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

来源：

PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH | 2003年 / 25卷

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

页码：2841 / 2844

页数：4

共 50 条

[21] Gender classification using principal geodesic analysis and Gaussian mixture models
Wu, Jing
Smith, William A. P.
Hancock, Edwin R.
[J]. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, 2006, 4225 : 58 - 67
[22] Unsupervised classification with non-Gaussian mixture models using ICA
Lee, TW
Lewicki, MS
Sejnowski, T
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 11, 1999, 11 : 508 - 514
[23] Processing of myoelectric signals by feature selection and dimensionality reduction for the control of powered upper-limb prostheses
Buchenrieder, Klaus
[J]. COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007, 2007, 4739 : 1057 - 1065
[24] Secure sound classification: Gaussian mixture models
Shashanka, Madhusudana V. S.
Smaragdis, Paris
[J]. 2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13, 2006, : 3539 - 3542
[25] Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields
Li, Wei
Prasad, Saurabh
Fowler, James E.
[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (01) : 153 - 157
[26] Real Life Emotion Classification using Spectral Features and Gaussian Mixture Models
Koolagudi, Shashidhar G.
Barthwal, Anurag
Devliyal, Swati
Rao, K. Sreenivasa
[J]. INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING, 2012, 38 : 3892 - 3899
[27] Vehicle acoustic classification in netted sensor systems using Gaussian mixture models
Necioglu, BF
Christou, CT
George, EB
Jacyna, CM
[J]. SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XIV, 2005, 5809 : 409 - 419
[28] Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks
Zhou, Tian-Yi
Huo, Xiaoming
[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2024, 25 : 1 - 54
[29] Moving Vehicle Classification Using Pixel Quantity Based on Gaussian Mixture Models
Putra, Bayu Charisma
Setiyono, Budi
Sulistyaningrum, Dwi Ratna
Soetrisno
Mukhlash, Imam
[J]. PROCEEDINGS OF 2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS), 2018, : 254 - 257
[30] Real Life Emotion Classification from Speech Using Gaussian Mixture Models
Koolagudi, Shashidhar G.
Barthwal, Anurag
Devliyal, Swati
Rao, K. Sreenivasa
[J]. CONTEMPORARY COMPUTING, 2012, 306 : 250 - +

← 1 2 3 4 5 →