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University of Waterloo
Country: Canada
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9 Projects, page 1 of 2
  • Funder: NSF Project Code: 9296133
  • Funder: NSF Project Code: 66B4064
  • Funder: EC Project Code: 824160
    Overall Budget: 4,206,390 EURFunder Contribution: 4,188,890 EUR

    EnTimeMent aims at a radical change in scientific research and enabling technologies for human movement qualitative analysis, entrainment and prediction, based on a novel neuro-cognitive approach of the multiple, mutually interactive time scales characterizing human behaviour. Our approach will afford the development of computational models for the automated detection, measurement, and prediction of movement qualities from behavioural signals, based on multi-layer parallel processes at non-linearly stratified temporal dimensions, and will radically transform technology for human movement analysis. EnTimeMent new innovative scientifically-grounded and time-adaptive technologies operate at multiple time scales in a multi-layered approach: motion capture and movement analysis systems will be endowed with a completely novel functionality, achieving a novel generation of time-aware multisensory motion perception and prediction systems. The proposed model and technologies will be iteratively tested and refined, by designing and performing controlled and ecological experiments, ranging from action prediction in a controlled laboratory setting, to prediction in dyadic and small group interaction. EnTimeMent scenarios include health (healing and support of everyday life of persons with chronic pain and disability), performing arts (e.g. dance), sports, and entertainment group activities, with and without living architectures. EnTimeMent will create and support community-building and exploitation with concrete initiatives, including a community of users and stakeholders, innovation hubs and SME incubators, as premises for the consolidation beyond the end of the project in a broader range of market areas.

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  • Funder: EC Project Code: 681109
    Overall Budget: 2,709,300 EURFunder Contribution: 2,325,540 EUR

    Smoking and other forms of tobacco consumption are considered the single most important cause of preventable morbidity and premature mortality worldwide. Efforts to reduce the devastation of tobacco-related deaths and illness in the EU consist of the Tobacco Products Directive (TPD), and the ongoing implementation of the WHO Framework Convention on Tobacco Control (FCTC). The main objective of EUREST-PLUS is to monitor and evaluate the impact of the TPD within the context of FCTC ratification at an EU level. Our 4 specific objectives hence are: 1) To evaluate the psychosocial and behavioral impact of TPD implementation and FCTC implementation, through the creation of a longitudinal cohort of adult smokers in 6 EU MS (Germany, Greece, Hungary, Poland, Romania, Spain; total n=6000) in a pre- vs. post-TPD study design. 2) To assess support for TPD implementation through secondary dataset analyses of the Special Eurobarometer on Tobacco Surveys (SETS), cross-sectional surveys performed among 27,000 adults in all 28 EU MS, before the TPD is implemented and to monitor progress in FCTC implementation in the EU over the past years through trend analyses on the merged datasets of the 2009, 2012 and 2015 SETS datasets (n=80,000). 3) To document changes in e-cigarette product parameters (technical design, labelling/packaging and chemical composition) following implementation of Article 20 of the TPD. 4) To enhance innovative joint research collaborations, through the pooling and comparisons across both other EU countries of the ITC Project (UK, NL, FR), and other non-EU countries . Tackling tobacco use is quintessential to reducing the impact of chronic NCDs, a topic EUREST-PLUS will stride to lead.

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  • Funder: EC Project Code: 965341
    Overall Budget: 6,492,420 EURFunder Contribution: 5,832,540 EUR

    BACKGROUND Optimal care for older patients with complex chronic conditions (CCC) is challenging. Not only do older patients with CCC present with multiple conditions and functional impairments, these often interact with each other, as well as with their treatments. Patients with CCC are concentrated in home care and nursing home settings. Professionals working in these settings often lack appropriate decision support that mirrors the medical and functional complexity of these persons. AIM To improve prognoses and estimation of treatment impact for older care recipients with CCC in home care and nursing homes settings, and develop, validate, and test next generation individualised decision support. IMPACT Better informed decision making for clinical management of older care recipients with CCC in home care and nursing homes, through (1) high quality internationally validated predictive algorithms on disease trajectories and treatment outcomes; (2) a multi-nationally tested e-platform for health professionals to receive predictive scenarios on course and treatment outcomes of newly assessed care recipients at point of care; and (3) dissemination among health professionals working in nursing homes and home care. APPROACH We collated longitudinal data from 52 million older recipients of home care and nursing home care from eight countries including (1) highly reliable, valid and harmonised comprehensive assessments of functional capacities, diseases, and treatments, linked with (2) administrative repositories on mortality and care use. We develop and validate decision support algorithms using a variety of techniques including machine learning to better predict (i) outcomes (eg death, acute admissions, quality of life) and the modifying impact of (ii) pharmacological and (iii) non-pharmacological treatments. We co-create decision support output with health professionals and patients and pilot it's applicability at point of care with an e-platform.

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