Hidden Markov model for human to computer interaction: a study on human hand gesture recognition

2011 ◽  
Vol 40 (4) ◽  
pp. 495-516 ◽  
Author(s):  
Sara Bilal ◽  
Rini Akmeliawati ◽  
Amir A. Shafie ◽  
Momoh Jimoh E. Salami
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand ◽  
Computer Interaction

Hand Gesture Recognition using Colour Based Segmentation and Hidden Markov Model

2015 ◽  
Vol 4 (5) ◽  
pp. 386-389
Author(s):  
Kshitish Milind Deo ◽  
Tirtha Suresh Girolkar ◽  
Avanti Yashwant Kulkarni
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Hand Gesture Recognition ◽  
Hand Gesture

scholarly journals Hand Gesture Recognition Using Hidden Markov Model Algorithm

MATICS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Roro Inda Melani
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Dominant Frequency ◽  
Sensor Data ◽  
Hand Gesture ◽  
Human Hand ◽  
Hand Position ◽  
The Difference

<p><em>A</em><em>bstract</em>— Recognizing human hand gesture through the use of INS (Inertial navigation System) sensor, Hidden Markov Model (HMM) was used as a tool to recognize pattern statistically. Employing INS sensor to admit data input , it is assumed that hand gesture could be detected by analizing the acceleration and fluctuation from data sensor and the difference of hand-position in 3-axis. The INS sensor that was being used was came with 6 channels to generate signals of a 3-axis gyroscope and a 3-axis accelerometer. The acceleration fluctuated in three perpendicular directions due to different hand gestures was detected by the accelerometer, while the change of hand-position in 3-axis was detected by gyroscope. Data from sensor was exported to computer via USB (Universal Serial Bus) port.</p><p class="Abstract">During the stage of data collection, a cut algorithm was developed to pick the most significant part of the sensor data. After finishing data comparison stage, DCT (Discrete Cosine Transform) was selected to transform the signal from time domain to frequency domain. Sequences of calculation were performed to analyze the best sampling frequency to select dominant frequency of every gesture to be picked as parameter value. The parameter value used in HMM as the approach to recognize and differs gestures.</p><p> </p><em>Index Terms</em>—<em>3-axis gyroscope,</em><em> accelerometer, gesture, gesture recognition, hand gesture, human gesture</em>.

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