Fall accident is a significant problem associated with our society both in terms of economic losses and human suffering [1]. In 2016, more than 800,000 people were hospitalized and over 33,000 deaths resulted from falling. Health costs associated with falling in 2016 yielded at 33% of total medical expenses in the US- mounting to approximately $31 billion per year. As such, it is imperative to find intervention strategies to mitigate deaths and injuries associated with fall accidents. In order for this goal to be realized, it is necessary to understand the mechanisms associated with fall accidents and more specifically, the movement profiles that may represent the cogent behavior of the locomotor system that may be amendable to rehabilitation and intervention strategies. In this light, this Thesis is focused on better understanding the factors influencing dynamic stability measure (as measured by Lyapunov exponents) during over-ground ambulation utilizing wireless Inertial Measurement Unit (IMU).

Four pilot studies were conducted: the First study was carried out to verify if IMU system was sophisticated enough to determine different load-carrying conditions. Second, to test the effects of walking inclinations, three incline levels on gait dynamic stability were examined. Third, tested whether different sections from the total gait cycle can be stitched together to assess LDS using the laboratory collected data. Finally, the fourth study examines the effect of “stitching” the data on dynamic stability measure from a longitudinally assessed (3-day continuous data collection) data to assess the effects of free-range data on assessment of dynamic stability.

Results indicated that load carrying significantly influenced dynamic stability measure but not for the floor inclination levels – indicating that future use of such measure should further implicate normalization of dynamic stability measures associated with different activities and terrain conditions. Additionally, stitching method was successful in obtaining dynamic stability measure utilizing free-living IMU data.