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In this paper, we present a tracking failure detection method by imitating human visual system. By adopting log-polar transformation, we could simulate properties of retina image, such as rotation and scaling invariance and foveal predominance. The rotation and scaling invariance helps to reduce false alarms caused by pose changes and intensify(More)
In the past decade, model learning techniques have provided appealing approaches for determining the dynamic model of robots from data. These techniques strongly capture the complicated effects of robot dynamics, which are often neglected in hand-crafted dynamic models. However, unlike robust performance shown in trained tasks, learned models do not exhibit(More)
In this study, we evaluated a fast training algorithm to decode human hand configuration from sEMG signals on the forearms of five subjects. Eight skin surface electrodes were placed on the forearm of each subject to detect the sEMG signals corresponding to four different hand configurations and relax state. The preamplifier, which has 100 - 10000 times(More)
This study presents an online remote control of a robotic hand using sEMG signals on a forearm. Eight skin surface electrodes were mounted on a forearm to detect the sEMG signals that correspond to four hand configurations and rest condition. In order to enhance learning speed and performance of the classifier, a supervised feature extraction method and a(More)
This paper proposes a novel fast classification system consisting of feature extraction and classifier to decode human hand configurations from multi-channel surface electromyogram (sEMG) signals that allows real-time classification of human movement intention as well as prothesis control. In order to enhance the learning speed and the performance of the(More)