Biomechanics meets Deep Learning

Abstract: Biomechanics is the study of human movement. Until recently, artificial intelligence (AI) or deep learning was hardly used in biomechanics research, but instead it was mainly based on physical models and experiments. However, recently deep learning has also become increasingly important in the field of biomechanics. This talk will discuss different ways how biomechanics and deep learning can be combined to improve research outcomes in movement analysis. In the first part of the talk, we start with a general introduction into movement analysis, and discuss more traditional methods that are used in the field. Mainly, we will cover how gait simulations can be created by solving trajectory optimization problems, since here many benefits of adding AI/deep learning can be identified. In the second part of the talk, we will discuss the combination of biomechanics and deep learning. First, we will discuss different ways to improve biomechanics models with deep learning, and highlight one example regarding energy expenditure models. Finally, we will discuss how gait simulations can be used to improve outcomes of deep learning models, by creating larger datasets.

machine learningnumerical analysisoptimization and control

Audience: researchers in the topic

Mathematics of Deep Learning

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Slides: drive.google.com/drive/folders/1w9lNCGWZyzGFxxuVvhJOcjlc92X2toJg?usp=sharing

Videos: www.fau.tv/course/id/878