HCI Based Input Device for Differently Abled

Tremendous advancements in the technology has lead to the monumental growth of various branches of computing such as Computer Vision and Human-Computer Interaction (HCI). Computer’s input has data about different properties of users, objects or places. For instance, mouse and keyboard works by the movement performed by the end user’s hands. These approaches are not appropriate for differently abled people. Away to create an application which replaces the input devices such as mouse and keyboard by using face of the user is proposed. This paper introduces how head motion of the user can be used to control the mouse cursor and how gaze tracking can be used to control the keyboard. A face detecting system precisely records the motion parameters from video at real-time using a typical webcam. While the pace reduces while using the virtual mouse and keyboard, the performance of the system is intact for differently abled people whose only means of communication are the head movements and gaze.

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A Comparison of Computer Input Devices: Linus Pen, Mouse, Cursor Keys and Keyboard

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128903300521 ◽

1989 ◽

Vol 33 (5) ◽

pp. 330-334 ◽

Cited By ~ 2

Author(s):

Karen Renee Mahach

Keyword(s):

Handwriting Recognition ◽

Data Entry ◽

Recognition System ◽

Input Device ◽

Input Devices ◽

Mouse Cursor ◽

Computer Input ◽

Computer Input Devices ◽

Speed And Accuracy ◽

Optical Mouse

Four input devices were compared in a data entry task by speed and accuracy scores. The input devices were: Linus pen (a handwriting recognition system), optical mouse, cursor keys, and alphabetic keys on a keyboard. Data entry consisted of twenty 5-letter words and 100 single letters. Two different screen designs (QWERTY and ALPHA) were used for the mouse and cursor keys conditions. Results showed the keyboard to be fastest and the cursor keys to be slowest in data entry. The mouse and Linus pen had comparable latency scores. Overall, five-letter words were entered faster than five single letters. Latency decreased over trials, and ALPHA conditions required more time than QWERTY conditions. The Linus pen was the least accurate input device. The cursor QWERTY condition produced the highest accuracy scores for letter entry while the keyboard produced the highest accuracy scores for word entry.

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Iris Movement and Eye Gaze Tracking

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2021.04.04 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1224-1233

Author(s):

Villuri Gnaneswar

Keyword(s):

Computer Vision ◽

Human Computer Interaction ◽

Eye Tracking ◽

Eye Gaze ◽

Industrial Area ◽

Research Field ◽

Efficient Technique ◽

Gaze Tracking ◽

The Past ◽

Active Research

Iris Movement and gaze tracking has been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human-computer interaction. The goal of this paper is to present a study on the existing literature on Iris Movement and Gaze Tracking and Develop an Efficient Technique that can revolutionize the field of Computer Vision. With the uptrend of systems based on eye Tracking in many different areas of life in recent years, this subject has gained much more attention by in the academic and industrial area.

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Eyeball Movement based Cursor Control using Raspberry Pi and OpenCV

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5187.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 392-395

Keyword(s):

Eye Movements ◽

Aspect Ratio ◽

Human Computer Interaction ◽

Personal Computer ◽

Raspberry Pi ◽

Data Input ◽

Input Devices ◽

Cursor Control ◽

Mouse Cursor ◽

The People

In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses raspberry pi board to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with OpenCV to detect the pupil. This system tracks the eye movements of the user with an IP cam (Internet Protocol camera) and simulates the eye movements into mouse cursor movements on screen and also detects user’s eye staring on icon and will translate it into click operation on screen. The main aim of this system is to help the user to control the cursor without the use of hands and is of great use especially for the people with disability

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Development of CNN-based Virtual Mouse

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35169 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 800-807

Author(s):

Mohammed Anasuddin

Keyword(s):

Neural Network ◽

Computer Vision ◽

Human Computer Interaction ◽

Convolutional Neural Network ◽

Neural Network Model ◽

Hand Gestures ◽

Battery Power ◽

Virtual Mouse ◽

Device Free ◽

Mouse System

The technique of building a process of interaction between human and computer is evolving since the invention of technology. The mouse is a superb invention in HCI (Human-Computer Interaction) technology. Though wireless mouse technology is invented still, that technology isn't completely device free. A Bluetooth mouse has the need of battery power and connecting dongle. The proposed mouse system is beyond this limitation. This paper proposes a virtual mouse system supported HCI using computer vision and hand gestures. Gestures captured with a built-in camera or webcam and processed by a Convolutional Neural Network Model for classification among the desired mouse operations. The users are going to be allowed to regulate a number of the pc cursor functions with their hand gestures. Primarily, a user can perform left clicks, right clicks, and double clicks, scrolling up or down using their hand in several gestures. This technique captures frames employing a webcam or built-in cam and processes the frames to make them track-able and then recognizes different gestures made by users and perform the mouse functions. Therefore the proposed mouse system eliminates device dependency so as to use a mouse.

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International Conferences Interfaces and Human Computer Interaction 2019; Game and Entertainment Technologies 2019; and Computer Graphics, Visualization, Computer Vision and Image Processing 2019

10.33965/g2019 ◽

2019 ◽

Keyword(s):

Image Processing ◽

Computer Vision ◽

Computer Graphics ◽

Human Computer Interaction ◽

Computer Interaction

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Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3033 ◽

2020 ◽

Vol 6 (3) ◽

pp. 127-130

Author(s):

Max B. Schäfer ◽

Kent W. Stewart ◽

Nico Lösch ◽

Peter P. Pott

Keyword(s):

Virtual Reality ◽

Real Time ◽

Robotic Arm ◽

Input Device ◽

Robot Assisted ◽

Promising Alternative ◽

Input Devices ◽

Current Input ◽

Current Systems ◽

Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

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Gaze tracking for multimodal human-computer interaction

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing ◽

10.1109/icassp.1997.595325 ◽

2002 ◽

Cited By ~ 20

Author(s):

R. Stiefelhagen ◽

J. Yang

Keyword(s):

Human Computer Interaction ◽

Gaze Tracking ◽

Computer Interaction

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Active head movements facilitate compensation for effects of prism displacement on dynamic gait12

Journal of Vestibular Research ◽

10.3233/ves-2006-161-203 ◽

2006 ◽

Vol 16 (1-2) ◽

pp. 29-33

Author(s):

Kim R. Gottshall ◽

Michael E. Hoffer ◽

Helen S. Cohen ◽

Robert J. Moore

Keyword(s):

Head Movement ◽

Sensory Modality ◽

Normal Subjects ◽

The Body ◽

Head Movements ◽

Head Motion ◽

Control Group ◽

Entire Body ◽

Group 3 ◽

Group 2

Study design: Four groups, between-subjects study. Objectives: To investigate the effects of exercise on adaptation of normal subjects who had been artificially spatially disoriented. Background: Many patients referred for rehabilitation experience sensory changes, due to age or disease processes, and these changes affect motor skill. The best way to train patients to adapt to these changes and to improve their sensorimotor skills is unclear. Using normal subjects, we tested the hypothesis that active, planned head movement is needed to adapt to modified visual input. Methods and measures: Eighty male and female subjects who had normal balance on computerized dynamic posturography (CDP) and the dynamic gait index (DGI), were randomly assigned to four groups. All groups donned diagonally shift lenses and were again assessed with CDP and DGI. The four groups were then treated for 20 min. Group 1 (control group) viewed a video, Group 2 performed exercise that involved translating the entire body through space, but without separate, volitional head movement, Group 3 performed exercises which all incorporated volitional, planned head rotations, and Group 4 performed exercises that involved translating the body (as in Group 2) and incorporated volitional, planned head motion (as in Group 3). All subjects were post-tested with CDP and DGI, lenses were removed, and subjects were retested again with CDP and DGI. Results: The groups did not differ significantly on CDP scores but Groups 3 and 4 had significantly better DGI scores than Groups 1 and 2. Conclusions: Active head movement that is specifically planned as part of the exercise is more effective than passive attention or head movements that are not consciously planned, for adapting to sensorimotor change when it incorporates active use of the changed sensory modality, in this case head motion.

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