Literature DB >> 29255654

Evaluation of Computer-Based Target Achievement Tests for Myoelectric Control.

Jacob Gusman1, Enzo Mastinu2, Max Ortiz-Catalan2,3.   

Abstract

Real-time evaluation of novel prosthetic control schemes is critical for translational research on artificial limbs. Recently, two computer-based, real-time evaluation tools, the target achievement control (TAC) test and the Fitts' law test (FLT), have been proposed to assess real-time controllability. Whereas TAC tests provides an anthropomorphic visual representation of the limb at the cost of confusing visual feedback, FLT clarifies the current and target locations by simplified non-anthropomorphic representations. Here, we investigated these two approaches and quantified differences in common performance metrics that can result from the chosen method of visual feedback. Ten able-bodied and one amputee subject performed target achievement tasks corresponding to the FLT and TAC test with equivalent indices of difficulty. Able-bodied subjects exhibited significantly (p <0.05) better completion rate, path efficiency, and overshoot when performing the FLT, although no significant difference was seen in throughput performance. The amputee subject showed significantly better performance in overshoot at the FLT, but showed no significant difference in completion rate, path efficiency, and throughput. Results from the FLT showed a strong linear relationship between the movement time and the index of difficulty (R2 = 0.96), whereas TAC test results showed no apparent linear relationship (R2 = 0.19). These results suggest that in relatively similar conditions, the confusing location of virtual limb representation used in the TAC test contributed to poorer performance. Establishing an understanding of the biases of various evaluation protocols is critical to the translation of research into clinical practice.

Entities:  

Keywords:  Electromyography (EMG); fitts’law; myoelectric control; target achievement control (TAC); user interfaces

Year:  2017        PMID: 29255654      PMCID: PMC5731324          DOI: 10.1109/JTEHM.2017.2776925

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


  23 in total

1.  Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity.

Authors:  Colin M Light; Paul H Chappell; Peter J Kyberd
Journal:  Arch Phys Med Rehabil       Date:  2002-06       Impact factor: 3.966

2.  Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses.

Authors:  Ann M Simon; Levi J Hargrove; Blair A Lock; Todd A Kuiken
Journal:  J Rehabil Res Dev       Date:  2011

3.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

Authors:  Erik Scheme; Kevin Englehart
Journal:  J Rehabil Res Dev       Date:  2011

4.  Analog Front-Ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition EMBC 2015.

Authors:  Enzo Mastinu; Max Ortiz-Catalan; Bo Hakansson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015

5.  Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control.

Authors:  Max Ortiz-Catalan; Faezeh Rouhani; Rickard Branemark; Bo Hakansson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015-08

6.  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

7.  Assessment of capacity for myoelectric control: evaluation of construct and rating scale.

Authors:  Helen Y N Lindner; John M Linacre; Liselotte M Norling Hermansson
Journal:  J Rehabil Med       Date:  2009-05       Impact factor: 2.912

8.  Support vector machine-based classification scheme for myoelectric control applied to upper limb.

Authors:  Mohammadreza Asghari Oskoei; Huosheng Hu
Journal:  IEEE Trans Biomed Eng       Date:  2008-08       Impact factor: 4.538

9.  Selective classification for improved robustness of myoelectric control under nonideal conditions.

Authors:  Erik J Scheme; Kevin B Englehart; Bernard S Hudgins
Journal:  IEEE Trans Biomed Eng       Date:  2011-02-10       Impact factor: 4.538

10.  Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer.

Authors:  Ludger van Dijk; Corry K van der Sluis; Hylke W van Dijk; Raoul M Bongers
Journal:  PLoS One       Date:  2016-08-24       Impact factor: 3.240

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  3 in total

Review 1.  Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.

Authors:  Andrés Jaramillo-Yánez; Marco E Benalcázar; Elisa Mena-Maldonado
Journal:  Sensors (Basel)       Date:  2020-04-27       Impact factor: 3.576

2.  Can spatial filtering separate voluntary and involuntary components in children with dyskinetic cerebral palsy?

Authors:  Cassie N Borish; Matteo Bertucco; Denise J Berger; Andrea d'Avella; Terence D Sanger
Journal:  PLoS One       Date:  2021-04-14       Impact factor: 3.240

3.  A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.

Authors:  Asim Waris; Muhammad Zia Ur Rehman; Imran Khan Niazi; Mads Jochumsen; Kevin Englehart; Winnie Jensen; Heidi Haavik; Ernest Nlandu Kamavuako
Journal:  Sensors (Basel)       Date:  2020-06-15       Impact factor: 3.576

  3 in total

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