Prediction and Situational Option Generation in Soccer

Paul Ward, Michigan Technological University Naturalistic models of decision making, such as the Recognition- Primed Decision (RPD) model (e.g., Klein, Calderwood, & Clinton-Cirocco, 1986; Klein, 1997), suggest that as individuals become more experienced within a domain they automatically recognize situational patterns as familiar which, in turn, activates an associated situational response. Typically, this results in a workable course of action being generated first, and subsequent options generated only if the initial option proves ineffective

Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach