Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device-Reference-Cited by-同舟云学术

Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device

Published:2019-11-27 Issue:2 Volume:10 Page:275-284
ISSN:2093-9868
Container-title:Biomedical Engineering Letters
language:en
Short-container-title:Biomed. Eng. Lett.

Author:

Falcari Thays^ORCID,Saotome Osamu,Pires Ricardo,Campo Alexandre Brincalepe

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s13534-019-00141-9.pdf

Reference23 articles.

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3. Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International workshop on ambient assisted living. New York: Springer; 2012. pp. 216–23.

4. Anguita D, Ghio A, Oneto L, Llanas Parra FX, Reyes Ortiz JL. Energy efficient smartphone-based activity recognition using fixed-point arithmetic. J Univ Comput Sci. 2013;19(9):1295–314.

5. Benatti S, Casamassima F, Milosevic B, Farella E, Schönle P, Fateh S, Burger T, Huang Q, Benini L. A versatile embedded platform for emg acquisition and gesture recognition. IEEE Trans Biomed Circuits Syst. 2015;9(5):620–30.

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