Intelligent Intent-Aware Touchscreen Systems Using Gesture Tracking with Endpoint Prediction

2015 ◽  
pp. 3-14 ◽  
Author(s):  
Bashar I. Ahmad ◽  
Patrick M. Langdon ◽  
Robert Hardy ◽  
Simon J. Godsill
Keyword(s):  
Endpoint Prediction ◽  
Gesture Tracking

New AAC Access Strategy for Gesture Tracking: A Technical Note

2012 ◽  
Vol 21 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Susan Fager ◽  
Tom Jakobs ◽  
David Beukelman ◽  
Tricia Ternus ◽  
Haylee Schley
Keyword(s):  
Augmentative And Alternative Communication ◽  
Technical Note ◽  
Head Tracking ◽  
Alternative Communication ◽  
Access Methods ◽  
Cord Injury ◽  
Physical Limitations ◽  
Gesture Tracking ◽  
Communication Needs ◽  
Tracking Device

Abstract This article summarizes the design and evaluation of a new augmentative and alternative communication (AAC) interface strategy for people with complex communication needs and severe physical limitations. This strategy combines typing, gesture recognition, and word prediction to input text into AAC software using touchscreen or head movement tracking access methods. Eight individuals with movement limitations due to spinal cord injury, amyotrophic lateral sclerosis, polio, and Guillain Barre syndrome participated in the evaluation of the prototype technology using a head-tracking device. Fourteen typical individuals participated in the evaluation of the prototype using a touchscreen.

scholarly journals An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hong-Min Zhu ◽  
Chi-Man Pun
Keyword(s):  
Template Matching ◽  
Tracking System ◽  
Recognition System ◽  
Appearance Model ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Motion Trajectory ◽  
Hand Motion ◽  
Target Hand ◽  
Gesture Tracking

We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.

2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors

2014 ◽  
Vol 14 (4) ◽  
pp. 1160-1170 ◽  
Author(s):  
Shengli Zhou ◽  
Fei Fei ◽  
Guanglie Zhang ◽  
John D. Mai ◽  
Yunhui Liu ◽  
...  
Keyword(s):  
Vision Sensors ◽  
Gesture Tracking ◽  
Tracking And Recognition

Moving gesture tracking algorithm based on vibe and space-time context

2018 ◽  
Vol 33 (1) ◽  
pp. 92-98
Author(s):  
王 民 WANG Min ◽  
石新源 SHI Xin-yuan ◽  
王稚慧 WANG Zhi-hui ◽  
李泽洋 LI Ze-yang
Keyword(s):  
Space Time ◽  
Tracking Algorithm ◽  
Gesture Tracking
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