@inproceedings{M{\"o}biusKadenStapsetal.2021,
  author    = {M{\"o}bius, Danny and Kaden, Marika and Staps, Daniel and Villmann, Thomas},
  title     = {Intelligent Gait Analysis using Marker Based Motion Capturing System},
  booktitle = {26. Interdisziplin{\"a}re Wissenschaftliche Konferenz Mittweida},
  number    = {002},
  organization    = {Institut f{\"u}r Mechatronik Chemnitz e.V.},
  issn      = {1437-7624},
  doi       = {10.48446/opus-12294},
  pages     = {133 -- 136},
  year      = {2021},
  abstract  = {Marker-based systems can digitally record human movements in detail. Using the digital biomechanical human model Dynamicus, which was developed by the Institut f{\"u}r Mechatronik, it is possible to model joint angles and their velocities such accurately that it can be used to improve motion analysis in competitive sports or for ergonomic evaluation of motion sequences. In this paper, we use interpretable machine learning techniques to analyze the gait. Here, the focus is on the classification between foot touchdown and drop-off during normal walking. The motion data for training the model is labeled using force plates. We analyze how we could apply our machine learning models directly on new motion data recorded in a different scenario compared to the initial training, more precise on a treadmill. We use the properties of the interpretable model to detect drift and to transfer our model if necessary.},
  subject      = {Motion Capturing},
  language  = {en}
}