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 - TY - CHAP A1 - Stenger, David A1 - Altherr, Lena A1 - Abel, Dirk T1 - Machine learning and metaheuristics for black-box optimization of product families: a case-study investigating solution quality vs. computational overhead T2 - Operations Research Proceedings 2018 N2 - In product development, numerous design decisions have to be made. Multi-domain virtual prototyping provides a variety of tools to assess technical feasibility of design options, however often requires substantial computational effort for just a single evaluation. A special challenge is therefore the optimal design of product families, which consist of a group of products derived from a common platform. Finding an optimal platform configuration (stating what is shared and what is individually designed for each product) and an optimal design of all products simultaneously leads to a mixed-integer nonlinear black-box optimization model. We present an optimization approach based on metamodels and a metaheuristic. To increase computational efficiency and solution quality, we compare different types of Gaussian process regression metamodels adapted from the domain of machine learning, and combine them with a genetic algorithm. We illustrate our approach on the example of a product family of electrical drives, and investigate the trade-off between solution quality and computational overhead. KW - Product family optimization KW - Mixed-integer nonlinear black-box optimization KW - Engineering optimization KW - Machine learning Y1 - 2019 SN - 978-3-030-18499-5 (Print) SN - 978-3-030-18500-8 (Online) U6 - http://dx.doi.org/10.1007/978-3-030-18500-8_47 SP - 379 EP - 385 PB - Springer CY - Cham ER - TY - CHAP A1 - Kolditz, Melanie A1 - Albin, Thivaharan A1 - Fasse, Alessandro A1 - Brüggemann, Gert-Peter A1 - Abel, Dirk A1 - Albracht, Kirsten T1 - Simulative Analysis of Joint Loading During Leg Press Exercise for Control Applications T2 - IFAC-PapersOnLine Y1 - 2015 U6 - http://dx.doi.org/10.1016/j.ifacol.2015.10.179 N1 - IFAC-PapersOnLine 48-20; Conference Paper Archive VL - 48 IS - 20 SP - 435 EP - 440 ER - TY - CHAP A1 - Kolditz, Melanie A1 - Albracht, Kirsten A1 - Fasse, Alessandro A1 - Albin, Thivaharan A1 - Brüggemann, Gert-Peter A1 - Abel, Dirk T1 - Evaluation of an industrial robot as a leg press training device T2 - XV International Symposium on Computer Simulation in Biomechanics July 9th – 11th 2015, Edinburgh, UK Y1 - 2015 SP - 41 EP - 42 ER - TY - JOUR A1 - Kolditz, Melanie A1 - Albin, Thivaharan A1 - Brüggemann, Gert-Peter A1 - Abel, Dirk A1 - Albracht, Kirsten T1 - Robotergestütztes System für ein verbessertes neuromuskuläres Aufbautraining der Beinstrecker JF - at - Automatisierungstechnik N2 - Neuromuskuläres Aufbautraining der Beinstrecker ist ein wichtiger Bestandteil in der Rehabilitation und Prävention von Muskel-Skelett-Erkrankungen. Effektives Training erfordert hohe Muskelkräfte, die gleichzeitig hohe Belastungen von bereits geschädigten Strukturen bedeuten. Um trainingsinduzierte Schädigungen zu vermeiden, müssen diese Kräfte kontrolliert werden. Mit heutigen Trainingsgeräten können diese Ziele allerdings nicht erreicht werden. Für 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 möglichen Stellgrößen wird der Aufbau eines robotischen Systems vorgestellt, das sowohl für Forschungszwecke als auch zur Entwicklung neuartiger Trainingsgeräte verwendet werden kann. Y1 - 2016 U6 - http://dx.doi.org/10.1515/auto-2016-0044 SN - 2196-677X VL - 64 IS - 11 SP - 905 EP - 914 PB - De Gruyter CY - Berlin ER - TY - CHAP A1 - Kolditz, Melanie A1 - Albin, Thivaharan A1 - Albracht, Kirsten A1 - Brüggemann, Gert-Peter A1 - Abel, Dirk T1 - Isokinematic leg extension training with an industrial robot T2 - 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) June 26-29, 2016. UTown, Singapore Y1 - 2016 U6 - http://dx.doi.org/10.1109/BIOROB.2016.7523750 SP - 950 EP - 955 ER - TY - JOUR A1 - Kolditz, Melanie A1 - Albin, Thivaharan A1 - Abel, Dirk A1 - Fasse, Alessandro A1 - Brüggemann, Gert-Peter A1 - Albracht, Kirsten T1 - Evaluation of foot position and orientation as manipulated variables to control external knee adduction moments in leg extension training JF - Computer methods and programs in biomedicine N2 - 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. KW - External knee adduction moments KW - Manipulated variables KW - Inverse dynamic problem KW - Inverse kinematic problem KW - Musculoskeletal model Y1 - 2016 U6 - http://dx.doi.org/10.1016/j.cmpb.2016.09.005 SN - 0169-2607 N1 - Part of special issue: "SI: Personalised Models and System Identification" VL - 171 SP - 81 EP - 86 PB - Elsevier CY - Amsterdam ER -