Papers by Author: Arthur D. Kuo

Abstract: We hypothesized that multi-sensory processing at the central nervous system (CNS) in human postural control can be described using an optimal estimator model. The estimates on body dynamics from multi-sensory signals contain sensory noise, transmission delays, and process disturbances. The state estimates approximate actual body movement. Erroneous estimates degrade the performance of feedback control and could cause a loss of balance if distorted severely. To test the hypothesis, we examined the frequency response of a visually-induced postural sway with stimulus frequency ranging from 0.075 to 1Hz and established an optimal estimator model. Two healthy young (33yrs ± 1) subjects stood on a force platform located 1.25m behind a projection screen with their arms crossed over their chests. They were asked to maintain an upright posture against the sinusoidal visual field stimuli. Each sinusoidal visual stimulus was generated by a projector for 200secs in pitch direction with a maximum pitch angle of 20o. Kinematics data was recorded to calculate the frequency response function of the center of mass (COM). There were three components in the modeling procedure: a biomechanical model of body and sensor dynamics, a linear feedback control model to stabilize the biomechanical model, and a state estimator to estimate body dynamic states based on multi-sensory outputs. We modeled the sensor dynamics of the semicircular canal, otolth, vision, and muscle spindles at the ankle and hip joint. We used the Kalman filter and linear quadratic regulator to determine feedback gains. Results showed that the frequency response function of a visually-induced postural sway decreased as stimulus frequency increased, and this low-pass filtering characteristic with an approximate cutoff frequency of 0.2Hz was also simulated by the postural feedback control model with optimal estimator. Low-pass filtering characteristics of the frequency response are mainly due to body and sensor dynamics, which show reduced responses for high frequency stimulus. The Kalman filter represents that the CNS utilizes redundant sensory information in a way that minimizes discrepancies between actual body dynamics and estimated body dynamics based on sensory output and an internal model. The results suggest that the CNS may make use of an internal representation of body dynamics, and can integrate sensory information in an optimal way to best estimate human postural responses.

Abstract: We examined how the central nervous system adjusts postural responses to an increased postural challenge due to an initial lean. Postural feedback responses scale to accommodate biomechanical constraints, such as an allowable ankle joint torque. Initial forward leaning, which is observed among the elderly who are inactive or afraid of falling, brings subjects near to the limit of stability and makes the biomechanical constraints more difficult to obey. We hypothesized that the central nervous system is aware of body dynamics and restrains postural responses when subjects initially lean forward. To test this hypothesis, fast backwards perturbations of various magnitudes were applied to 12 healthy young subjects (3 male, 9 female) aged 20 to 32 years. The subjects were instructed to stand quietly on a hydraulic servo-controlled force platform with their arms crossed over their chests, then to recover from a perturbation by returning to their upright position, without stepping or lifting their heels off the ground, if possible. Initially, the subjects were either standing upright or leaning forward. The force platform was movable in the translational direction and programmed to move backward with various ramp displacements ranging from 1.2 to 15 cm, all with the duration of 275 msec. For each trial, the kinematics and ground reaction force data were recorded, then used to compute the net joint torques, employing a least squares inverse dynamics method. Optimization methods were used to identify a set of equivalent feedback control gains for each trial so that the biomechanical model incorporating this feedback control would reproduce the empirical response. The results showed that the kinematics, joint torque, and feedback gains gradually scaled as a function of the perturbation magnitude before they reached the biomechanical constraint, and the scaling became more severe with an initial forward lean. For example, the model suggested that the magnitude of the ankle joint angle feedback to ankle torque was smaller in the leaning trials than in the initially upright trials, as if the subjects experienced a larger postural perturbation in the leaning trials. These results imply that the central nervous system restrained the postural responses to accommodate the additional biomechanical constraint imposed by the forward posture, thereby suggesting that the central nervous system is aware of body dynamics and biomechanical constraints. The scaling of the postural feedback gains with the perturbation magnitude and initial lean indicates that the postural control can be interpreted as a feedback scheme with scalable gains.