It has been repeatedly shown that females have lower stability and increased risk of ankle injury when compared to males participating in similar sports activities (e.g., basketball and soccer), yet sex differences in neuromuscular control of the ankle, including the modulation of ankle stiffness, and their contribution to stability remain unknown. To identify sex differences in human ankle stiffness, this study quantified 2- dimensional (2D) ankle stiffness in 20 young, healthy men and 20 young, healthy women during upright standing over a range of tasks, specifically, ankle muscle co-contraction tasks (4 levels up to 20% maximum voluntary co-contraction of ankle muscles), weight-bearing tasks (4 levels up to 90% of body weight), and ankle torque generation tasks accomplished by maintaining offset center-of-pressure (5 levels up to +6 cm to the center-of-pressure during quiet standing). A dual-axial robotic platform, capable of perturbing the ankle in both the sagittal and frontal planes and measuring the corresponding ankle torques, was used to reliably quantify the 2D ankle stiffness during upright standing. In all task conditions and in both planes of ankle motion, ankle stiffness in males was consistently greater than that in females. Among all 26 experimental conditions, all but 2 conditions in the frontal plane showed statistically significant sex differences. Further analysis on the normalized ankle stiffness scaled by weight times height suggests that while sex differences in ankle stiffness in the sagittal plane could be explained by sex differences in anthropometric factors as well as neuromuscular factors, the differences in the frontal plane could be mostly explained by anthropometric factors. This study also demonstrates that the sex differences in the sagittal plane were significantly higher as compared to those in the frontal plane. The results indicate that females have lower ankle stiffness during upright standing thereby providing the neuromuscular basis for further investigations on the correlation of ankle stiffness and the higher risk of ankle injury in females.

There are many inconsistencies in the literature regarding how to estimate the Lyapunov Exponent (LyE) for gait. In the last decade, many papers have been published using Lyapunov Exponents to determine differences between young healthy and elderly adults and healthy and frail older adults. However, the differences in methodologies of data collection, input parameters, and algorithms used for the LyE calculation has led to conflicting numerical values for the literature to build upon. Without a unified methodology for calculating the LyE, researchers can only look at the trends found in studies. For instance, LyE is generally lower for young adults compared to elderly adults, but these values cannot be correlated across studies to create a classifier for individuals that are healthy or at-risk of falling. These issues could potentially be solved by standardizing the process of computing the LyE.

This dissertation examined several hurdles that must be overcome to create a standardized method of calculating the LyE for gait data when collected with an accelerometer. In each of the following investigations, both the Rosenstein et al. and Wolf et al. algorithms as well as three normalization methods were applied in order to understand the extent at which these factors affect the LyE. First, the a priori parameters of time delay and embedding dimension which are required for phase space reconstruction were investigated. This study found that the time delay can be standardized to a value of 10 and that an embedding dimension of 5 or 7 should be used for the Rosenstein and Wolf algorithm respectively. Next, the effect of data length on the LyE was examined using 30 to 1300 strides of gait data. This analysis found that comparisons across papers are only possible when similar amounts of data are used but comparing across normalization methods is not recommended. And finally, the reliability and minimum required number of strides for each of the 6 algorithm-normalization method combinations in both young healthy and elderly adults was evaluated. This research found that the Rosenstein algorithm was more reliable and required fewer strides for the calculation of the LyE for an accelerometer.

Falls are the leading cause of fatal and non-fatal injuries in the older adult population with more than 27,000 fall related deaths reported every year[1]. Adults suffering from lower extremity arthritis have more than twice the likelihood of experiencing multiple falls resulting in increased fall-related injuries compared to healthy adults. People with lower extremity end-stage osteoarthritis(KOA), experience a number of fall risk factors such as knee instability, poor mobility, and knee pain/stiffness. At end-stage knee OA, the space between the bones in the joint of the knee is significantly reduced, resulting in bone to bone frictional wearing causing bone deformation. In addition, an impaired stepping response during a postural perturbation is seen in people with OA related knee instability. The most common treatment for end-stage knee osteoarthritis is a surgical procedure called, total knee replacement (TKR). It is known that TKR significantly reduces pain, knee stiffness, and restores musculoskeletal functions such as range of motion. Despite studies concluding that knee OA increases fall-risk, it remains unknown if standard treatments, such as TKR, can effectively decrease fall-risk. Analyzing the compensatory step response during a fall is a significant indicator of whether a fall or a recovery will occur in the event of a postural disturbance and is key to determining fall risk among people. Studies have shown reduced trunk stability and step length, as well as increased trunk velocities, correspond to an impaired compensatory step. This study looks at these populations to determine whether TKR significantly enhances compensatory stepping response by analyzing trunk velocities and flexions among other kinematic/kinetic variable analysis during treadmill induced perturbations and clinical assessments.

Every year, 3 million older people are treated for fall injuries, and nearly 800,000 are hospitalized, many of which due to head injuries or hip fractures. In 2015 alone, Medicare and Medicaid paid nearly 75% of the $50 Billion in medical costs generated by falls. As the US population continues to age, more adults are beginning to deal with movement related disorders, and the need to be able to detect and mitigate these risks is becoming more necessary. Classical metrics of fall risk can capture static stability, but recent advancements have yielded new metrics to analyze balance and stability during movement, such as the Maximum Lyapunov Exponent (MLE). Much work has been devoted to characterizing gait, but little has explored novel way to reduce fall risk with interventional therapy. Targeting certain cranial nerves using electrical stimulation has shown potential for treatment of movement disorders such as Parkinson’s Disease (PD) in certain animal models. For human models, based on ease of access, connection to afferents leading to the lower lumber region and key brain regions, as well as general parasympathetic response, targeting the cervical nerves may have a more significant effect on balance and posture. This project explored the effects of transcutaneous Cervical Nerve Stimulation (CNS) on posture stability and gait with the practical application of ultimately applying this treatment to fall risk populations. Data was collected on each of the 31 healthy adults (22.3 ± 6.3 yrs) both pre and post stimulation for metrics representative of fall risk such as postural stability both eyes open and closed, Timed-Up-and-Go (TUG) time, gait velocity, and MLE. Significant differences manifested in the postural stability sub-metric of sway area with subject eyes open in the active stimulation group. The additional 8 metrics and sub-metrics did not show statistically significant differences among the active or sham groups. It is reasonable to conclude that transcutaneous CNS does not significantly affect fall risk metrics in healthy adults. This can potentially be attributed to either the stimulation method chosen, internal brain control mechanisms of posture and balance, analysis methods, and the Yerkes-Dodson law of optimal arousal. However, no adverse events were reported in the active group and thus is a safe therapy option for future experimentation.

According to the CDC in 2010, there were 2.8 million emergency room visits costing $7.9 billion dollars for treatment of nonfatal falling injuries in emergency departments across the country. Falls are a recognized risk factor for unintentional injuries among older adults, accounting for a large proportion of fractures, emergency department visits, and urgent hospitalizations. The objective of this research was to identify and learn more about what factors affect balance using analysis techniques from nonlinear dynamics. Human balance and gait research traditionally uses linear or qualitative tests to assess and describe human motion; however, it is growing more apparent that human motion is neither a simple nor a linear task. In the 1990s Collins, first started applying stochastic processes to analyze human postural control system. Recently, Zakynthinaki et al. modeled human balance using the idea that humans will remain erect when perturbed until some boundary, or physical limit, is passed. This boundary is similar to the notion of basins of attraction in nonlinear dynamics and is referred to as the basin of stability. Human balance data was collected using dual force plates and Vicon marker position data for leans using only ankle movements and leans that were unrestricted. With this dataset, Zakynthinaki’s work was extended by comparing different algorithms used to create the critical curve (basin of stability boundary) that encloses the experimental data points as well as comparing the differences between the two leaning conditions.