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IET CV Journal - CV in Healthcare & AAL 2017 : IET Computer Vision Journal - Special Issue: Computer Vision in Healthcare and Assisted Living

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Link: http://digital-library.theiet.org/files/IET_CFP_CV_HEALTH_ASSIS_LIV.pdf
 
When N/A
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Submission Deadline Jan 23, 2017
Categories    computer vision   healthcare   active and assisted living   ambient and assisted living
 

Call For Papers

In our ageing society, the proportion of people who need healthcare support and a safe and assistive environment is clearly increasing. Hence, there
is a growing demand for technological solutions for the purpose of prevention, diagnosis and rehabilitation, not only to support clinicians, but also to
enable patients to self-manage their health and wellbeing. Computer Vision is well placed to provide such solutions. In the last decade, enormous
advances have been made with regard to automated and reliable recognition of image or video content, such as face, object and motion recognition,
and gesture and activity recognition. Additionally, the affordability of modern depth sensors, such as the Microsoft Kinect, Asus Xtion Pro live,
PrimeSense Carmine and Leap Motion, represent a huge leap forward, enabling 3D modelling and body pose estimation in real time with low cost and
mostly simple setup solutions. In active and assisted living (AAL), the aim is to develop intelligent environments within which people’s health can be
monitored and assistance can be provided to deliver comfort, safety and eHealth services, among others.

Binary sensors were traditionally deployed, however they are limited in their ability to help with complex scenarios, while visual sensors, either located
in the environment or worn by the person, provide a richer modality to analyse the person’s activities and the environment. In healthcare, non-intrusive
visual sensors may estimate and track the human body’s physical state to provide real-time feedback to clinicians and/or support interactive and
stimulating rehabilitation activities. As such, computer vision, pattern recognition and machine learning techniques are gaining popularity in healthcare
and assisted living solutions. The aim of this Special Issue is to provide a platform for presenting those techniques and applications.

Please see more details and submission instructions at http://digital-library.theiet.org/files/IET_CFP_CV_HEALTH_ASSIS_LIV.pdf.

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