This project focuses on the complex task of maintaining standing balance, which requires the central nervous system (CNS) to continuously integrate feedback from multiple sensory modalities, primarily vision and proprioception. While it is widely recognized that these individual sensory systems play an essential role in postural control, current literature frequently relies on linear time-invariant models that fail to capture the interactions and dynamic sensory reweighting that occur under varying environmental conditions. 

To address this gap, this project aims to thoroughly quantify both the individual and interacting contributions of visual and proprioceptive sensory systems to the CNS during upright stance. By moving beyond simplified linear assumptions, the research seeks to uncover the complex, nonlinear principles governing how humans maintain equilibrium when faced with differing levels of sensory availability and accuracy. 

The analytical framework will initially apply non-parametric frequency domain analyses to estimate frequency response functions (FRF) between the sensory inputs and the resulting mechanical (CoP) and neural (EMG) outputs. To capture the anticipated nonlinearities and non-stationarities inherent in human postural control, the methodology will also incorporate nonlinear block-oriented models (such as Wiener or Hammerstein structures) and time-varying (TV) identification approaches. This multi-input multi-output (MIMO) modeling strategy will enable the precise quantification of both ipsilateral and contralateral neuromuscular responses.  

Ultimately, this research is expected to yield a comprehensive, quantitative model of postural control that explicitly integrates cross-responses between the legs and links sensory reweighting directly to muscle recruitment. The project anticipates confirming that unilateral proprioceptive inputs elicit measurable contralateral neuromuscular reflexes, and that combining visual and proprioceptive stimuli generates distinctly nonlinear integration behaviors within the CNS. By establishing a dynamic model of sensorimotor integration, this project will provide a highly transferable methodology for evaluating unilateral sensory deficits. These insights will significantly enhance the precision of clinical balance assessments, inform the development of targeted rehabilitation protocols, and guide new strategies for assistive technologies aimed at mitigating balance impairments and preventing falls.