Estimation of Muscle Force with EMG Signals Using Hammerstein-Wiener Model

被引：0
|
作者：
Abbasi-Asl, R. [1 ]
Khorsandi, R. [1 ]
Farzampour, S. [2 ]
Zahedi, E. [1 ]
机构：
[1] Sharif Univ Technol, Sch Elect Engn, Tehran, Iran
[2] Artesh Univ Med Sci, Sch Med, Tehran, Iran
来源：
5TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2011 (BIOMED 2011) | 2011年 / 35卷
关键词：
Force estimation; EMG signal; Hammerstein-Wiener model;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Estimation of muscle force is needed for monitoring or control purposes in many studies and applications that include direct human involvement such as control of prosthetic arms and human-robot interaction. A new model is introduced to estimate the force of muscle from the EMG signals. Estimation is based on Hammerstein-Wiener Model which consists of three blocks. These blocks are used to describe the nonlinearity of input and output and linear behavior of the model. The nonlinear network is designed base on the sigmoid network. The introduced model is trained by some data sets which are recorded from different people and tested by some other data sets. The simulation results show low error rate between measured force and estimated force.
引用
收藏
页码：157 / +
页数：2
相关论文
共 50 条
  • [1] Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals
    Chihi, Ines
    Sidhom, Lilia
    Kamavuako, Ernest Nlandu
    [J]. BIOSENSORS-BASEL, 2022, 12 (02):
  • [2] OPTIMIZATION OF BAYESIAN FILTERS AND HAMMERSTEIN-WIENER MODELS FOR EMG-FORCE SIGNALS USING GENETIC ALGORITHM
    Sebastian, Anish
    Kumar, Parmod
    Anugolu, Madhavi
    Schoen, Marco P.
    Urfer, Alex
    Naidu, D. Subbaram
    [J]. PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2009, PTS A AND B, 2010, : 713 - 720
  • [3] ANALYSIS OF EMG-FORCE RELATION USING SYSTEM IDENTIFICATION AND HAMMERSTEIN-WIENER MODELS
    Sebastian, Anish
    Kumar, Parmod
    Schoen, Marco P.
    Urfer, Alex
    Creelman, Jim
    Naidu, D. Subbaram
    [J]. PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2010, VOL 1, 2010, : 381 - 388
  • [4] Parameter estimation of Hammerstein-Wiener nonlinear system with noise using special test signals
    Li, Feng
    Jia, Li
    [J]. NEUROCOMPUTING, 2019, 344 : 37 - 48
  • [5] Identification Approach of the Hammerstein-Wiener Model Applying Combined Signals
    Zhou, Shibo
    Ding, Zhenyu
    Li, Feng
    [J]. 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 938 - 942
  • [6] Estimation of an N-L-N Hammerstein-Wiener model
    Zhu, YC
    [J]. AUTOMATICA, 2002, 38 (09) : 1607 - 1614
  • [7] Identification of pH Process using Hammerstein-Wiener Model
    Abinayadhevi, P.
    Prasad, S. J. Suji
    [J]. PROCEEDINGS OF 2015 IEEE 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND CONTROL (ISCO), 2015,
  • [8] Parameters estimation for the Hammerstein-Wiener models with colored noise based on hybrid signals
    Li, Feng
    Han, Jiahu
    [J]. INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2024, 38 (03) : 921 - 937
  • [9] Hammerstein-Wiener Model: A New Approach to the Estimation of Formal Neural Information
    Abbasi-Asl, Reza
    Khorsandi, Rahman
    Vosooghi-Vahdat, Bijan
    [J]. BASIC AND CLINICAL NEUROSCIENCE, 2012, 3 (04) : 45 - 51
  • [10] Combined separable signals based neuro-fuzzy Hammerstein-Wiener model
    Jia Li
    Feng Qiliang
    [J]. MEMETIC COMPUTING, 2017, 9 (03) : 245 - 259
12345