NDLI: Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Sebastian, Anish Kumar, Parmod Anugolu, Madhavi Marco, P. Schoen Urfer, Alex Naidu, D. Subbaram
Copyright Year	2009
Abstract	Processing electromyographic (EMG) signals for force estimation has many unknown variables that can influence the outcome or interpretation of the recorded EMG signal significantly. An array of filtering methods have been proposed over the past few years with the objective to classify motion for use in prosthetic hands. In this paper, we explore the optimal parameter settings of a set of Bayesian based EMG filters with the objective to use the filtered EMG data for system identification. System identification is utilized to establish a relationship between the measured EMG data and the generated force developed by fingers in a human hand. The proposed system identification is based on nonlinear Hammerstein-Wiener models. Optimization is also applied to find the optimal parameter settings for these nonlinear models. Genetic Algorithm (GA) is used to conduct the optimization for both, the optimal parameter settings for the Bayesian filters as well as the Hammerstein-Wiener model. The experimental results and optimization analysis indicate that the optimization can yield significant improvement in data accuracy and interpretation.
Sponsorship	Dynamic Systems and Control Division
Starting Page	713
Ending Page	720
Page Count	8
File Format	PDF
ISBN	9780791848920
DOI	10.1115/DSCC2009-2658
e-ISBN	9780791838600
Volume Number	ASME 2009 Dynamic Systems and Control Conference, Volume 1
Conference Proceedings	ASME 2009 Dynamic Systems and Control Conference
Language	English
Publisher Date	2009-10-12
Publisher Place	Hollywood, California, USA
Access Restriction	Subscribed
Subject Keyword	Artificial limbs Filters Filtration Electromyography Signals Performance Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Analysis of EMG-Force Relation Using System Identification and Hammerstein-Wiener Models

Surface EMG Array Sensor Based Model Fusion Using Bayesian Approaches for Prosthetic Hands

Realization of human-computer interaction of lower limbs rehabilitation robot based on sEMG

Nonlinear system identification of Hammerstein-Wiener model using AWPSO

Using LBG algorithm for extracting the features of EMG signals

Nonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms

Control of a Robotic Prosthetic Hand Using an EMG Signal Based Counter

Nonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms

Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm

Similar Documents

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Analysis of EMG-Force Relation Using System Identification and Hammerstein-Wiener Models

Surface EMG Array Sensor Based Model Fusion Using Bayesian Approaches for Prosthetic Hands

Realization of human-computer interaction of lower limbs rehabilitation robot based on sEMG

Nonlinear system identification of Hammerstein-Wiener model using AWPSO

Using LBG algorithm for extracting the features of EMG signals

Nonlinear model predictive control of Hammerstein and Wiener models using genetic algorithms

Control of a Robotic Prosthetic Hand Using an EMG Signal Based Counter

Nonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms

Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm