Development of smart inner city recreational facilities to encourage active living

Lowfield Park in Sheffield, UK is a green recreational space main-tained by the City Council. Lowfield Park was selected as the primary Sheffield FieldLab for the ProFit project which ended in 2015. The ProFit project was European Interreg IVbNWE funded with the aim of encouraging physical activ-ity through innovations in products, services and ICT systems. In 2014 the Sheffield Hallam University City Athletics Stadium (SHUCAS) was introduced as a secondary FieldLab. A number of innovative systems have been installed into the FieldLabs, these include: Pan Tilt Zoom cameras, automatically timed sprint and running tracks, outdoor displays/touchscreen and a gait analyser. This paper describes the hardware, software and cloud infrastructure created to enable these systems. Pilot testing has been carried out over the last year and has found a positive effect on both sites. The systems created will be taken for-ward to Sheffield’s Olympic Legacy Park, which is currently under develop-ment

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial

Dense Disparity Estimation via Global and Local Matching

A new divide-and-conquer technique for disparity estimation is proposed in this paper. This technique performs feature matching recursively, starting with the strongest feature point in the left scanline. Once the fu'st matching pair is established, the ordering constraint in disparity estimation allows the original intra-scanline matching problem to be divided into two smaller subproblems. Each subproblem can then be solved recursively, or via a disparity space technique. An extension to the standard disparity space technique is also proposed to compliment the divide-andconquer algorithm. Experimental results demonstrate the effectiveness of the proposed approaches

The world of micro-surgery

Hundreds of diabetes self-management apps are available for smart phones, typically using a diary or logging methodology. This paper investigates how well such approaches help participants to make sense of collected data. We found that, while such systems typically support data and trend review, they are ill suited to helping users understand complex correlations in the data. The cognitively demanding user interfaces (UI’s) of these apps are poorly adapted both to the restricted real estate of smartphone displays and to the daily needs of users. Many participants expressed the desire for intelligent, personalized and contextually aware near-term advice. By contrast, users did not see tools for reflection on prior data and behavior, seen as indispensable by many researchers, as a priority. We argue that while designers of future mobile health (mHealth) systems need to take advantage of automation through connected sensors, and the increasing subtlety of intelligent processing, it is also necessary to evolve current graphs and dashboards UI paradigms to assist users in long-term self-management health practices