Authors:
Vittorio Lippi
1
;
2
;
Christoph Maurer
2
and
Stefan Kammermeier
3
Affiliations:
1
Institute of Digitalization in medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
;
2
Clinic of Neurology and Neurophysiology, Medical Centre-University of Freiburg, Faculty of Medicine, University of Freiburg, Breisacher Straße 64, 79106, Freiburg im Breisgau, Germany
;
3
Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Neurologische Klinik und Poliklinik, Marchioninistraße 15, 81377 München, Germany
Keyword(s):
Modelling, Computational Model, Feedback Control Systems, Parameters Identification, Posture Control, Human Motor Control.
Abstract:
Human and humanoid posture control models usually rely on single or multiple degrees of freedom inverted pendulum representation of upright stance associated with a feedback controller. In models typically focused on the action between ankles, hips, and knees, the control of head position is often neglected, and the head is considered one with the upper body. However, two of the three main contributors to the human motion sensorium reside in the head: the vestibular and the visual system. As the third contributor, the proprioceptive system is distributed throughout the body. In human neurodegenerative brain diseases of motor control, like Progressive Supranuclear Palsy PSP and Idiopathic Parkinson’s Disease IPD, clinical studies have demonstrated the importance of head motion deficits. is work sp ecifically addresses the control of the head during a perturbed upright stance. A control model for the neck is proposed following the hypothesis of a modular posture control from previous s
tudies. Data from human experiments are used to fit the model and retrieve sets of parameters representative of the behavior obtained in different conditions. e result of the analysis is twofold: validate the model and its underlying hypothesis and provide a system to assess the differences in posture control that can be used to identify the differences between healthy subjects and patients with different conditions. Implications for clinical pathology and application in humanoid and assistive robotics are discussed.
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