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.
Highlights
The sports community is an acknowledged early adopter of technologies for the quantification of movement demands of athletes such as inertial sensors [1]
This paper presents a multidisciplinary approach grounded in feedback from wearables fused with various sources that will be presented in the remainder of this document
Existing development on the athlete data (Athdata) structure saw the use of the MATLAB data science environment as the primary platform of implementation
Summary
The sports community is an acknowledged early adopter of technologies for the quantification of movement demands of athletes such as inertial sensors [1]. The sports community use a range of technologies for movement analysis [4] that typically produce time series data These include 3D motion capture systems, 2D video, force plates, heart rate and electromyography data, and, more recently, ambulatory body worn sensors (such as inertial sensors) and even smart phones [5]. To meet this ongoing demand necessitates moving from single small data sets to large multimodal data from various sources ranging from wearables to laboratory equipment and video for effective intervention The growth in this demand is evidenced by the 30+ year paralleled expansion in sport science professional recruitment as supporting staff as well as the world wide establishment of government backed institutes of sport in part to facilitate the increased need to analyse the larger volumes of data in order to gain or maintain a competitive edge.
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