@article{KolditzAlbinBrueggemannetal.2016,
  author    = {Kolditz, Melanie and Albin, Thivaharan and Br{\"u}ggemann, Gert-Peter and Abel, Dirk and Albracht, Kirsten},
  title     = {Robotergest{\"u}tztes System f{\"u}r ein verbessertes neuromuskul{\"a}res Aufbautraining der Beinstrecker},
  series = {at - Automatisierungstechnik},
  volume    = {64},
  journal   = {at - Automatisierungstechnik},
  number    = {11},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {2196-677X},
  doi       = {10.1515/auto-2016-0044},
  pages     = {905 -- 914},
  year      = {2016},
  abstract  = {Neuromuskul{\"a}res Aufbautraining der Beinstrecker ist ein wichtiger Bestandteil in der Rehabilitation und Pr{\"a}vention von Muskel-Skelett-Erkrankungen. Effektives Training erfordert hohe Muskelkr{\"a}fte, die gleichzeitig hohe Belastungen von bereits gesch{\"a}digten Strukturen bedeuten. Um trainingsinduzierte Sch{\"a}digungen zu vermeiden, m{\"u}ssen diese Kr{\"a}fte kontrolliert werden. Mit heutigen Trainingsger{\"a}ten k{\"o}nnen diese Ziele allerdings nicht erreicht werden. F{\"u}r ein sicheres und effektives Training sollen durch den Einsatz der Robotik, Sensorik, eines Regelkreises sowie Muskel-Skelett-Modellen Belastungen am Zielgewebe direkt berechnet und kontrolliert werden. Auf Basis zweier Vorstudien zu m{\"o}glichen Stellgr{\"o}ßen wird der Aufbau eines robotischen Systems vorgestellt, das sowohl f{\"u}r Forschungszwecke als auch zur Entwicklung neuartiger Trainingsger{\"a}te verwendet werden kann.},
  language  = {de}
}
@article{KetelhutGoellBraunsteinetal.2018,
  author    = {Ketelhut, Maike and G{\"o}ll, Fabian and Braunstein, Bj{\"o}rn and Albracht, Kirsten and Abel, Dirk},
  title     = {Comparison of different training algorithms for the leg extension training with an industrial robot},
  series = {Current Directions in Biomedical Engineering},
  volume    = {4},
  journal   = {Current Directions in Biomedical Engineering},
  number    = {1},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {2364-5504},
  doi       = {10.1515/cdbme-2018-0005},
  pages     = {17 -- 20},
  year      = {2018},
  abstract  = {In the past, different training scenarios have been developed and implemented on robotic research platforms, but no systematic analysis and comparison have been done so far. This paper deals with the comparison of an isokinematic (motion with constant velocity) and an isotonic (motion against constant weight) training algorithm. Both algorithms are designed for a robotic research platform consisting of a 3D force plate and a high payload industrial robot, which allows leg extension training with arbitrary six-dimensional motion trajectories. In the isokinematic as well as the isotonic training algorithm, individual paths are defined i n C artesian s pace by sufficient s upport p oses. I n t he i sotonic t raining s cenario, the trajectory is adapted to the measured force as the robot should only move along the trajectory as long as the force applied by the user exceeds a minimum threshold. In the isotonic training scenario however, the robot's acceleration is a function of the force applied by the user. To validate these findings, a simulative experiment with a simple linear trajectory is performed. For this purpose, the same force path is applied in both training scenarios. The results illustrate that the algorithms differ in the force dependent trajectory adaption.},
  language  = {en}
}