Development of a Single 3-Axis Accelerometer Sensor Based Wearable Gesture Recognition Band

2007 ◽  
pp. 43-52 ◽  
Author(s):  
Il-Yeon Cho ◽  
John Sunwoo ◽  
Yong-Ki Son ◽  
Myoung-Hwan Oh ◽  
Cheol-Hoon Lee
Keyword(s):  
Gesture Recognition ◽  
Accelerometer Sensor

A Gesture Recognition Based on Accelerometer and Hidden Markov Model for Human Computer Communication

2013 ◽  
Vol 479-480 ◽  
pp. 938-942
Author(s):  
Shu Lin Wang ◽  
Zhe George Zhang ◽  
You Gang Guo
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Gesture Recognition ◽  
Hidden Markov ◽  
Computer Applications ◽  
Interactive System ◽  
Nintendo Wii ◽  
Accelerometer Sensor ◽  
Computer Communication ◽  
Communication Needs

Along with the continuous changes and improvement of information technology, remote controls are widely used in smart living, robot control, Sign language systems, and so on. However, the human-computer communication needs to be diversified in the future. In particular, users are not intuitive when applying a special application or remote control environment, thus, this study applying Nintendo Wii remote as a human-computer interface device, to capture motion data by built-in three-axis accelerometer sensor, and training and recognize with Hidden Markov Model. First, we apply a gesture recognize system, to enhance the interactive ability of three-axis accelerometer sensor, gesture commands are trainable by user on-demand, and users can interactive with different computer applications through the gesture command has been trained. Finally, this study had issued a Gesture Recognition approach for intelligent interactive system design and for future study.

Customized Gesture Recognition for Educational Games

2016 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Seong Jeong ◽  
HongJun Ju ◽  
Hyo-Rim Choi ◽  
TaeYong Kim
Keyword(s):  
Gesture Recognition ◽  
Educational Games

Mobile Robot Operation by Gesture Recognition using Iterative Motion

2015 ◽  
Vol 135 (8) ◽  
pp. 944-953 ◽  
Author(s):  
Kazuki Tanaka ◽  
Hiroyuki Ukida
Keyword(s):  
Mobile Robot ◽  
Gesture Recognition

HUMAN GESTURE RECOGNITION USING BISPECTRUM-BASED WIRELESS SIGNAL PROCESSING

2020 ◽  
Vol 79 (1) ◽  
pp. 47-57
Author(s):  
O. G. Viunytskyi ◽  
A. V. Totsky ◽  
Karen O. Egiazarian
Keyword(s):  
Signal Processing ◽  
Gesture Recognition ◽  
Wireless Signal

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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