Fachbereich 06: Psychologie und Sportwissenschaft - Sportwissenschaft - Master of Science Human Movement Analytics: Biomechanics, Motor Control and Learning (gültig ab Wintersemester 2021/22)

The Applied Mathematics lecture covers important mathematical tools for analyzing human movements from the field of linear algebra and calculus as well as their implementation with MATLAB.

The mathematical tools covered in the lecture are deepened in the seminar "Applied Mathematics" using application examples from the field of motion analysis. The implementation of the mathematical solutions with the software MATLAB plays an important role. Therefore, it is strongly recommended to also take the module "Computer Programming in Human Movement Analytics".

In this lecture, I will try to provide a multidisciplinary, multilevel perspective on human motor control (and learning). Despite the multifaceted nature of connecting psychology, human movement science and neuroscience, I hope to achieve a certain degree of coherence in addressing the core problems of human motor control. More specifically, I will focus on
- how specific movements are selected
- how selected movements are planned in advance
- how executed movements are modulated by incoming perceptual information
- how existing movements are adapted and novel movements are acquired

This seminar will complement the lecture with respect to the following topics:
- Human Information Processing
- Attention and Performance
- Principles of Speed and Accuracy
- Coordination
The seminar will use team-based learning as an instructional strategy focusing on regular readings and interactive work in small groups.

The lecture deals with the central terms and concepts of electrical measurement technology that are relevant to the application area of motion analysis. First, the basic terms of measurement and the structure of measurement chains are introduced and you will learn about typical measures for determining measurement errors. During the course, the various measuring elements of the measurement chain will be discussed in more detail. A central aspect of this is getting to know sensors for recording typical measured variables when carrying out movement analyses (forces, angles, accelerations, positions, EMG activity). You will learn about different sensors and their measuring principles, properties and possible applications.

In this hands-on seminar, the content of the lecture is applied and deepened. You will work on measurement tasks in small groups using state-of-the-art measurement materials. You will independently set up different measurement chains for a range of purposes and will be able to experience the properties and metrological features directly in practice.

In this hands-on seminar, the content of the lecture is applied and deepened. You will work on measurement tasks in small groups using state-of-the-art measurement materials. You will independently set up different measurement chains for a range of purposes and will be able to experience the properties and metrological features directly in practice.

Please make sure that you are registered for the seminar and the tutorial in FlexNow.
Note: A laptop (PC; no tablet) with an installed version of Matlab is required for active participation in the course.

Please make sure that you are registered for the seminar and the tutorial in FlexNow.
Note: A laptop (PC; no tablet) with an installed version of Matlab is required for active participation in the course.

This profile module will build on the previous module specific data analysis I. Students will implement current advanced inference statistical techniques for continuous data sets in matlab programs. In a second major project, students will apply basic algorithms for categorisation of data embedded in a project on image processing. The seminar will be complemented by a tutorial.

In the Measurement Projects in Movement Analytics profile module, the knowledge and skills acquired in the first two semesters are applied and deepened. You will develop a concept for answering a question from the field of movement analysis. To do this, you must decide which movement characteristics are relevant, how these can be recorded, and which data processing and analysis steps are required to determine and appropriately illustrate meaningful characteristic values. You will develop the measurement setup, carry out measurements to determine reliability and validity as well as pilot measurements in order to be able to run preliminary results to answer the questions. Finally, the project is documented in writing and presented and discussed in a presentation.

https://deeplearning-jupyterbook.github.io/

In this seminar, you will get an introduction to the human visual system, as a model system for understanding the principles of neural coding and how we can reveal them using neuro-computational methods. Based on attendee preferences, the course will be taught in German or English.

In this lecture, you will get an introduction to basic principles of human brain organization and the methods to investigate them experimentally, with a focus on computational analyses of functional brain data, including neural decoding and artificial neural network models. Based on attendee preferences, the course will be taught in German or English.

(for an English description, see below)

Diese Veranstaltung ist eine praktische Einführung zur Durchführung von neurowissenschaftlichen Experimenten mithilfe von Elektroenzephalografie (EEG). Der spezifische Fokus dieses Praktikums besteht darin, zu lernen, wie man visuelle Inhalte, die Probanden in einem Computerexperiment präsentiert bekommen haben, mithilfe von Classifier-Algorithmen aus deren Gehirnaktivität dekodiert. Teilnehmende Studierende müssen selbst ein EEG-Experiment an Probanden durchführen und erlernen, wie man EEG-Daten vorverarbeitet und die Decoding-Analysen in MATLAB programmiert.

This course is a practical introduction to conducting neuroscientific experiments using electroencephalography (EEG). The specific focus of this practical course is learning how to decode visual contents that participants were presented in a computer experiment from their brain activity using classifier algorithms. Participating students have to conduct an EEG experiment themselves and learn how to preprocess EEG data and program the decoding analyses in MATLAB.