Johannes Burdack

Johannes Burdack
Johannes Gutenberg-Universität Mainz | JGU · Institute of Sports Science

Master of Education

About

8
Publications
14,017
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35
Citations
Introduction
Johannes Burdack is a research assistant and PhD student at the Institute for Sports Science at the University of Mainz. He works in the fields of training and movement science with a focus on biomechanics. His main research interests are complex movement analysis using machine learning and the effects of movement on cognitive learning and brain activity.

Publications

Publications (8)
Article
Full-text available
Abstract: Traditionally, studies on learning have mainly focused on the acquisition and stabilization of only single movement tasks. In everyday life and in sports, however, several new skills often must be learned in parallel. The extent to which the similarity of the movements or the order in which they are learned influences success has only rece...
Article
Full-text available
A variety of approaches have been proposed for teaching several volleyball techniques to beginners, ranging from general ball familiarization to model-oriented repetition to highly variable learning. This study compared the effects of acquiring three volleyball techniques in parallel with three approaches. Female secondary school students (N = 42;...
Article
Full-text available
The scientific and practical fields—especially high-performance sports—increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes ov...
Data
You can find the the description of the dataset and all relevant information about it on: https://data.mendeley.com/datasets/y55wfcsrhz/2
Data
Supplementary Material of the article "Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning" including tables with prediction accuracy, precision-, and recall-scores.
Article
Full-text available
Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniq...
Preprint
Full-text available
Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis e.g. in increasing the classification accuracy. In order to ensure the generalizability of the machin...
Poster
Full-text available
Introduction: In gait analysis, various approaches based on machine learning classifications have been suggested in recent years to help clinicians classify gait patterns into clinically relevant groups. The majority of machine learning applications used kinematic and kinetic data derived from three- dimensional gait analysis (3DGA) [1,2]. Disadvan...

Questions

Questions (2)
Question
Hi, I am looking for deep learning approaches to pattern recognition on very small data sets.
Previous attempts using convolutional neural networks have been far less powerful than common machine learning classifiers like support vector machines, random forests or multi layer perceptrons.
Are there new alternatives or approaches that suggest promising and satisfying multi-class (3 - 10 classes) predictions on very small data sets (size approx. 50x500 - 100x10,000)?
Question
I am looking for a way to compare two time series and to find a measure of similarity between them. Previous ideas were to compare the distance between both series and to count the number of crossings.
Could this be a promising approach or are there better suggestions?

Network

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Projects

Projects (2)
Project
The aim of this project was (since 1990) and is the identification of latent patterns of multiple signals that are related to movements by means of mathematical algorithms. Examples of signal sources are instants and time courses of kinematic, kinetic, muscular, cardiological, brain, infrared, or team variables.