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【期刊论文】A Remote Assessment System with a Vision Robot and Wearable Sensors
王珏, Tong Zhang, Jue Wang, Yumiao Ren, and Jianjun Li
San Francisco, CA, USA,-0001,():
-1年11月30日
This paper describes an ongoing researched remote rehabilitation assessment system that has a 6-freedom double-eyes vision robot to catch vision information, and a group of wearable sensors to acquire biomechanical signals. A server computer is fixed on the robot, to provide services to the robot’s controller and all the sensors. The robot is connected to Internet by wireless channel, and so do the sensors to the robot. Rehabilitation professionals can semi-automatically practise an assessment program via Internet. The preliminary results show that the smart device, including the robot and the sensors, can improve the quality of remote assessment, and reduce the complexity of operation at a distance.
remote assessment,, Client/, Server,, vision robot,, wearable sensor.,
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王珏, LIU Mingyp, WANG Jue, YAN Nan
2005 First International Conference on Neural Interface and Control Proceedings; 26-28 May 2005; Wuhan, China,-0001,():
-1年11月30日
Electroencephalogram (EEG) based biofeedback is widely employed to treat certain kinds of diseases especially Attention Deficit Hyperactivity Disorder (ADD/ADHD). Thus to design a system capable of tearning a particular mapping between EEG features and different attention-level mental tasks is of great significance. Event Related Potentials (EM) is such a powerful feature which is traditionally extracted by averaging. The paper proposed a new EFW extraction algorithm using radial basis function (RBF) neural network. It discussed the configuration, learning and running of the designed network. In order to reduce computational complexity and the influence of noise in, estimating EFW, the partial least square regression was introduced to train the RBF network. Series experiments’showed that the method is effective and is suitable for single-trail ERP estimation.
Event-related potential,, Radial basis function Neural network,, Partial least square regression
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