Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition

Krishnamurthi, R; Patan, R; Gandomi, AH

Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition

Krishnamurthi, R Patan, R Gandomi, AH

Permalink

Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2019, 98 pp. 650 - 659
Issue Date:: 2019-09-01

Closed Access

	Filename	Description	Size
	Assistive pointer device for limb impaired people A novel Frontier Point Method for hand movement recognition.pdf	Published Version	1.45 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Krishnamurthi, R	en_US
dc.contributor.author	Patan, R	en_US
dc.contributor.author	Gandomi, AH https://orcid.org/0000-0002-2798-0104	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2019, 98 pp. 650 - 659	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136893
dc.description.abstract	© 2019 Elsevier B.V. In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2019.02.010	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition	en_US
dc.type	Journal Article
utslib.citation.volume	98	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	98	en_US

Abstract:

© 2019 Elsevier B.V. In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/136893