Investigating scale invariant dynamics in minimum toe clearance variability of the young and elderly during treadmill walking
AuthorKhandoker, AH; Taylor, SB; Karmakar, CK; Begg, RK; Palaniswami, M
Source TitleIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
AffiliationElectrical And Electronic Engineering
Document TypeJournal Article
CitationsKhandoker, A. H., Taylor, S. B., Karmakar, C. K., Begg, R. K. & Palaniswami, M. (2008). Investigating scale invariant dynamics in minimum toe clearance variability of the young and elderly during treadmill walking. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 16 (4), pp.380-389. https://doi.org/10.1109/TNSRE.2008.925071.
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C1 - Journal Articles Refereed
Current research applying variability measures of gait parameters has demonstrated promise for helping to solve one of the "holy grails" of geriatric research by defining markers that can be used to prospectively identify persons at risk of falling . The minimum toe clearance (MTC) event occurs during the leg swing phase of the gait cycle and is a task highly sensitive to the spatial and balance control properties of the locomotor system. The aim of this study is to build upon the current state of research by investigating the magnitude and dynamic structure from the MTC time series fluctuations due to aging and locomotor disorder. Thirty healthy young (HY), 27 healthy elderly (HE), and 10 falls risk (FR) elderly individuals (who presented a prior history of trip-related falls) participated in treadmill walking for at least 10 min at their preferred speed. Continuous MTC data were collected and the first 512 data points were analyzed. The following variability indices were quantified: 1) MTC mean and standard deviation (SD), 2) PoincarE plot indices of MTC variability (SD1, SD2, SD1/SD2), 3) a wavelet based multiscale exponent beta to describe the dynamic structure of MTC fluctuations, and 4) detrended fluctuation analysis exponent alpha to investigate the presence of long-range correlations in MTC time series data. Results showed that stride-to-stride MTC time series has a nonlinear structure in all three groups when compared against randomly shuffled surrogate MTC data. Test on aging effects showed the MTC central tendency was significantly lower (p < 0.01) and the magnitude of the MTC variability significantly higher (p < 0.01). This trend changed when comparing FR subjects against age-matched HE as both the central tendency (p < 0.01) and magnitude of the variability (p < 0.01) increased significantly in FR. Although the magnitude of MTC variability increased with age, the nonlinear indices represented by alpha, beta, and SD1/SD2 demonstrated that the nonlinear structure of MTC does not change significantly due to aging (p > 0.05). There were, however, significant differences between HY and FR for beta (between scale 1 and 2; p < 0.01) and alpha (p < 0.05). Out of all the variability measures applied, beta(Wv2-4), SD1/SD2, SD2 of critical MTC parameter were found to be potential markers to be able to reliably identify FR from HE subjects. Further research is required to understand the mechanisms underlying the cause of MTC variability.
KeywordsBiomedical Engineering not elsewhere classified; Pattern Recognition and Data Mining; Rehabilitation Engineering; Biomedical Engineering not elsewhere classified; Pattern Recognition; Rehabilitation Engineering ; Medical Instrumentation; Computer Equipment; Medical Instruments; Computer and Electronic Office Equipment
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