Stride Interval Research Articles

The effect of footwear and footfall pattern on running stride interval long-range correlations and distributional variability

The presence of long-range correlations (self-similarity) in the stride-to-stride fluctuations in running stride interval has been used as an indicator of a healthy adaptable system. Changes to footfall patterns when running with minimalist shoes could cause a less adaptable running gait. The purpose of this study was to investigate stride interval variability and the degree of self-similarity of stride interval in runners wearing minimalist and conventional footwear. Twenty-six trained habitual rearfoot footfall runners, unaccustomed to running in minimalist footwear, performed 6-min sub-maximal treadmill running bouts at 11, 13 and 15 km·h−1 in minimalist and conventional shoes. Force sensitive resistors were placed in the shoes to quantify stride interval (time between successive foot contacts). Footfall position, stride interval mean and coefficient of variation (CV), were used to assess performance as a function of shoe type. Long-range correlations of stride interval were assessed using detrended fluctuation analysis (α). Mean stride interval was 1-1.3% shorter (P=0.02) and 27% of runners adopted a midfoot footfall (MFF) in the minimalist shoe. There was a significant shoe effect on α and shoe*speed*footfall interaction effect on CV (P<0.05). Runners that adopted a MFF in minimalist shoes, displayed reduced long-range correlations (P<0.05) and CV (P<0.06) in their running stride interval at the 15 km·h−1 speed. The reduced variability and self-similarity observed for runners that changed to a MFF in the minimalist shoe may be suggestive of a system that is less flexible and more prone to injury.

Cognitive tasks during walking affect cerebral blood flow signal features in middle cerebral arteries and their correlation to gait characteristics.

Gait is a complex process involving both cognitive and sensory ability and is strongly impacted by the environment. In this paper, we propose to study of the impact of a cognitive task during gait on the cerebral blood flow velocity, the blood flow signal features and the correlation of gait and blood flow features through a dual task methodology. Both cerebral blood flow velocity and gait characteristics of eleven participants with no history of brain or gait conditions were recorded using transcranial Doppler on mid-cerebral artery while on a treadmill. The cognitive task was induced by a backward counting starting from 10,000 with decrement of 7. Central blood flow velocity raw and envelope features were extracted in both time, frequency and time-scale domain; information-theoretic metrics were also extracted and statistical significances were inspected. A similar feature extraction was performed on the stride interval signal. Statistical differences between the cognitive and baseline trials, between the left and right mid-cerebral arteries signals and the impact of the antropometric variables where studied using linear mixed models. No statistical differences were found between the left and right mid-cerebral arteries flows or the baseline and cognitive state gait features, while statistical differences for specific features were measured between cognitive and baseline states. These statistical differences found between the baseline and cognitive states show that cognitive process has an impact on the cerebral activity during walking. The state was found to have an impact on the correlation between the gait and blood flow features.

Assessing interactions among multiple physiological systems during walking outside a laboratory: An Android based gait monitor

Gait function is traditionally assessed using well-lit, unobstructed walkways with minimal distractions. In patients with subclinical physiological abnormalities, these conditions may not provide enough stress on their ability to adapt to walking. The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system. There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions. To address this need, an Android-based gait-monitoring device was developed that enabled the recording of a patient's physiological systems during walking. The gait-monitoring device was tested during self-regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18–35 years. The gait-monitoring device measures the patient's stride interval, acceleration, electrocardiogram, skin conductance and respiratory rate. The data is stored on an Android phone and is analyzed offline through the extraction of features in the time, frequency and time–frequency domains. The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects. These interactions included locomotion-electrodermal, locomotion-respiratory and cardiolocomotion couplings. The current results depicting strong interactions between the locomotion system and the other considered systems (i.e., electrodermal, respiratory and cardiovascular systems) warrant further investigation into multisystem interactions during walking, particularly in challenging walking conditions with older adults.

Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model.

Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LOO) cross-validation algorithm showed that the FHMM method provides better recognition of ALS and healthy subjects compared to standard HMM. Moreover, comparing our method with a state-of-the art method named least square support vector machine (LS-SVM) showed the efficiency of the FHMM in distinguishing ALS subjects from healthy ones.

A simple method to detect stride intervals in continuous acceleration and gyroscope data recorded during treadmill running

Inertial measurement units (IMUs) are becoming increasingly popular to analyse walking and running gait. The data usually consist of time series of continuously sampled acceleration and gyroscope d...

Effect of leg length discrepancy on the dynamics of stride interval during self-paced walking

Effect of leg length discrepancy on the dynamics of stride interval during self-paced walking

Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal

The purpose of this paper is to address the question of the existence of auto regressive moving average (ARMA) models with reduced order for neurodegenerative disorder signals by using Huberian approach. Since gait rhythm dynamics between Parkinson׳s disease (PD) or Huntington׳s disease (HD) and healthy control (CO) differ, and since the stride interval presents great variability, we propose a different ARMA modeling approach based on a Huberian function to assess parameters. Huberian function as a mixture of L2 and L1 norms, tuned with a threshold γ from a new curve, is chosen to deal with stride signal disorders. The choice of γ is crucial to ensure a good treatment of NO and allows to reduce the model order. The disorders induce disturbances in the classical estimation methods and increase of the number of parameters of the ARMA model. Here, the use of the Huberian function reduces the number of parameters of the estimated models leading to a disease transfer function with low order for PD and HD. Mathematical approach is discussed and experimental results based on a database containing 16 CO, 15 PD, and 19 HD are presented.

HMM for classification of Parkinson's disease based on the raw gait data.

The central nervous system (CNS) plays an important role in regulation of human gait. Parkinson's disease (PD) is a common neurodegenerative disease that may cause neurophysiologic change in the CNS and as a result change the gait cycle duration (stride interval). This article used the Hidden Markov Model (HMM) with Gaussian Mixtures to separate the patients with PD from healthy subjects. The results showed that the performance of the HMM classifier in classifying the gait data corresponding to 16 healthy and 15 PD subjects is comparable to the results obtained from the least squares support vector machine (LS-SVM) classifier. In this study, the leave-one-out cross-validation method was used to evaluate the performance of each classifier. The HMM method could effectively separate the gait data in terms of stride interval obtained from healthy subjects and PD patients with an accuracy rate of 90.3 % . All in all, the results showed that the proposed method can be used for distinguishing PD patients from healthy subjects based on the gait data classification.

Foot gait time series estimation based on support vector machine.

A new algorithm for the estimation of stride interval time series from foot gait signals is proposed. The algorithm is based on the detection of beginning of heel strikes in the signal by using the support vector machine. Morphological operations are used to enhance the accuracy of detection. By taking backward differences of the detected beginning of heel strikes, stride interval time series is estimated. Simulation results are presented which shows that the proposed algorithm yields fairly accurate estimation of stride interval time series where estimation error for mean and standard deviation of the time series is of the order of 10(-4).

Assessment of gait nonlinear dynamics by inhomogeneous point-process models.

Point-process linear models of stride intervals have been recently proven to provide a unique characterization of human gait dynamics through instantaneous time domain features. In this study we propose novel instantaneous measures characterizing nonlinear gait dynamics using a quadratic autoregressive inhomogeneous point-process model recently devised for the instantaneous assessment of physiological, natural, and physical discrete dynamical systems. Our mathematical framework accounts for long-term information given by the past events of non-stationary non-Gaussian time series, expressed by a Laguerre expansion of the Wiener-Volterra terms. Here, we present a study of gait variability from data gathered from physionet.org, including 15 recordings from young and elderly healthy volunteers, and patients with Parkinson's disease. Results show that our instantaneous polyspectral characterization provides an informative tracking of the inherent nonlinear dynamics of human gait, which is significantly affected by aging and locomotor disabilities.

Characteristics of Stride Behavior During Treadmill Walking and Stationary Stepping

Much has been learned about the characteristics of gait in overground and treadmill walking. However, there are many contexts in which overground or treadmill walking might not be possible, such as in home-based physical therapy. In those cases, a surrogate task to index gait behavior would be a valuable tool. Thus, the purpose of this study was to evaluate the stride behavior characteristics of stationary stepping compared with treadmill walking. Healthy young adults (N = 10) preformed two 15-minute tasks: (1) treadmill walking and (2) stationary stepping. Several stride behavior characteristics were recorded, including the number of strides taken, minimum and maximum knee angle, stride interval mean, stride interval standard deviation, and detrended fluctuation analysis (DFA) alpha of the stride interval time series. The results showed that stride behavior was similar between tasks when examined at the group level. However, when individual level analyses were used to examine the reliability of each metric between tasks, poor reliability was observed in most metrics, indicating that stationary stepping may not be an appropriate surrogate task for overground or treadmill walking. These results are discussed in the context of a gait dynamics framework, with attention to task constraints that may have influenced the findings.

A pediatric correlational study of stride interval dynamics, energy expenditure and activity level.

The strength of time-dependent correlations known as stride interval (SI) dynamics has been proposed as an indicator of neurologically healthy gait. Most recently, it has been hypothesized that these dynamics may be necessary for gait efficiency although the supporting evidence to date is scant. The current study examines over-ground SI dynamics, and their relationship with the cost of walking and physical activity levels in neurologically healthy children aged nine to 15 years. Twenty participants completed a single experimental session consisting of three phases: 10 min resting, 15 min walking and 10 min recovery. The scaling exponent (α) was used to characterize SI dynamics while net energy cost was measured using a portable metabolic cart, and physical activity levels were determined based on a 7-day recall questionnaire. No significant linear relationships were found between a and the net energy cost measures (r < .07; p > .25) or between α and physical activity levels (r = .01, p = .62). However, there was a marked reduction in the variance of α as activity levels increased. Over-ground stride dynamics do not appear to directly reflect energy conservation of gait in neurologically healthy youth. However, the reduction in the variance of α with increasing physical activity suggests a potential exercise-moderated convergence toward a level of stride interval persistence for able-bodied youth reported in the literature. This latter finding warrants further investigation.

Persistent Fluctuations in Stride Intervals under Fractal Auditory Stimulation

Stride sequences of healthy gait are characterized by persistent long-range correlations, which become anti-persistent in the presence of an isochronous metronome. The latter phenomenon is of particular interest because auditory cueing is generally considered to reduce stride variability and may hence be beneficial for stabilizing gait. Complex systems tend to match their correlation structure when synchronizing. In gait training, can one capitalize on this tendency by using a fractal metronome rather than an isochronous one? We examined whether auditory cues with fractal variations in inter-beat intervals yield similar fractal inter-stride interval variability as isochronous auditory cueing in two complementary experiments. In Experiment 1, participants walked on a treadmill while being paced by either an isochronous or a fractal metronome with different variation strengths between beats in order to test whether participants managed to synchronize with a fractal metronome and to determine the necessary amount of variability for participants to switch from anti-persistent to persistent inter-stride intervals. Participants did synchronize with the metronome despite its fractal randomness. The corresponding coefficient of variation of inter-beat intervals was fixed in Experiment 2, in which participants walked on a treadmill while being paced by non-isochronous metronomes with different scaling exponents. As expected, inter-stride intervals showed persistent correlations similar to self-paced walking only when cueing contained persistent correlations. Our results open up a new window to optimize rhythmic auditory cueing for gait stabilization by integrating fractal fluctuations in the inter-beat intervals.

FLUCTUATIONS IN STRIKE INDEX AND SPATIOTEMPORAL PARAMETERS IN PREVIOUSLY INJURED VS UNINJURED RUNNERS

BackgroundLong-range correlations in running stride interval have been proposed as an indicator of running-related injury (RRI) and found to change with running speed. They have not yet been used to...

An fNIRS exploratory investigation of the cortical activity during gait in children with spastic diplegic cerebral palsy

The primary aim of this exploratory investigation was to determine if there are differences in cortical activation of children with spastic diplegic cerebral palsy (CP) and typically developing children during gait. Functional near-infrared spectroscopy was used to measure the concentration of oxygenated hemoglobin that was present in the supplementary motor area, pre-central gyrus, post-central gyrus and superior parietal lobule as the children walked on a treadmill. A sagittal plane video was concurrently collected and later digitized to quantify the temporal gait variations. (1) The children with CP had an increased amount of activation in the sensorimotor cortices and superior parietal lobule during gait, (2) the children with CP had a greater amount of variability or error in their stride time intervals, and (3) an increased amount of error in the temporal gait kinematics was associated with an increased amount of activity across the cortical network. Our results suggest that the perinatal damage and subsequent neural reorganization that occurs with spastic diplegic CP may impact the functional cortical activity for controlling gait. Furthermore, our results imply the increased cortical activity of the somatosensory cortices and superior parietal cortices may underlie the greater amount of error in the temporal gait kinematics.

Gait performance is not influenced by working memory when walking at a self-selected pace

Gait performance exhibits patterns within the stride-to-stride variability that can be indexed using detrended fluctuation analysis (DFA). Previous work employing DFA has shown that gait patterns can be influenced by constraints, such as natural aging or disease, and they are informative regarding a person's functional ability. Many activities of daily living require concurrent performance in the cognitive and gait domains; specifically working memory is commonly engaged while walking, which is considered dual-tasking. It is unknown if taxing working memory while walking influences gait performance as assessed by DFA. This study used a dual-tasking paradigm to determine if performance decrements are observed in gait or working memory when performed concurrently. Healthy young participants (N = 16) performed a working memory task (automated operation span task) and a gait task (walking at a self-selected speed on a treadmill) in single- and dual-task conditions. A second dual-task condition (reading while walking) was included to control for visual attention, but also introduced a task that taxed working memory over the long term. All trials involving gait lasted at least 10 min. Performance in the working memory task was indexed using five dependent variables (absolute score, partial score, speed error, accuracy error, and math error), while gait performance was indexed by quantifying the mean, standard deviation, and DFA α of the stride interval time series. Two multivariate analyses of variance (one for gait and one for working memory) were used to examine performance in the single- and dual-task conditions. No differences were observed in any of the gait or working memory dependent variables as a function of task condition. The results suggest the locomotor system is adaptive enough to complete a working memory task without compromising gait performance when walking at a self-selected pace.

A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

BackgroundThe time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New methodThis paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. ResultsThe results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with existing methodsClass dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. ConclusionsThe comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.

Patterns of optimization in single- and inter-leg gait dynamics

IntroductionWe examined the influence of walking speed on the fluctuation and synchronization dynamics of stride intervals and ground reaction force (GRF) profiles. Our aim was to identify patterns of optimization in the single-leg and inter-leg dynamics at preferred walking speed (PWS). PWS is thought to bring about the most stable walking pattern in terms of the attractor dynamics of the locomotion system. MethodsTwenty healthy subjects (29.1±1.8 years; 10 women) walked on a treadmill for 5-min periods at their PWS and at 20, 40, 70, and 80% of maximal walking speed. The coefficient of variation (CV) and long-range correlations α of GRF profile and stride time fluctuations as well as the phase synchronization ρ of inter-leg stride timing were analyzed. ResultsGRF profile α increased with increasing walking speed (p<0.001). In contrast, stride time CV and α showed a U-shaped speed-dependency with lowest values at PWS (p<0.05). The speed-dependency of single-leg stride time fluctuations was mirror-inverted in the speed-dependency of inter-leg stride timing ρ; its highest values occurred at PWS (p<0.001). ConclusionsFluctuations in GRF profiles become more consistent with increasing walking speed. In contrast, the dynamics of single-leg and inter-leg timing show a collective pattern of optimization at PWS. Less correlated noise in single-leg timing at PWS, imposed on the two coupled oscillating legs, increases the phase synchronization of bilateral timing, thereby enhancing gait stability at the attractor of self-paced walking. Thus, the attractor dynamics of locomotion appear to rely on the interaction of single- and inter-leg timing.

Interactive Rhythmic Cue Facilitates Gait Relearning in Patients with Parkinson's Disease

To develop a method for cooperative human gait training, we investigated whether interactive rhythmic cues could improve the gait performance of Parkinson's disease patients. The interactive rhythmic cues ware generated based on the mutual entrainment between the patient's gait rhythms and the cue rhythms input to the patient while the patient walked. Previously, we found that the dynamic characteristics of stride interval fluctuation in Parkinson's disease patients were improved to a healthy 1/f fluctuation level using interactive rhythmic cues and that this effect was maintained in the short term. However, two problems remained in our previous study. First, it was not clear whether the key factor underpinning the effect was the mutual entrainment between the gait rhythms and the cue rhythms or the rhythmic cue fluctuation itself. Second, it was not clear whether or not the gait restoration was maintained longitudinally and was relearned after repeating the cue-based gait training. Thus, the present study clarified these issues using 32 patients who participated in a four-day experimental program. The patients were assigned randomly to one of four experimental groups with the following rhythmic cues: (a) interactive rhythmic cue, (b) fixed tempo cue, (c) 1/f fluctuating tempo cue, and (d) no cue. It has been reported that the 1/f fluctuation of stride interval in healthy gait is absent in Parkinson's disease patients. Therefore, we used this dynamic characteristic as an evaluation index to analyze gait relearning in the four different conditions. We observed a significant effect in condition (a) that the gait fluctuation of the patients gradually returned to a healthy 1/f fluctuation level, whereas this did not occur in the other conditions. This result suggests that the mutual entrainment can facilitate gait relearning effectively. It is expected that interactive rhythmic cues will be widely applicable in the fields of rehabilitation and assistive technology.

Long-Range Correlations in Stride Intervals May Emerge from Non-Chaotic Walking Dynamics

Stride intervals of normal human walking exhibit long-range temporal correlations. Similar to the fractal-like behaviors observed in brain and heart activity, long-range correlations in walking have commonly been interpreted to result from chaotic dynamics and be a signature of health. Several mathematical models have reproduced this behavior by assuming a dominant role of neural central pattern generators (CPGs) and/or nonlinear biomechanics to evoke chaos. In this study, we show that a simple walking model without a CPG or biomechanics capable of chaos can reproduce long-range correlations. Stride intervals of the model revealed long-range correlations observed in human walking when the model had moderate orbital stability, which enabled the current stride to affect a future stride even after many steps. This provides a clear counterexample to the common hypothesis that a CPG and/or chaotic dynamics is required to explain the long-range correlations in healthy human walking. Instead, our results suggest that the long-range correlation may result from a combination of noise that is ubiquitous in biological systems and orbital stability that is essential in general rhythmic movements.