TY  - JOUR
A1  - Atlas, Glen
A1  - Brealey, David
A1  - Dhar, Sunil
A1  - Dikta, Gerhard
A1  - Singer, Meryvn
T1  - Additional hemodynamic measurements with an esophageal Doppler monitor: a preliminary report of compliance, force, kinetic energy, and afterload in the clinical setting
JF  - Journal of clinical monitoring and computing
N2  - 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.
KW  - Acceleration
KW  - Velocity
KW  - Kinetic energy
KW  - Force
KW  - Compliance
KW  - Afterload
KW  - Contractility
KW  - Volume status
KW  - Esophageal Doppler monitor
Y1  - 2012
SN  - 1573-2614
U6  - http://dx.doi.org/10.1007/s10877-012-9386-5
IS  - 26
SP  - 473
EP  - 482
PB  - Springer Nature
CY  - London
ER  - 
TY  - CHAP
A1  - Ketelhut, Maike
A1  - Göll, Fabian
A1  - Braunstein, Bjoern
A1  - Albracht, Kirsten
A1  - Abel, Dirk
T1  - Iterative learning control of an industrial robot for neuromuscular training
T2  - 2019 IEEE Conference on Control Technology and Applications
N2  - 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.
KW  - Knee
KW  - Training
KW  - Load modeling
KW  - Force
KW  - Iterative learning control
Y1  - 2019
SN  - 978-1-7281-2767-5 (ePub)
SN  - 978-1-7281-2766-8 (USB)
SN  - 978-1-7281-2768-2 (PoD)
U6  - http://dx.doi.org/10.1109/CCTA.2019.8920659
N1  - 2019 IEEE Conference on Control Technology and Applications (CCTA) Hong Kong, China, August 19-21, 2019
PB  - IEEE
CY  - New York
ER  -