Zoekresultaten

On the validation of the initial step of an upfront system implementation evaluation framework:

This technical report describes "validation of the 'Target system definition"

Trendonderzoek in het studiejaar 2014-2015

Presentatie op de Inholland Themadag Langstuderen, locatie Haarlem.

Automated Analysis of Human Performance Data

Automated Analysis of Human Performance Data could help to understand and possibly predict the performance of the human. To inform future research and enable Automated Analysis of Human Performance Data a systematic mapping study (scoping study) on the state-of-the-art knowledge is performed on three interconnected components(i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis. Using a systematic method of Kitchenham and Charters for performing the systematic mapping study, resulted in a comprehensive search for studies and a categorisation the studies using a qualitative method. This systematic mapping review extends the philosophy of Shyr and Spisic, and Knuth and represents the state-of-art knowledge on Human Performance,Monitoring Human Performance and Automated Data Analysis

Prediction of running injuries from training load: a machine learning approach

The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to sustain an injury. As a first step Self-reported data on training parameters and injuries from high-level runners (duration=37 weeks, n=23, male=16, female=7) were used to identify the most predictive variables for injuries, and train a machine learning tree algorithm to predict an injury. The model was validated by splitting the data in training and a test set. The 10 most important variables were identified from 85 possible variables using the Random Forest algorithm. To predict at an earliest stage, so the runner or the coach is able to intervene, the variables were classified by time to build tree algorithms up to 7 weeks before the occurrence of an injury. By building machine learning algorithms using existing self-reported training data can enable prospective identification of high-level runners who are likely to develop an injury. Only the established prediction model needs to be verified as correct.

Redenen voor een mismatch in trainingsbelasting tussen coaches en sporters

De daadwerkelijke trainingsbelasting van een sporter komt vaak niet overeen met de door de coach beoogde trainingsbelasting. Bekende oorzaken zijn een verkeerde inschatting van de belastbaarheid van de sporter en moeite met het gebruik van de RPE schaal. Op basis van recent onderzoek wordt het belang van een goede instructie bij het gebruik van de RPE schaal benadrukt.

Ontwikkeling en implementatie van een geriatrisch revalidatieprogramma CO-FIT+