Assessment Of Gait Variability Research Articles

Gait variability analysis using continuous RF data streams of human activity

The detection and rapid response to critical health events, such as falls, as well as monitoring of balance and gait for fall risk assessment are important to ensuring positive outcomes, longevity, and quality-of-life, especially in older adults. Mobility limitations affect more than one-third of adults and older adults with a history of falls have exhibited increased gait variability relative to younger adults. However, quantitative gait analysis in laboratories equipped with gold standard sensors, are expensive and may not be easily accessible for aging and rural communities. Thus, radio frequency (RF) sensing is an emerging technology for in-home health monitoring because they are non-contact devices that are effective in the dark and do not acquire private visual recordings. This work proposes a technique for the estimation of step-time variability from continuous streams of RF data acquired while a person does daily activities. The proposed approach is first validated through comparison with a Vicon motion capture system in a lab environment, after which the proposed approaches are used to analyze a continuous stream of daily activity data acquired from 5 different participants. Our results show that RF sensing is a promising, effective tool for gait variability assessment in natural, in-home environments. • Step-time estimation from continuous RF data streams of sequential activity. • Segmentation, human activity classification, and gait variability assessment. • Enables gait analysis and fall risk assessment in home environments. • Radar enables non-intrusive, remote assessment of natural gait.

Reliability of the Modified Four-Square Step Test in Revision Total Knee Arthroplasty: A Cross-Sectional Study

Abstract Background The modified four-square step test (mFSST) assesses dynamic balance by rapid stepping in different directions. This study aimed to evaluate the reliability of the mFSST in patients who had undergone revision total knee artroplasty (rTKA). Methods The study was designed as a cross-sectional study. The mFSST was administered on 22 patients with a history of rTKA. Two trials were performed and the patients rested between trials to avoid fatigue. Results The intraclass correlation coefficient (ICC) for the mFSST was 0.83. The standard error of measurement and the smallest real difference (95%. confidence level) were 0.67 and 1.85 respectively. Conclusion The mFSST showed good reliability in patients who had undergone rTKA. The mFSST is a reliable measure for the assessment of gait variability, dynamic balance, walking skills and mobility. It can easily detect changes in rTKA patients with simple equipment.

Laboratory-Based Gait Variability and Habitual Gait Entropy Do Not Differentiate Community-Dwelling Older Adults from Those with Subjective Memory Complaints

BackgroundAge-related cognitive decline may be delayed with appropriate interventions if those at high risk can be identified prior to clinical symptoms arising. Gait variability assessment has emerged as a promising candidate prognostic indicator, however, it remains unclear how sensitive gait variability is to early changes in cognitive abilities. Research questionDo community-dwelling adults over 65 years of age with subjective memory complaints differ from those with no subjective memory concerns in terms of laboratory-measured or free-living gait variability? MethodsThis cross-sectional study recruited 24 (age = 73.5(SD 6.4) years) community-dwelling people with subjective memory complaints and twenty seven (age = 70.9(4.3) years) individuals with no subjective memory concerns. A sample of 9 individuals with diagnosed mild dementia were also assessed (age = 86.5(7.0) years). Gait variability was assessed in a laboratory during walking at preferred pace (single-task) and while counting backward by seven (dual-task). Sixteen passes over a 4.88 m walkway in each condition were recorded and step length and duration variability was analysed. Free-living gait was assessed with a waist-worn accelerometer by identifying gait bouts of at least one min duration, and the mean multiscale sample entropy in one mins non-overlapping epochs is reported. Statistical inferences were based on analysis of variance using sex and group as the factors. ResultsNo difference between those with subjective memory complaints and those without were observed in either laboratory- or free-living gait variability estimates. Both laboratory- and free-living gait variability were higher in those with mild dementia compared to the other groups. SignificanceAssuming that subjective memory complaints are on the pathway from cognitively intact to cognitively frail, the findings raise the hypothesis that subjective memory complaints occur earlier in the pathophysiology than measurable changes in laboratory or free living gait. Alternatively the gait variability assessments utilised may have been too insensitive.

Gait Complexity and Regularity Are Differently Modulated by Treadmill Walking in Parkinson's Disease and Healthy Population.

Variability raises considerable interest as a promising and sensitive marker of dysfunction in physiology, in particular in neurosciences. Both internally (e.g., pathology) and/or externally (e.g., environment) generated perturbations and the neuro-mechanical responses to them contribute to the fluctuating dynamics of locomotion. Defective internal gait control in Parkinson's disease (PD), resulting in typical timing gait disorders, is characterized by the breakdown of the temporal organization of stride duration variability. Influence of external cue on gait pattern could be detrimental or advantageous depending on situations (healthy or pathological gait pattern, respectively). As well as being an interesting rehabilitative approach in PD, treadmills are usually implemented in laboratory settings to perform instrumented gait analysis including gait variability assessment. However, possibly acting as an external pacemaker, treadmill could modulate the temporal organization of gait variability of PD patients which could invalidate any gait variability assessment. This study aimed to investigate the immediate influence of treadmill walking (TW) on the temporal organization of stride duration variability in PD and healthy population. Here, we analyzed the gait pattern of 20 PD patients and 15 healthy age-matched subjects walking on overground and on a motorized-treadmill (randomized order) at a self-selected speed. The temporal organization and regularity of time series of walking were assessed on 512 consecutive strides and assessed by the application of non-linear mathematical methods (i.e., the detrended fluctuation analysis and power spectral density; and sample entropy, for the temporal organization and regularity of gait variability, respectively). A more temporally organized and regular gait pattern seems to emerge from TW in PD while no influence was observed on healthy gait pattern. Treadmill could afford the necessary framework to regulate gait rhythmicity in PD. Overall, the results support the hypothesis of a greater dependence to regulatory inputs as an explanatory factor of treadmill influence observed in PD. Also, since treadmill misrepresents the gait as more healthy than it is, the present findings underline that gait analysis using treadmill devices should be cautiously considered in PD and especially for gait variability assessment in gait lab.

Relationship between stride interval variability and aging: use of linear and non-linear estimators for gait variability assessment in assisted living environments

The aim of this study is to assess the impact of aging process in gait variability. Stride interval variability is estimated by using two approaches: (1) a non-linear fractal analysis (detrended fluctuation analysis) which evaluates the presence of long-range correlations in stride interval time series; and (2) a statistical dispersion measure (coefficient of variation) to quantify the magnitude of the stride interval fluctuations. Two groups of physically independent older adults, with different walking ability, have participated in the gait trials performed in an elderly care home. The estimated stride interval variability of the elders is compared to each other and to the variability coming from a young adult group used as control. To accomplish this, an infrastructure which uses wearables to acquire inertial data from the trunk of each participant is provided. Stride interval time series are made up of the estimated heel-strike events from previous inertial data. In addition, a service segments straight paths within the gait trials, discarding turns. The stride intervals from the segmented straight paths are stitched together to produce the long time series required to analyze gait variability. Despite the study has not provided conclusive results from an individual perspective, finding older adults who have less stride interval variability than younger ones. The inter-class analysis conducted has shown interesting findings about the relation between the subjective characterization of gait, aging and stride interval variability estimated through the two proposed approaches.

A Pilot Study Using Entropy as a Noninvasive Assessment of Running.

Running performance is influenced by the interaction of biomechanical and physiological factors. Miniaturized accelerometers worn by athletes can be used to quantify mechanical aspects of running and as a noninvasive tool to assess training status and progression. The aim of this study was to define and validate a method to assess running regularity and allow the estimation of an individual's oxygen uptake (V̇O2) and/or blood lactate-[La]b-based on data collected with accelerometers and heart rate. Male adolescent endurance athletes completed an incremental submaximal aerobic stage test where V̇O2 and [La]b were measured. The test was terminated when [La]b concentration at the end of the stage exceeded 4 mmol/L. Two wireless triaxial accelerometers were placed on participants' right shank and lower back throughout the test. The root mean square (RMS) and sample entropy (SampEn) were calculated for the vertical, mediolateral, and anteroposterior components of acceleration. There were significant positive correlations of acceleration and entropy variables with [La]b and V̇O2, with moderate to high coefficients (r = .43-.87). RMS of the shank acceleration was the most highly related with both physiological variables. When the accelerometer was attached on the trunk, SampEn of the vertical acceleration had the strongest relationship with V̇O2 (r = .76, P < .01). The described method analyzing running complexity may allow an assessment of gait variability, which noninvasively tracks V̇O2 and/or [La]b, allowing monitoring of fatigue or training readiness for trained adolescent individuals.

Gait variability assessment in neuro-degenerative patients by measuring complexity of independent sources.

Patients suffering from neuro-degenerative diseases have difficulties with normal locomotion. This problem progresses with the course of disease. Gait assessment is an effective way of diagnosing the disease and quantifying its progress which can effectively prevent falls. In this paper, an automatic assessment method for analyzing gait data obtained by force sensor insoles is introduced. The gait analysis method is based on measuring the complexity of gait data after extracting independent sources. The results are promising an average accuracy of 94% for three different diseases.

SMARTPHONE ACCELEROMETRY FOR GAIT VARIABILITY ASSESSMENT AND REHABILITATION IN OLDER ADULTS

This session will focus on the use of body-fixed sensors (particularly accelerometers) to assess gait variability in older adults. An overview of different gait variability measures and processing methods will be provided (e.g., gait symmetry, stride regularity, fractal structure) including information on the relationship of these measures to diminished gait capacity and fall risk in older adults with and without physical impairments. This session will also present information on the advantages to be gained from using Smartphone technology for the purpose of gait variability assessment and monitoring in and out of the clinic. The potential application of using Smartphone technology as a clinical tool to assess various aspects of gait control and stability for the purpose of evaluating physical activity intervention (i.e., gait rehabilitation programs) will also be discussed.

How many steps are enough? Assessment of gait variability in realistically confined clinical settings

How many steps are enough? Assessment of gait variability in realistically confined clinical settings

Power considerations for the application of detrended fluctuation analysis in gait variability studies.

The assessment of gait variability using stochastic signal processing techniques such as detrended fluctuation analysis (DFA) has been shown to be a sensitive tool for evaluation of gait alterations due to aging and neuromuscular disease. However, previous studies have suggested that the application of DFA requires relatively long recordings (600 strides), which is difficult when working with clinical populations or older adults. In this paper we propose a model for predicting DFA variance in experimental data and conduct a Monte Carlo simulation to estimate the sample size and number of trials required to detect a change in DFA scaling exponent. We illustrate the model in a simulation to detect a difference of 0.1 (medium effect) between two groups of subjects when using short gait time series (100 to 200 strides) in the context of between- and within-subject designs. We assumed that the variance of DFA scaling exponent arises due to individual differences, time series length, and experimental error. Results showed that sample sizes required to achieve acceptable power of 80% are practically feasible, especially when using within-subject designs. For example, to detect a group difference in the DFA scaling exponent of 0.1, it would require either 25 subjects and 2 trials per subject or 12 subjects and 4 trials per subject using a within-subject design. We then compared plausibility of such power predictions to the empirically observed power from a study that required subjects to synchronize with a persistent fractal metronome. The results showed that the model adequately predicted the empirical pattern of results. Our power simulations could be used in conjunction with previous design guidelines in the literature when planning new gait variability experiments.

Applying different mathematical variability methods to identify older fallers and non-fallers using gait variability data.

The clinical assessment of gait variability may be a particularly powerful tool in the screening of older adults at risk of falling. Measurement of gait variability is important in the assessment of fall risk, but the variability metrics used to evaluate gait timing have not yet been adequately studied. The aims of this study were (1) to identify the best mathematical method of gait variability analysis to discriminate older fallers and non-fallers and (2) to identify the best temporal, kinematic parameter of gait to discriminate between older fallers and non-fallers. Thirty-five physically active volunteers participated in this study including 16 older women fallers (69.6±8.1years) and 19 older women non-fallers (66.1±6.2years). Volunteers were instructed to walk for 3min on the treadmill to record the temporal kinematic gait parameters including stance time, swing time and stride time by four footswitches sensors placed under the volunteers' feet. Data analysis used 40 consecutive gait cycles. Six statistical methods were used to determine the variability of the stance time, swing time and stride time. These included: (1) standard deviation of all the time intervals; (2) standard deviation of the means of these intervals taken every five strides; (3) mean of the standard deviations of the intervals determined every five strides; (4) root-mean-square of the differences between intervals; (5) coefficient of variation calculated as the standard deviation of the intervals divided by the mean of the intervals; and (6) a geometric method calculated based on the construction of a histogram of the intervals. The standard deviation of 40 consecutive gait cycles was the most sensitive (100%) and specificity (100%) parameter to discriminate older fallers and non-fallers. The standard deviation of stance time is the kinematic gait variability parameter that demonstrated the best ability to discriminate older fallers from non-fallers. RBR-6rytw2.

Reliability of gait variability assessment in older individuals during a six-minute walk test

Gait variability is an important indicator of gait performance. However, the reliability of the parameters used for its quantification, obtained from trunk linear accelerations, has still not been thoroughly investigated. The aim of this study is to assess the reliability of gait variability assessment in healthy older individuals based on lower trunk accelerations during a six-minute walk test and to examine the reliability of the data acquired in shorter periods. Twenty-nine subjects (84±5 years) performed the test while wearing one inertial sensor on the lower trunk. Gait variability parameters (standard deviation and coefficient of variation of the stride duration, and correlation coefficients of accelerations between neighbouring strides) were calculated from the accelerations over 35 rectilinear strides observed during six series of one-minute intervals extracted from the original signal. The reliability of these parameters was assessed using intraclass correlation coefficients (ICC). Results showed no significant changes across the six series for any of the parameters, with very high ICC values (0.93–0.95), indicating a strong reliability of the observed quantities. Therefore, gait variability analysis based on lower trunk acceleration data is a reliable and informative quantity in gait performance assessment in older individuals, and 1min interval is sufficient to ensure reliable results.

Assessment of Gait Variability: Towards an Optimal Testing Protocol

Gait variability is frequently used to identify rehabilitation progresses in orthopedic and neurologic diseases by means of repeated testing. However, learning effects are a significant issue. This study examined if the inter-day reliability of gait variability parameters improves with an additional familiarization trial.

Is gait variability reliable? An assessment of spatio-temporal parameters of gait variability during continuous overground walking

The assessment of gait variability has become an important indicator for quantifying motor performance. However, the use of treadmills is known to influence the temporal rhythm of gait, while non-continuous (i.e. stop-start) overground walking alters gait variability, leading to erroneous results. Through establishing the “8-walk”, an overground walking protocol that allows the collection of a high number of consecutive gait cycles, the aim of this study was to determine the conditions under which gait variability can be assessed reliably.Twelve healthy subjects performed continuous barefoot walking at their preferred speed in a path shaped as an “8”. Kinematic data of the dominant foot was collected while subjects walked along the straight 10m sections of the 8-walk during sessions on two different days. Mean spatio-temporal parameters of gait and gait variability were computed for 10, 20, 30, 40, 50 and 60 consecutive cycles.All mean parameters of gait showed excellent reliability (ICC: 0.88–0.98) with only 10 cycles included in the analysis. However, the reliability of spatial and temporal parameters of gait variability improved with increasing number of cycles (ICC: 0.60–0.90) but levelled-off after 50 consecutive cycles, revealing an inter-day test–retest variability of ∼13%.To reliably assess gait variability and evaluate human motor performance, we propose the collection of at least 50 cycles and the use of an 8-walk protocol, which avoids the limitations of treadmill and non-consecutive walking protocols.

SIG 8 Perspectives Vol. 12, No. 1, December 2011

SIG 8 Perspectives Vol. 12, No. 1, December 2011