@inproceedings{KetelhutGoellBraunsteinetal.2019,
  author    = {Ketelhut, Maike and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk},
  title     = {Iterative learning control of an industrial robot for neuromuscular training},
  series = {2019 IEEE Conference on Control Technology and Applications},
  booktitle = {2019 IEEE Conference on Control Technology and Applications},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-7281-2767-5 (ePub)},
  doi       = {10.1109/CCTA.2019.8920659},
  pages     = {7 Seiten},
  year      = {2019},
  abstract  = {Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.},
  language  = {en}
}
@article{AtlasBrealeyDharetal.2012,
  author    = {Atlas, Glen and Brealey, David and Dhar, Sunil and Dikta, Gerhard and Singer, Meryvn},
  title     = {Additional hemodynamic measurements with an esophageal Doppler monitor: a preliminary report of compliance, force, kinetic energy, and afterload in the clinical setting},
  series = {Journal of clinical monitoring and computing},
  journal   = {Journal of clinical monitoring and computing},
  number    = {26},
  publisher = {Springer Nature},
  address   = {London},
  isbn      = {1573-2614},
  doi       = {10.1007/s10877-012-9386-5},
  pages     = {473 -- 482},
  year      = {2012},
  abstract  = {The esophageal Doppler monitor (EDM) is a minimally-invasive hemodynamic device which evaluates both cardiac output (CO), and fluid status, by estimating stroke volume (SV) and calculating heart rate (HR). The measurement of these parameters is based upon a continuous and accurate approximation of distal thoracic aortic blood flow. Furthermore, the peak velocity (PV) and mean acceleration (MA), of aortic blood flow at this anatomic location, are also determined by the EDM. The purpose of this preliminary report is to examine additional clinical hemodynamic calculations of: compliance (C), kinetic energy (KE), force (F), and afterload (TSVRi). These data were derived using both velocity-based measurements, provided by the EDM, as well as other contemporaneous physiologic parameters. Data were obtained from anesthetized patients undergoing surgery or who were in a critical care unit. A graphical inspection of these measurements is presented and discussed with respect to each patient's clinical situation. When normalized to each of their initial values, F and KE both consistently demonstrated more discriminative power than either PV or MA. The EDM offers additional applications for hemodynamic monitoring. Further research regarding the accuracy, utility, and limitations of these parameters is therefore indicated.},
  language  = {en}
}