A Multi-Class SVM for Decoding the Human Activity Mode from sEMG Signals

被引：0

作者：

Kalani, Hadi ^{[1
]}

Tahamipour-Z, S. Mohammad ^{[2
]}

Kardan, Iman ^{[3
]}

Akbarzadeh, Alireza ^{[3
]}

Ebrahimi, Amirali ^{[4
]}

Sede, Reza ^{[2
]}

机构：

[1] Sadjad Univ Technol, Dept Mech Engn, Mashhad, Razavi Khorasan, Iran

[2] Ferdowsi Univ Mashhad, Elect Engn Dept, Mashhad, Razavi Khorasan, Iran

[3] Ferdowsi Univ Mashhad, Mech Engn Dept, Mashhad, Razavi Khorasan, Iran

[4] Ferdowsi Univ Mashhad, Comp Engn Dept, Mashhad, Razavi Khorasan, Iran

来源：

2019 7TH INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM 2019) | 2019年

关键词：

Surface Electromyography (sEMG); Support Vector Machine (SVM); Flexion and Extension Muscles; Exoskeleton Robot; DRIVEN MUSCULOSKELETAL MODEL; FEEDBACK ASSISTIVE CONTROL; POWERED EXOSKELETON; EMG; PREDICTION; ROBOT; REHABILITATION; GENERATION; DESIGN;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Nowadays, the relationship between muscles' electrical activity and body movements has been investigated in many medical applications. This Paper proposes the classification of activity mode of healthy human subjects based on surface Electromyography (sEMG) signals. Support vector machine (SVM) methodology is used to predict human activity mode, using the sEMG signals recorded from four main muscles in flexion and extension of the left leg. The presented method shows promising results with classification accuracies of up to 93%. This method provides a reliable solution for the classification of human activity modes, required in many applications like control of exoskeleton robots.

引用

页码：265 / 269

页数：5

共 40 条

[21] Multi-Class Learning from Label Proportions for Bank Customer Classification
Qian, Yaxing
Tong, Qiang
Wang, Bo
7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2019): INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE, 2019, 162 : 421 - 428
[22] Multi-Class Clustering of Cancer Subtypes through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification
Mukhopadhyay, Anirban
Bandyopadhyay, Sanghamitra
Maulik, Ujjwal
PLOS ONE, 2010, 5 (11):
[23] Detecting Network Transmission Anomalies using Autoencoders-SVM Neural Network on Multi-class NSL-KDD Dataset
Khan, Shehram Sikander
Mailewa, Akalanka Bandara
2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC, 2023, : 835 - 843
[24] A Robust and Accurate Method for Feature Selection and Prioritization from Multi-Class OMICs Data
Fortino, Vittorio
Kinaret, Pia
Fyhrquist, Nanna
Alenius, Harri
Greco, Dario
PLOS ONE, 2014, 9 (09):
[25] A Multi-View SVM Approach for Seizure Detection from Single Channel EEG Signals
Jana G.C.
Praneeth M.S.
Agrawal A.
IETE Journal of Research, 2023, 69 (06) : 3120 - 3131
[26] Segmentation of specific speech signals from multi-dialog environment using SVM and wavelet
Truong, T. K.
Lin, Chien-Chang
Chen, Shi-Huang
PATTERN RECOGNITION LETTERS, 2007, 28 (11) : 1307 - 1313
[27] New imbalanced bearing fault diagnosis method based on Sample-characteristic Oversampling TechniquE (SCOTE) and multi-class LS-SVM
Wei, Jianan
Huang, Haisong
Yao, Liguo
Hu, Yao
Fan, Qingsong
Huang, Dong
APPLIED SOFT COMPUTING, 2021, 101
[28] Detection of the gas-bearing zone in a carbonate reservoir using multi-class relevance vector machines (RVM): comparison of its performance with SVM and PNN
Mohebian, Reza
Riahi, Mohammad Ali
Afjeh, Mona
CARBONATES AND EVAPORITES, 2018, 33 (03) : 347 - 357
[29] A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data
Yuan, Xiaohui
Xie, Lijun
Abouelenien, Mohamed
PATTERN RECOGNITION, 2018, 77 : 160 - 172
[30] Enhancement of multi-class support vector machine construction from binary learners using generalization performance
Songsiri, Patoomsiri
Phetkaew, Thimaporn
Kijsirikul, Boonserm
NEUROCOMPUTING, 2015, 151 : 434 - 448

← 1 2 3 4 →