A Progressive Refinement of Postural Human Balance Models Based on Experimental Data Using Topological Data Analysis

Injuries related to falling occur with high frequency and severity in geriatric individuals as well as those medically impaired by neuromuscular diseases (such as Parkinson's, multiple sclerosis, concussions, etc.). It has been shown that modeling a human's postural standing position as an inverted pendulum with a continuous feedback loop and analyzing an individual's center of pressure location during quiet standing can provide insight into stability states through topological data analysis. This stability state then offers information on the healthiness of an individual signal. However, only synthetically created signals derived from the model itself have undergone state classification. This thesis investigated different models of human balance by analyzing their similarities with experimental data using topological data analysis. The objective of this thesis was to competitively iterate through model types and variations to convincingly support a superior model type for use in future stability state classification research and studies.