Sobre o Desenvolvimento do Talento no Desporto: Um Contributo dos Modelos Teóricos do Desenvolvimento Desportivo

ABSTRACT: With this article, we aim to offer a conceptual synthesis of some of the most important developments in past decades on the subject of talent in sport, while also helping sports stakeholders, particularly managers and coaches, to recognize and apply these conclusions in their practices. The article starts with a brief historical review, which explores how there has been a shift from a talent detection perspective to a talent development perspective and to a holistic vision of athletes and their background context. Secondly, the article presents an overview of the main theoretical models put forward in literature on sport psychology, including career-transition-based models and talent-and-expertise-based models. Finally, as the conceptual model most widely referred to in literature, a detailed analysis of the Development Model of Sports Participation (Côté, Baker & Abernethy, 2007), is made, especially with regard to development processes relating to standards of practice (e.g. diversification and specialization) and psychosocial influences, aspects that form the basis of all-round athlete development.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.