DeepGesture: Improving Touchscreen Gesture Recognition using Convolutional Neural Network for Users with Varying Motor Skill Levels

• Main Authors: Tzu-Chuan Chen, 陳子權
• Other Authors: Mike Y. Chen
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/2ktru7

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Elderly people and the motor impaired have difficulty in interacting with touch screen devices. Commonly-used mobile system uses a general model for gesture recognition. However, the general threshold-based model may not meet their special needs. Hence, we present DeepGesture, a 2-stage model providing self-learning function for gesture recognition. In first stage, convolution neutral network is used to classify gesture.It remarkably improves the success rate of recognizing common gestures, such as tap and pan etc. After tapping gesture recognized,a novel tap optimizer is used to choose most important touch point to obtain higher tapping success rate. The results show that DeepGesture achieves a higher success rate than iOS default recognizer.