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Items: 5

1.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

Betthauser JL, Hunt CL, Osborn LE, Masters MR, Levay G, Kaliki RR, Thakor NV.

IEEE Trans Biomed Eng. 2018 Apr;65(4):770-778. doi: 10.1109/TBME.2017.2719400. Epub 2017 Jun 23.

PMID:
28650804
2.

Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations.

Betthauser JL, Hunt CL, Osborn LE, Kaliki RR, Thakor NV.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:6373-6376. doi: 10.1109/EMBC.2016.7592186.

PMID:
28325032
3.

User training for pattern recognition-based myoelectric prostheses: improving phantom limb movement consistency and distinguishability.

Powell MA, Kaliki RR, Thakor NV.

IEEE Trans Neural Syst Rehabil Eng. 2014 May;22(3):522-32. doi: 10.1109/TNSRE.2013.2279737. Epub 2013 Oct 7.

PMID:
24122566
4.

Evaluation of a noninvasive command scheme for upper-limb prostheses in a virtual reality reach and grasp task.

Kaliki RR, Davoodi R, Loeb GE.

IEEE Trans Biomed Eng. 2013 Mar;60(3):792-802. doi: 10.1109/TBME.2012.2185494. Epub 2012 Jan 23.

PMID:
22287229
5.

The effects of training set on prediction of elbow trajectory from shoulder trajectory during reaching to targets.

Kaliki RR, Davoodi R, Loeb GE.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:5483-6.

PMID:
17946704

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