The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of CIRCUITS, SYSTEMS and SIGNAL PROCESSING
Transactions ID Number: 20-875
Full Name: Marco Schoen
Position: Professor
Age: ON
Sex: Male
Address: 921 South 8th Avenue, Stop 8060, Pocatello, Idaho 83209
Country: UNITED STATES
Tel: 001 208 282 4377
Tel prefix:
Fax: 001 208 282 4538
E-mail address: schomarc@isu.edu
Other E-mails: sebaanis@isu.edu,caldmet70@gmail.com
Title of the Paper: Modeling surface electromyogram dynamics using Hammerstein-Wiener models with comparison of IIR and spatial filtering techniques
Authors as they appear in the Paper: Anish Sebastian, Parmod Kumar, Marco P. Schoen
Email addresses of all the authors: sebaanis@isu.edu,caldmet70@gmail.com,schomarc@isu.edu
Number of paper pages: 12
Abstract: The national limb loss statistics paints a grim picture. Given the staggering limb loss numbers, the need to develop a "Smart Prosthetic Device" has never been more exigent. Despite years of effort by various government organizations and dedicated work on part of many scientists, we are still quite a ways away from creating the "perfect" prosthetic. Using electromyogram (EMG) signals to control prosthetic devices is and has been in the past, one of the most promising directions for this research. However, most of the control schemes being used, are based on either pre-programming the motion using threshold values of the EMG signal as reference, or using the root-mean-squared values of the EMG signal to actuate the prosthetic device. Using such a control strategy, makes it impossible to capture the underlying dynamics between EMG signals and the intended finger movements and forces. As a result of which the user needs to make an effort to learn to use the device, w!
hich can be very exhaustive. We propose to use system identification based dynamic models which are extracted from recorded surface EMG (sEMG) signals and the corresponding finger forces. A key influence on the resulting quality of such models is the filtering methods used for the EMG signals. This paper presents a thorough analysis of spatial filtering and other filtering methods as a possible solution to capture the dynamics of the sEMG signals, and perhaps in the future use these models to implement control schemes which would mimic the intricate force changes for a prosthetic hand. The different filters are compared on the basis of the EMG-finger force model fit percentages, obtained from System Identification using various Non-Linear Hammerstein-Wiener models. The nonlinear spatial filters gave better fit values as compared to the standard filtering techniques.
Keywords: Spatial Filtering, Hammerstein-Wiener, Surface Electromyogram (sEMG), System Identification, Sensor Array, Modeling
EXTENSION of the file: .pdf
Special (Invited) Session: Evaluation of filtering techniques applied to surface EMG data and comparison based on Hammerstein-Wiener models
Organizer of the Session: 658_230
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