Tsukuba, Ibaraki, Japan
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ABSTRACT: The Simon Effect is a phenomenon in which reaction times are usually faster when the stimulus location and the response correspond, even if the stimulus location is irrelevant to the task. Recent studies have demonstrated the Simon effect in a three-dimensional (3-D) display. The present study examined whether two-dimensional (2-D) and 3-D locations simultaneously affected the Simon effect for stimuli in which a target and fixation were located on the same plane (ground or ceiling) at different 3-D depths, and the perspective effect produced a difference in the 2-D vertical location of the target stimulus relative to the fixation. The presence of the ground and ceiling plane was controlled to examine the contextual effects of background. The results showed that the 2-D vertical location and 3-D depth simultaneously affected the speed of responses, and they did not interact. The presence of the background did not affect the magnitude of either the 2-D or the 3-D Simon effect. These results suggest that 2-D vertical location and 3-D depth are coded simultaneously and independently, and both affect response selection in which 2-D and 3-D representations overlap.Frontiers in Psychology 09/2015; 6:1302. DOI:10.3389/fpsyg.2015.01302
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ABSTRACT: In this study, we introduce a novel variant and application of the Collaborative Representation based Classification in spectral domain for recognition of the hand gestures using the raw surface Electromyography signals. The intuitive use of spectral features are explained via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set. The worst recognition result which is the best in the literature is obtained as 97.3\% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation.Biomedical Signal Processing and Control 06/2015; 24. DOI:10.1016/j.bspc.2015.09.001
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ABSTRACT: Personal Mobility Robots, such as the Seqway may be the remedy for the transportation related problems in the congested environment, especially for the last and first mile problems of the elderly people. However, the vehicle segmentation issues for the mobility robots, impede the use of these devices on the shared paths. The mobility reports can only be used in the designated areas and private facilities. The traffic regulatory institutions lack of robot-society interaction database. In this study, we proposed methods and algorithms which can be employed on a widespread computing device, such as an Android tablet, to gather travel information and rider behavior making use of the motion and position sensors of the tablet PC. The methods we developed, first filter the noisy sensor readings using a complementary filter, then align the body coordinate system of the device to the Segway’s motion coordinate. A couple of state of the art classification methods are integrated to classify the braking states of the Segway. The classification algorithms are not limited to classification of the braking states, but they can be used for other motion related maneuvers the road surfaces. The detected braking states and the other classified features related to the motion are reflected to the screen of the Android tablet to inform the rider about the riding and motion conditions. The developed Android application also gather these travel information to build a National database for further statisccal analysis of the robot-society interaction.Robotics and Autonomous Systems 04/2015; 2073. DOI:10.1016/j.robot.2015.04.008
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