A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-09
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation.
In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated.
Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory.
Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented.
The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.
American Psychological Association (APA)
Chen, Guoliang& Ge, Kaikai. 2020. A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1138928
Modern Language Association (MLA)
Chen, Guoliang& Ge, Kaikai. A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1138928
American Medical Association (AMA)
Chen, Guoliang& Ge, Kaikai. A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1138928
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138928