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A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis

Published on Jan 1, 2016in IEEE Journal of Translational Engineering in Health and Medicine2.075
· DOI :10.1109/JTEHM.2016.2616123
Todd A. Kuiken43
Estimated H-index: 43
(Rehabilitation Institute of Chicago),
Laura A. Miller16
Estimated H-index: 16
(Rehabilitation Institute of Chicago)
+ 1 AuthorsLevi J. Hargrove33
Estimated H-index: 33
(Rehabilitation Institute of Chicago)
Sources
Abstract
With existing conventional prosthesis control (direct control), individuals with a transradial amputation use two opposing muscle groups to control each prosthesis motor. As component complexity increases, subjects must switch the prosthesis into different modes to control each component in sequence. Pattern recognition control offers the ability to control multiple movements in a seamless manner without switching. In this paper, three individuals with a transradial amputation completed a home trial to compare direct control and pattern recognition control of a multiple degree-of-freedom prosthesis. Outcome measures before and after the home trial, together with subject questionnaires, were used to evaluate functional control. Although small, this trial has implications for the implementation of pattern recognition in commercial control systems and for future research studies.
  • References (19)
  • Citations (25)
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References19
Newest
#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
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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...
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#1Jiayuan He (SJTU: Shanghai Jiao Tong University)H-Index: 8
#2Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 20
Last. Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 23
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Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was...
48 CitationsSource
#1Sean Deeny (NU: Northwestern University)H-Index: 3
#2Caitlin L. Chicoine (Rehabilitation Institute of Chicago)H-Index: 2
Last. Arun Jayaraman (NU: Northwestern University)H-Index: 20
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Common goals in the development of human-machine interface (HMI) technology are to reduce cognitive workload and increase function. However, objective and quantitative outcome measures assessing cognitive workload have not been standardized for HMI research. The present study examines the efficacy of a simple event-related potential (ERP) measure of cortical effort during myoelectric control of a virtual limb for use as an outcome tool. Participants trained and tested on two methods of control, ...
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#1Erik Scheme (UNB: University of New Brunswick)H-Index: 20
#2Blair A. Lock (Rehabilitation Institute of Chicago)H-Index: 19
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This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method...
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Aug 1, 2012 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
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#2Ann M. Simon (Rehabilitation Institute of Chicago)H-Index: 17
Last. Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 33
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Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the pros...
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#1Ann M. SimonH-Index: 17
#2Blair A. LockH-Index: 19
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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...
43 CitationsSource
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#2Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 33
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Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Nonamputee and amputee subjects controlled a prosthesis in real time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a sig...
76 CitationsSource
Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when control- ling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viab...
424 CitationsSource
#1Lauren H. Smith (NU: Northwestern University)H-Index: 11
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Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifier...
174 CitationsSource
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A remaining barrier to the clinical accessibility of pattern recognition systems is the lack of practical methods to acquire the myoelectric signals required to train the system. Many current methods involve screen-guided training (SGT), where wearers connected to an external computer perform muscle contractions synchronized with a sequence of visual cues. The system complexity prevents easy retraining when signal conditions change. We have developed a method called prosthesis-guided training (P...
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