Classification of team sport activities using a single wearable tracking device

Wundersitz, Daniel; Josman, Casey; Gupta, Ritu; Netto, Kevin; Gastin, Paul B.; Robertson, Sam

doi:10.1016/j.jbiomech.2015.09.015

Cited by 74 publications

(78 citation statements)

References 35 publications

(41 reference statements)

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“…Global Positioning System (GPS) technology has been used extensively to quantify the average running demands of rugby union. [1][2][3] Despite the volume of research using GPS technology in rugby union, no studies have reported simultaneously on GPS metrics and performance. In rugby league, it has been reported that condensed periods of repeated highintensity efforts (RHIE) are common prior to scoring or conceding a try.…”

Section: Introductionmentioning

confidence: 99%

Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries

Tierney

Tobin

Blake

et al. 2016

Scandinavian Med Sci Sports

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Global Positioning System (GPS) technology is commonly utilized in team sports, including rugby union. It has been used to describe the average running demands of rugby union. This has afforded an enhanced understanding of the physical fitness requirements for players. However, research in team sports has suggested that training players relative to average demands may underprepare them for certain scenarios within the game. To date, no research has investigated the running demands of attacking 22 entries in rugby union. Additionally, no research has been undertaken to determine whether differences exist in the running intensity of successful and unsuccessful attacking 22 entries in rugby union. The first aim of this study was to describe the running intensity of attacking 22 entries. The second aim of this study was to investigate whether differences exist in the running intensity of successful and unsuccessful attacking 22 entries. Running intensity was measured using meters per minute (m min ) for (a) total distance, (b) running distance, (c) high-speed running distance, and (d) very high-speed running distance. This study provides normative data for the running intensity of attacking 22 entries in rugby union. Forwards achieved greater high-speed running intensity in successful (3.6 m min ) compared to unsuccessful (1.8 m min ) attacking 22 entries. Forwards should try and achieve greater high-speed running intensity in attacking 22 entries to increase the likelihood of successful outcomes during this period of gameplay.

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Section: Introductionmentioning

confidence: 99%

Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries

Tierney

Tobin

Blake

et al. 2016

Scandinavian Med Sci Sports

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“…The aim is to develop an algorithm based on sample data that can be used to classify subjects or predict outcomes in other datasets. Some examples in health‐related research include the use of NNs or SVMs to classify activity patterns, to detect obesity, to predict those who will reduce salt intake and to predict if dietary patterns can predict acute coronary syndrome . In the analysis we have reported here, the error rate was high and the error under the curve lower than the CART model.…”

Section: Discussionmentioning

confidence: 81%

Data mining: Potential applications in research on nutrition and health

Batterham

Neale

Martin

et al. 2017

Nutrition & Dietetics

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“…As shown in Figure 1A, these algorithms showed different performance and some of them displayed high AUCs. For example, Simple Logistic and LMT algorithm [30] covered the maximum area under the curve (AUC=0.975, standard deviation (SD), 0.04), followed by Random Forest (AUC=0.94, SD, 0.05) and Bayesian methods (AUC=0.93, SD, 0.06), while lazy. IB1 model [31]only achieved an AUC of 0.693 (SD, 0.13).…”

Section: Resultsmentioning

confidence: 99%

Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer

Tian

Chen

et al. 2017

Oncotarget

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Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs). As a result, Bayes Net and Random Forest displayed higher accuracies than other algorithms in both populations, although Bayes Net was found with a lower false negative rate than that of Random Forest. Gut microbiota-based prediction was more accurate than the standard fecal occult blood test (FOBT), and the combination of both approaches further improved the prediction accuracy. Moreover, when unclassified OTUs were used as input, the BayesDMNB text algorithm achieved higher accuracy in the Chinese population (AUC=0.994). Taken together, our results suggest that Bayes Net classification model combined with unclassified OTUs may present an accurate method for predicting CRC based on the compositions of gut microbiota.

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Classification of team sport activities using a single wearable tracking device

Cited by 74 publications

References 35 publications

Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries

Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries

Data mining: Potential applications in research on nutrition and health

Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer

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