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This work has aimed to contribute to the prothesis-bionic hand studies. Four hundred eighty signals used in this work correspond to position of adduction motion of thumb, flexion motion of thumb, abduction motion of fingers were collected by surface electrodes. Eight healthy has participated for collecting by surface electromyogram (SEMG). The wavelet based autoregressive models of collected signals...
In this paper, we introduce a novel noise suppression method for electromyography (EMG) signals, based on statistical modeling of wavelet coefficients. First, we demonstrate that Generalized Autoregressive Conditional Heteroscedasticity (GARCH) effect exists in wavelet coefficients of EMG signals. Then, we use GARCH model for these coefficients. In consequence, we introduce a maximum a-posteriori...
An EMG-driven arm wrestling robot (AWR) is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The AWR arm have 2-DOF, integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera, is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). The surface...
A five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines Levenberg-Marquardt (LM) or variable learning rate (VLR) based neural network with parametric autoregressive (AR) model and wavelet transform. This motion pattern classifier can successfully...
In this paper, the surface electromyographic (EMG) signals is acquired from the upper limb when the experimenter competes with the arm wrestling robot (AWR) which is integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with human on a table with pegs for entertainment...
Toward the goal of elbow and wrist prostheses control by characterizing events in surface myoelectric signals, this paper presents a dynamic method to simultaneously detect and classify such events. Dynamic cumulative sum of local generalized likelihood ratios using wavelet decomposition of the myoelectric signal is used for on-line detection. Frequency as well as energy changes are detected with...
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