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Head operated electric wheelchair
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik. (Digital Media Lab)
KTH Royal Institute of Technology, Department of Media Technology and Interaction Design.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik. (Digital Media Lab)
KTH Royal Institute of Technology, Department of Media Technology and Interaction Design.
2014 (engelsk)Inngår i: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI 2014), IEEE , 2014, s. 53-56Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Currently, the most common way to control an electric wheelchair is to use joystick. However, there are some individuals unable to operate joystick-driven electric wheelchairs due to sever physical disabilities, like quadriplegia patients. This paper proposes a novel head pose estimation method to assist such patients. Head motion parameters are employed to control and drive an electric wheelchair. We introduce a direct method for estimating user head motion, based on a sequence of range images captured by Kinect. In this work, we derive new version of the optical flow constraint equation for range images. We show how the new equation can be used to estimate head motion directly. Experimental results reveal that the proposed system works with high accuracy in real-time. We also show simulation results for navigating the electric wheelchair by recovering user head motion.

sted, utgiver, år, opplag, sider
IEEE , 2014. s. 53-56
Serie
IEEE Southwest Symposium on Image Analysis and Interpretation, ISSN 1550-5782
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-86746ISI: 000355255900014ISBN: 978-1-4799-4053-0 (tryckt)OAI: oai:DiVA.org:umu-86746DiVA, id: diva2:703474
Konferanse
IEEE Southwest Symposium on Image Analysis and Interpretation SSIAI
Tilgjengelig fra: 2014-03-06 Laget: 2014-03-06 Sist oppdatert: 2018-06-08bibliografisk kontrollert
Inngår i avhandling
1. Bring Your Body into Action: Body Gesture Detection, Tracking, and Analysis for Natural Interaction
Åpne denne publikasjonen i ny fane eller vindu >>Bring Your Body into Action: Body Gesture Detection, Tracking, and Analysis for Natural Interaction
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are essential to what makes us human. This motivated us to introduce new interaction methods that will support human values, particularly human well-being.

The aim of this thesis is to design new interaction methods that will empower human to have a healthy, intuitive, and pleasurable interaction with tomorrow’s digital world. In order to achieve this aim, this research is concerned with developing theories and techniques for exploring interaction methods beyond keyboard and mouse, utilizing human body. Therefore, this thesis addresses a very fundamental problem, human motion analysis.

Technical contributions of this thesis introduce computer vision-based, marker-less systems to estimate and analyze body motion. The main focus of this research work is on head and hand motion analysis due to the fact that they are the most frequently used body parts for interacting with computers. This thesis gives an insight into the technical challenges and provides new perspectives and robust techniques for solving the problem.

sted, utgiver, år, opplag, sider
Umeå: Umeå Universitet, 2014. s. 70
Serie
Digital Media Lab, ISSN 1652-6295 ; 19
Emneord
Human Well-Being, Bodily Interaction, Natural Interaction, Human Motion Analysis, Active Motion Estimation, Direct Motion Estimation, Head Pose Estimation, Hand Pose Estimation.
HSV kategori
Forskningsprogram
signalbehandling; datoriserad bildanalys
Identifikatorer
urn:nbn:se:umu:diva-88508 (URN)978-91-7601-067-9 (ISBN)
Disputas
2014-06-04, Naturvetarhuset, N420, Umeå universitet, Umeå, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-05-14 Laget: 2014-05-08 Sist oppdatert: 2018-06-07bibliografisk kontrollert

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