Literature DB >> 28814002

Joint-based velocity feedback to virtual limb dynamic perturbations.

Eric J Earley, Kyle J Kaveny, Reva E Johnson, Levi J Hargrove, Jon W Sensinger.   

Abstract

Despite significant research developing myoelectric prosthesis controllers, many amputees have difficulty controlling their devices due in part to reduced sensory feedback. Many attempts at providing supplemental sensory feedback have not significantly aided control. We hypothesize this is because the feedback provided contains redundant information already provided by vision. However, whereas vision provides egocentric, position-based feedback, sensory feedback tied to joint coordinates may provide information complementary to vision. In this study, we tested if providing audio feedback of joint velocities can improve performance and adaptation to dynamic perturbations while controlling a virtual limb. While subjects performed time-controlled center-out reaches, we perturbed the dynamics of the system and measured the rate subjects adapted to this change. Our results suggest that initial errors were reduced in the presence of audio feedback, and we theorize this is due to subjects identifying the perturbed limb dynamics sooner. We also noted other possible benefits including improved muscle activation detection.

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Year:  2017        PMID: 28814002      PMCID: PMC8164835          DOI: 10.1109/ICORR.2017.8009430

Source DB:  PubMed          Journal:  IEEE Int Conf Rehabil Robot        ISSN: 1945-7898


  19 in total

1.  Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss using a myoelectric prosthesis.

Authors:  Matthew Schiefer; Daniel Tan; Steven M Sidek; Dustin J Tyler
Journal:  J Neural Eng       Date:  2015-12-08       Impact factor: 5.379

2.  Humans integrate visual and haptic information in a statistically optimal fashion.

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Journal:  Nature       Date:  2002-01-24       Impact factor: 49.962

3.  User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control.

Authors:  Jiayuan He; Dingguo Zhang; Ning Jiang; Xinjun Sheng; Dario Farina; Xiangyang Zhu
Journal:  J Neural Eng       Date:  2015-06-01       Impact factor: 5.379

4.  Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

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Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-03       Impact factor: 3.802

5.  Interaction of visual and proprioceptive feedback during adaptation of human reaching movements.

Authors:  Robert A Scheidt; Michael A Conditt; Emanuele L Secco; Ferdinando A Mussa-Ivaldi
Journal:  J Neurophysiol       Date:  2005-01-19       Impact factor: 2.714

Review 6.  Closed-loop control in prosthetic systems: historical perspective.

Authors:  D S Childress
Journal:  Ann Biomed Eng       Date:  1980       Impact factor: 3.934

7.  Motor-output variability: a theory for the accuracy of rapid motor acts.

Authors:  R A Schmidt; H Zelaznik; B Hawkins; J S Frank; J T Quinn
Journal:  Psychol Rev       Date:  1979-09       Impact factor: 8.934

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

Authors:  Todd A Kuiken; Laura A Miller; Kristi Turner; Levi J Hargrove
Journal:  IEEE J Transl Eng Health Med       Date:  2016-11-22       Impact factor: 3.316

9.  Classification of simultaneous movements using surface EMG pattern recognition.

Authors:  Aaron J Young; Lauren H Smith; Elliott J Rouse; Levi J Hargrove
Journal:  IEEE Trans Biomed Eng       Date:  2012-12-10       Impact factor: 4.538

10.  Estimating the sources of motor errors for adaptation and generalization.

Authors:  Max Berniker; Konrad Kording
Journal:  Nat Neurosci       Date:  2008-11-16       Impact factor: 24.884

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  3 in total

1.  Modeling Expected Reaching Error and Behaviors for Motor Adaptation.

Authors:  Eric J Earley; Levi J Hargrove
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2019-07

2.  Joint Speed Discrimination and Augmentation For Prosthesis Feedback.

Authors:  Eric J Earley; Reva E Johnson; Levi J Hargrove; Jon W Sensinger
Journal:  Sci Rep       Date:  2018-12-10       Impact factor: 4.379

3.  EMG feedback outperforms force feedback in the presence of prosthesis control disturbance.

Authors:  Jack Tchimino; Jakob Lund Dideriksen; Strahinja Dosen
Journal:  Front Neurosci       Date:  2022-09-20       Impact factor: 5.152

  3 in total

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