Fachbereich 7 - Psychologie und Sportwissenschaft

The application of AI methods in the field of human motion analysis has increased significantly in recent years. Machine learning can be applied both as a tool to provide us extra insight into data we have, or to improve our capturing of these data. For example, machine learning has been applied very successfully in the field of human motion analysis and measurement, where they allow us to determine the 3D motion of a human with only one (depth-sensing) camera combined with body shell recognition. Furthermore, biologically inspired machine learning types - the artificial neural networks - can be used to increase our understanding of how biological neural networks function. By allowing us to manipulate the interconnections, learning rules and network architectures and comparing these findings to recordings of biological neural networks, we can generate hypotheses for how the biological networks function. 

In this course, we’re not very interested in the design and functioning of the machine learning models themselves (for this, you should check out the WiSe course 'Recurrent Neural Networks & Motor Control), but we view them as tools in our scientific toolbox. We will discuss several ways in which machine learning models can be used in the field of movement sciences, such as markerless motion capture, gait event detection, injury/performance prediction, prosthesis control and more.

In the first half of the course, you will work your way through several content blocks (with texts, videos, practice exercises, etc.) to get an overview of the field and to have reference materials. In the second half of the course, you will apply what you have learned so far and work on a mini-project for a more hands-on learning experience.

The course can be followed fully online, but optional support classes will be scheduled for those who appreciate face-to-face contact. Furthermore, it is both possible to audit the course (i.e. check out the course contents at your own pace, any time you want) or to follow the credit point tract (this requires handing in assignments and a final project at the end of the course and can only be done within the semester).

To join the course, make sure to sign up for the learnweb page (enrolment key: ML24). Here you will find all the course contents. 

InterKI

This course is part of the InterKI project, an interdisciplinary teaching programme, that includes introductory and advanced lectures, seminars and self-study courses on machine learning (ML) and artificial intelligence (AI, German: Künstliche Intelligenz, KI) methods as well as courses on their application in current fields of research and on various AI-related issues that are philosophically and societally relevant. In the ‘Machine Learning for Movement Science’ module (link), both these aspects will be covered in two separate courses: Recurrent Neural Networks & Motor control (taught in the WiSe) and Machine Learning Applications in Movement Science (taught in the SoSe). We aim to offer interesting courses to bothmovement scientists wanting to increase their skill set by learning machine learning as well as informatics students interested in moving into the field of movement science (and, of course, to anyone else that is interested in combining these fields!).

Kurs im HIS-LSF