Healthy Gait Patterns Research Articles

Smartphone usage during walking decreases the positive persistency in gait cycle variability

Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.

Freezing of gait, gait initiation, and gait automaticity share a similar neural substrate in Parkinson's disease

Individuals with Parkinson’s disease (PD) and freezing of gait (FOG) have difficulty initiating and maintaining a healthy gait pattern; however, the relationship among FOG severity, gait initiation, and gait automaticity, in addition to the neural substrate of this relationship has not been investigated. This study investigated the association among FOG severity during turning (FOG-ratio), gait initiation (anticipatory postural adjustment [APA]), and gait automaticity (dual-task cost [DTC]), and the neural substrates of these associations. Thirty-four individuals with FOG of PD were assessed in the ON-medication state. FOG-ratio during a turning test, gait automaticity using DTC on stride length and gait speed, and APA during an event-related functional magnetic resonance imaging protocol to assess brain activity from the regions of interest (e.g., dorsolateral prefrontal cortex [DLPFC] and mesencephalic locomotor region [MLR]) were assessed in separated days. Results showed that FOG-ratio, APA amplitude, and DTC on stride length are negatively associated among them (P < 0.05). APA amplitude and DTC on stride length explained 59% of the FOG-ratio variance (P < 0.05). Although the activity of the right DLPFC and right MLR explained 55% of the FOG-ratio variance (P < 0.05) and 30% of the DTC on stride length variance (P ≤ 0.05), only the activity of the right MLR explained 23% of the APA amplitude (P < 0.05). FOG severity during turning, APA amplitude, and stride length automaticity are associated among them and share a similar locomotor substrate, as the MLR activity was a common brain region in explaining the variance of these variables.

A unilateral robotic knee exoskeleton to assess the role of natural gait assistance in hemiparetic patients

BackgroundHemiparetic gait is characterized by strong asymmetries that can severely affect the quality of life of stroke survivors. This type of asymmetry is due to motor deficits in the paretic leg and the resulting compensations in the nonparetic limb. In this study, we aimed to evaluate the effect of actively promoting gait symmetry in hemiparetic patients by assessing the behavior of both paretic and nonparetic lower limbs. This paper introduces the design and validation of the REFLEX prototype, a unilateral active knee–ankle–foot orthosis designed and developed to naturally assist the paretic limbs of hemiparetic patients during gait.MethodsREFLEX uses an adaptive frequency oscillator to estimate the continuous gait phase of the nonparetic limb. Based on this estimation, the device synchronically assists the paretic leg following two different control strategies: (1) replicating the movement of the nonparetic leg or (2) inducing a healthy gait pattern for the paretic leg. Technical validation of the system was implemented on three healthy subjects, while the effect of the generated assistance was assessed in three stroke patients. The effects of this assistance were evaluated in terms of interlimb symmetry with respect to spatiotemporal gait parameters such as step length or time, as well as the similarity between the joint’s motion in both legs.ResultsPreliminary results proved the feasibility of the REFLEX prototype to assist gait by reinforcing symmetry. They also pointed out that the assistance of the paretic leg resulted in a decrease in the compensatory strategies developed by the nonparetic limb to achieve a functional gait. Notably, better results were attained when the assistance was provided according to a standard healthy pattern, which initially might suppose a lower symmetry but enabled a healthier evolution of the motion of the nonparetic limb.ConclusionsThis work presents the preliminary validation of the REFLEX prototype, a unilateral knee exoskeleton for gait assistance in hemiparetic patients. The experimental results indicate that assisting the paretic leg of a hemiparetic patient based on the movement of their nonparetic leg is a valuable strategy for reducing the compensatory mechanisms developed by the nonparetic limb.

Hybrid Zero Dynamics Control for Gait Guidance of a Novel Adjustable Pediatric Lower-Limb Exoskeleton.

Exoskeleton technology has undergone significant developments for the adult population but is still lacking for the pediatric population. This paper presents the design of a hip–knee exoskeleton for children 6 to 11 years old with gait abnormalities. The actuators are housed in an adjustable exoskeleton frame where the thigh part can adjust in length and the hip cradle can adjust in the medial-lateral and posterior-anterior directions concurrently. Proper control of exoskeletons to follow nominal healthy gait patterns in a time-invariant manner is important for ease of use and user acceptance. In this paper, a hybrid zero dynamics (HZD) controller was designed for gait guidance by defining the zero dynamics manifold to resemble healthy gait patterns. HZD control utilizes a time-invariant feedback controller to create dynamically stable gaits in robotic systems with hybrid models containing both discrete and continuous dynamics. The effectiveness of the controller on the novel pediatric exoskeleton was demonstrated via simulation. The presented preliminary results suggest that HZD control provides a viable method to control the pediatric exoskeleton for gait guidance.

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature.

BackgroundThe purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns.MethodsLiterature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines.ResultsA total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy.ConclusionsCurrent applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

A Retrospective Single-Center Study of 23 Patients to Compare Gait Before and After Total Hip Arthroplasty Using the S-ROM Modular Hip System.

BackgroundThis retrospective study used the Harris hip score (HHS) and range of motion (ROM) to compare gait before and after total hip arthroplasty using the modular S-ROM® hip prosthesis in 23 patients treated at a single center.Material/MethodsFor this study, 23 patients with severe hip deformity, who were treated with a total hip replacement using the S-ROM® modular hip system by DePuy® in the period from 2003 until 2008, underwent a 3-dimensional gait analysis before and after surgery. Values were compared with a validated data set of healthy subjects. Gait analysis took place using 8 infrared cameras (50 Hz), 2 force platforms of Advanced Medical Technology, Inc. (AMTI)®, and the Vicon® 512 system. The HHS and ROM of the hip joint were determined preoperatively and postoperatively. The follow-up period was 16.7±15.2 months.ResultsA healthy gait pattern is characterized by an even stride length of both legs in relation to body height and relative symmetry of certain gait phases. These characteristics are influenced by the range of motion of the hip joint and by pelvic tilt.Symmetry could be achieved postoperatively in the stance phase and in the single-leg stance phase. However, the gait phases could not normalize. HHS and ROM improved postoperatively.ConclusionsThe findings from this retrospective study showed that ROM and HHS significantly improved following THA with the S-ROM® hip prosthesis, but that gait did not completely return to normal.

Predictive simulation of post-stroke gait with functional electrical stimulation

Post-stroke patients present various gait abnormalities such as drop foot, stiff-knee gait (SKG), and knee hyperextension. Functional electrical stimulation (FES) improves drop foot gait although the mechanistic basis for this effect is not well understood. To answer this question, we evaluated the gait of a post-stroke patient walking with and without FES by inverse dynamics analysis and compared the results to an optimal control framework. The effect of FES and cause-effect relationship of changes in knee and ankle muscle strength were investigated; personalized muscle–tendon parameters allowed the prediction of pathologic gait. We also predicted healthy gait patterns at different speeds to simulate the subject walking without impairment. The passive moment of the knee played an important role in the estimation of muscle force with knee hyperextension, which was decreased during FES and knee extensor strengthening. Weakening the knee extensors and strengthening the flexors improved SKG. During FES, weak ankle plantarflexors and strong ankle dorsiflexors resulted in increased ankle dorsiflexion, which reduced drop foot. FES also improved gait speed and reduced circumduction. These findings provide insight into compensatory strategies adopted by post-stroke patients that can guide the design of individualized rehabilitation and treatment programs.

Age and sex differences in normative gait patterns.

A comprehensive understanding of healthy gait patterns is a critical first step towards understanding age-related pathologies and disorders that are commonly associated with mobility limitations throughout aging. Further, consideration of sex-specific gait patterns throughout the lifespan is important, considering biological differences between males and females that can manifest biomechanically, and epidemiological evidence of female sex being a risk factor for some age-related pathologies such as osteoarthritis. The aim of this study was to characterize the differences in lower extremity joint kinematics and kinetics during gait between asymptomatic adult women and men in different age groups (20-40 years, 41-50 years, 51-59 years, 60+ years). This was a secondary analysis conducted on instrumented gait data from 154 asymptomatic adult participants (94 females, 60 males). Three-dimensional hip, knee and ankle joint angles and net external moments were calculated and waveform principal component analysis (PCA) was applied to extract major patterns of variability from each. PC scores were examined for significant sex, age and interaction effects using a two-factor ANOVA analysis (p = 0.001). 13 PC features differed between asymptomatic male and female gait patterns, and were independent of age category. No PC features significantly differed between the age groups, and there were no significant sex by age interactions. There are significant magnitude and pattern differences in hip, knee and ankle kinematics and kinetics between asymptomatic women and men. As study participants were asymptomatic, these differences do not necessarily correlate with any injury or disease mechanisms. However, these results do suggest the importance of considering sex-specific analyses in gait study design, and the use of sex-specific normative data in clinical gait studies. These results further suggest that consideration of strict age-matching for gait analysis studies using adult controls is not as critical as sex considerations.

Irregular Metronomes as Assistive Devices to Promote Healthy Gait Patterns.

Older adults and people suffering from neurodegenerative disease often experience difficulty controlling gait during locomotion, ultimately increasing their risk of falling. To combat these effects, researchers and clinicians have used metronomes as assistive devices to improve movement timing in hopes of reducing their risk of falling. Historically, researchers in this area have relied on metronomes with isochronous interbeat intervals, which may be problematic because normal healthy gait varies considerably from one step to the next. More recently, researchers have advocated the use of irregular metronomes embedded with statistical properties found in healthy populations. In this paper, we explore the effect of both regular and irregular metronomes on many statistical properties of interstride intervals. Furthermore, we investigate how these properties react to mechanical perturbation in the form of a halted treadmill belt while walking. Our results demonstrate that metronomes that are either isochronous or random break down the inherent structure of healthy gait. Metronomes with statistical properties similar to healthy gait seem to preserve those properties, despite a strong mechanical perturbation. We discuss the future development of this work in the context of networked augmented reality metronome devices.

Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH.

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.

Gait Guidance Control for Damping of Unnatural Motion in a Powered Pediatric Lower-Limb Orthosis.

The nominal gait of each individual is unique and varies with the walking speed of the person. This poses a difficult problem for powered rehabilitative orthoses since control strategies often require a reference trajectory and give little control to the patient. This paper describes a simple control approach which applies torque resistive to joint movement that is unnatural for healthy individuals in the hip and knee joints during the swing phase of gait. The controller uses a configuration-dependent orthonormal basis to represent vectors in terms of components which are tangent and normal to healthy gait patterns for a continuum of gait speeds. The controller damps motion in the normal direction, thereby resisting movement which is unnatural for healthy individuals. With this control law, subjects are not restricted to a particular reference trajectory and have a large degree of volition over spatiotemporal gait parameters (e.g., stride length, swing time, and cadence). Experiments are conducted to check the feasibility of the control law in a provisional powered pediatric lower-limb orthosis. The gait guidance controller is used in conjunction with a human controller representing an individual with gait impairment. The main results compare gait shape quality when the gait guidance controller is enabled versus disabled, and show how the gait guidance controller is able to reshape gait to more closely resemble that of a healthy individual for various cadences.

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.

Gait assessment system based on novel gait variability measures.

In this paper, a novel gait assessment system based on measures of gait variability reflected through the variability of shapes of gait cycles trajectories is proposed. The presented gait assessment system is based on SVM (support vector machine) classifier and on gait variability-based features calculated from the hip and knee joint angle trajectories recorded using wearable IMUs during walking trials. A system classifier was trained to distinguish healthy gait patterns from the pathological ones. The features were extracted by calculating the distances between the joint trajectories of the individual gait cycles using 4 different distance functions. As result, the system is able to provide a Gait Variability Index (GVI), which is a numeric value that can be used as an indicator of a degree to which a pathological gait pattern is close to a healthy gait pattern. The system and GVI were tested in three experiments, involving subjects suffering from gait disorders caused by different neurological diseases. The results demonstrated that the proposed gait assessment system would be suitable for supporting clinicians in the evaluation of gait performances during the gait rehabilitation procedures.

Does metronome really help timing gait in Parkinson's disease?

Unstructured gait variability is a hallmark of timing gait disorders of Parkinson's disease (PD). Recently, complex temporal structure of stride duration variability has been demonstrated in healthy gait pattern, displaying long-range autocorrelations (LRA). Conversely, the breakdown of such temporal organization was associated to dynamic instability in PD. To improve timing gait in PD, synchronization of walking with rhythmic auditory stimulation (RAS) like music or metronome is largely used in clinical settings. However, while structure of RAS can modulate LRA in healthy gait pattern, it remains unknown in PD. Patients performed overground walking trials at a comfortable speed while listening different structures of RAS (counterbalanced order across patients). Each structure was adapted to the gait cadence of each patient as previously measured in a 10-meter walking test. The RAS varied in term of fluctuation of the metronome period in the sound sequence, yielding four conditions: strictly metronomic, randomly fluctuating around the metronome period, fluctuating according to an LRA structure (Hurst exponent = 0.80) and no auditory stimulus. Cadence, speed, stride length, temporal organization (LRA) and magnitude of stride duration variability were measured on 512 consecutive gait cycles. The presence of LRA was based on scaling properties of the series variability (Hurst exponent) and the shape of the power spectral density (α exponent). Those measures were compared across the four conditions using a one-way repeated ANOVA. Our results show that temporal organization of PD gait may be modulated using different auditory structures. Spearman's coefficients indicate adequate correlations between LRA of gait and auditory cue. This matching indicates strong adaptation and synchronization of the gait to the RAS in those patients. However, LRA were systematically lower during auditory conditions compared to spontaneous walking session. Furthermore, magnitude of stride duration variability, walking cadence, walking speed, stride length were not statistically different across different conditions. Consequently, strictly metronomic auditory stimuli do not seem to be an optimal way to improve timing gait in PD, as it induces the disappearance of LRA. Future work will investigate whether structured auditory stimuli induce gait improvement in PD.

Use of the Gait Profile Score for the Quantification of Gait Pattern in Down Syndrome

Gait Analysis can characterize alterations in walking patterns, but results in a considerable amount of data that requires complex interpretation. The literature proposes some global indexes that represent the deviation of pathological gait from healthy gait pattern. In this study, the Gait Profile Score (GPS) and the Gait Variable Scores (GVSs) were selected among the summary indexes for describing gait pattern of individuals with Down Syndrome (DS). Twenty-four participants with DS and 15 healthy individuals took part in this study. From their gait analysis data, the GPS and the GVSs were computed. The Mann-Whitney U test compared data of DS group and control group (p < 0.05). In participants with DS, the GPS presented global statistical difference in the gait pattern in comparison with the control group (p < 0.05). Moreover, all the GVSs pointed out statistical differences (p < 0.05), displaying abnormal gait pattern at all lower limb joints. In this study, the GPS and GVSs revealed to be suitable in detecting the typical features of gait pattern of DS participants. Thus, the GPS seems to have validity as a summary measure of functional limitation during walking in DS individuals.

Day-to-day consistency of lower extremity kinematics during stair ambulation in 24–45 years old athletes

Before making interpretations on the effects of interventions or on the features of pathological gait patterns during stair ambulation, the day-to-day consistency of the investigated variables must be established. In this article, the day-to-day consistency was determined for kinematic variables during barefoot stair ambulation. Ten healthy athletes performed two gait analysis sessions, at least one week apart, utilizing a marker set of 47 skin markers, and a functional joint center/axes determination. Being found on limits of agreement and mean differences between the repeated stair ambulation sessions, totally 43 ranges of motions were examined at the hip, knee, ankle, and midfoot joints. The day-to-day consistency was generally in the magnitude of three degrees, irrespective of test condition, investigated joint, or regarded cardinal body plane. The reported values of the day-to-day consistency provide guidelines to distinguish between pathological and healthy gait patterns, and thresholds to determine minimal effects of interventions during stair ambulation.

Dynamic Principles of Gait and Their Clinical Implications

A healthy gait pattern depends on an array of biomechanical features, orchestrated by the central nervous system for economy and stability. Injuries and other pathologies can alter these features and result in substantial gait deficits, often with detrimental consequences for energy expenditure and balance. An understanding of the role of biomechanics in the generation of healthy gait, therefore, can provide insight into these deficits. This article examines the basic principles of gait from the standpoint of dynamic walking, an approach that combines an inverted pendulum model of the stance leg with a pendulum model of the swing leg and its impact with the ground. The heel-strike at the end of each step has dynamic effects that can contribute to a periodic gait and its passive stability. Biomechanics, therefore, can account for much of the gait pattern, with additional motor inputs that are important for improving economy and stability. The dynamic walking approach can predict the consequences of disruptions to normal biomechanics, and the associated observations can help explain some aspects of impaired gait. This article reviews the basic principles of dynamic walking and the associated experimental evidence for healthy gait and then considers how the principles may be applied to clinical gait pathologies.

Design and test of neural networks and statistical classifiers in computer-aided movement analysis: a case study on gait analysis

Objective. To describe the methods for designing and testing diagnostic systems in movement analysis and to verify the clinical usefulness of neural networks and statistical classifiers in a case study. Design. Connectionist and statistical models trained and tested with measured data. Background. A basic need in rehabilitation and related fields is to efficiently manage the vast information obtained from a movement analysis laboratory. Many studies have dealt with the interpretation of measured variables in order to correlate objective descriptors to the presence and/or severity of specific neuromusculoskeletal disorders or their consequences. This traditional analytical approach has been complemented in the last decade by new non-linear classification tools called neural networks. Methods. A gait analysis study on 148 lower limb arthrosis patients and 88 age-matched control subjects. Pathological and healthy gait patterns obtained from force plates were discriminated by means of multilayer perceptrons and statistical classifiers. Results. Ten input features were enough to train a multilayer perceptron with six hidden neurons. The discrimination rate of the neural net was 80% after cross-validation, significantly higher ( P < 0.05) than the performance of a Bayes quadratic classifier (about 75%). A great variance due to a small cross-validation set could be demonstrated. Conclusions. Strict statistical requirements must be observed for designing a neural network. Although these models attain a better performance than conventional statistical approaches, the benefits they bring are sometimes not sufficient to justify their use. Further-more, clinicians routinely involved in critical decisions may not consider such diagnostic systems reliable enough.