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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.
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.
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.
Achilles tendon rupture (ATR) patients have persistent functional deficits in the triceps surae muscle–tendon unit (MTU). The complex remodeling of the MTU accompanying these deficits remains poorly understood. The purpose of the present study was to associate in vivo and in silico data to investigate the relations between changes inMTU properties and strength deficits inATR patients. Methods: Elevenmale subjects who had undergone surgical repair of complete unilateral ATR were examined 4.6 ± 2.0 (mean ± SD) yr after rupture. Gastrocnemius medialis (GM) tendon stiffness, morphology, and muscle architecture were determined using ultrasonography. The force–length relation of the plantar flexor muscles was assessed at five ankle joint angles. In addition, simulations (OpenSim) of the GM MTU force–length properties were performed with various iterations of MTU properties found between the unaffected and the affected side. Results: The affected side of the patients displayed a longer, larger, and stiffer GM tendon (13% ± 10%, 105% ± 28%, and 54% ± 24%, respectively) compared with the unaffected side. The GM muscle fascicles of the affected side were shorter (32% ± 12%) and with greater pennation angles (31% ± 26%). A mean deficit in plantarflexion moment of 31% ± 10% was measured. Simulations indicate that pairing an intact muscle with a longer tendon shifts the optimal angular range of peak force outside physiological angular ranges, whereas the shorter muscle fascicles and tendon stiffening seen in the affected side decrease this shift, albeit incompletely. Conclusions: These results suggest that the substantial changes in MTU properties found in ATR patients may partly result from compensatory remodeling, although this process appears insufficient to fully restore muscle function.