Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Artificial Neural Networks
Device Independence and Extensibility in Gesture Recognition
VR '03 Proceedings of the IEEE Virtual Reality 2003
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ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
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MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
A tuned eigenspace technique for articulated motion recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
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Applied Soft Computing
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Acquisition of the behavioural skills of a human operator and recreating them in an intelligent autonomous system has been a critical but rather challenging step in the development of complex intelligent autonomous systems. Development of a systematic and generic method for realising this process by acquiring human postural and motor movements is explored. This is achieved by breaking down the human motion into a number of segments called motion or skill primitives. The proposed methodology is developed based on studying the movement of the human hand. The motion is measured by a dual-axis accelerometer and a gyroscope mounted on the hand. The gyroscope locates the position and configuration of the hand, whereas the accelerometer measures the kinematics parameters of the movement. The covariance and the mean of the data produced by the sensors are used as features in the clustering process. A fuzzy clustering method is developed and applied to identify different movements of the human hand. The proposed clustering approach identifies the sequence of the motion primitives embedded in the data produced from the human wrist movement. A review of the previous work in the area is carried out and the developed methodology is described. An overview of the experimental setup and procedures to validate the approach is given. The results of the validation are analysed critically and some conclusions are drawn.