A self-organizing approach to generate raining data for EMG signal classification

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 data are generated. These training data are employed for the classification of the user´s intended motions. It is necessary to determine the number of feature patterns required for motion classification. Therefore, we verify appropriate thresholds which determine the number of feature patterns with consideration of classification rate and learning time.