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Items: 1 to 20 of 103

1.

Stable myoelectric control of a hand prosthesis using non-linear incremental learning.

Gijsberts A, Bohra R, Sierra González D, Werner A, Nowak M, Caputo B, Roa MA, Castellini C.

Front Neurorobot. 2014 Feb 25;8:8. doi: 10.3389/fnbot.2014.00008. eCollection 2014.

2.

Stable force-myographic control of a prosthetic hand using incremental learning.

Rasouli M, Ghosh R, Lee WW, Thakor NV, Kukreja S.

Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:4828-31. doi: 10.1109/EMBC.2015.7319474.

PMID:
26737374
3.

Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis.

Matrone GC, Cipriani C, Carrozza MC, Magenes G.

J Neuroeng Rehabil. 2012 Jun 15;9:40. doi: 10.1186/1743-0003-9-40.

4.

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications.

Fani S, Bianchi M, Jain S, Pimenta Neto JS, Boege S, Grioli G, Bicchi A, Santello M.

Front Neurorobot. 2016 Oct 17;10:11. eCollection 2016.

5.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

Edwards AL, Dawson MR, Hebert JS, Sherstan C, Sutton RS, Chan KM, Pilarski PM.

Prosthet Orthot Int. 2016 Oct;40(5):573-81. doi: 10.1177/0309364615605373. Epub 2015 Sep 30.

PMID:
26423106
6.

EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis.

Dosen S, Markovic M, Somer K, Graimann B, Farina D.

J Neuroeng Rehabil. 2015 Jun 19;12:55. doi: 10.1186/s12984-015-0047-z.

7.

Online Bimanual Manipulation Using Surface Electromyography and Incremental Learning.

Strazzulla I, Nowak M, Controzzi M, Cipriani C, Castellini C.

IEEE Trans Neural Syst Rehabil Eng. 2017 Mar;25(3):227-234. doi: 10.1109/TNSRE.2016.2554884. Epub 2016 Apr 27.

PMID:
28113557
8.

Electromyography data for non-invasive naturally-controlled robotic hand prostheses.

Atzori M, Gijsberts A, Castellini C, Caputo B, Hager AG, Elsig S, Giatsidis G, Bassetto F, Müller H.

Sci Data. 2014 Dec 23;1:140053. doi: 10.1038/sdata.2014.53. eCollection 2014.

9.

Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

Gonzalez-Vargas J, Dosen S, Amsuess S, Yu W, Farina D.

PLoS One. 2015 Jun 12;10(6):e0127528. doi: 10.1371/journal.pone.0127528. eCollection 2015.

10.

Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.

Gailey A, Artemiadis P, Santello M.

Front Neurol. 2017 Feb 1;8:7. doi: 10.3389/fneur.2017.00007. eCollection 2017.

11.

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

Downey JE, Weiss JM, Muelling K, Venkatraman A, Valois JS, Hebert M, Bagnell JA, Schwartz AB, Collinger JL.

J Neuroeng Rehabil. 2016 Mar 18;13:28. doi: 10.1186/s12984-016-0134-9.

12.

Changes in performance over time while learning to use a myoelectric prosthesis.

Bouwsema H, van der Sluis CK, Bongers RM.

J Neuroeng Rehabil. 2014 Feb 25;11:16. doi: 10.1186/1743-0003-11-16.

13.

Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning.

Vasan G, Pilarski PM.

IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1457-1464. doi: 10.1109/ICORR.2017.8009453.

PMID:
28814025
14.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

Matsubara T, Morimoto J.

IEEE Trans Biomed Eng. 2013 Aug;60(8):2205-13. doi: 10.1109/TBME.2013.2250502. Epub 2013 Mar 7.

PMID:
23475334
15.

Effects of prosthesis use on the capability to control myoelectric robotic prosthetic hands.

Atzori M, Hager AG, Elsig S, Giatsidis G, Bassetto F, Muller H.

Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:3456-9. doi: 10.1109/EMBC.2015.7319136.

PMID:
26737036
16.

Closed-loop control of grasping with a myoelectric hand prosthesis: which are the relevant feedback variables for force control?

Ninu A, Dosen S, Muceli S, Rattay F, Dietl H, Farina D.

IEEE Trans Neural Syst Rehabil Eng. 2014 Sep;22(5):1041-52. doi: 10.1109/TNSRE.2014.2318431. Epub 2014 Apr 29.

PMID:
24801625
17.

First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand.

Pasquina PF, Evangelista M, Carvalho AJ, Lockhart J, Griffin S, Nanos G, McKay P, Hansen M, Ipsen D, Vandersea J, Butkus J, Miller M, Murphy I, Hankin D.

J Neurosci Methods. 2015 Apr 15;244:85-93. doi: 10.1016/j.jneumeth.2014.07.016. Epub 2014 Aug 4.

18.

Surface EMG in advanced hand prosthetics.

Castellini C, van der Smagt P.

Biol Cybern. 2009 Jan;100(1):35-47. doi: 10.1007/s00422-008-0278-1. Epub 2008 Nov 18.

PMID:
19015872
19.

Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis.

Markovic M, Dosen S, Popovic D, Graimann B, Farina D.

J Neural Eng. 2015 Dec;12(6):066022. doi: 10.1088/1741-2560/12/6/066022. Epub 2015 Nov 3.

PMID:
26529274
20.

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

Hahne JM, Biessmann F, Jiang N, Rehbaum H, Farina D, Meinecke FC, Muller KR, Parra LC.

IEEE Trans Neural Syst Rehabil Eng. 2014 Mar;22(2):269-79. doi: 10.1109/TNSRE.2014.2305520.

PMID:
24608685

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