scholarly journals Design of an Arm Gesture Recognition System Using Feature Transformation and Hidden Markov Models

2013 ◽  
Vol 2 (10) ◽  
pp. 723-730 ◽  
Author(s):  
Se-Kyeong Heo ◽  
Ye-Seul Shin ◽  
Hye-Suk Kim ◽  
In-Cheol Kim
Keyword(s):  
Hidden Markov Models ◽  
Gesture Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Feature Transformation

scholarly journals Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hyesuk Kim ◽  
Incheol Kim
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Gesture Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition Performance ◽  
Graph Model ◽  
Feature Transformation ◽  
Probabilistic Graph

We introduce a vision-based arm gesture recognition (AGR) system using Kinect. The AGR system learns the discrete Hidden Markov Model (HMM), an effective probabilistic graph model for gesture recognition, from the dynamic pose of the arm joints provided by the Kinect API. Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles of the corresponding arm part to obtain view-invariant and more discriminative features. We confirmed high recognition performance of the proposed AGR system through experiments with two different datasets.

Gesture Recognition Using Pseudo 3D Hidden Markov Models

2000 ◽  
pp. 420-427
Author(s):  
Ilhan K. Yalcin ◽  
A. Tolga Kilinc ◽  
Stefan Müller ◽  
Gerhard Rigoll
Keyword(s):  
Hidden Markov Models ◽  
Gesture Recognition ◽  
Markov Models ◽  
Hidden Markov
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