Continuous classification of myoelectric signals for powered prostheses using Gaussian mixture models

被引:20
|
作者
Chan, ADC [1 ]
Englehart, KB [1 ]
机构
[1] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
关键词
EMG; Gaussian mixture model; myoelectric signals; pattern recognition; prosthesis;
D O I
10.1109/IEMBS.2003.1280510
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Pattern recognition is a, key element of myoelectrically controlled prostheses. Improvements in classification accuracy have been achieved using various feature extraction and classification methodologies. In this paper, it is demonstrated that using a simple and direct approach can achieve high classification accuracy, while maintaining a low computational load; important characteristics for a real-time embedded system. An average classification accuracy of 94.06% was achieved for a six class problem, using a single mixture Gaussian mixture model, along with majority vote post-processing.
引用
收藏
页码:2841 / 2844
页数:4
相关论文
共 50 条
  • [1] A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses
    Huang, YH
    Englehart, KB
    Hudgins, B
    Chan, ADC
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2005, 52 (11) : 1801 - 1811
  • [2] Continuous myoelectric control for powered prostheses using hidden Markov models
    Chan, ADC
    Englehart, KB
    [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2005, 52 (01) : 121 - 124
  • [3] Classification and compression of ICEGS using gaussian mixture models
    Coggins, R
    Jabri, M
    [J]. NEURAL NETWORKS FOR SIGNAL PROCESSING VII, 1997, : 226 - 235
  • [4] Using Wavelets and Gaussian Mixture Models for Audio Classification
    Chuan, Ching-Hua
    Vasana, Susan
    Asaithambi, Asai
    [J]. 2012 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2012, : 421 - 426
  • [5] Classification of facial images using Gaussian mixture models
    Liao, P
    Gao, W
    Shen, L
    Chen, XL
    Shan, SG
    Zeng, WB
    [J]. ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS, 2001, 2195 : 724 - 731
  • [6] Emotional speech classification using Gaussian mixture models
    Ververidis, D
    Kotropoulos, C
    [J]. 2005 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), VOLS 1-6, CONFERENCE PROCEEDINGS, 2005, : 2871 - 2874
  • [7] Distribution based classification using Gaussian Mixture Models
    Gudnason, J
    Brookes, M
    [J]. 2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 4159 - 4159
  • [8] Evolving Gaussian Mixture Models for Classification
    Reichhuber, Simon
    Tomforde, Sven
    [J]. ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3, 2022, : 964 - 974
  • [9] SPEAKER PHONE MODE CLASSIFICATION USING GAUSSIAN MIXTURE MODELS
    Eghbal-zadeh, H.
    Sobhan-manesh, F.
    Sameti, H.
    BabaAli, B.
    [J]. SPA 2011: SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS CONFERENCE PROCEEDINGS, 2011, : 112 - +
  • [10] Towards autonomous habitat classification using Gaussian Mixture Models
    Steinberg, Daniel M.
    Williams, Stefan B.
    Pizarro, Oscar
    Jakuba, Michael V.
    [J]. IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, : 4424 - 4431