TY - JOUR A1 - Ketelhut, Maike A1 - Göll, Fabian A1 - Braunstein, Björn A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Comparison of different training algorithms for the leg extension training with an industrial robot JF - Current Directions in Biomedical Engineering N2 - 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. KW - Rehabilitation Technology and Prosthetics KW - Surgical Navigation and Robotics Y1 - 2018 U6 - http://dx.doi.org/10.1515/cdbme-2018-0005 SN - 2364-5504 VL - 4 IS - 1 SP - 17 EP - 20 PB - De Gruyter CY - Berlin 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 - TY - JOUR A1 - Ketelhut, Maike A1 - Kolditz, Melanie A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Admittance control of an industrial robot during resistance training JF - IFAC-PapersOnLine N2 - Neuromuscular strength training of the leg extensor muscles plays an important role in the rehabilitation and prevention of age and wealth related diseases. In this paper, we focus on the design and implementation of a Cartesian admittance control scheme for isotonic training, i.e. leg extension and flexion against a predefined weight. For preliminary testing and validation of the designed algorithm an experimental research and development platform consisting of an industrial robot and a force plate mounted at its end-effector has been used. Linear, diagonal and arbitrary two-dimensional motion trajectories with different weights for the leg extension and flexion part are applied. The proposed algorithm is easily adaptable to trajectories consisting of arbitrary six-dimensional poses and allows the implementation of individualized trajectories. KW - Assistive technology KW - Rehabilitation engineering KW - Human-Computer interaction KW - Automatic control Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.ifacol.2019.12.102 SN - 2405-8963 N1 - 14th IFAC Symposium on Analysis, Design, and Evaluation of Human Machine Systems HMS 2019 Tallinn, Estonia, 16–91 September 2019 VL - 52 IS - 19 SP - 223 EP - 228 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ketelhut, Maike A1 - Brügge, G. M. A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Adaptive iterative learning control of an industrial robot during neuromuscular training JF - IFAC PapersOnLine N2 - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur. KW - Iterative learning control KW - Robotic rehabilitation KW - Adaptive control Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.ifacol.2020.12.741 SN - 2405-8963 VL - 53 IS - 2 SP - 16468 EP - 16475 PB - Elsevier CY - Amsterdam ER -