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Falls are the leading cause of disability in older adults with a third of adults over the age of 65 falling every year. Quantitative fall risk assessments using inertial measurement units and local dynamics stability (LDS) have shown that it is possible to identify at-risk persons. However, there are inconsistencies in the literature on how to calculate LDS and how much data is required for a reliable result. This study investigates the reliability and minimum required strides for 6 algorithm-normalization method combinations when computing LDS using young healthy and community dwelling elderly individuals. Participants wore an accelerometer at the lower lumbar while they walked for three minutes up and down a long hallway. This study concluded that the Rosenstein et al. algorithm was successfully and reliably able to differentiate between both populations using only 50 strides. It was also found normalizing the gait time series data by either truncating the data using a fixed number of strides or using a fixed number of strides and normalizing the entire time series to a fixed number of data points performed better when using the Rosenstein et al. algorithm.
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Effect of data length on time delay and embedding dimension for calculating the Lyapunov exponent in walking
The Lyapunov exponent (LyE) is a trending measure for characterizing gait stability. Previous studies have shown that data length has an effect on the resultant LyE, but the origin of why it changes is unknown. This study investigates if data length affects the choice of time delay and embedding dimension when reconstructing the phase space, which is a requirement for calculating the LyE. The effect of three different preprocessing methods on reconstructing the gait attractor was also investigated. Lumbar accelerometer data were collected from 10 healthy subjects walking on a treadmill at their preferred walking speed for 30 min. Our results show that time delay was not sensitive to the amount of data used during calculation. However, the embedding dimension had a minimum data requirement of 200 or 300 gait cycles, depending on the preprocessing method used, to determine the steady-state value of the embedding dimension. This study also found that preprocessing the data using a fixed number of strides or a fixed number of data points had significantly different values for time delay compared to a time series that used a fixed number of normalized gait cycles, which have a fixed number of data points per stride. Thus, comparing LyE values should match the method of calculation using either a fixed number of strides or a fixed number of data points.
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- Award ID(s):
- 1650566
- PAR ID:
- 10315420
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 17
- Issue:
- 168
- ISSN:
- 1742-5689
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rsif.2020.0311
