@article{KetelhutBrueggeGoelletal.2020, author = {Ketelhut, Maike and Br{\"u}gge, G. M. and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk}, title = {Adaptive iterative learning control of an industrial robot during neuromuscular training}, series = {IFAC PapersOnLine}, volume = {53}, journal = {IFAC PapersOnLine}, number = {2}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8963}, doi = {10.1016/j.ifacol.2020.12.741}, pages = {16468 -- 16475}, year = {2020}, abstract = {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.}, language = {en} } @article{KetelhutKolditzGoelletal.2019, author = {Ketelhut, Maike and Kolditz, Melanie and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk}, title = {Admittance control of an industrial robot during resistance training}, series = {IFAC-PapersOnLine}, volume = {52}, journal = {IFAC-PapersOnLine}, number = {19}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8963}, doi = {10.1016/j.ifacol.2019.12.102}, pages = {223 -- 228}, year = {2019}, abstract = {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.}, language = {en} } @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} } @inproceedings{KolditzAlbrachtFasseetal.2015, author = {Kolditz, Melanie and Albracht, Kirsten and Fasse, Alessandro and Albin, Thivaharan and Br{\"u}ggemann, Gert-Peter and Abel, Dirk}, title = {Evaluation of an industrial robot as a leg press training device}, series = {XV International Symposium on Computer Simulation in Biomechanics July 9th - 11th 2015, Edinburgh, UK}, booktitle = {XV International Symposium on Computer Simulation in Biomechanics July 9th - 11th 2015, Edinburgh, UK}, pages = {41 -- 42}, year = {2015}, language = {en} } @article{KolditzAlbinAbeletal.2016, author = {Kolditz, Melanie and Albin, Thivaharan and Abel, Dirk and Fasse, Alessandro and Br{\"u}ggemann, Gert-Peter and Albracht, Kirsten}, title = {Evaluation of foot position and orientation as manipulated variables to control external knee adduction moments in leg extension training}, series = {Computer methods and programs in biomedicine}, volume = {171}, journal = {Computer methods and programs in biomedicine}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0169-2607}, doi = {10.1016/j.cmpb.2016.09.005}, pages = {81 -- 86}, year = {2016}, abstract = {Background and Objective Effective leg extension training at a leg press requires high forces, which need to be controlled to avoid training-induced damage. In order to avoid high external knee adduction moments, which are one reason for unphysiological loadings on knee joint structures, both training movements and the whole reaction force vector need to be observed. In this study, the applicability of lateral and medial changes in foot orientation and position as possible manipulated variables to control external knee adduction moments is investigated. As secondary parameters both the medio-lateral position of the center of pressure and the frontal-plane orientation of the reaction force vector are analyzed. Methods Knee adduction moments are estimated using a dynamic model of the musculoskeletal system together with the measured reaction force vector and the motion of the subject by solving the inverse kinematic and dynamic problem. Six different foot conditions with varying positions and orientations of the foot in a static leg press are evaluated and compared to a neutral foot position. Results Both lateral and medial wedges under the foot and medial and lateral shifts of the foot can influence external knee adduction moments in the presented study with six healthy subjects. Different effects are observed with the varying conditions: the pose of the leg is changed and the direction and center of pressure of the reaction force vector is influenced. Each effect results in a different direction or center of pressure of the reaction force vector. Conclusions The results allow the conclusion that foot position and orientation can be used as manipulated variables in a control loop to actively control knee adduction moments in leg extension training.}, language = {en} } @inproceedings{KolditzAlbinAlbrachtetal.2016, author = {Kolditz, Melanie and Albin, Thivaharan and Albracht, Kirsten and Br{\"u}ggemann, Gert-Peter and Abel, Dirk}, title = {Isokinematic leg extension training with an industrial robot}, series = {6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) June 26-29, 2016. UTown, Singapore}, booktitle = {6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) June 26-29, 2016. UTown, Singapore}, doi = {10.1109/BIOROB.2016.7523750}, pages = {950 -- 955}, year = {2016}, language = {de} } @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{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} } @inproceedings{KolditzAlbinFasseetal.2015, author = {Kolditz, Melanie and Albin, Thivaharan and Fasse, Alessandro and Br{\"u}ggemann, Gert-Peter and Abel, Dirk and Albracht, Kirsten}, title = {Simulative Analysis of Joint Loading During Leg Press Exercise for Control Applications}, series = {IFAC-PapersOnLine}, volume = {48}, booktitle = {IFAC-PapersOnLine}, number = {20}, doi = {10.1016/j.ifacol.2015.10.179}, pages = {435 -- 440}, year = {2015}, language = {en} }