Institute of Technology, Tralee

About: Institute of Technology, Tralee is a based out in . It is known for research contribution in the topics: Higher education & Action learning. The organization has 137 authors who have published 217 publications receiving 3906 citations. The organization is also known as: IT Tralee & Tralee IT.

World Health Organization 2020 guidelines on physical activity and sedentary behaviour

Abstract: Objectives To describe new WHO 2020 guidelines on physical activity and sedentary behaviour. Methods The guidelines were developed in accordance with WHO protocols. An expert Guideline Development Group reviewed evidence to assess associations between physical activity and sedentary behaviour for an agreed set of health outcomes and population groups. The assessment used and systematically updated recent relevant systematic reviews; new primary reviews addressed additional health outcomes or subpopulations. Results The new guidelines address children, adolescents, adults, older adults and include new specific recommendations for pregnant and postpartum women and people living with chronic conditions or disability. All adults should undertake 150-300 min of moderate-intensity, or 75-150 min of vigorous-intensity physical activity, or some equivalent combination of moderate-intensity and vigorous-intensity aerobic physical activity, per week. Among children and adolescents, an average of 60 min/day of moderate-to-vigorous intensity aerobic physical activity across the week provides health benefits. The guidelines recommend regular muscle-strengthening activity for all age groups. Additionally, reducing sedentary behaviours is recommended across all age groups and abilities, although evidence was insufficient to quantify a sedentary behaviour threshold. Conclusion These 2020 WHO guidelines update previous WHO recommendations released in 2010. They reaffirm messages that some physical activity is better than none, that more physical activity is better for optimal health outcomes and provide a new recommendation on reducing sedentary behaviours. These guidelines highlight the importance of regularly undertaking both aerobic and muscle strengthening activities and for the first time, there are specific recommendations for specific populations including for pregnant and postpartum women and people living with chronic conditions or disability. These guidelines should be used to inform national health policies aligned with the WHO Global Action Plan on Physical Activity 2018-2030 and to strengthen surveillance systems that track progress towards national and global targets.

Active commuting to school: How far is too far?

Abstract: Walking and cycling to school provide a convenient opportunity to incorporate physical activity into an adolescent's daily routine. School proximity to residential homes has been identified as an important determinant of active commuting among children. The purpose of this study is to identify if distance is a barrier to active commuting among adolescents, and if there is a criterion distance above which adolescents choose not to walk or cycle. Data was collected in 2003–05 from a cross-sectional cohort of 15–17 yr old adolescents in 61 post primary schools in Ireland. Participants self-reported distance, mode of transport to school and barriers to active commuting. Trained researchers took physical measurements of height and weight. The relation between mode of transport, gender and population density was examined. Distance was entered into a bivariate logistic regression model to predict mode choice, controlling for gender, population density socio-economic status and school clusters. Of the 4013 adolescents who participated (48.1% female, mean age 16.02 ± 0.661), one third walked or cycled to school. A higher proportion of males than females commuted actively (41.0 vs. 33.8%, χ2 (1) = 22.21, p < 0.001, r = -0.074). Adolescents living in more densely populated areas had greater odds of active commuting than those in the most sparsely populated areas (χ2 (df = 3) = 839.64, p < 0.001). In each density category, active commuters travelled shorter distances to school. After controlling for gender and population density, a 1-mile increase in distance decreased the odds of active commuting by 71% (χ2 (df = 1) = 2591.86, p < 0.001). The majority of walkers lived within 1.5 miles and cyclists within 2.5 miles. Over 90% of adolescents who perceived distance as a barrier to active commuting lived further than 2.5 miles from school. Distance is an important perceived barrier to active commuting and a predictor of mode choice among adolescents. Distances within 2.5 miles are achievable for adolescent walkers and cyclists. Alternative strategies for increasing physical activity are required for individuals living outside of this criterion.

Deep Learning vs. Traditional Computer Vision

Abstract: Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

Sensor Technology in Autonomous Vehicles : A review

Abstract: This paper will review the main sensor technologies used to create an autonomous vehicle. Sensors are key components for all types of autonomous vehicles because they can provide the data required to perceive the surrounding environment and therefore aid the decision-making process. This paper explains how each of these sensors work, their advantages and disadvantages and how sensor fusion techniques can be utilised to create a more optimum and efficient system for autonomous vehicles.