0000000000639456

AUTHOR

Christian Rasche

showing 4 related works from this author

Performance Estimation using the Fitness-Fatigue Model with Kalman Filter Feedback

2016

Abstract Tracking and predicting the performance of athletes is of great interest, not only in training science but also, increasingly, for serious hobbyists. The increasing availability and use of smart watches and fitness trackers means that abundant data is becoming available, and the interest to optimally use this data for performance tracking and training optimization is great. One competitive model in this domain is the 3-time-constant fitness-fatigue model by Busso based on the model by Banister and colleagues. In the following, we will show that this model can be written equivalently as a linear, time-variant state-space model. With this understanding, it becomes clear that all meth…

03 medical and health sciences0302 clinical medicineGeneral Computer ScienceComputer scienceControl theoryPerformance estimation05 social sciencesBiomedical Engineering050301 education030229 sport sciencesKalman filterddc:7960503 educationInternational Journal of Computer Science in Sport
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A biomechanical comparison of countermovement performance after short-term traditional and daily-undulated loaded vertical jump training.

2017

In order to assess lower extremity muscle mechanical properties in athletes, power-load characteristics during multi-joint tasks are frequently examined. This work compared 6 weeks of traditional (TP) and daily-undulated (DUP) periodized loaded countermovement jumping (CMJ). 20 amateur athletes (age: 24.2 ± 2.6 years, height: 175.6 ± 7.1 cm, body mass: 71.5 ± 7.7 kg, 10 males/10 females) exercised three times weekly using maximal CMJs with loads corresponding to 0%, 15% and 30% of body mass. Prior to the training period, subjects were once-only assigned by random to either the TP or DUP training scheme. Pre-to-post training, maximal center of mass (COM) -height, -take-off velocity, -power o…

MalePhysical Therapy Sports Therapy and RehabilitationPlyometric ExerciseAthletic Performancemedicine.disease_cause03 medical and health sciencesVertical jumpYoung Adult0302 clinical medicineJumpingCountermovementmedicinePlyometricsHumansOrthopedics and Sports MedicineMuscle StrengthMuscle SkeletalMathematicsOrthodonticsWork (physics)Repeated measures designResistance Training030229 sport sciencesBiomechanical PhenomenaLower ExtremitydupFemaleAnalysis of variance030217 neurology & neurosurgeryJournal of sports sciences
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Linear vs. non-linear classification of winners, drawers and losers at FIFA World Cup 2014

2017

Purpose: Tactical analyses to distinguish between football teams that were more or less successful have been conducted up to now only by means of linear methods (like discriminant analysis). Concer...

EngineeringOperations researchbusiness.industry030209 endocrinology & metabolismPhysical Therapy Sports Therapy and Rehabilitation030229 sport sciencesFootballLinear discriminant analysisMachine learningcomputer.software_genreLinear methods03 medical and health sciencesNonlinear system0302 clinical medicineTourism Leisure and Hospitality ManagementNotational analysisOrthopedics and Sports MedicineArtificial intelligencebusinesscomputerScience and Medicine in Football
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Including the Past: Performance Modeling Using a Preload Concept by Means of the Fitness-Fatigue Model

2019

Abstract In mathematical modeling by means of performance models, the Fitness-Fatigue Model (FF-Model) is a common approach in sport and exercise science to study the training performance relationship. The FF-Model uses an initial basic level of performance and two antagonistic terms (for fitness and fatigue). By model calibration, parameters are adapted to the subject’s individual physical response to training load. Although the simulation of the recorded training data in most cases shows useful results when the model is calibrated and all parameters are adjusted, this method has two major difficulties. First, a fitted value as basic performance will usually be too high. Second, without mo…

03 medical and health sciencesPreload0302 clinical medicineGeneral Computer ScienceComputer science05 social sciencesBiomedical Engineering050301 education030229 sport sciences0503 educationddc:006Simulation
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