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.     

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.     

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.     

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.     

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.     

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.     

Using Multilevel Mixtures to Evaluate Intervention Effects in Group Randomized Trials

There is evidence to suggest that the effects of behavioral interventions may be limited to specific types of individuals, but methods for evaluating such outcomes have not been fully developed. This study proposes the use of finite mixture models to evaluate whether interventions, and, specifically, group randomized trials, impact participants with certain characteristics or levels of problem behaviors. This study uses latent classes defined by clustering of individuals based on the targeted behaviors and illustrates the model by testing whether a preventive intervention aimed at reducing problem behaviors affects experimental users of illicit substances differently than problematic substance users or those individuals engaged in more serious problem behaviors. An illustrative example is used to demonstrate the identification of latent classes, specification of random effects in a multilevel mixture model, independent validation of latent classes, and the estimation of power for the proposed models to detect intervention effects. This study proposes specific steps for the estimation of multilevel mixture models and their power and suggests that this model can be applied more broadly to understand the effectiveness of interventions.     

Effects of visual focus and gait speed on walking balance in the frontal plane

We investigated how head position and gait speed influenced frontal plane balance responses to external perturbations during gait. Thirteen healthy participants walked on a treadmill at three different gait speeds. Visual conditions included either focus downward on lower extremities and walking surface only or focus forward on a stationary scene with horizontal and vertical lines. The treadmill was positioned on a platform that was stationary (non-perturbed) or moving in a pattern that appeared random to the subjects (perturbed). In non-perturbed walking, medial–lateral upper body motion was very similar between visual conditions. However, in perturbed walking, there was significantly less body motion when focus was on the stationary visual scene, suggesting visual feedback of stationary vertical and horizontal cues are particularly important when balance is challenged. Sensitivity of body motion to perturbations was significantly decreased by increasing gait speed, suggesting that faster walking was less sensitive to frontal plane perturbations. Finally, our use of external perturbations supported the idea that certain differences in balance control mechanisms can only be detected in more challenging situations, which is an important consideration for approaches to investigating sensory contribution to balance during gait.     

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.     

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.     

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.     

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.     

Validity of Functional Ambulation Performance Score for the evaluation of spatiotemporal parameters of children's gait

Gait characteristics of a healthy adult population have been used to develop the Functional Ambulation Profile (FAP) score to evaluate gait in patients with neuromuscular or musculoskeletal involvement (A. J. Nelson, 1974). Further technological progress allowed a more precise recording of walk parameters and propitiated the development of the Functional Ambulation Performance Score (FAPS). The authors aimed to explore the evolution of the FAPS in healthy children to determine what the lower limit of age would be to ensure reliability of this score. Participants were 32 children with normal development. A GAITRite? walkway was used to log the spatiotemporal parameters. Compared with values obtained in adults, the average FAPS was significantly lower for children under 12 years old. The interparticipant variability was particularly high for the younger children and decreased with age. Similar trends were observed regarding the intraparticipant variability. In conclusion, the authors observed that the FAPS is not suitable to compare the gait of different children younger than 12 years old. At least, the adult standards used to calculate FAPS would need to be modified if the score has to be applied to a pediatric population.     

Selection of gait parameters during constrained walking

It is commonly thought that at prescribed speeds humans choose gait parameters that minimize the cost of transportation. However, it is unclear whether and how the relationship between step length and step frequency is affected by the additional physiological factors caused by constraints. We performed a series of experiments to understand the selection of gait parameters under different constraints from a probabilistic perspective. First, we show that the effect of constraining step length on step frequency (i.e., monotonically decrease, Experiment I) is different from the effect of constraining step frequency on step length (i.e., inverted-U, Experiment II). Using the results from Experiment I and II, we summarized the marginal distribution of step length and step frequency and built their joint distribution in a probabilistic model. The probabilistic model predicts the selection of gait parameters by achieving the maximum probability of joint distribution of step length and step frequency. In Experiment III, the probabilistic model could well predict gait parameters at prescribed speeds, and it is similar to minimizing the cost of transportation. Finally, we show that the distribution of step length and step frequency were completely different between constrained and non-constrained walking. We argue that constraints in walking are major factors determining how humans choose gait parameters due to their involvement of mediators, i.e., attention or active control. Using the probabilistic model to account for gait parameters has an advantage compared with fixed-parameter models in that it can still include the effect of hidden mechanical, neurophysiological, or psychological variables by grouping them into distribution curves.     

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.     

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.     

Learning the moves: the effect of familiarity and facial motion on person recognition across large changes in viewing format

Familiarity with a face or person can support recognition in tasks that require generalization to novel viewing contexts. Using naturalistic viewing conditions requiring recognition of people from face or whole body gait stimuli, we investigated the effects of familiarity, facial motion, and direction of learning/test transfer on person recognition. Participants were familiarized with previously unknown people from gait videos and were tested on faces (experiment 1a) or were familiarized with faces and were tested with gait videos (experiment 1b). Recognition was more accurate when learning from the face and testing with the gait videos, than when learning from the gait videos and testing with the face. The repetition of a single stimulus, either the face or gait, produced strong recognition gains across transfer conditions. Also, the presentation of moving faces resulted in better performance than that of static faces. In experiment 2, we investigated the role of facial motion further by testing recognition with static profile images. Motion provided no benefit for recognition, indicating that structure-from-motion is an unlikely source of the motion advantage found in the first set of experiments.