@article{MoratFaudeHanssenetal.2020,
  author    = {Morat, Mareike and Faude, Oliver and Hanssen, Henner and Ludyga, Sebastian and Zacher, Jonas and Eibl, Angi and Albracht, Kirsten and Donath, Lars},
  title     = {Agility Training to Integratively Promote Neuromuscular, Cognitive, Cardiovascular and Psychosocial Function in Healthy Older Adults: A Study Protocol of a One-Year Randomized-Controlled Trial},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {17},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph17061853},
  pages     = {1 -- 14},
  year      = {2020},
  abstract  = {Exercise training effectively mitigates aging-induced health and fitness impairments. Traditional training recommendations for the elderly focus separately on relevant physiological fitness domains, such as balance, flexibility, strength and endurance. Thus, a more holistic and functional training framework is needed. The proposed agility training concept integratively tackles spatial orientation, stop and go, balance and strength. The presented protocol aims at introducing a two-armed, one-year randomized controlled trial, evaluating the effects of this concept on neuromuscular, cardiovascular, cognitive and psychosocial health outcomes in healthy older adults. Eighty-five participants were enrolled in this ongoing trial. Seventy-nine participants completed baseline testing and were block-randomized to the agility training group or the inactive control group. All participants undergo pre- and post-testing with interim assessment after six months. The intervention group currently receives supervised, group-based agility training twice a week over one year, with progressively demanding perceptual, cognitive and physical exercises. Knee extension strength, reactive balance, dual task gait speed and the Agility Challenge for the Elderly (ACE) serve as primary endpoints and neuromuscular, cognitive, cardiovascular, and psychosocial meassures serve as surrogate secondary outcomes. Our protocol promotes a comprehensive exercise training concept for older adults, that might facilitate stakeholders in health and exercise to stimulate relevant health outcomes without relying on excessively time-consuming physical activity recommendations.},
  language  = {en}
}
@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{GossmannThomasHorvathetal.2020,
  author    = {Gossmann, Matthias and Thomas, Ulrich and Horv{\´a}th, Andr{\´a}s and Dragicevic, Elena and Stoelzle-Feix, Sonja and Jung, Alexander and Raman, Aravind Hariharan and Staat, Manfred and Linder, Peter},
  title     = {A higher-throughput approach to investigate cardiac contractility in vitro under physiological mechanical conditions},
  series = {Journal of Pharmacological and Toxicological Methods},
  volume    = {105},
  journal   = {Journal of Pharmacological and Toxicological Methods},
  number    = {Article 106843},
  publisher = {Elsevier},
  address   = {New York, NY},
  doi       = {10.1016/j.vascn.2020.106843},
  year      = {2020},
  language  = {en}
}