Title: STARR – Decision Support and Self-Management System for Stroke Survivors
Funding source: H2020 PHC-28-2015
Partners: CEA (coordinator), University of Lund, Osakidetza, The Stroke Association, Fondation Hopale, Fiz Karlsruhe, Blulinea, RT-RK, Telefonica
Principal investigator: Dr. Djamila Aouada
Researchers: Dr. Enjie Ghorbel, Dr. Abd El Rahman Shabayek, Renato Baptista
Starting date/ Duration: 01/02/2016 – 42 months
STARR
Stroke a leading cause of death and disability, with an estimated total cost of €65 billion per year in Europe. Even though preventive measures are in place to reduce the incidence of stroke, the number of persons having a stroke in Europe is likely to increase from 1.1 million/year in 2000 to more than 1.5 million/year in 2025 because of the increasing ageing population. Secondary stroke carries with it a greater risk than first-ever stroke for death and disability. Also, as mortality from first strokes has decreased recently, the number of people at risk for a secondary stroke has increased, with an associated increase in healthcare costs. In order to reduce these stroke statistics and the associated cost, the self-management of stroke risk factors is particularly suitable and necessary for the following reasons: 1) risk factors for stroke are well-known, and 2) 90% of strokes or secondary stroke events are preventable if the risk factors are managed appropriately. The Decision SupporT and self-mAnagement system for stRoke survivoRs (STARR) project and the system developed in it are targeting the self-management of stroke risk factors. Based on existing computational predictive models of stroke risk, we will develop a modular, affordable, and easy-to-use system, which will inform stroke survivors about the relation between their daily activities (e.g., medication intake, physical and cognitive exercises, diet, social contacts) and the risk of having a secondary stroke. This will lead to better prevention and a reduction of the number of secondary stroke events, as well as to a more efficient participation of patients in medical decision-making. A multidisciplinary consortium has been built for achieving the objectives of this ambitious project, involving stroke survivors’ associations, healthcare actors, sensing and human-machine interfaces experts. The consortium also comprises 3 European companies which will exploit the results of the project after its end.
Publications
Temporal 3D Human Pose Estimation for Action Recognition from Arbitrary Viewpoints
; ; ; in 6th Annual Conf. on Computational Science & Computational Intelligence, Las Vegas 5-7 December 2019 (2019, December)
VIEW-INVARIANT ACTION RECOGNITION FROM RGB DATA VIA 3D POSE ESTIMATION
; ; ; ; ; in IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, 12–17 May 2019 (2019, May)
Home Self-Training: Visual Feedback for Assisting Physical Activity for Stroke Survivors
; ; ; ; ; ; ; ; in Computer Methods and Programs in Biomedicine (2019)
A View-invariant Framework for Fast Skeleton-based Action Recognition Using a Single RGB Camera
; ; ; ; ; ; in 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, 25-27 February 2018 (2019, February)
Two-stage RGB-based Action Detection using Augmented 3D Poses
; ; ; ; in 18th International Conference on Computer Analysis of Images and Patterns SALERNO, 3-5 SEPTEMBER, 2019 (2019)
Anticipating Suspicious Actions using a Small Dataset of Action Templates
; ; ; in 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2018, January)
Deformation-Based Abnormal Motion Detection using 3D Skeletons
; ; ; in IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA) (2018, November)
Key-Skeleton Based Feedback Tool for Assisting Physical Activity
; ; ; ; in 2018 Zooming Innovation in Consumer Electronics International Conference (ZINC), 30-31 May 2018 (2018, May 31)
Flexible Feedback System for Posture Monitoring and Correction
; ; ; ; in IEEE International Conference on Image Information Processing (ICIIP) (2017)
Video-Based Feedback for Assisting Physical Activity
; ; ; in 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2017)
STARR – Decision SupporT and self-mAnagement system for stRoke survivoRs Vision based Rehabilitation System
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; in European Project Space on Networks, Systems and Technologies (2017)
Visual and human-interpretable feedback for assisting physical activity
; ; ; ; in European Conference on Computer Vision (ECCV) Workshop on Assistive Computer Vision and Robotics Amsterdam, (2016)