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SUMMARY:Anne Koelewijn (FAU Erlangen-Nürnberg)
DTSTART:20210713T101500Z
DTEND:20210713T114500Z
DTSTAMP:20260423T022602Z
UID:MathDeep/13
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MathDeep/13/
 ">Biomechanics meets Deep Learning</a>\nby Anne Koelewijn (FAU Erlangen-N
 ürnberg) as part of Mathematics of Deep Learning\n\n\nAbstract\nBiomechan
 ics is the study of human movement. Until recently\, artificial intelligen
 ce (AI) or deep learning was hardly used in biomechanics research\, but in
 stead it was mainly based on physical models and experiments. However\, re
 cently deep learning has also become increasingly important in the field o
 f biomechanics. This talk will discuss different ways how biomechanics and
  deep learning can be combined to improve research outcomes in movement an
 alysis. In the first part of the talk\, we start with a general introducti
 on into movement analysis\, and discuss more traditional methods that are 
 used in the field. Mainly\, we will cover how gait simulations can be crea
 ted by solving trajectory optimization problems\, since here many benefits
  of adding AI/deep learning can be identified. In the second part of the t
 alk\, we will discuss the combination of biomechanics and deep learning. F
 irst\, we will discuss different ways to improve biomechanics models with 
 deep learning\, and highlight one example regarding energy expenditure mod
 els. Finally\, we will discuss how gait simulations can be used to improve
  outcomes of deep learning models\, by creating larger datasets.\n
LOCATION:https://researchseminars.org/talk/MathDeep/13/
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