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This paper exploited the single trial decoding of cortical-muscular activities (CMAs). The CMAs were measured by acquiring Electromyographic (EMG) signal, and Electroencephalographic (EEG) signal. We focused on CMAs related to sustained muscular contractions (SMC), and investigated on the classification performance of four types of CMAs with different combinations of three types of features. The four...
For achieving a closed-loop, bidirectional control over myoelectric prosthetic hands, adopting the electrical stimulation in the sensory feedback channel for providing electrotactile substitution is currently a big trend. However, the electrical pulses used for stimulation may spread to the EMG collection sites which significantly interferes the controlling stability. In this paper, a novel noise...
A common source for controlling hand prosthesis is the myoelectric signal (MES, also termed electromyography, EMG) that are collected from human body. For a pattern recognition-based EMG control scheme, research has found that the classification accuracy obtained offline may deteriorate owing to signal instinct or changed environment, which results in a reduced system stability. Based on support vector...
A five-fingered, multi-sensory biomechatronic hand with sEMG interface is presented. The cambered palm is specially designed to enhance the stability while grasping. The location of the thumb is designed by maximizing interaction area between the thumb and other fingers. The opposite thumb could grasp along a cone surface, while maintaining its function. By taken the advantage of coupling linkage...
The multi-DOF prosthetic hand's myocontrol needs to recognize more hand gestures (or motions) based on myoelectric signals. This paper presents a classification method, which is based on the support vector machine (SVM), to classify 19 different hand gesture modes through electromyographic (EMG) signals acquired from six surface myoelectric electrodes. All hand gestures are based on a 3-DOF configuration,...
In the force control of multi-functional prosthetic hands, it is important to extract grasp force information besides mode specifications directly from the myoelectric signals. In this paper, a force sensor is adopted to record the hand's enveloping force when the hand is performing several grasp modes, synchronously with 6 channels surface electromyography (EMG) which are extracting from the subject's...
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...
A five-fingered underactuated prosthetic hand controlled by surface EMG (electromyographic) signals is presented in this paper. The prosthetic hand is designed with simplicity, lightweight and dexterity on the requirement of anthropomorphic hands. Underactuated self-adaptive theory is adopted to decrease the number of motors and weight. The fingers of the hand with multi phalanges have the same size...
A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle...
This paper presents a five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals. The prosthetic hand control part is based on an EMG motion pattern classifier which combines variable learning rate (VLR) based neural network with parametric autoregressive (AR) model and wavelet transform. This motion pattern classifier can successfully identify flexion and extension...
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