What shapes the face of human-computer interaction in higher education? A framework of the influences

1996 ◽  
Vol 8 (1) ◽  
pp. 97-123 ◽  
Author(s):  
Jean B. Gasen ◽  
Jenny Preece
Keyword(s):  
Higher Education ◽  
Human Computer Interaction ◽  
The Face ◽  
Computer Interaction

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.

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

2017 ◽  
pp. 67-96
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.

Robot-assisted feeding: A technical application that combines learning from demonstration and visual interaction

2020 ◽  
pp. 1-6
Author(s):  
Fei Liu ◽  
Peng Xu ◽  
Hongliu Yu
Keyword(s):  
Human Computer Interaction ◽  
Recognition Rate ◽  
Interaction Method ◽  
Robot Assisted ◽  
Technical Application ◽  
Interaction Mode ◽  
Visual Interaction ◽  
The Face ◽  
Programming Ability ◽  
Computer Interaction

BACKGROUND: The traditional meal assistance robots use human-computer interaction such as buttons, voice, and EEG. However, most of them rely on excellent programming technology for development, in parallelism with exhibiting inconvenient interaction or unsatisfactory recognition rates in most cases. OBJECTIVE: To develop a convenient human-computer interaction mode with a high recognition rate, which allows users to make the robot show excellent adaptability in the new environment without programming ability. METHODS: A visual interaction method based on deep learning was used to develop the feeding robot: when the camera detects that the user’s mouth is open for 2 seconds, the feeding command is turned on, and the feeding is temporarily conducted when the eyes are closed for 2 seconds. A programming method of learning from the demonstration, which is simple and has strong adaptability to different environments, was employed to generate a feeding trajectory. RESULTS: The user is able to eat independently through convenient visual interaction, and it only requires the caregiver to drag and teach the robotic arm once in the face of a new eating environment.

scholarly journals Hand gesture recognition using machine learning algorithms

2020 ◽  
Vol 1 (3) ◽  
pp. 116-120
Author(s):  
Abhishek B. ◽  
Kanya Krishi ◽  
Meghana M. ◽  
Mohammed Daaniyaal ◽  
Anupama H. S.
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Human Gestures ◽  
The Face ◽  
Mathematical Algorithms ◽  
User Friendly ◽  
Computer Interaction

Gesture recognition is an emerging topic in today’s technologies. The main focus of this is to recognize the human gestures using mathematical algorithms for human computer interaction. Only a few modes of Human-Computer Interaction exist, they are: through keyboard, mouse, touch screens etc. Each of these devices has their own limitations when it comes to adapting more versatile hardware in computers. Gesture recognition is one of the essential techniques to build user-friendly interfaces. Usually gestures can be originated from any bodily motion or state, but commonly originate from the face or hand. Gesture recognition enables users to interact with the devices without physically touching them. This paper describes how hand gestures are trained to perform certain actions like switching pages, scrolling up or down in a page.

Sign in / Sign up

Export Citation Format

Share Document