Literature DB >> 23459166

A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses.

Michael A Powell1, Nitish V Thakor.   

Abstract

Pattern recognition-based control of myoelectric prostheses offers amputees a natural, intuitive way of controlling the increasing functionality of modern myoelectric prostheses. While this approach to prosthesis control is certainly attractive, it is a significant departure from existing control methods. The transition from the more traditional methods of direct or proportional control to pattern recognition-based control presents a training challenge that will be unique to each amputee. In this paper we describe specific ways that a transradial amputee, prosthetist, and occupational therapist team can overcome these challenges by developing consistent and distinguishable muscle patterns. A central part of this process is the employment of a computer-based pattern recognition training system with which an amputee can learn and improve pattern recognition skills throughout the process of prosthesis fitting and testing. We describe in detail the manner in which four transradial amputees trained to improve their pattern recognition-based control of a virtual prosthesis by focusing on building consistent, distinguishable muscle patterns. We also describe a three-phase framework for instruction and training: 1) initial demonstration and conceptual instruction, 2) in-clinic testing and initial training, and 3) at-home training.

Entities:  

Keywords:  motor learning; myoelectric prosthesis; pattern recognition

Year:  2013        PMID: 23459166      PMCID: PMC3581303          DOI: 10.1097/JPO.0b013e31827af7c1

Source DB:  PubMed          Journal:  J Prosthet Orthot        ISSN: 1040-8800


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Journal:  IEEE Trans Biomed Eng       Date:  2005-01       Impact factor: 4.538

Review 5.  Myoelectric signal processing for control of powered limb prostheses.

Authors:  P Parker; K Englehart; B Hudgins
Journal:  J Electromyogr Kinesiol       Date:  2006-10-11       Impact factor: 2.368

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Authors:  Erik J Scheme; Kevin B Englehart; Bernard S Hudgins
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9.  Patient training for functional use of pattern recognition-controlled prostheses.

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3.  A Myoelectric Postural Control Algorithm for Persons With Transradial Amputations: A Consideration of Clinical Readiness.

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4.  A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics.

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7.  Upper-Limb Prosthetic Myocontrol: Two Recommendations.

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8.  Initial Clinical Evaluation of the Modular Prosthetic Limb.

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9.  Virtual Integration Environment as an Advanced Prosthetic Limb Training Platform.

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10.  User experience of controlling the DEKA Arm with EMG pattern recognition.

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