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Title:
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REAL-TIME HAND GESTURE IDENTIFICATION FOR HUMAN COMPUTER INTERACTION BASED ON ICA OF SURFACE ELECTROMYOGRAM |
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Author(s):
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Ganesh R. Naik , Hans Weghorn , Dinesh K. Kumar , Vijay P. Singh |
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ISBN:
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978-972-8924-39-3 |
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Editors:
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António Palma dos Reis, Katherine Blashki and Yingcai Xiao (series editors:Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Human Computer Interface (HCI), Hand Gesture Sensing, Bio-signal Analysis, Surface Electromyography (SEMG),
Independent Component Analysis (ICA). |
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Type:
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Full Paper |
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First Page:
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83 |
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Last Page:
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90 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Today, there exists a variety of interfaces that allow human users to interact with computerized systems. Many of these
input and output devices force the user to adapt to the requirements of the machine construction, like e.g. numeric
keyboards on tiny devices often have to be used also for letter input. In contradiction to such technically-driven concepts,
the aim of the investigation presented here is to provide a reliable input mode, which enables machine control for
rehabilitation and human-computer interaction applications in a quite natural way. The processing in this new input
system consists of three major stages: At first, hand gestures are sensed from non-invasive surface electromyograms, and
in the second step the activities of the involved individual muscles are decomposed by semi-blind independent
component analysis (ICA). In the last step, the particular hand action is identified with an artificial neural network
(ANN). In this model-based approach, the order and magnitude ambiguity of ICA have been overcome by using a priori
knowledge of the hand muscle anatomy and a fixed un-mixing matrix for the signal decomposition. In 360 single-shot
experiments, this system was able to classify all tested hand gestures fully correct. These experimental results
demonstrate that the proposed approach yields a high recognition rate with various gestures, and the system was verified
being insensitive against electrode positions. A comparative evaluation of applying the same recognition mechanism in
identifying facial movement yields new findings about the properties of the derived ICA mixing matrix, which can be
exploited as indicator for the reliability and efficiency of the pattern classification mechanism in a distinct application. |
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