Multi-joint coordination in upright stance: The uncontrolled manifold helps decouple control of body-in-space from control of other degrees of freedom
Final Report Abstract
Analysis of upright stance in humans traditionally models the body as an inverted pendulum with a single degree of freedom at the ankle. In this paradigm, the prevention of falling translates into transforming noisy, delayed sensor information about the state of the plant into a suitable torque applied at the ankle that counteracts gravitation and keeps the center of mass over the support of the feet. Other joints that also affect the center of mass position, like the knee and hip joints, are assumed to be stiff. While this approach has been remarkably successful in explaining many phenomena, its generality is challenged by two major issues. Recent experimental data (Hsu et al., 2007) have shown that in quiet standing other joints than the ankle are far from stiff, but move in similar ranges as the ankle. Instead of forcing mainly the ankle joint, variations are distributed over all joints in a manner that minimizes the deviation of the center of mass, not that of the joints. Second, the assumption that the CNS can directly generate a specific desired torque at a joint fails to capture the dynamics of muscles, whose generation of force is regulated by local feedback loops inducing time delay and dependence on joint angle and velocity. We developed a neural process model of how the nervous system controls upright stance that addresses these issues. A multi-link inverted pendulum model is used instead of a single joint. The model is based on two feedback loops. An inner loop characterizes each muscle-joint system by a nonlinear muscle model that captures the force-length relationship that is generated by the spinal stretch reflex jointly with the passive visco-elastic properties of muscles. An outer loop is closed by sensory information (vision, vestibular input, foot pressure sense etc.) about the kinematic state of the body in space. A control law stabilize the body in space by counter-acting accelerations and non-zero velocities of the body in space. The control law generates motor commands that are distributed to the muscle-joint systems through in inverse transform that sends stronger signals to joints that contribute more strongly to motion of the body in space, that encounter larger inertial moments, and that are stiffened less by concontraction of agonist and antagonist muscles. Simulations of the model demonstrate that the multi-link body remains upright under the impact of gravity. Weakening of the inner, local feedback loops leads to collapse of the body as observed in patients lacking proprioception. Weakening of the outer feedback loop leads to falls as observed under conditions of reduced sensory information from the feet, the eyes, or the vestibular system (e.g., in patients with peripheral neuropathy or vestibular patients). The model also accounts for the time structure of postural sway under normal conditions The signature of the uncontrolled manifold observed in experiment is predicted by the model with more variance in directions of joint space that leave the body in space invariant than in directions in joint space that move the body. That signature derives from the outer feedback loop: any joint moving the body in space elicits a control signal that counter-acts the body’s movement, but is distributed across all joints. This account replaces the traditional inverted-pendulum approximation and provides a new understanding for how the body is stabilized in space.
Publications
- Motor equivalence and self-motion induced by different movement speeds. Experimental Brain Research 209(3):319-32 (2011)
Scholz, J.P., Dwight-Higgin, T., Lynch, J., Tseng, Y., Martin, V., Schöner, G.
- Functional Synergies Underlying Control of Upright Posture during Changes in Head Orientation. PLoS One 7(8):e41583. (2012)
Park, E., Schöner G., Scholz, J.P.
(See online at https://doi.org/10.1371/journal.pone.0041583) - How visual information links to multijoint coordination during quiet standing. Experimental Brain Research 222 (3):229-39 (2012)
Scholz, J.P., Park, E., Jeka, J.J., Schöner, G., Kiemel, T.
(See online at https://doi.org/10.1007/s00221-012-3210-9) - Coordination Dynamics. Encyclopedia of Computational Neuroscience, 2014 (6 pages)
Schöner., G., Nowak, E.
- Use of the Uncontrolled Manifold (UCM) approach to understand motor variability, motor equivalence, and self-motion. In: Levin MF (ed) Progress in Motor Control: Skill Learning, Performance, Health, and Injury. Springer, New York (2014), pages 91-100
Scholz, J.P., Schöner, G.
- A multi-joint model of quiet, upright stance accounts for the ”uncontrolled manifold” structure of joint variance. Biological Cybernetics, December 2017, Volume 111, Issue 5–6, pp 389–403
Reimann, H., Schöner, G.
(See online at https://doi.org/10.1007/s00422-017-0733-y)
