scholarly journals Acoustic-sensing-based Gesture Recognition Using Hierarchical Classifier

2020 ◽  
Vol 32 (9) ◽  
pp. 2981
Author(s):  
Miki Kawato ◽  
Kaori Fujinami
Keyword(s):  
Gesture Recognition ◽  
Acoustic Sensing ◽  
Hierarchical Classifier

scholarly journals HCI on the Table: Robust Gesture Recognition Using Acoustic Sensing in Your Hand

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 31481-31498 ◽  
Author(s):  
Gan Luo ◽  
Panlong Yang ◽  
Mingshi Chen ◽  
Ping Li
Keyword(s):  
Gesture Recognition ◽  
Acoustic Sensing

scholarly journals Gesture Recognition Method Utilizing Ultrasonic Active Acoustic Sensing

2017 ◽  
Vol 25 (0) ◽  
pp. 331-340 ◽  
Author(s):  
Hiroki Watanabe ◽  
Tsutomu Terada ◽  
Masahiko Tsukamoto
Keyword(s):  
Gesture Recognition ◽  
Recognition Method ◽  
Acoustic Sensing

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

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.

Sign in / Sign up

Export Citation Format

Share Document