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Grant Abt - Wearable technology for sport and health

Grant Abt

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Department of Sport, Health and Exercise Science, The University of Hull, UK

Performance Analysis Section Editor for the Journal of Sports Sciences

Theme: Wearables technology for sport and health

Keynote Title: Wearable technology for sport and health

Abstract:Although ‘wearable’ technology has been around for decades (e.g. Polar HR monitor), only recently has the combination of adequate battery power, pervasive wireless networks, and miniaturisation of electronic components, allowed wearable technology to reach mainstream use. The current poster-child of wearable technology for sport is the Global Positioning System (GPS). GPS provides coaches and sport scientists with information on player location and its derivatives - speed and direction. Current GPS systems also include heart rate monitoring, together with triaxial accelerometers and gyroscopes, which are increasingly being used to measure rapid changes in direction and speed. Despite wide adoption of GPS, we have only just begun to explore the potential of wearable technology for the monitoring of athletes and analysis of sports performance data. For example, we now have the capability to measure a wider range of physiological data, including temperature, hydration status, heart rate variability, and even blood lactate. These measures can be obtained through new methods of data acquisition such as flexible skin-mounted sensors (MC10), electronic circuits sewn into fabric (OMsignal), electromyography (Leo), or near-infrared spectroscopy (BSX Insight). Despite the focus on performance there are also uses for wearable technology to monitor athlete and general population health. For example, recent smartwatches (e.g. Apple Watch, Samsung Gear S2) not only measure heart rate and accelerometer data, but their manufacturers also provide Software Development Kits to enable third-party developers to create apps that extend their functionality. All of the data generated is available in real-time and connected to the cloud for storage and sharing via wireless networks. Vast amounts of data collected from this array of wearable technology will increasingly allow the use of multivariate approaches to analysis as used in machine learning and data mining.

   

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