Modeling sensorimotor control of human upright stance.
We model human postural control of upright stance during external disturbances and voluntary lean. Our focus is on how data from various sensors are combined to estimate these disturbances. Whereas most current engineering models of multisensory estimation rely on "internal observers" and complex processing, we compute our estimates by simple sensor fusion mechanisms, i.e., weighted sums of sensory signals combined with thresholds. We show with simulations that this simple device mimics human-like postural behavior in a wide range of situations and diseases. We have now embodied our mechanism in a biped humanoid robot to show that it works in the real world with complex, noisy, and imperfectly known sensors and effectors. On the other hand, we find that the more complex, internal-observer approach, when applied to bipedal posture, can also yield human-like behavior. We suggest that humans use both mechanisms: simple, fast sensor fusions with thresholding for automatic reactions (default mechanism), and more complex methods for voluntary movements. We suggest also that the fusion with thresholding mechanisms are optimized during phylogenesis but are mainly hardwired in any one organism, whereas sensorimotor learning and optimization is mainly a domain of the internal observers.
Mergner
Thomas
T
Neurological University Clinic, Neurocenter, Breisacher Street 64, 79106 Freiburg, Germany. mergner@uni-freiburg.de
D006801:Humans; D008954:Models, Biological; D004856:Postural Balance; D011187:Posture; D011434:Proprioception / Q000502:physiology*