Identification of the Plant for Upright Stance in Humans: Multiple Movement Patterns From a Single Neural Strategy

Abstract

We determined properties of the plant during human upright stance using a closed-loop system identification method originally applied to human postural control by another group. To identify the plant, which was operationally defined as the mapping from muscle activation (rectified EMG signals) to body segment angles, we rotated the visual scene about the axis through the subject's ankles using a sum-of-sines stimulus signal. Because EMG signals from ankle muscles and from hip and lower trunk muscles showed similar responses to the visual perturbation across frequency, we combined EMG signals from all recorded muscles into a single plant input. Body kinematics were described by the trunk and leg angles in the sagittal plane. The phase responses of both angles to visual scene angle were similar at low frequencies and approached a difference of ∼150° at higher frequencies. Therefore we considered leg and trunk angles as separate plant outputs. We modeled the plant with a two-joint (ankle and hip) model of the body, a second-order low-pass filter from EMG activity to active joint torques, and intrinsic stiffness and damping at both joints. The results indicated that the in-phase (ankle) pattern was neurally generated, whereas the out-of-phase pattern was caused by plant dynamics. Thus a single neural strategy leads to multiple kinematic patterns. Moreover, estimated intrinsic stiffness in the model was insufficient to stabilize the plant.