Literature DB >> 32396094

Understanding Limb Position and External Load Effects on Real-Time Pattern Recognition Control in Amputees.

Yuni Teh, Levi J Hargrove.   

Abstract

Limb position is a factor that negatively affects myoelectric pattern recognition classification accuracy. However, prior studies evaluating impact on real-time control for upper-limb amputees have done so without a physical prosthesis on the residual limb. It remains unclear how limb position affects real-time pattern recognition control in amputees when their residual limb is supporting various weights. We used a virtual reality target achievement control test to evaluate the effects of limb position and external load on real-time pattern recognition control in fourteen intact limb subjects and six major upper limb amputee subjects. We also investigated how these effects changed based on different control system training methods. In a static training method, subjects kept their unloaded arm by their side with the elbow bent whereas in the dynamic training method, subjects moved their arm throughout a workspace while supporting a load. When static training was used, limb position significantly affected real-time control in all subjects. However, amputee subjects were still able to adequately complete tasks in all conditions, even in untrained limb positions. Moreover, increasing external loads decreased controller performance, albeit to a lesser extent in amputee subjects. The effects of limb position did not change as load increased, and vice versa. In intact limb subjects, dynamic training significantly reduced the limb position effect but did not completely remove them. In contrast, in amputee subjects, dynamic training eliminated the limb position effect in three out of four outcome measures. However, it did not reduce the effects of load for either subject population. These findings suggest that results obtained from intact limb subjects may not generalize to amputee subjects and that advanced training methods can substantially improve controller robustness to different limb positions regardless of limb loading.

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Year:  2020        PMID: 32396094      PMCID: PMC7391097          DOI: 10.1109/TNSRE.2020.2991643

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  32 in total

1.  A survey of the satisfaction of upper limb amputees with their prostheses, their lifestyles, and their abilities.

Authors:  Judith Davidson
Journal:  J Hand Ther       Date:  2002 Jan-Mar       Impact factor: 1.950

2.  Classification of Multiple Finger Motions During Dynamic Upper Limb Movements.

Authors:  Dapeng Yang; Wei Yang; Qi Huang; Hong Liu
Journal:  IEEE J Biomed Health Inform       Date:  2015-10-14       Impact factor: 5.772

3.  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

4.  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

5.  A Reliable Multi-User EMG Interface Based on A Generic-Musculoskeletal Model against Loading Weight Changes.

Authors:  Lizhi Pan; Andrew Harmody; He Huang
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07

6.  Clinical results of an investigation of paediatric upper limb myoelectric prosthesis fitting at the Quebec Rehabilitation Institute.

Authors:  F Routhier; C Vincent; M J Morissette; L Desaulniers
Journal:  Prosthet Orthot Int       Date:  2001-08       Impact factor: 1.895

7.  Effect of arm position on the prediction of kinematics from EMG in amputees.

Authors:  Ning Jiang; Silvia Muceli; Bernhard Graimann; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2012-10-23       Impact factor: 2.602

8.  Functional outcome of patients with proximal upper limb deficiency--acquired and congenital.

Authors:  Dipak Datta; Kanther Selvarajah; Nicola Davey
Journal:  Clin Rehabil       Date:  2004-03       Impact factor: 3.477

9.  Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees.

Authors:  Yanjuan Geng; Oluwarotimi Williams Samuel; Yue Wei; Guanglin Li
Journal:  Biomed Res Int       Date:  2017-04-24       Impact factor: 3.411

10.  Adapting myoelectric control in real-time using a virtual environment.

Authors:  Richard B Woodward; Levi J Hargrove
Journal:  J Neuroeng Rehabil       Date:  2019-01-16       Impact factor: 4.262

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  5 in total

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Journal:  Front Bioeng Biotechnol       Date:  2022-05-04

2.  Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.

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Journal:  Front Neurorobot       Date:  2022-06-07       Impact factor: 3.493

3.  A Multi-User Transradial Functional-Test Socket for Validation of New Myoelectric Prosthetic Control Strategies.

Authors:  Taylor C Hansen; Abigail R Citterman; Eric S Stone; Troy N Tully; Christopher M Baschuk; Christopher C Duncan; Jacob A George
Journal:  Front Neurorobot       Date:  2022-06-17       Impact factor: 3.493

4.  Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

Authors:  Michael D Paskett; Mark R Brinton; Taylor C Hansen; Jacob A George; Tyler S Davis; Christopher C Duncan; Gregory A Clark
Journal:  J Neuroeng Rehabil       Date:  2021-02-25       Impact factor: 4.262

5.  Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Jianan Li; William J Boyd; Carlos Martinez-Luna; Chenyun Dai; Haopeng Wang; He Wang; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2022-04-11       Impact factor: 4.528

  5 in total

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