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The surface electromyogram (EMG) signal collected from multiple channels has frequently been investigated for use in controlling upper-limb prostheses. One common control method is EMG-based motion classification. Time and frequency features derived from the EMG have been investigated. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening...
We propose a method for generating training data by using a self-organized clustering technique for electromyography (EMG) signal classification. In this method, EMG signals are measured during motions, and representative feature patterns are extracted from the EMG signals by using the self-organized clustering method. A user determines the connections between feature patterns and motions, and training...
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control prosthetic devices or human-assisting manipulators. This paper proposes a task model using a Bayesian network (BN) for motion prediction. Given information of the previous motion, this task model is able...
Feature extraction is a key element of pattern recognition for myoelectric control. In this paper, recurrence plots and recurrence quantification analysis (RQA) are used as the feature extractor for surface EMG signals. For eight different hand motions, two-channel EMG signals are recorded. Ten individual RQA parameters are calculated for each channel of EMG signals. With different combinations of...
This paper reports a new myoelectric interface for robotic hand control consisting of two main parts. The first part concerns the motion classification using electromyogram (EMG) signals of a support vector machine (SVM). Because there has been little research on the application of the SVM to motion classification using EMG signals, its effectiveness has not yet been established. The SVM has some...
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