Literature DB >> 30680253

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

Linda Resnik1,2, Frantzy Acluche1, Matt Borgia1, Gail Latlief3, Sam Phillips4.   

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.

Entities:  

Keywords:  Patient satisfaction; pattern recognition; prosthesis; usability

Year:  2018        PMID: 30680253      PMCID: PMC6331198          DOI: 10.1109/JTEHM.2018.2883943

Source DB:  PubMed          Journal:  IEEE J Transl Eng Health Med        ISSN: 2168-2372            Impact factor:   3.316


  17 in total

1.  A robust, real-time control scheme for multifunction myoelectric control.

Authors:  Kevin Englehart; Bernard Hudgins
Journal:  IEEE Trans Biomed Eng       Date:  2003-07       Impact factor: 4.538

2.  A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment.

Authors:  L Hargrove; Y Losier; B Lock; K Englehart; B Hudgins
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2007

3.  Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control.

Authors:  Levi J Hargrove; Guanglin Li; Kevin B Englehart; Bernard S Hudgins
Journal:  IEEE Trans Biomed Eng       Date:  2009-05       Impact factor: 4.538

4.  Controlling a multi-degree of freedom upper limb prosthesis using foot controls: user experience.

Authors:  Linda Resnik; Shana Lieberman Klinger; Katherine Etter; Christopher Fantini
Journal:  Disabil Rehabil Assist Technol       Date:  2013-07-31

5.  The DEKA Arm: its features, functionality, and evolution during the Veterans Affairs Study to optimize the DEKA Arm.

Authors:  Linda Resnik; Shana L Klinger; Katherine Etter
Journal:  Prosthet Orthot Int       Date:  2013-10-22       Impact factor: 1.895

6.  Using virtual reality environment to facilitate training with advanced upper-limb prosthesis.

Authors:  Linda Resnik; Katherine Etter; Shana Lieberman Klinger; Charles Kambe
Journal:  J Rehabil Res Dev       Date:  2011

7.  Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation.

Authors:  Levi J Hargrove; Blair A Lock; Ann M Simon
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

8.  Factor structure of the Trinity Amputation and Prosthesis Experience Scales (TAPES) with individuals with acquired upper limb amputations.

Authors:  Deirdre M Desmond; Malcolm MacLachlan
Journal:  Am J Phys Med Rehabil       Date:  2005-07       Impact factor: 2.159

9.  A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control.

Authors:  Ann M Simon; Levi J Hargrove; Blair A Lock; Todd A Kuiken
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

10.  Patient training for functional use of pattern recognition-controlled prostheses.

Authors:  Ann M Simon; Blair A Lock; Kathy A Stubblefield
Journal:  J Prosthet Orthot       Date:  2012-04
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  3 in total

1.  Suitability of the Openly Accessible 3D Printed Prosthetic Hands for War-Wounded Children.

Authors:  John-John Cabibihan; Farah Alkhatib; Mohammed Mudassir; Laurent A Lambert; Osama S Al-Kwifi; Khaled Diab; Elsadig Mahdi
Journal:  Front Robot AI       Date:  2021-01-11

2.  Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm.

Authors:  Mark R Brinton; Elliott Barcikowski; Tyler Davis; Michael Paskett; Jacob A George; Gregory A Clark
Journal:  Front Robot AI       Date:  2020-09-25

3.  A Roadmap Towards Standards for Neurally Controlled End Effectors.

Authors:  Andrew Y Paek; Justin A Brantley; Akshay Sujatha Ravindran; Kevin Nathan; Yongtian He; David Eguren; Jesus G Cruz-Garza; Sho Nakagome; Dilranjan S Wickramasuriya; Jiajun Chang; Md Rashed-Al-Mahfuz; Md Rafiul Amin; Nikunj A Bhagat; Jose L Contreras-Vidal
Journal:  IEEE Open J Eng Med Biol       Date:  2021-02-12
  3 in total

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