index: 6
(Providence VA Medical Center),
Katherine Etter4
Estimated H-index: 4
(Brown University)
Background and aim:DEKA Integrated Solutions Corp. (DEKA) was charged by the Defense Advanced Research Project Agency to design a prosthetic arm system that would be a dramatic improvement compared with the existing state of the art. The purpose of this article is to describe the two DEKA Arm prototypes (Gen 2 and Gen 3) used in the Veterans Affairs Study to optimize the DEKA Arm.Technique:This article reports on the features and functionality of the Gen 2 and Gen 3 prototypes discussing weight,...
79 Citations Source Cite
Published on Jan 1, 2014in Journal of Neuroengineering and Rehabilitation 3.58
Aaron J. Young19
Estimated H-index: 19
(NU: Northwestern University),
Lauren H. Smith11
Estimated H-index: 11
(NU: Northwestern University)
+ 1 AuthorsLevi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University)
Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditional...
41 Citations Source Cite
Linda Resnik23
Estimated H-index: 23
(VA: United States Department of Veterans Affairs),
Shana Lieberman Klinger6
Estimated H-index: 6
,
Katherine Etter4
Estimated H-index: 4
INTRODUCTION Background The development of the DEKA Arm was funded by the Defense Advanced Research Projects Agency's (DARPA's) Revolutionizing Prosthetics program in 2006 [1]. By 2008, DEKA had built and tested the first-generation DEKA Arm and developed the second-generation (gen 2) prototype. Because the gen 2 DEKA Arm was designed as an experimental platform, it included many test features that had not yet been finalized or miniaturized. Before moving to the next prototype, the Department of...
11 Citations Source Cite
Published on Jan 1, 2014in Journal of Neuroengineering and Rehabilitation 3.58
Sophie M. Wurth6
Estimated H-index: 6
(EPFL: École Polytechnique Fédérale de Lausanne),
Levi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University)
Background Pattern recognition (PR) based strategies for the control of myoelectric upper limb prostheses are generally evaluated through offline classification accuracy, which is an admittedly useful metric, but insufficient to discuss functional performance in real time. Existing functional tests are extensive to set up and most fail to provide a challenging, objective framework to assess the strategy performance in real time.
32 Citations Source Cite
Published on Jul 1, 2013 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Levi J. Hargrove29
Estimated H-index: 29
(Rehabilitation Institute of Chicago),
Blair A. Lock18
Estimated H-index: 18
(Rehabilitation Institute of Chicago),
Ann M. Simon17
Estimated H-index: 17
(Rehabilitation Institute of Chicago)
Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric contr...
26 Citations Source Cite
Published on Apr 1, 2012in Jpo Journal of Prosthetics and Orthotics
Ann M. Simon17
Estimated H-index: 17
,
Blair A. Lock18
Estimated H-index: 18
,
Kathy A. Stubblefield14
Estimated H-index: 14
Pattern recognition control systems have the potential to provide better, more reliable myoelectric prosthesis control for individuals with an upper-limb amputation. However, proper patient training is essential. We begin user training by teaching the concepts of pattern recognition control and progress to teaching how to control, use, and maintain prostheses with one or many degrees of freedom. Here we describe the training stages, with relevant case studies, and highlight several tools that ca...
34 Citations Source Cite
Linda Resnik23
Estimated H-index: 23
,
Katherine Etter4
Estimated H-index: 4
+ 1 AuthorsCharles J Kambe1
Estimated H-index: 1
INTRODUCTION Technological advances in upper-limb prosthetic design offer dramatically increased possibilities for powered movement. The full DEKA Arm (DEKA Research & Development Corporation; Manchester, New Hampshire), for example, allows up to 10 degrees of powered movement in addition to passive degrees of freedom associated with active degrees of freedom (Figure 1). No previous prosthetic device has given users control over so many degrees of freedom. While these new capabilities are exciti...
41 Citations Source Cite
Published on Sep 1, 2009 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Ann M. Simon17
Estimated H-index: 17
(Rehabilitation Institute of Chicago),
Levi J. Hargrove29
Estimated H-index: 29
(Rehabilitation Institute of Chicago)
+ 1 AuthorsTodd A. Kuiken40
Estimated H-index: 40
(Rehabilitation Institute of Chicago)
Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of ~95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited movement speed when there was a change in classifie...
9 Citations Source Cite
Published on May 1, 2009in IEEE Transactions on Biomedical Engineering 4.49
Levi J. Hargrove29
Estimated H-index: 29
(UNB: University of New Brunswick),
Guanglin Li22
Estimated H-index: 22
(Rehabilitation Institute of Chicago)
+ 1 AuthorsBernard Hudgins17
Estimated H-index: 17
(UNB: University of New Brunswick)
Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each specific muscle being modified by the dispersive propagation through the volume conductor between the muscle and the detection points. In this paper, th...
127 Citations Source Cite
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