Effects of vibration and target size on the use of varied computer input devices in basic human‐computer interaction tasks

2021 ◽  
Author(s):  
Hailiang Wang ◽  
Da Tao ◽  
Jian Cai ◽  
Xingda Qu
Keyword(s):  
Human Computer Interaction ◽  
Target Size ◽  
Input Devices ◽  
Computer Input ◽  
Computer Input Devices ◽  
Computer Interaction

Computer input devices: Quantification of use and variation in use

2004 ◽  
Author(s):  
Carolyn M. Sommerich ◽  
Sahika Vatan ◽  
Amy Asmus
Keyword(s):  
Input Devices ◽  
Computer Input ◽  
Computer Input Devices

Physical workload during use of speech recognition and traditional computer input devices

Ergonomics ◽  
2004 ◽  
Vol 47 (2) ◽  
pp. 119-133 ◽  
Author(s):  
B. JUUL-KRISTENSEN†*, ◽  
B. LAURSEN‡, ◽  
M. PILEGAARD§ and ◽  
B.R. JENSEN§
Keyword(s):  
Speech Recognition ◽  
Physical Workload ◽  
Input Devices ◽  
Computer Input ◽  
Computer Input Devices

Design and Evaluation of Vision-Based Head and Face Tracking Interfaces for Assistive Input

2014 ◽  
pp. 180-205 ◽  
Author(s):  
Chamin Morikawa ◽  
Michael J. Lyons
Keyword(s):  
Human Computer Interaction ◽  
Qualitative Evaluation ◽  
Facial Feature ◽  
Face Tracking ◽  
Input Devices ◽  
Interaction Styles ◽  
Facial Feature Tracking ◽  
The Face ◽  
Gesture Interfaces ◽  
Computer Interaction

Interaction methods based on computer-vision hold the potential to become the next powerful technology to support breakthroughs in the field of human-computer interaction. Non-invasive vision-based techniques permit unconventional interaction methods to be considered, including use of movements of the face and head for intentional gestural control of computer systems. Facial gesture interfaces open new possibilities for assistive input technologies. This chapter gives an overview of research aimed at developing vision-based head and face-tracking interfaces. This work has important implications for future assistive input devices. To illustrate this concretely the authors describe work from their own research in which they developed two vision-based facial feature tracking algorithms for human computer interaction and assistive input. Evaluation forms a critical component of this research and the authors provide examples of new quantitative evaluation tasks as well as the use of model real-world applications for the qualitative evaluation of new interaction styles.

Human Computer Interaction for Computer-Based Classroom Teaching

2009 ◽  
pp. 1436-1458
Author(s):  
Wolfgang Hürst ◽  
Khaireel A. Mohamed
Keyword(s):  
Human Computer Interaction ◽  
Software Design ◽  
Data Type ◽  
Classroom Teaching ◽  
Teaching Process ◽  
Input Devices ◽  
Computer Based ◽  
Digital Ink ◽  
Computer Interaction

This chapter focuses on HCI aspects to overcome problems arising from technologies and applications that may hinder the normal teaching process in ICT-ready classrooms. It investigates different input devices on their usage and interactivity for classroom teaching and argues that pen-based computing is the mode of choice for lecturing in modern lecture halls. It also discusses the software design of the interface where digital ink, as a “first class” data type is used to communicate visual contents and interact with the ICT.

Range of motion of the wrist: implications for designing computer input devices for the elderly

2000 ◽  
Vol 22 (13-14) ◽  
pp. 633-637 ◽  
Author(s):  
Alex Chaparro ◽  
Michael Rogers ◽  
Jeffrey Fernandez ◽  
Mike Bohan ◽  
Choi Sang Dae ◽  
...  
Keyword(s):  
Range Of Motion ◽  
The Elderly ◽  
Input Devices ◽  
Computer Input ◽  
Computer Input Devices

Speed of Speech Recognition versus Keyboarding as Computer Input Devices for the Severely Disabled

1988 ◽  
Vol 16 (3) ◽  
pp. 283-293 ◽  
Author(s):  
L. E. Hiner
Keyword(s):  
Speech Recognition ◽  
Word Processing ◽  
Recognition System ◽  
Upper Body ◽  
Experimental Conditions ◽  
Input Devices ◽  
Keyboard Input ◽  
Computer Input ◽  
Recognition Systems ◽  
Computer Input Devices

Speech recognition systems promise to facilitate access to computer for users with disability. This study examined the usefulness of the Texas Instruments Speech Recognition System in completing word processing tasks under the experimental conditions of 1) keyboard input only, 2) speech recognition only, and 3) a combination of keyboard input and speech recognition. Five subjects with some degree of upper-body disability were tested; the results indicate that performance was 1) greatest under the keyboard only condition, 2) lowest under the speech only condition, and 3) somewhat lower under the combined condition than under the keyboard only condition. Based on the findings, suggestions for further research were made.

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