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Updated: Jun 21, 2026

Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
Vestibular humanoid postural control
Thomas Mergner1, Georg Schweigart, Luminous Fennell
1Neurologie der Universität Freiburg, Neurozentrum, Breisacher Strasse 64, Freiburg, Germany. mergner@uni-freiburg.de
Abstract:
Many of our motor activities require stabilization against external disturbances. This especially applies to biped stance since it is inherently unstable. Disturbance compensation is mainly reactive, depending on sensory inputs and real-time sensor fusion. In humans, the vestibular system plays a major role. When there is no visual space reference, vestibular-loss clearly impairs stance stability. Most humanoid robots do not use a vestibular system, but stabilize upright body posture by means of center of pressure (COP) control. We here suggest using in addition a vestibular sensor and present a biologically inspired vestibular sensor along with a human-inspired stance control mechanism. We proceed in two steps. First, in an introductory review part, we report on relevant human sensors and their role in stance control, focusing on own models of transmitter fusion in the vestibular sensor and sensor fusion in stance control. In a second, experimental part, the models are used to construct an artificial vestibular system and to embed it into the stance control of a humanoid. The robot's performance is investigated using tilts of the support surface. The results are compared to those of humans. Functional significance of the vestibular sensor is highlighted by comparing vestibular-able with vestibular-loss states in robot and humans. We show that a kinematic body-space sensory feedback (vestibular) is advantageous over a kinetic one (force cues) for dynamic body-space balancing. Our embodiment of human sensorimotor control principles into a robot is more than just bionics. It inspired our biological work (neurorobotics: 'learning by building', proof of principle, and more). We envisage a future clinical use in the form of hardware-in-the-loop simulations of neurological symptoms for improving diagnosis and therapy and designing medical assistive devices.
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