scholarly journals A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guoliang Chen ◽  
Kaikai Ge
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Calculation Model ◽  
Opening Angle ◽  
Hand Gesture ◽  
Leap Motion ◽  
Recognition Method ◽  
Hand Gestures ◽  
Multiple Features

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.

scholarly journals A NEURAL-NETWORK-BASED APPROACH TO RECOGNIZING 3D ARM MOVEMENTS

2003 ◽  
Vol 15 (01) ◽  
pp. 17-26 ◽  
Author(s):  
MU-CHUN SU ◽  
YU-XIANG ZHAO ◽  
EUGENE LAI
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Arm Movements ◽  
Movement Pattern ◽  
Arm Movement ◽  
Hand Gesture ◽  
Spatio Temporal

Gesture recognition is needed for a variety of applications. One particular application of gesture-based systems is to implement a speaking aid for the deaf. Among several factors constituting a hand gesture, the arm movement pattern is one of the most challenging features to recognize. In this paper, we propose a neural-network-based approach to recognition of spatio-temporal patterns of nonlinear 3D arm movements. Compared to Hidden-Markov-Model-based methods, the most appealing property of the proposed method is its simplicity. The effectiveness of this method is evaluated by a database consisted of 10 persons.

Recognition of Offline Handwritten Chinese Characters of Amount in Words Based on Integrated Features and HMM

2012 ◽  
Vol 263-266 ◽  
pp. 2639-2642
Author(s):  
Cai Feng Liu ◽  
Xue Dong Tian ◽  
Fang Yang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Experimental Results ◽  
Chinese Characters ◽  
Recognition Method ◽  
Voting Rule

A recognition method of offline handwritten Chinese characters of amount in words is presented. The method uses elastic mesh strategy to divide character images written by special men into meshes, and extracts directional element and key point features in every mesh to produce a vector. Based on independent Hidden Markov Model classifiers, this paper uses voting rule to integrate the Hidden Markov Model classifiers. The experimental results show that this method has a relative high recognition rate.

State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model

2016 ◽  
Vol 231 (11) ◽  
pp. 2135-2146 ◽  
Author(s):  
Bing Wang ◽  
Ping Yan ◽  
Qiang Zhou ◽  
Libing Feng
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Machining Process ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Recognition Method ◽  
State Recognition ◽  
Model Recognition

Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.

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