Human Postural Sway Modeling and Balance Impairment Detection

Falls, Fear of falling (FoF), and low balance confidence pose significant threats to health and independence among older people. This thesis aims to study humans’ postural and balance mechanisms, from analyzing the effect of multisensory inputs on balance performance and balance-related concerns to modeling the postural sway through physical dynamics and data-driven approaches. Mimic human biomechanics and motor control systems on robotic balance applications; and finally, investigate and propose novel technological methods to diagnose and measure the postural sway clinical concerns. Within this context, this thesis will establish the following main contributions: First, with signal processing and machine learning algorithms, new analysis and methods are developed to detect the balance impairment applied to the posturography data of the quiet stance. These results underscore the importance of considering the sensori motor function and fall-related concerns when examining human postural sway and balance performance and suggest potential affordable avenues for early detection of balance impairment. Secondly, applying estimation theory, a novel approach has been developed to detect internal disturbances in the neuromuscular control of balance mechanisms. This innovative method holds the potential for identifying FoF in individuals, offering valuable insights into balance-related concerns. The third main contribution is in the field of human postural sway modeling by perceiving both data-driven and model-based approaches. A new structure for the postural sway that offers a more comprehensive understanding of the underlying neural mechanisms involved in the balance is introduced. Moreover, it has also examined existing model-based approaches for human postural sway modeling, comparing their ability to mimic human data in the upright stance. By carefully evaluating and benchmarking these approaches, each method’s strengths and limitations are examined, and areas for future development are highlighted. Finally, the proposed data-driven model’s efficacy through simulation and experimental results using a single-link inverted pendulum is demonstrated. Specifically, the data-driven control model achieved a proper balance, proving the approach’s effectiveness. These findings have important implications for developing new interventions to improve balance and reduce the risk of falls in real-world settings, particularly in aging and other high-risk populations