title: Learning an intermittent control strategy for postural balancing using an EMG-based human-computer interface.
abstract: It has been considered that the brain stabilizes unstable body dynamics by regulating co-activation levels of antagonist muscles. Here we critically reexamined this established theory of impedance control in a postural balancing task using a novel EMG-based human-computer interface, in which subjects were asked to balance a virtual inverted pendulum using visual feedback information on the pendulum's position. The pendulum was actuated by a pair of antagonist joint torques determined in real-time by activations of the corresponding pair of antagonist ankle muscles of subjects standing upright. This motor-task raises a frustrated environment; a large feedback time delay in the sensorimotor loop, as a source of instability, might favor adopting the non-reactive, preprogrammed impedance control, but the ankle muscles are relatively hard to co-activate, which hinders subjects from adopting the impedance control. This study aimed at discovering how experimental subjects resolved this frustrated environment through motor learning. One third of subjects adapted to the balancing task in a way of the impedance-like control. It was remarkable, however, that the majority of subjects did not adopt the impedance control. Instead, they acquired a smart and energetically efficient strategy, in which two muscles were inactivated simultaneously at a sequence of optimal timings, leading to intermittent appearance of periods of time during which the pendulum was not actively actuated. Characterizations of muscle inactivations and the pendulum¡Çs sway showed that the strategy adopted by those subjects was a type of intermittent control that utilizes a stable manifold of saddle-type unstable upright equilibrium that appeared in the state space of the pendulum when the active actuation was turned off.
authors.affiliation: Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Asai
authors.forename: Shota
authors.initials: S
authors.lastname: Tateyama
authors.forename: Taishin
authors.initials: T
authors.lastname: Nomura
mesh_terms: D000328:Adult; D000843:Ankle Joint / Q000502:physiology; D003201:Computers; D004576:Electromyography / Q000379:methods; D005246:Feedback; D006801:Humans; D018482:Muscle, Skeletal / Q000502:physiology*; D004856:Postural Balance / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology; D019415:Torque; D055815:Young Adult