Gestural flick input-based non-touch interface for character input

被引:0
|
作者
Md. Abdur Rahim
Jungpil Shin
Md. Rashedul Islam
机构
[1] The University of Aizu,School of Computer Science and Engineering
来源
The Visual Computer | 2020年 / 36卷
关键词
Human–computer interaction; Hand gestures; Non-touch input; Gesture recognition; Kinect sensor;
D O I
暂无
中图分类号
学科分类号
摘要
A non-touch character input is a modern system for communication between humans and computers that can help the user to interact with a computer, a machine, or a robot in unavoidable circumstances or industrial life. There have been many studies in the field of touch and non-touch character input systems (i.e., hand gesture languages), such as aerial handwriting, sign languages, and the finger alphabet. However, many previously developed systems require substantial effort in terms of learning and overhead processing for character recognition. To address this issue, this paper proposes a gesture flick input system that offers a quick and easy input method using a hygienic and safe non-touch character input system. In the proposed model, the position and state of the hands (i.e., open or closed) are recognized to enable flick input and to relocate and resize the on-screen virtual keyboard for the user. In addition, this system recognizes hand gestures that perform certain motion functions, such as delete, add a space, insert a new line, and select language, an approach which reduces the need for recognition of a large number of overhead gestures for the characters. To reduce the image-processing overhead and eliminate the surrounding noise and light effects, body index skeleton information from the Kinect sensor is used. The proposed system is evaluated based on the following factors: (a) character selection, recognition and speed of character input (in Japanese hiragana, English, and numerals); and (b) accuracy of gestures for the motion functions. The system is then compared to state-of-the-art algorithms. A questionnaire survey was also conducted to measure the user acceptance and usability of this system. The experimental results show that the average recognition rates for characters and motion functions were 98.61% and 97.5%, respectively, thus demonstrating the superiority of the proposed model compared to the state-of-the-art algorithms.
引用
收藏
页码:1559 / 1572
页数:13
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