Conference Paper · July 2012with292 Reads

Conference: CONEM 2012
  • [Show abstract] [Hide abstract] ABSTRACT: The aim of this study was to use a machine learning approach combining fuzzy modeling with an immune algorithm to model sport training, in particular swimming. A proposed algorithm mines the available data and delivers the results in a form of a set of fuzzy rules “IF (fuzzy conditions) THEN (class)”. Fuzzy logic is a powerful method to cope with continuous data, to overcome problem of overlapping class definitions, and to improve the rule comprehensibility. Sport training is modeled at the level of microcycle and training unit by 12 independent attributes. The data was collected in two months (February–March 2008), among swimmers from swimming sections in Wrocław, Poland. The swimmers had minimum of 7 years of training and reached the II class level in swimming classification from 2005 to 2008. The goal of the performed experiments was to find the rules answering the question – how does the training unit influence swimmer’s feelings while being in water the next day? The fuzzy rules were inferred for two different scales of the class to be predicted. The effectiveness of the learned set of rules reached 68.66%. The performance, in terms of classification accuracy, of the proposed approach was compared with traditional classifier schemes. The accuracy of the result of compared methods is significantly lower than the accuracy of fuzzy rules obtained by a method presented in this study (paired t-test, P < 0.05).
    Article · Sep 2011
  • [Show abstract] [Hide abstract] ABSTRACT: Competitive sports techniques can be modeled as a many-layered pyramid. The most advanced competitive state - i.e. the apex of the pyramid - is what we call the "Sport Frontier Technique" (SFT). Six important features and developing trends of sport frontier technique innovations are found and discussed in this research. (C) 2009 Published by Elsevier Ltd.
    Full-text · Article · Jun 2010
  • [Show abstract] [Hide abstract] ABSTRACT: This paper proposes a boosting EigenActions algorithm for human action recognition. A spatio-temporal Information Saliency Map (ISM) is calculated from a video sequence by estimating pixel density function. A continuous human action is segmented into a set of primitive periodic motion cycles from information saliency curve. Each cycle of motion is represented by a Salient Action Unit (SAU), which is used to determine the EigenAction using principle component analysis. A human action classifier is developed using multi-class Adaboost algorithm with Bayesian hypothesis as the weak classifier. Given a human action video sequence, the proposed method effectively locates the SAUs in the video, and recognizes the human actions by categorizing the SAUs. Two publicly available human action databases, namely KTH and Weizmann, are selected for evaluation. The average recognition accuracy are 81.5% and 98.3% for KTH and Weizmann databases, respectively. Comparative results with two recent methods and robustness test results are also reported.
    Article · May 2010
Show more