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Algorithms 2017, 10(1), 23; doi:10.3390/a10010023

An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications

1
Physiolytics Laboratory, School of Psychological and Clinical Sciences, Charles Darwin University, Ellengowan Drive, Casuarina, NT 0810, Australia
2
Sport and Biomedical Engineering Laboratories (SABEL), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia
3
Information Technology Center for Sports Sciences, National Institute of Fitness and Sports, Kanoya 891-2393, Japan
4
Centre of Excellence for Applied Sport Science Research, Queensland Academy of Sport, Queensland Sport and Athletics Centre, Kessels Road, Nathan, QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Received: 30 May 2016 / Accepted: 20 January 2017 / Published: 1 February 2017
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Abstract

The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming. View Full-Text
Keywords: Python; data science; cloud computing; MATLAB; feedback Python; data science; cloud computing; MATLAB; feedback
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MDPI and ACS Style

Lee, J.; Rowlands, D.; Jackson, N.; Leadbetter, R.; Wada, T.; James, D.A. An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications. Algorithms 2017, 10, 23.

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