ANALYSIS OF THE CORRELATION BETWEEN MUSCLE REACTION AND STRIDE INTERVAL VARIABILITY IN SINGLE-TASK AND DUAL-TASK WALKING

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the alterations of leg muscle activation and gait variability in single-task and dual-task walking. Since leg muscle activation in the form of electromyogram (EMG) signals and gait variability in the form of stride interval time series have complex structures, fractal theory and approximate entropy were used to evaluate their correlation at various walking conditions. Sixty subjects walked at their preferred speed for 10 min under the single-task condition and for 90[Formula: see text]s under the cognitive dual-task condition, and we evaluated the variations of the fractal dimension and approximate entropy of EMG signals and stride interval time series. According to the results, dual-task walking caused reductions in the complexity of EMG signals and stride interval time series than single-task walking. This technique can be used to evaluate the correlation between other organs during different locomotion.

Gait Stability Measurement by Using Average Entropy

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals.

Added Value and Clinical Significance of Nonlinear Variability Indices of Walking Stride Interval in Neurodegenerative Diseases

Though self-paced walking is highly stereotyped, the stride interval fluctuates from one stride to the next around an average value with a measurable statistical variability. In clinical gait analysis, this variability is usually assessed with indices such the standard deviation or the coefficient of variation (CV). The aim of this study is to understand the added value that nonlinear indices of walking stride interval variability, such as Hurst exponent (H) and Minkowski fractal dimension (D), can provide in a clinical context and to suggest a clinical significance of these indices in the most common neurodegenerative diseases: Parkinson, Huntington, and amyotrophic lateral sclerosis. Although evidence have been accumulated that the stride interval organization at long range displays a more random, less autocorrelated, gait pattern in neurodegenerative diseases compared with young healthy individuals, it is today necessary to recompute CV, H, and D indices in a unified way and to take into account aging impact on these indices. In fact, computed nonlinear indices of variability are mainly dependent on stride interval time series length and algorithms used, making quantitative comparisons between different studies difficult or even impossible. Here, we recompute these indices from available stride interval time series, either coming from our lab or from online databases, or made available to us by the authors of previously conducted research. We confirm that both linear and nonlinear variability indices are relevant indicators of aging process and neurodegenerative diseases. CV is sensitive to aging process and pathology but does not allow to discriminate between specific neurodegenerative diseases. D shows no significative change in pathological groups. However, since H index is correlated with Hoehn & Yahr scores and significantly lowered in patients suffering from Huntington’s disease, we recommend it as a relevant supplement to CV.

An Internet of Things infrastructure for gait characterization in assisted living environments and its application in the discovery of associations between frailty and cognition

This article presents an easy-to-deploy and low-cost Internet of Things infrastructure for gait characterization based on a set of wireless inertial sensors, called nodes, connected to the same local area network. These nodes allow acquiring inertial raw data from the trunk of each frail elder involved in explicit gait trials carried out directly in the elderly care homes. The Internet of Things infrastructure has been validated for Quantitative Gait Analysis showing an adequate accuracy in the demarcation of relevant gait events and in the estimation of stride interval variability. The latter, in combination with other characteristics that are commonly used to assess the state of frail elders and which come from anthropometric, biological, nutritional, functional, and mobility domains, allows us to perform a cross-sectional cohort study and a subsequent multiple logistic regression to evaluate their impact on cognitive functioning. The cohort study and the multivariate regression are performed using a sample of 81 frail elders from two nursing homes in Spain. The results obtained indicate that frail elders aged 90 years or older, with moderate dependence in daily functioning and with a stride interval gait variability greater than 6%, were most likely to suffer cognitive impairment, representing what is called cognitive frail.