The effect of treadmill walking on the stride interval dynamics of children

IEEE Trans. Neural Syst. Rehabil. Eng.

Lowry

Bellanca

et al. 2014

119

105

Gait accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we propose a comprehensive set of signal features that may be used to extract clinically valuable information from gait accelerometry signals. To achieve our goal, we collected tri-axial gait accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, ten participants were diagnosed with Parkinson’s disease and eleven participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait accelerometry data and should be utilized in future studies.

Section: Methodsmentioning

confidence: 99%

A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains

IEEE Trans. Neural Syst. Rehabil. Eng.

Lowry

Bellanca

et al. 2014

119

105

“…The extracted features are then compared to the same features extracted from the stride interval time series obtained from reflective markers using the procedure outlined in [43] , which has a mean maximal error of 11.9 milliseconds. In particular, we calculated mean stride intervals, coefficients of variations (CoV) (e.g., [5] , [46] ), but other typical stride features such right and left stances, single and double support times and swing percentages were calculated as well. Also, using these stride time series, harmonic ratios ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$HR$ \end{document} ) (e.g., [2] , [13] , [14] , [47] , [48] ) were calculated based on the acceleration signals.…”

Section: Segmentation Of Gait Accelerometry Signalsmentioning

confidence: 99%

“…In general, walking arises from complex interactions of cerebellum, the motor cortex, basal ganglia and feedback from vestibular, visual and peripheral receptors [3] . Nevertheless, walking consists of repeatable movement patterns, and thus, it generally exhibits a low level of variability [4] – [6] . Initially, it was believed that observed stride-to-stride variations are a normal random process, but over the years it has been shown that stride interval time series behave more like fractal processes [1] , [3] .…”

Section: Introductionmentioning

confidence: 99%

Extraction of Stride Events From Gait Accelerometry During Treadmill Walking

IEEE J. Transl. Eng. Health Med.

Lowry

Bellanca

et al. 2016

Objective Evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method We proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson’s disease and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results Our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe contact events from both feet. We used t-tests, ANOVA and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be identified similarly using measures with the two methods. Conclusions A simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical Impact: The proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step towards the assessment of stride events using tri-axial accelerometers in real-life settings.

“…Lower α values have been seen in the elderly and those with neurological diseases like Parkinson's and Huntington's diseases who have less stable walking patterns (Jordan et al (2007), Bollens et al (2012)). It has been also found that elevated values of α are associated with stride dynamics in children (Fairley et al (2010a)). These results lead to the hypothesis that the higher values associated with the younger children may represent immature stride dynamics, suggesting that there is an optimal value for α , above which indicates immature stride dynamics and below which denotes impaired gait rhythmicity.…”

Section: Discussionmentioning

confidence: 96%

The effects of listening to music or viewing television on human gait

Computers in Biology and Medicine

Findlay

Merey

et al. 2013

Self Cite

This paper presents a two-part study with walking conditions involving music and television (TV) to investigate their effects on human gait. In the first part, we observed seventeen able-bodied adults as they participated in three 15-minute walking trials: 1. without music, 2. with music and 3. without music again. In the second part, we observed fifteen able-bodied adults as they walked on a treadmill for fifteen minutes while watching 1. TV with sound 2. TV without sound and 3. TV with subtitles but no sound. Gait timing was recorded via bilateral heel sensors and center-of-mass accelerations were measured by tri-axial accelerometers. Measures of statistical persistence, dynamic stability and gait variability were calculated. Our results showed that none of the considered gait measures were statistically different when comparing music with no-music trials. Therefore, walking to music did not appear to affect intrinsic walking dynamics in the able-bodied adult population. However, stride interval variability and stride interval dynamics were significantly greater in the TV with sound walking condition when compared to the TV with subtitles condition. Treadmill walking while watching TV with subtitles alters intinsic gait dynamics but potentially offers greater gait stability.