Fall-related injuries are one of the major issues today in terms of human suffering and economic loss. Falls are caused by a number of multifactorial risk factors. These factors are mainly classified into two categories, intrinsic factors such as aging, disease and impaired balance or mobility, and extrinsic factors such as environmental hazard. Among these risk factors, we focused on impaired (dynamic) balance during human walking. Since impaired balance and gait are associated with fall risk, it is necessary to detect abnormal movement patterns due to gait impairment in order to predict fall risk. Gait analysis, by quantifying dynamic movement patterns during ambulation, can be used to discover and diagnose gait impairment and the underlying causes.

The aims of this dissertation were to explore new methods for quantifying gait motion patterns and to characterize possible fall-prone gait behaviors in population groups with high fall risk. Condition signature analysis was developed as a novel method to better examine spatiotemporal coupling characteristics of the lower extremity joints through the use of temporal cross-correlations. This analysis technique has the added feature of being able to assess multiple parameter pairings to improve understanding of bilateral compensation strategies;​ previous techniques have only studied one or two pairings.

This technique along with other recently developed quantitative analysis methods were used to examine gait behaviors in three population groups with high fall risk (older adults, persons with Parkinson’s disease (PD), and firefighters). Recently developed gait analysis methods were applied to assess changes in lower extremity movement symmetry, variability, complexity, and joint coupling due to the effects of aging, PD, and the use of implanted bilateral subthalamic nucleus deep brain stimulation on persons with severe PD symptoms. Biomechanical analysis using kinetic and kinematic metrics were also performed to investigate the effects of novel equipment design on level walking and obstacle crossing gait behavior and fall risk in firefighters. These new gait analysis techniques were found to provide effective and efficient ways to assess possible fall-prone gait behavior in a variety of populations.