Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 371621 14 pages doi 2008 371621 Research Article Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques G. Yang 1 Y. Lin 2 and P. Bhattacharya3 1 College of Information Engineering Central University for Nationalities Beijing 100081 China 2 Department of Mechanical and Industrial Engineering Northeastern University 360 Huntington Avenue Boston MA 02115 USA 3 Concordia Institute for Information Systems Engineering Concordia University Montreal QC Canada H3G 1M8 Correspondence should be addressed to Y. Lin yilin@ Received 11 December 2006 Revised 25 April 2007 Accepted 9 August 2007 Recommended by Dimitrios Tzovaras To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually there is a need for the machine to understand the human state especially cognitive state when the human s operation task demands an intensive cognitive activity. Due to a well-known fact with the human being a highly uncertain cognitive state and behavior as well as expressions or cues the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method we take the driver fatigue detection as an example. The proposed method has in particular the following new features. First human expressions are classified into four categories i casual or contextual feature ii contact feature iii contactless feature and iv performance feature. Second the fuzzy neural network technique in particular Takagi-Sugeno-Kang TSK model is employed to cope with uncertain behaviors. Third the sensor fusion technique in particular ordered .
