Dr. Lee has never been shy to follow his passion for sports technology a journey that has taken him to Japan for a year with Ohgi laboratory, and several teaching and research positions at a number of leading Austrlian universities. Since landing at Chares Darwin university just a few years ago he has transformed their teaching programmes through the adoption of online and social media technologies to create a more engaging programme, led several research initiatives to help solve problems unique to the Northern areas, coordinated the areas participation in a CRC bid (fingers crossed) and continued to collaborate widely.
Recently this has also leading to a publication on cloud based wearable sensor technologies and data analytics platform, as a partnership between SABEL Labs members and the Kanoya National Institute of Sports and Fitness.
An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications
James Lee 1,* David Rowlands 2, Nicholas Jackson 2, Raymond Leadbetter 2, Tomohito Wada 3 and Daniel A. James
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.
You can download the full version Here PDF Version: http://www.mdpi.com/1999-4893/10/1/23/pdf