Matching Items (2)

Standardizing the Calculation of the Lyapunov Exponent for Human Gait using Inertial Measurement Units

Description

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

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.

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Date Created
  • 2019

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Startle Distinguishes Task Expertise

Description

Recently, it was demonstrated that startle-evoked-movements (SEMs) are present during individuated finger movements (index finger abduction), but only following intense training. This demonstrates that changes in motor planning, which occur

Recently, it was demonstrated that startle-evoked-movements (SEMs) are present during individuated finger movements (index finger abduction), but only following intense training. This demonstrates that changes in motor planning, which occur through training (motor learning - a characteristic which can provide researchers and clinicians with information about overall rehabilitative effectiveness), can be analyzed with SEM. The objective here was to determine if SEM is a sensitive enough tool for differentiating expertise (task solidification) in a common everyday task (typing). If proven to be true, SEM may then be useful during rehabilitation for time-stamping when task-specific expertise has occurred, and possibly even when the sufficient dosage of motor training (although not tested here) has been delivered following impairment. It was hypothesized that SEM would be present for all fingers of an expert population, but no fingers of a non-expert population. A total of 9 expert (75.2 ± 9.8 WPM) and 8 non-expert typists, (41.6 ± 8.2 WPM) with right handed dominance and with no previous neurological or current upper extremity impairment were evaluated. SEM was robustly present (all p < 0.05) in all fingers of the experts (except the middle) and absent in all fingers of non-experts except the little (although less robust). Taken together, these results indicate that SEM is a measurable behavioral indicator of motor learning and that it is sensitive to task expertise, opening it for potential clinical utility.

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Created

Date Created
  • 2018