Sökning: id:"swepub:oai:lup.lub.lu.se:779b71ae-65a6-46ec-a11f-548ec8086899" >
EMG pattern recogni...
EMG pattern recognition using decomposition techniques for constructing multiclass classifiers
-
- Huang, Huaiqi (författare)
- Swiss Federal Institute of Technology
-
- Li, Tao (författare)
- Bern University of Applied Sciences
-
- Bruschini, Claudio (författare)
- Swiss Federal Institute of Technology
-
visa fler...
-
- Enz, Christian (författare)
- Swiss Federal Institute of Technology
-
- Koch, Volker M. (författare)
- Bern University of Applied Sciences
-
- Justiz, Jorn (författare)
- Bern University of Applied Sciences
-
- Antfolk, Christian (författare)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH
-
visa färre...
-
(creator_code:org_t)
- 2016
- 2016
- Engelska 6 s.
-
Ingår i: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, BioRob 2016. - 9781509032877 ; , s. 1296-1301
- Relaterad länk:
-
http://dx.doi.org/10...
-
visa fler...
-
https://lup.lub.lu.s...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multiclass classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap -- Övrig annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences -- Other Medical and Health Sciences not elsewhere specified (hsv//eng)
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas