Literature DB >> 19162627

Continuous decoding of finger position from surface EMG signals for the control of powered prostheses.

Ryan J Smith1, Francesco Tenore, David Huberdeau, Ralph Etienne-Cummings, Nitish V Thakor.   

Abstract

As development toward multi-fingered dexterous prosthetic hands continues, there is a growing need for more flexible and intuitive control schemes. Through the use of generalized electrode placement and well-established methods of pattern recognition, we have developed a basis for asynchronous decoding of finger positions. With the present method, correlations as large as 0.91 and mean overall decoding errors of approximately 11% have been achieved with average decoding errors of between decoded and actual conformation of the metacarpophalangeal joints of individual fingers. It is hoped that these results will serve as a foundation from which to encourage further investigation into more intuitive methods of myoelectric control of powered upper limb prostheses.

Mesh:

Year:  2008        PMID: 19162627     DOI: 10.1109/IEMBS.2008.4649124

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  10 in total

1.  Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes.

Authors:  Edward A Clancy; Carlos Martinez-Luna; Marek Wartenberg; Chenyun Dai; Todd R Farrell
Journal:  J Electromyogr Kinesiol       Date:  2017-03-29       Impact factor: 2.368

2.  Model-Based Control of Individual Finger Movements for Prosthetic Hand Function.

Authors:  Dimitra Blana; Antonie J Van Den Bogert; Wendy M Murray; Amartya Ganguly; Agamemnon Krasoulis; Kianoush Nazarpour; Edward K Chadwick
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-01-20       Impact factor: 3.802

3.  Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model.

Authors:  Jimson G Ngeo; Tomoya Tamei; Tomohiro Shibata
Journal:  J Neuroeng Rehabil       Date:  2014-08-14       Impact factor: 4.262

Review 4.  Myoelectric control of prosthetic hands: state-of-the-art review.

Authors:  Purushothaman Geethanjali
Journal:  Med Devices (Auckl)       Date:  2016-07-27

5.  Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model.

Authors:  Qinghua Gao; Shuo Jiang; Peter B Shull
Journal:  Sensors (Basel)       Date:  2020-05-24       Impact factor: 3.576

6.  SEMG Feature Extraction Based on StockwellTransform Improves Hand MovementRecognition Accuracy.

Authors:  Haotian She; Jinying Zhu; Ye Tian; Yanchao Wang; Hiroshi Yokoi; Qiang Huang
Journal:  Sensors (Basel)       Date:  2019-10-14       Impact factor: 3.576

7.  Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis.

Authors:  Agamemnon Krasoulis; Kianoush Nazarpour
Journal:  Sci Rep       Date:  2020-10-09       Impact factor: 4.379

8.  Decoding upper limb residual muscle activity in severe chronic stroke.

Authors:  Ander Ramos-Murguialday; Eliana García-Cossio; Armin Walter; Woosang Cho; Doris Broetz; Martin Bogdan; Leonardo G Cohen; Niels Birbaumer
Journal:  Ann Clin Transl Neurol       Date:  2014-12-09       Impact factor: 4.511

Review 9.  Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees: A Scientific Research and Market Overview.

Authors:  Manfredo Atzori; Henning Müller
Journal:  Front Syst Neurosci       Date:  2015-11-30

10.  Regressing grasping using force myography: an exploratory study.

Authors:  Rana Sadeghi Chegani; Carlo Menon
Journal:  Biomed Eng Online       Date:  2018-10-23       Impact factor: 2.819

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.