User experience of controlling the DEKA Arm with EMG pattern recognition

Linda J Resnik, Frantzy Acluche, Shana Lieberman Klinger, Linda J Resnik, Frantzy Acluche, Shana Lieberman Klinger

Abstract

Introduction: A commercially available EMG Pattern Recognition (EMG-PR) control system was adapted to interface with the multi-degree of freedom (DOF) DEKA Arm.

Purpose: To describe users' experience of controlling the DEKA Arm using EMG-PR.

Methods: Sample: Twelve persons with upper limb amputation participated, 10 with transradial (TR), 2 with transhumeral (TH) level amputation. Ten were male, and 11 were users of a prosthesis at baselines. Design: This was a two-part study consisting of in-laboratory prosthetic training (Part A) and up to 12 weeks of home use of the prosthesis (Part B). Data collection: Qualitative data were collected through open-ended survey questions and semi-structured interviews. Data Analysis: The study used a qualitative case series design with a constant comparative approach to identify common categories of experience. Coding categories were iteratively refined until saturation of categories was achieved. The data were organized in a primary category, major categories of experience, factors impacting experience, and broader contextual factors.

Results: Users had mixed perspectives on the desirability of the EMG-PR control system in combination with the DEKA Arm. Major aspects of user experience related to the system complexity, process of calibrating, and functional benefits. Factors influencing user experience included training and acclimation, fatigue, prosthesis design, technical issues and control changes. Broader contextual factors, both personal and environmental, also impacted users' experience.

Discussion/conclusion: This study provided an in-depth description of user experience operating the DEKA Arm using EMG-PR control. The majority of participants expressed a preference for the controls of their personal prosthesis and controls rather than the iteration of EMG-PR controlled DEKA Arm used in this study. Most were positive about the future potential of EMG-PR as a control method. An understanding of patient experience will assist clinicians and patients choosing prosthetic options.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Prototype 1 RC socket.
Fig 1. Prototype 1 RC socket.
Fig 2. Summary of coding process.
Fig 2. Summary of coding process.
Fig 3. Theoretical model resulting from the…
Fig 3. Theoretical model resulting from the analysis.

References

    1. Resnik L, Klinger SL, Etter K, Fantini C. Controlling a multi-degree of freedom upper limb prosthesis using foot controls: user experience. Disabil Rehabil Assist Technol. 2014. Jul;9(4):318–29. 10.3109/17483107.2013.822024
    1. Resnik L, Klinger SL, Etter K. The DEKA Arm: its features, functionality, and evolution during the Veterans Affairs Study to optimize the DEKA Arm. Prosthet Orthot Int. 2014;38(6):492–504. Epub 2013/10/24. 10.1177/0309364613506913
    1. Phillips SL, Resnik L, Fantini C, Latlief G. Endpoint Control for a Powered Shoulder Prosthesis. J ProsthOrthotics. 2013;25(4):8.
    1. Cowley J, Resnik L, Wilken J, Smurr Walters L, Gates D. Movement quality of conventional prostheses and the DEKA Arm during everyday tasks. Prosthet Orthot Int. 2017;41(1):33–40. 10.1177/0309364616631348
    1. Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng. 2003;50(7):848–54. Epub 2003/07/10. 10.1109/TBME.2003.813539
    1. Hargrove L, Losier Y, Lock B, Englehart K, Hudgins B. A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:4842–5. Epub 2007/11/16. 10.1109/IEMBS.2007.4353424
    1. Simon AM, Hargrove LJ, Lock BA, Kuiken TA. A strategy for minimizing the effect of misclassifications during real time pattern recognition myoelectric control. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:1327–30. Epub 2009/12/08. 10.1109/IEMBS.2009.5334135
    1. Hargrove LJ, Li G, Englehart KB, Hudgins BS. Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control. IEEE Trans Biomed Eng. 2009;56(5):1407–14. Epub 2009/05/29. 10.1109/TBME.2008.2008171
    1. Hargrove LJ, Lock BA, Simon AM. Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:1599–602. Epub 2013/10/11. 10.1109/EMBC.2013.6609821
    1. Lock BA, Schultz AE, Kuiken TA. CLINICALLY PRACTICAL APPLICATIONS OF PATTERN RECOGNITION FOR MYOELECTRIC PROSTHESES MEC’08: Measuring Success in Upper Limb Prosthetics: University of New Brunswick's Myoelectric Controls/Powered Prosthetics Symposium; August 13-15, 2008; Fredericton, N.B., Canada: Myoelectric Symposium
    1. Sensinger JW, Lock BA, Kuiken TA. ADAPTIVE PATTERN RECOGNITION TO ENSURE CLINICAL VIABILITY OVER TIME. MEC’08: Measuring Success in Upper Limb Prosthetics: University of New Brunswick's Myoelectric Controls/Powered Prosthetics Symposium; August 13-15, 2008; Fredericton, N.B., Canada: Myoelectric Symposium
    1. Wurth SM, Hargrove LJ. A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts' law style assessment procedure. J Neuroeng Rehabil. 2014;11:91 Epub 2014/06/03. 10.1186/1743-0003-11-91
    1. Wurth SM, Hargrove LJ. Real-time comparison of conventional direct control and pattern recognition myoelectric control in a two-dimensional Fitts' law style test. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:3630–3. 10.1109/EMBC.2013.6610329
    1. Tkach DC, Young AJ, Smith LH, Rouse EJ, Hargrove LJ. Real-time and offline performance of pattern recognition myoelectric control using a generic electrode grid with targeted muscle reinnervation patients. IEEE Trans Neural Syst Rehabil Eng. 2014;22(4):727–34. Epub 2014/04/25. 10.1109/TNSRE.2014.2302799
    1. Young AJ, Smith LH, Rouse EJ, Hargrove LJ. A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements. J Neuroeng Rehabil. 2014;11:5 Epub 2014/01/15. 10.1186/1743-0003-11-5
    1. Simon AM, Lock BA, Stubblefield KA. Patient training for functional use of pattern recognition-controlled prostheses. J Prosth Orthotics.2012;24(2):56–64.
    1. Chicoine CL, Simon AM, Hargrove LJ. Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:1876–9. Epub 2013/02/01. 10.1109/EMBC.2012.6346318
    1. Yin R. Case study research: design and methods Los Angeles, CA: Sage Publications; 2009.
    1. Kuiken T. Targeted reinnervation for improved prosthetic function. Phys Med Rehabil Clin N Am. 2006;17(1):1–13. Epub 2006/03/07. 10.1016/j.pmr.2005.10.001
    1. Hargrove LJ, Miller LA, Turner K, Kuiken TA. Myoelectric Pattern Recognition Outperforms Direct Control for Transhumeral Amputees with Targeted Muscle Reinnervation: A Randomized Clinical Trial. Scientific reports. 2017;7(1):13840 10.1038/s41598-017-14386-w
    1. Castellini C, Artemiadis P, Wininger M, Ajoudani A, Alimusaj M, Bicchi A, et al. Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography. Frontiers in neurorobotics. 2014;8(22).
    1. Powell MA, Kaliki RR, Thakor NV. User training for pattern recognition-based myoelectric prostheses: improving phantom limb movement consistency and distinguishability. IEEE Trans Neural Syst Rehabil Eng. 2014;22(3):522–32. 10.1109/TNSRE.2013.2279737
    1. Powell MA, Thakor NV. A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses. J Prosth Orthotics, 2013;25(1):30–41.
    1. Scheme E, Biron K, Englehart K. Improving myoelectric pattern recognition positional robustness using advanced training protocols. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:4828–31. Epub 2012/01/19. 10.1109/IEMBS.2011.6091196

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