xiii, 109 p. : ill. / Falls are a significant source of physical, social, and psychological suffering among elderly adults. Falls lead to morbidity and even mortality. Over one-third of adults over the age of 65 years will fall within a calendar year, with almost 10,000 deaths per year attributed to falls. The direct cost of falls exceeds $10 billion a year in the United States. Fall incidents have been linked to multiple risk factors, including cognitive function, muscle strength, and balance control. The ability to properly identify balance impairment is a tremendous challenge to the medical community, with accurate assessment of fall risk lacking. Therefore, the purpose of this study was to assess balance control during gait among young adults, elderly adults, and elderly fallers; determine which biomechanical measures can best identify fallers retrospectively; demonstrate longitudinal changes in elderly adults and prospectively assess fall risk; and provide a method for mapping clinical variables to sensitive balance control measures using artificial neural networks. The interaction of the whole body center of mass (CoM) in relation to the base of support (BoS) assessed static and dynamic balance control throughout gait. Elderly fallers demonstrated reduced balance control ability, specifically a decreased time to contact with the boundary of the BoS, when compared to young adults at heel strike. This decreased time might predispose older adults to additional falls due to an inability to properly respond to perturbations or slips. Inclusion of these balance control measures along with the Berg Balance Scale and spatiotemporal measures demonstrated sensitivity and specificity values of up to 90% when identifying 98 elderly fallers and non-fallers, respectively. Additionally, 27 older adults were followed longitudinally over a period of one year, with only the interaction of the CoM with the BoS demonstrating an ability to differentiate fallers and non-fallers prospectively. As the collection and analysis of these biomechanics measures can be time consuming and expensive, an artificial neural network demonstrated that clinical measures can accurately predict balance control during ambulation. This model approached a solution quickly and provides a means for assessing longitudinal changes, intervention effects, and future fall risk. This dissertation includes both previously published and unpublished co-authored material. / Committee in charge: Dr. Li-Shan Chou, Chair; Dr. Andrew Karduna, Member; Dr. Marjorie Woollacott, Member; Dr. Ronald Stock, Member; Dr. Arthur Farley, Outside Member