Comprehensive quantitative investigation of arm swing during walking at various speed and surface slope conditions

Previous studies have shown that inclusion of arm swing in gait rehabilitation leads to more effective walking recovery in patients with walking impairments. However, little is known about the correct arm-swing trajectories to be used in gait rehabilitation given the fact that changes in walking conditions affect arm-swing patterns. In this paper we present a comprehensive look at the effects of a variety of conditions on arm-swing patterns during walking. The results describe the effects of surface slope, walking speed, and physical characteristics on arm-swing patterns in healthy individuals. We propose data-driven mathematical models to describe arm-swing trajectories. Thirty individuals (fifteen females and fifteen males) with a wide range of height (1.58–1.91 m) and body mass (49–98 kg), participated in our study. Based on their self-selected walking speed, each participant performed walking trials with four speeds on five surface slopes while their whole-body kinematics were recorded. Statistical analysis showed that walking speed, surface slope, and height were the major factors influencing arm swing during locomotion. The results demonstrate that data-driven models can successfully describe arm-swing trajectories for normal gait under varying walking conditions. The findings also provide insight into the behavior of the elbow during walking.     

The Human Body Model versus conventional gait models for kinematic gait analysis in children with cerebral palsy

With the rise of biofeedback in gait training in cerebral palsy there is a need for real-time measurements of gait kinematics. The Human Body Model (HBM) is a recently developed model, optimized for the real-time computing of kinematics. This study evaluated differences between HBM and two commonly used models for clinical gait analysis: the Newington Model, also known as Plug-in-Gait (PiG), and the calibrated anatomical system technique (CAST). Twenty-five children with cerebral palsy participated. 3D instrumented gait analyses were performed in three laboratories across Europe, using a comprehensive retroreflective marker set comprising three models: HBM, PiG and CAST. Gait kinematics from the three models were compared using statistical parametric mapping, and RMSE values were used to quantify differences. The minimal clinically significant difference was set at 5°. Sagittal plane differences were mostly less than 5°. For frontal and transverse planes, differences between all three models for almost all segment and joint angles exceeded the value of minimal clinical significance. Which model holds the most accurate information remains undecided since none of the three models represents a ground truth. Meanwhile, it can be concluded that all three models are equivalent in representing sagittal plane gait kinematics in clinical gait analysis.     

What factors determine the preferred gait transition speed in humans? A review of the triggering mechanisms

Human locomotion is a fundamental skill that is required for daily living, yet it is not completely known how human gait is regulated in a manner that seems so effortless. Gait transitions have been analyzed to gain insight into the control mechanisms of human locomotion since there is a known change that occurs as the speed of locomotion changes. Specifically, as gait speed changes, there is a spontaneous transition between walking and running that occurs at a particular speed. Despite the growing body of research on the determinants of this preferred transition speed and thus the triggering mechanisms of human gait transitions, a clear consensus regarding the control mechanisms of gait is still lacking. Therefore, this article reviews the determinants of the preferred transition speed using concepts of the dynamic systems theory and how these determinants contribute to four proposed triggers (i.e. metabolic efficiency, mechanical efficiency, mechanical load and cognitive and perceptual) of human gait transitions. While individual anthropometric and strength characteristics influence the preferred transition speed, they do not act to trigger a gait transition. The research has more strongly supported the mechanical efficiency and mechanical load determinants as triggering mechanisms of human gait transitions. These mechanical determinants, combined with cognitive and perceptual processes may thus be used to regulate human gait patterns through proprioceptive and perceptual feedback as the speed of locomotion changes.     

Gait measurement in chronic mild traumatic brain injury: A model approach

IntroductionMild traumatic brain injury (mTBI) can impact gait, with deficits linked to underlying neural disturbances in cognitive, motor and sensory systems. Gait is complex as it is comprised of multiple characteristics that are sensitive to underlying neural deficits. However, there is currently no clear framework to guide selection of gait characteristics in mTBI. This study developed a model of gait in chronic mTBI and replicated this in a separate group of controls, to provide a comprehensive and structured methodology on which to base gait assessment and analysis.MethodsFifty-two people with chronic mTBI and 59 controls completed a controlled laboratory gait assessment; walking for two minutes back and forth over a 13 m distance while wearing five wirelessly synchronized inertial sensors. Thirteen gait characteristics derived from the inertial sensors were selected for entry into the principle component analysis based on previous literature, robustness and novelty. Principle component analysis was then used to derive domains (components) of gait.ResultsFour gait domains were derived for our chronic mTBI group (variability, rhythm, pace and turning) and this was replicated in a separate control cohort. Domains totaled 80.8% and 77.4% of variance in gait for chronic mTBI and controls, respectively. Gait characteristic loading was unambiguous for all features, with the exception of gait speed in controls that loaded on pace and rhythm domains.ConclusionThis study contributes a four component model of gait in chronic mTBI and controls that can be used to comprehensively assess and analyze gait and underlying mechanisms involved in impairment, or examine the influence of interventions.     

Gait patterns during free choice ladder ascents

Twenty two male subjects each performed five climbing trials of a portable straight ladder. Each subject was instructed to ascend the ladder at a “comfortable” pace using only the rungs for support. For the first, third and fifth trials, the temporal and movement characteristics of the performances were recorded using capacitive touch sensors mounted on each of the rungs and high-speed cinematographical techniques. The results revealed little evidence to suggest a preferred climbing gait. The two most commonly utilized methods of ascent for all trials were the lateral and four-beat lateral gaits. Only 31.8% of the subjects adopted the same gait pattern during each of the three trials. The temporal characteristics of each gait pattern showed a relatively longer time for each segment contact phase than for the corresponding airborne phase. The shortest average period was found for the four-beat diagonal gait followed, in order, by the lateral, diagonal and four beat lateral gaits. Variability measures assumed the same ranking in reverse order with the four-beat diagonal gait producing the most variable period times.     

Transitions to and from asymmetrical gait patterns

Asymmetrical gait patterns such as the gallop provide insight into the complexity of human locomotion. The nature of spontaneous (e.g., walk-run), quasi-spontaneous (e.g., gallop-walk), and intentional (e.g., walk-gallop) transitions was analyzed in 2 ways in the present study. In Analysis 1, the authors used step-wise regression to associate 10 physical characteristics with gait transitions. Transition predictability was moderate; thigh length best predicted 3 of 6 transitions. In Analysis 2, the dynamic characteristics of transitions (order parameters, phase shifts, multistability, and critical fluctuations) were described; those characteristics existed for all transition types. The results of the analyses suggest that intentional transitions are less biomechanically predictable than are spontaneous transitions and that transitions between gait pairs (e.g., walk-gallop and gallop-walk), regardless of velocity direction, have more in common than do transitions requiring specific intention.     

The influence of aging on the spatial and temporal variables of gait during usual and fast speeds in older adults aged 60 to 102 years

BackgroundWith increases in life expectancy, it is important to understand the influence of aging on gait, given that this activity is related to the independence of older adults and may help in the development of health strategies that encourage successful aging in all phases of this process.Research questionTo compare gait parameters with usual and fast speeds for independent and autonomous older adults throughout the aging process (60 to 102 years old), and also to identify which of the gait variables are best for identifying differences across the different age groups.MethodsTwo hundred older adults aged between 60 and 102 years were evaluated. The sample was divided into 3 age groups: 60 to 79 years, 80 to 89 years and 90 years and over. The analyzed gait variables were: speed (meters/s), cadence (steps/min), stride time (seconds), step length (centimeters), double support (percentage of the gait cycle), swing (percentage of the gait cycle), step length variability (CoV%) and stride time variability (CoV%).ResultsGroup comparison regarding usual gait and fast gait revealed a significant difference in all gait variables. In addition, it can be seen that variables such as gait speed and step length showed greater effect sizes in intergroup comparison (usual gait: 0.48 and 0.47; fast gait: 0.36 and 0.40; respectively), possibly showing that these variables can better detect the changes observed with increasing age.ConclusionThere are differences in the gait performance of older adults from different age groups for usual and fast gait speeds, which is more evident regarding gait speed and step length variables. We recommend the use of usual gait for the identification of the effects of aging because, besides showing a higher effect size values it is more comfortable and requires less effort from older subjects.     

The SYBAR system: Integrated recording and display of video, EMG, and force plate data

A new system—called SYBAR—is introduced, that employs digital video for registration of the movements of a patient while simultaneously recording electromyogram signals of relevant muscles and ground reaction forces (for the lower extremities in gait studies). All information is stored in a multimedia record, which can be viewed by the clinician with a simple user interface. This setup allows an integrated and more detailed view of the movement of the patient and related information (i.e., muscle physiology). It is used by clinicians to assess the causes of movement disorders in their patients. This paper describes the SYBAR system and focuses on the employed methods of data synchronization for both the time and the spatial domains. It is concluded that, although SYBAR was developed for clinical gait studies, the technology can be applied in all situations in which the relation between physiological signals and human or animal behavior is studied.     

Effect of walking surface,late-cueing,physiological characteristics of aging,and gait parameters on turn style preference in healthy,older adults

Turning while walking is a crucial component of locomotion, often performed on irregular surfaces with little planning time. Turns can be difficult for some older adults due to physiological age-related changes. Two different turning strategies have been identified in the literature. During step turns, which are biomechanically stable, the body rotates about the outside limb, while for spin turns, generally performed with closer foot-to-foot distance, the inside limb is the main pivot point. Turning strategy preferences of older adults under challenging conditions remains unclear. The aim of this study was to determine how turning strategy preference in healthy older adults is modulated by surface features, cueing time, physiological characteristics of aging, and gait parameters. Seventeen healthy older adults (71.5 ± 4.2 years) performed 90° turns for two surfaces (flat, uneven) and two cue conditions (pre-planned, late-cue). Gait parameters were identified from kinematic data. Measures of lower-limb strength, balance, and reaction-time were also recorded. Generalized linear (logistic) regression mixed-effects models examined the effect of (1) surface and cuing, (2) physiological characteristics of ageing, and (3) gait parameters on turn strategy preference. Step turns were preferred when the condition was pre-planned (p < 0.001) (model 1) and when the gait parameters of stride regularity and maximum acceleration decreased (p = 0.010 and p = 0.039, respectively) (model 3). Differences in turn strategy selection under dynamic conditions ought to be evaluated in future fall-risk research and rehabilitation utilizing real-world activity monitoring.     

Investigation of factors impacting mobility and gait in Parkinson disease

Mobility and gait limitations are major issues for people with Parkinson disease (PD). Identification of factors that contribute to these impairments may inform treatment and intervention strategies. In this study we investigated factors that predict mobility and gait impairment in PD. Participants with mild to moderate PD and without dementia (n = 114) were tested in one session ‘off’ medication. Mobility measures included the 6-Minute Walk test and Timed-Up-and-Go. Gait velocity was collected in four conditions: forward preferred speed, forward dual task, forward fast as possible and backward walking. The predictors analyzed were age, gender, disease severity, balance, balance confidence, fall history, self-reported physical activity, and executive function. Multiple regression models were used to assess the relationships between predictors and outcomes. The predictors, in different combinations for each outcome measure, explained 55.7% to 66.9% of variability for mobility and 39.5% to 52.8% for gait velocity. Balance was the most relevant factor (explaining up to 54.1% of variance in mobility and up to 45.6% in gait velocity). Balance confidence contributed to a lesser extent (2.0% to 8.2% of variance) in all models. Age explained a small percentage of variance in mobility and gait velocity (up to 2.9%). Executive function explained 3.0% of variance during forward walking only. The strong predictive relationships between balance deficits and mobility and gait impairment suggest targeting balance deficits may be particularly important for improving mobility and gait in people with PD, regardless of an individual’s age, disease severity, fall history, or other demographic features.     

Against Relative Timing Invariance in Movement Kinematics

The kinematics of stair climbing were examined to test the assertion that relative timing is an invariant feature of human gait. Six male and four female subjects were video-recorded (at 60 Hz) while they climbed a flight of stairs 10 times at each of three speeds. Each gait cycle was divided into three segments by the maximum and minimum angular displacement of the left knee and left foot contact. Gentner's (1987) analysis methods were applied to the individual subject data to determine whether the duration of the segments remained a fixed proportion of gait cycle duration across changes in stair-climbing speed. A similar analysis was performed using knee velocity maxima to partition the gait cycle. Regardless of how the gait cycle was divided, relative timing was not found to remain strictly invariant across changes in speed. This conclusion is contrary to previous studies of relative timing that involved less conservative analysis but is consistent with the wider gait literature. Strict invariant relative timing may not be a fundamental feature of movement kinematics.     

Treadmill Gait Analysis Does Not Detect Motor Deficits in Animal Models of Parkinson's Disease or Amyotrophic Lateral Sclerosis

Computerized treadmill gait analysis in models of toxicant exposure and neurodegenerative disorders holds much potential for detection and therapeutic intervention in these models, and researchers must validate the technology that assists in that data collection and analysis. The present authors used a commercially available computerized gait analysis system that used (a) a motorized treadmill on retired breeder male C57BL/6J mice, (b) the toxicant-induced (1-methyl-1-, 2-, 3-, 6-tetrahydropyridine) MPTP mouse model of Parkinson's disease (PD), and (c) the superoxide dismutase 1 (SOD1) G93A transgenic mouse model of amyotrophic lateral sclerosis (ALS). The authors compared the detection of deficits by computerized treadmill gait analysis in MPTP-treated mice with inked-paw stride length and correlated these measures to dopamine (DA) loss. The authors found that the computerized treadmill gait analysis system did not distinguish MPTP-treated mice from vehicle controls, despite a nearly 90% deficit of striatal DA. In contrast, decreases in inked-paw stride length correlated strongly with DA losses in these same animals. Computerized treadmill gait analysis could neither reliably distinguish SOD1 G93A mutant mice from controls from 6 to 12 weeks of age nor detect any consistent early motor deficits in these mice. On the basis of the authors' findings, they inferred that computerized gait analysis on a motorized treadmill is not suited to measuring motor deficits in either the MPTP mouse model of PD or the SOD1 G93A mouse model of ALS.     

Treadmill gait analysis does not detect motor deficits in animal models of Parkinson's disease or amyotrophic lateral sclerosis

Computerized treadmill gait analysis in models of toxicant exposure and neurodegenerative disorders holds much potential for detection and therapeutic intervention in these models, and researchers must validate the technology that assists in that data collection and analysis. The present authors used a commercially available computerized gait analysis system that used (a) a motorized treadmill on retired breeder male C57BL/6J mice, (b) the toxicant-induced (1-methyl-1-, 2-, 3-, 6-tetrahydropyridine) MPTP mouse model of Parkinson's disease (PD), and (c) the superoxide dismutase 1 (SOD1) G93A transgenic mouse model of amyotrophic lateral sclerosis (ALS). The authors compared the detection of deficits by computerized treadmill gait analysis in MPTP-treated mice with inked-paw stride length and correlated these measures to dopamine (DA) loss. The authors found that the computerized treadmill gait analysis system did not distinguish MPTP-treated mice from vehicle controls, despite a nearly 90% deficit of striatal DA. In contrast, decreases in inked-paw stride length correlated strongly with DA losses in these same animals. Computerized treadmill gait analysis could neither reliably distinguish SOD1 G93A mutant mice from controls from 6 to 12 weeks of age nor detect any consistent early motor deficits in these mice. On the basis of the authors' findings, they inferred that computerized gait analysis on a motorized treadmill is not suited to measuring motor deficits in either the MPTP mouse model of PD or the SOD1 G93A mouse model of ALS.     

Feature extraction via KPCA for classification of gait patterns

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.     

Consistency of vertebral motion and individual characteristics in gait sequences

Vertebral motion reveals complex patterns, which are not yet understood in detail. This applies to vertebral kinematics in general but also to specific motion tasks like gait. For gait analysis, most of existing publications focus on averaging characteristics of recorded motion signals. Instead, this paper aims at analyzing intra- and inter-individual variation specifically and elaborating motion parameters, which are consistent during gait cycles of particular persons. For this purpose, a study design was utilized, which collected motion data from 11 asymptomatic test persons walking at different speed levels (2, 3, and 4 km/h). Acquisition of data was performed using surface topography. The motion signals were preprocessed in order to separate average vertebral orientations (neutral profiles) from basic gait cycles. Subsequently, a k-means clustering technique was applied to figure out, whether a discrimination of test persons was possible based on the preprocessed motion signals. The paper shows that each test sequence could be assigned to the particular test person without additional prior information. In particular, the neutral profiles appeared to be highly consistent intra-individually (across the gait cycles as well as speed levels), but substantially different between test persons. A full discrimination of test persons was achieved using the neutral profiles with respect to flexion/extension data. Based on this, these signals can be considered as individual characteristics for the particular test persons.     

Mathematical models of central pattern generators in locomotion: II. Single limb models for locomotion in the cat

Three mathematical models of central pattern generation for locomotion in the single limb of the cat are presented. In each model, the activities in populations of neurons controlling limb joint flexors and extensors are described by a system of nonlinear differential equations. Each solution of the system for a different set of parameters corresponds to a simulation of some gait of the cat. Model I is based on unit generators for each limb joint muscle group and assumes that flexors inhibit their paired extensors, but not vice-versa. Model IIa assumes that flexors and extensors are mutually inhibitory, but that only the flexors have inherent oscillatory capability. Model IIb assumes flexors and extensors are mutually inhibitory and that both flexors and extensors have oscillatory capability. The properties of each of these models are explored, compared and contrasted, and discussed in relation to the experimental literature. All three models are shown to be capable of generating patterns consistent with various stepping rates of the cat and to show appropriate muscle sequencing and flexor-extensor interactions. Further, all three models exhibit smooth initiation and termination of stepping. However, Model I seems to provide a more parsimonious account of producing changes in stepping rate and is preferred, therefore, over models IIa and IIb.     

Comparison and evaluation of two common methods to measure center of mass displacement in three dimensions during gait

Center of mass displacement during gait has frequently been used as an indicator of gait efficiency or as a complement to standard gait analysis. With technological advances, measuring the center of mass as the centroid of a multi-segment system is practical and feasible, but must first be compared to the well-established Newtonian computation of double-integrating the ground reaction force. This study aims to verify that the kinematic centroid obtained from a commonly-used model (Vicon Peak Plug-In-Gait) provides at least as reliable measurements of center of mass displacement as those obtained from the ground reaction forces. Gait data was collected for able-bodied children and children with myelomeningocele who use larger lateral center of mass excursions during gait. Reasonable agreement between methods was found in fore-aft and vertical directions, where the methods' excursions differed by an average of less than 10 mm in either direction, and the average RMS differences between methods' computed curves were 6 and 13 mm. Particularly good agreement was observed in the lateral direction, where the calculated excursions differed by an average of less than 2 mm and the RMS difference was 5 mm. Error analyses in computing the center of mass displacement from ground reaction forces were performed. A 5% deviation in mass estimation increased the computed vertical excursion twofold, and a 5% deviation in the integration constant of initial velocity increased the computed fore-aft excursions by 10%. The suitability of calculating center of mass displacement using ground reaction forces in a patient population is questioned. The kinematic centroid is susceptible to errors in segment parameters and marker placement, but results in plausible results that are at least within the range of doubt of the better-established ground reaction force integration, and are more useful when interpreting 3-D gait data.     

Nonlinear Bayesian analysis for single case designs

Several authors have suggested the use of multilevel models for the analysis of data from single case designs. Multilevel models are a logical approach to analyzing such data, and deal well with the possible different time points and treatment phases for different subjects. However, they are limited in several ways that are addressed by Bayesian methods. For small samples Bayesian methods fully take into account uncertainty in random effects when estimating fixed effects; the computational methods now in use can fit complex models that represent accurately the behavior being modeled; groups of parameters can be more accurately estimated with shrinkage methods; prior information can be included; and interpretation is more straightforward. The computer programs for Bayesian analysis allow many (nonstandard) nonlinear models to be fit; an example using floor and ceiling effects is discussed here.     

Analysis of toddlers' gait after six months of independent walking to identify autism: a preliminary study

This research analyzed gait in toddlers and tested the hypothesis that movement can be used as an early indicator of Autistic Disorder. It was proposed that an early identification method could indicate differences in the gait of toddlers with autism as opposed to those with typical development or with mental retardation. Observational methods were applied to retrospective home videos of 42 children after 6 mo. of independent walking. In particular, the Walking Observation Scale was used, which includes 11 items that analyze gait through three axes of foot movements, arm movements, and global movements. Analysis showed different distributions for the three groups, i.e., the autistic group differed from the other two on scores for the Walking Observation Scale and each axis. After 6 mo. of independent walking, different patterns in gait among groups were evident. These results agree with recently published evidence which acknowledges the importance of movement as an early indicator for differential diagnosis of autism.