Measuring gait parameters from a single chest-worn accelerometer in healthy individuals: a validation study

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants' wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland-Altman plots, Pearson's correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs.Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100).

Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors

This work addresses the lack of reliable wearable methods to assess walking gaits in underwater environments by evaluating the lateral hydrodynamic pressure exerted on lower limbs. Sixteen healthy adults were outfitted with waterproof wearable inertial and pressure sensors. Gait analysis was conducted on land in a motion analysis laboratory using an optoelectronic system as reference, and subsequently underwater in a rehabilitation swimming pool. Differences between the normalized land and underwater gaits were evaluated using temporal gait parameters, knee joint angles and the total water pressure on the lower limbs. The proposed method was validated against the optoelectronic system on land; gait events were identified with low bias (0.01s) using Bland-Altman plots for the stride time, and an acceptable error was observed when estimating the knee angle (10.96° RMSE, Bland-Altman bias -2.94°). The kinematic differences between the land and underwater environments were quantified, where it was observed that the temporal parameters increased by more than a factor of two underwater (p<0.001). The subdivision of swing and stance phases remained consistent between land and water trials. A higher variability of the knee angle was observed in water (CV = 60.75%) as compared to land (CV = 31.02%). The intra-subject variability of the hydrodynamic pressure on the foot ([Formula: see text] = 39.65%) was found to be substantially lower than that of the knee angle (CVz = 67.69%). The major finding of this work is that the hydrodynamic pressure on the lower limbs may offer a new and more reliable parameter for underwater motion analysis as it provided a reduced intra-subject variability as compared to conventional gait parameters applied in land-based studies.

Slowing gait during turning: how volition of modifying walking speed affects the gait pattern in healthy adults

Background

Turning during walking and volitionally modulating walking speed introduces complexity to gait and has been minimally explored.

Research question

How do the spatiotemporal parameters vary between young adults walking at a normal speed and a slower speed while making 90°, 180°, and 360° turns?

Methods

In a laboratory setting, the spatiotemporal parameters of 10 young adults were documented as they made turns at 90°, 180°, and 360°. A generalized linear model was utilized to determine the effect of both walking speed and turning amplitude.

Results

Young adults volitionally reducing their walking speed while turning at different turning amplitudes significantly decreased their cadence and spatial parameters while increasing their temporal parameters. In conditions of slower movement, the variability of certain spatial parameters decreased, while the variability of some temporal parameters increased.

Significance

This research broadens the understanding of turning biomechanics in relation to volitionally reducing walking speed. Cadence might be a pace gait constant synchronizing the rhythmic integration of several inputs to coordinate an ordered gait pattern output. Volition might up-regulate or down-regulate this pace gait constant (i.e., cadence) which creates the feeling of modulating walking speed.

On the relation between gait speed and gait cycle duration for walking on even ground

Gait models and reference motions are essential for the objective assessment of walking patterns and therapy progress, as well as research in the field of wearable robotics and rehabilitation devices in general. A human can achieve a desired gait speed by adjusting stride length and/or stride frequency. It is hypothesized that sex, age, and physique of a person have a significant influence on the combination of these parameters. A mathematical description of the relation between gait speed and its determinants is presented in the form of a parameterized analytic function. Based on the statistical significance of the parameters, three models are derived. The first two models are valid for slow to fast walking, which is defined as the interval of approximately 0.6-2.0ms-1, assuming a linear relation of gait speed and stride length, and a non-linear relation of gait speed and stride duration, respectively. The third model is valid for a defined range of walking speed centered at a certain (preferred or spontaneous) gait speed. The latter assumes a constant walk ratio, i.e. the ratio between step or stride length and step or stride frequency, and is recommended for walking at a speed of 1.0-1.6ms-1. On the basis of a large pool of gait datasets, regression coefficients with significance for age and/or body mass index are identified. The presented models allow to estimate the gait cycle duration based on gait speed, sex, age and body mass index of healthy persons walking on even ground.

Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone Location

Marker-based 3D motion capture systems, widely used for gait analysis, are accurate but have disadvantages such as cost and accessibility. Whereas markerless pose estimation has emerged as a convenient and cost-effective alternative for gait analysis, challenges remain in achieving optimal accuracy. Given the limited research on the effects of camera location and orientation on data collection accuracy, this study investigates how camera placement affects gait assessment accuracy utilizing five smartphones. This study aimed to explore the differences in data collection accuracy between marker-based systems and pose estimation, as well as to assess the impact of camera location and orientation on accuracy in pose estimation. The results showed that the differences in joint angles between pose estimation and marker-based systems are below 5°, an acceptable level for gait analysis, with a strong correlation between the two datasets supporting the effectiveness of pose estimation in gait analysis. In addition, hip and knee angles were accurately measured at the front diagonal of the subject and ankle angle at the lateral side. This research highlights the significance of careful camera placement for reliable gait analysis using pose estimation, serving as a concise reference to guide future efforts in enhancing the quantitative accuracy of gait analysis.

Reliability and Validity of the Variability Model Testing Procedure for Somatic Dysfunction Assessment: A Comparison with Gait Analysis Parameters in Healthy Subjects

Somatic dysfunction (SD) is an altered body function involving the musculoskeletal system. However, its clinical signs-tissue texture abnormalities, positional asymmetry, restricted range of motion, and tissue tenderness-did not achieve satisfactory results for reliability. A recent theoretical model proposed a revision assessing the movement variability around the joint rest position. The asymmetry and restriction of motion may characterize functional assessment in osteopathic clinical practice, demonstrating the reliability required. Hence, this study investigated the reliability of the new variability model (VM) with gait analysis (GA). Three blind examiners tested 27 young healthy subjects for asymmetry of motion around rest position and the SD grade on six body regions. The results were compared to the VICON procedure for 3D-GA. The inter-rater agreement for the detection of reduced movement variability ranged from 0.78 to 0.54, whereas for SD, grade ranged from 0.64 to 0.47. VM had a sensitivity and specificity of 0.62 and 0.53, respectively, in SD detection compared to step length normality. Global severity grade of SD demonstrated moderate to good correlation with spatial-temporal parameters. The VM showed palpatory reliability and validity with spatial-temporal parameters in GA. Those findings contribute to the innovation for SD examination with implications for the clinical practice.

Comparative profiling of cellular gait on adhesive micropatterns defines statistical patterns of activity that underlie native and cancerous cell dynamics

Cell dynamics are powered by patterns of activity, but it is not straightforward to quantify these patterns or compare them across different environmental conditions or cell-types. Here we digitize the long-term shape fluctuations of metazoan cells grown on micropatterned fibronectin islands to define and extract statistical features of cell dynamics without the need for genetic modification or fluorescence imaging. These shape fluctuations generate single-cell morphological signals that can be decomposed into two major components: a continuous, slow-timescale meandering of morphology about an average steady-state shape; and short-lived "events" of rapid morphology change that sporadically occur throughout the timecourse. By developing statistical metrics for each of these components, we used thousands of hours of single-cell data to quantitatively define how each axis of cell dynamics was impacted by environmental conditions or cell-type. We found the size and spatial complexity of the micropattern island modulated the statistics of morphological events-lifetime, frequency, and orientation-but not its baseline shape fluctuations. Extending this approach to profile a panel of triple negative breast cancer cell-lines, we found that different cell-types could be distinguished from one another along specific and unique statistical axes of their behavior. Our results suggest that micropatterned substrates provide a generalizable method to build statistical profiles of cell dynamics to classify and compare emergent cell behaviors.

Can the F-Scan in-shoe pressure system be combined with the GAITRite® temporal and spatial parameter-recording walkway as a cost-effective alternative in clinical gait analysis? A validation study

BACKGROUND

Clinical gait analysis is widely used to aid the assessment and diagnosis of symptomatic pathologies. Foot function pressure systems such as F-scan and analysis of the spatial-temporal parameters of gait using GAITRite® can provide clinicians with a more comprehensive assessment. There are systems however, such as Strideway™ that can measure these parameters simultaneously but can be expensive. F-Scan in-shoe pressure data is normally collected whilst the person is walking on a hard floor surface. The effects of the softer Gaitrite® mat upon the F-Scan in-shoe sensor pressure data is unknown. This study therefore aimed to assess the agreement between F-Scan pressure measurements taken from a standard walkway (normal hard floor), and those from a GAITRite® walkway to establish whether these two pieces of equipment (in-shoe F-Scan and GAITRite®) can be used simultaneously, as a cost-effective alternative.

METHOD

Twenty-three participants first walked on a standard floor and then on a GAITRite® walkway wearing F-Scan pressure sensor insoles with same footwear. They repeated these walks three times on each surface. Mid gait protocols were utilised by analysing the contact pressure of the first and second metatarsophalangeal joint of the third, fifth and seventh step from each walk. For both joints, 95% Bland-Altman Limits of Agreement was used to determine a level of agreement between the two surfaces, using mean values from pressure data collected from participants who successfully completed all required walks. The intraclass correlation coefficient (ICC) and Lin's concordance correlation coefficient were calculated as indices of reliability.

FINDINGS

ICC results for the hard surface and the GAITRrite® walkway at the first and second metatarsophalangeal joints were 0.806 and 0.991 respectively. Lin's concordance correlation coefficient for the first and second metatarsophalangeal joints were calculated to be 0.899 and 0.956 respectively. Both sets of statistics indicate very good reproducibility. Bland-Altman plots revealed good repeatability of data at both joints.

CONCLUSION

The level of agreement in F-Scan plantar pressures observed between walking on a normal hard floor and on a GAITRite® walkway was very high, suggesting that it is feasible to use F-Scan with GAITRite® together in a clinical setting, as an alternative to other less cost-effective standalone systems. Although it is assumed combining F-Scan with GAITRite® does not affect spatiotemporal analysis, this was not validated in this study.

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.

Mapping features and patterns of accelerometry data on human movement in different age groups and associated health problems: A cross-sectional study

PURPOSE

Human movement is considered one of the important factors for maintaining an independent life. Individuals in different age groups have different characteristics of locomotion patterns and some health conditions can affect or be affected by mobility changes. Few studies clarify or present data about the influence of different ages and biopsychosocial factors on accelerometry features. The aim of this study was to identify characteristics and variables in the frequency signals for different age groups and their relationship with associated health conditions in raw accelerometry data obtained from the use of a triaxial accelerometer during 7 days of activities of daily living.

METHOD

A cross-sectional study was conducted based on the database of the first evaluations of the Epidemiological Study of Movement (EPIMOV) cohort. Frequency, signal amplitude, and entropy accelerometry features of EPIMOV participants who used a triaxial accelerometer for 7 days were extracted. Sociodemographic, clinical, anthropometric and physical activity assessments were also performed. Two-way ANOVA was performed to compare accelerometry features within different age groups. A series of stepwise multiple regressions were performed on accelerometry variables to analyze their relationships with demographic, anthropometric and cardiovascular risk variables.

RESULTS

The sample consisted mostly of female, white, and high school graduates. The most prevalent cardiovascular risk factors were sedentary behavior and obesity. When analyzing the accelerometry variables, it was possible to observe that the entropy feature, and the counts, decrease in the group of older adults, while the feature of harmonic components of gait (frequency × amplitude) increases in the group of older adults. Regarding the amplitude feature, there were no significant differences between the groups. Through stepwise multiple linear regression, it was possible to observe that demographic, anthropometric and cardiovascular risk factors are associated with most accelerometry variables.

CONCLUSION

The results confirm that human movement can be influenced by different ages, sex, demographic, anthropometric and cardiovascular risk factors. Future studies and clinical analyzes can use the methods proposed in this research to adjust movement patterns for sex and different age groups, thus obtaining new interpretations about human movement.

Unilateral symptomatic Achilles tendinopathy has limited effects on bilateral lower limb ground reaction force asymmetries and muscular synergy attributes when walking at natural and fast speeds

BACKGROUND

Achilles tendinopathy (AT) may affect ground reaction force (GRF) and muscle synergy (MS) during walking due to pain, biological integrity changes in the tendon and neuroplastic adaptations. The objective of this study was to compare GRF asymmetries and MS attributes between symptomatic and asymptomatic lower limbs (LL) during walking at natural and fast speeds in adults with unilateral AT.

METHODS

A convenience sample consisting of twenty-eight participants walked on an instrumented treadmill at natural (1.3 m/s) and fast (1.6 m/s) speeds. Peak GRF were measured in mediolateral, anteroposterior and vertical directions. Individualized electromyography (EMG) activation profiles were time- and amplitude-normalized for three consecutive gait cycles and MS were extracted using non-negative matrix factorization algorithms. MS were characterized by the number, composition (i.e., weighting of each muscle) and temporal profiles (i.e., duration and amplitude) of the MS extracted during walking. Paired Student's t-tests assessed peak GRF and MS muscle weighting differences between sides whereas Pearson correlation coefficients characterized the similarities of the individualized EMG and MS activation temporal profiles within sides.

RESULTS

AT had limited effects on peak GRF asymmetries and the number, composition and temporal profiles of MS between symptomatic and asymptomatic LL while walking on a level treadmill at natural and fast speeds. In most participants, four MS with a specific set of predominantly activated muscles were extracted across natural (71 and 61%) and fast (54 and 50%) walking speeds for the symptomatic and asymptomatic side respectively. Individualized EMG activation profiles were relatively similar between sides (r = 0.970 to 0.999). As for MS attributes, relatively similar temporal activation profiles (r = 0.988 to 0.998) and muscle weightings (p < 0.05) were found between sides for all four MS and the most solicited muscles. Although the faster walking speed increased the number of merged MS for both sides, it did not significantly alter MS symmetry.

CONCLUSION

Faster walking speed increased peak GRF values but had limited effects on GRF symmetries and MS attribute differences between the LL. Corticospinal neuroplastic adaptations associated with chronic unilateral AT may explain the preserved quasi-symmetric LL motor control strategy observed during natural and fast walking among adults with chronic unilateral AT.

Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.

Evaluating the discriminatory power of the velocity field diagram and timed-up-and-go test in determining the fall status of community-dwelling older adults: a cross-sectional observational study

Background

Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults.

Method

This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD’s intersection point of the stride frequency, and velocity regression lines (E₁) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E₁ ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD’s and TUG’s sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05.

Results

The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC’s AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively).

Conclusion

The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls.

Non-age-related gait kinematics and kinetics in the elderly

BACKGROUND

The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects.

METHODS

Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results.

RESULTS

A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal.

CONCLUSIONS

The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.

The effects of custom-made foot orthoses on foot pain, foot function, gait function, and free-living walking activities in people with psoriatic arthritis (PsA): a pre-experimental trial

Introduction

Foot involvement is a significant concern in psoriatic arthritis (PsA) as it can lead to severe levels of foot pain and disability and reduced mobility and quality of life. Previous studies have shown moderate efficacy for custom-made foot orthoses (CFO) in reducing foot pain and disability in people with rheumatoid arthritis. However, evidence on the efficacy of CFO in people with PsA is lacking.

Objectives

To explore the effects of CFO on foot function, foot and lower limb pain, gait function, and free-living walking activities (FWA) in people with PsA.

Methods

A pre-experimental study including twenty participants with PsA (mean age: 54.10 ± 9.06 years and disease duration: 11.53 ± 10.22 years) was carried out. All the participants received and wore CFO for 7 weeks. Foot and lower limb pain and foot function were measured before and after the intervention using the numerical rating scale (NRS) and the foot function index (FFI). Gait function was assessed by recording spatiotemporal parameters (STPs) during a 10-m walk test using an instrumented gait analysis system (Mobility Lab). Free-living walking activities (step count, free-living cadence, time spent in different ambulatory physical activities (APA)) were recorded over 7 days using an accelerometer-instrumented sock.

Results

The FFI reported scores demonstrated severe baseline levels of foot pain (54.46 ± 14.58 %) and disability (46.65 ± 16.14%). Statistically and clinically significant improvements in foot pain and foot function and large effect sizes (Cohen’s effect size > 1, p < 0.005) were observed after the intervention period. A strong correlation (r = −0.64, p < 0.01) between the CFO wearing time and foot function was demonstrated. However, no significant changes were found for gait STP or free-living walking activities after 7 weeks of CFO use.

Conclusion

Results support the clinical and biomechanical plausibility of using CFO in people with PsA to reduce pain and improve foot function. Large-scale and controlled studies are needed to confirm these findings. Moreover, a multidisciplinary approach including the prescription of exercise therapy and physiotherapy combined with CFO could be required to improve STP and promote APA in people with PsA.

Trial registration

ClinicalTrials.gov, NCT05075343. Retrospectively registered on September 29, 2021

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02808-8.

Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review

INTRODUCTION

Type 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive methods to forecast complications and develop early strategies to guide treatments. To date, no research has systematically summarised the predictive methods used to assess type 2 diabetic gait. Therefore, this protocol aims to identify which predictive methods have been employed to assess the diabetic gait.

METHODS AND ANALYSIS

This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) statement. Electronic searches of articles from inception to January 2022 will be performed, from May 2021 to 31 January 2022, in the Web of Science, MEDLINE, Embase, IEEE Xplore Digital Library, Scopus, CINAHL, Google Scholar, APA PsycInfo, the Cochrane Library and in references of key articles and grey literature without language restrictions. We will include studies that examined the development and/or validation of predictive methods to assess type 2 diabetic gait in adults aged >18 years without amputations, use of assistive devices, ulcers or neuropathic pain. Two independent reviewers will screen the included studies and extract the data using a customised charting form. A third reviewer will resolve any disagreements. A narrative synthesis will be performed for the included studies. Risk of bias and quality of evidence will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.

ETHICS AND DISSEMINATION

Ethical approval is not required because only available secondary published data will be analysed. The findings will be disseminated through peer-reviewed journals and/or presentations at relevant conferences and other media platforms.

PROSPERO REGISTRATION NUMBER

CDR42020199495.

Improvement in postural alignment is associated with recovery of mobility after complex acquired brain injury: An observational study

PURPOSE

Determine how mobility changes over 6 months in people unable to walk at 8-weeks post-Acquired Brain Injury (ABI); if there is an association over time between postural alignment and mobility post-ABI; and if alignment after ABI becomes closer to healthy alignment over time.

METHODS

Fourteen adults with ABI, evaluated over 6 months, and a reference sample of 30 healthy adults were studied. The primary measure for changes in mobility was the Clinical Outcome Variables Scale (COVS). Secondary measures were sit-to-stand, timed standing holding rails, independent walking speed and number of testing conditions achieved. The Functional Independence Measure (FIM) was scored at rehabilitation admission and discharge. To analyze postural alignment, participants were recorded in sitting and standing, each repeated holding rails, and walking if able. Three-dimensional kinematic data were used to quantify whole-body postural alignment, equal to mean segment displacements from the base of support in the transverse plane. Associations between three-dimensional kinematic alignment scores and COVS scores were calculated using Linear Mixed-Effects Models.

RESULTS

Participants made significant improvements in COVS scores, most secondary mobility scores, and FIM scores over time (p ≤ .001). Relationships between increasing COVS scores and decreasing sitting and standing mal-alignment scores were statistically significant. Visual analysis of graphed segment positions indicated that sitting and standing alignment became more similar to healthy alignment over time; this was not clear for walking.

CONCLUSION

Improvement in postural alignment may be a factor for improving mobility in people with severe impairments after ABI.

Unilateral non-electric assistive walking device helps neurological and orthopedic patients to improve gait patterns

BACKGROUND

Pathological gait patterns are common in neurological and orthopedic patients. These put them at risk of falling and restrict their autonomy and social participation. Novel assistive walking devices are designed to actively support physiological gait patterns by means of motor guidance and mechanical support of the lower limbs.

RESEARCH QUESTION

Does a non-electric assistive walking device powered by a cam-spring mechanism (aLQ, Imasen) improve or otherwise affect pathological gait patterns in neurological and orthopedic patients?

METHODS

A three-dimensional instrumented gait analysis was conducted on a treadmill (quasar, hp cosmos) using spatiotemporal, kinetic, and kinematic data obtained from synchronized motion capturing (Miqus M3, Qualisys), surface EMG (sEMG; Ultium, Noraxon), and pressure distribution measurements (FMD-T, Zebris). Participants with impaired walking were tested in a randomized repeated measures design (assisted/unassisted; at preferred/fast speed) and analyzed with regard to their medical condition (orthopedic or neurological group, n = 20 each).

RESULTS

In both groups, participants showed a significant increase of step length and decrease of cadence during assisted walking compared to baseline. Immediate kinematic effects included enhanced sagittal hip flexion but reduced extension. On the contrary, knee joint angles and muscle activity of M. gastrocnemius and M. rectus femoris seemed to be unaffected by the aLQ device.

SIGNIFICANCE

Participants appear to benefit from the assistive walking device regarding gait and movement patterns, which suggests that the tested device may help to improve patients' functional health status and quality of life. Activities of daily living (ADLs) that involve extensive hip flexion like stairs or curb climbing are promising applications. We propose the implementation of an invertible cam-spring that provides an additional resistance training option.

Anipose: A toolkit for robust markerless 3D pose estimation

Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.

Real-Time Identification of Knee Joint Walking Gait as Preliminary Signal for Developing Lower Limb Exoskeleton

An exoskeleton is a device used for walking rehabilitation. In order to develop a proper rehabilitation exoskeleton, a user’s walking intention needs to be captured as the initial step of work. Moreover, every human has a unique walking gait style. This work introduced a wearable sensor, which aimed to recognize the walking gait phase, as the fundamental step before applying it into the rehabilitation exoskeleton. The sensor used in this work was the IMU sensor, used to recognize the pitch angle generated from the knee joint while the user walks, as information about the walking gait cycle, before doing the investigation on how to identify the walking gait cycle. In order to identify the walking gait cycle, Neural Network has been proposed as a method. The gait cycle identification was generated to recognize the gait cycle on the knee joint. To verify the performance of the proposed method, experiments have been done in real-time application. The experiments were carried out with different processes such as walking on a flat floor, climbing up, and walking down stairs. Five subjects were trained and tested using the system. The experiments showed that the proposed method was able to recognize each gait cycle for all users as they wore the sensor on their knee joints. This study has the potential to be applied on an exoskeleton rehabilitation robot as a further research experiment.

Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task-carrying a cup filled with water, cognitive task-subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

The effect of core stability-based corrective exercises on gait parameters in elite soccer players diagnosed with Middle Crossed Syndrome

BACKGROUND/AIMS

The aim of this study was to investigate the effect of eight weeks of core stability-based corrective exercises, on gait parameters in elite soccer players diagnosed with middle crossed syndrome.

METHODS

15 male elite soccer players (aged 18-28) were enrolled in a same-subject intervention trial to assess if the middle crossed syndrome could be influenced through core stability exercise. Core stability-based corrective exercises were completed 3 times per week for 8 weeks and changes in gait parameters (pre- and post- intervention) were measured.

RESULTS

The results showed that most gait parameters including stride length (p = 0.025), gait speed (p = 0.023), number of strides (p = 0.007), length of shots (p = 0.003), and also soccer players' height (p = 0.011) improved significantly in post-intervention in comparison to pre-intervention. Stride width in post-intervention did not show changes in comparison with pre-intervention (p = 0.083).

CONCLUSION

The results indicate the significant effectiveness of core stability-based corrective exercises on gait parameters in those with middle crossed syndrome. By doing corrective exercises based on core stability during the study period, gait parameters in the post-intervention surpass the results in the pre-intervention in most parameters. Therefore, it is proposed that corrective exercises based on core stability is a safe and useful method for improving function in those with middle crossed syndrome and it could be used as a therapy to help players identified with this finding. In this regard, it is suggested to researchers and coaches to correct imbalances in order to achieve better results in training programs.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Identification of Gait-Cycle Phases for Prosthesis Control

The major problem with transfemoral prostheses is their capacity to compensate for the loss of the knee joint. The identification of gait-cycle phases plays an important role in the control of these prostheses. Such control is completely up to the patient in passive prostheses or partly facilitated by the prosthesis in semiactive prostheses. In both cases, the patient recovers his/her walking ability through a suitable rehabilitation procedure that aims at recreating proprioception in the patient. Understanding proprioception passes through the identification of conditions and parameters that make the patient aware of lower-limb body segments’ postures, and the recognition of the current gait-cycle phase/period is the first step of this awareness. Here, a proposal is presented for the identification of the gait-cycle phases/periods under different walking conditions together with a control logic for a possible active/semiactive prosthesis. The proposal is based on the detection of different gait-cycle events as well as on different walking conditions through a load sensor, which is implemented by analyzing the variations in some gait parameters. The validation of the proposed method is done by using gait-cycle data present in the literature. The proposal assumes the prosthesis is equipped with an energy-storing foot without mobility.

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.

The development of mature gait patterns in children during walking and running

PURPOSE

We sought to identify the developing maturity of walking and running in young children. We assessed gait patterns for the presence of flight and double support phases complemented by mechanical energetics. The corresponding classification outcomes were contrasted via a shotgun approach involving several potentially informative gait characteristics. A subsequent clustering turned out very effective to classify the degree of gait maturity.

METHODS

Participants (22 typically developing children aged 2-9 years and 7 young, healthy adults) walked/ran on a treadmill at comfortable speeds. We determined double support and flight phases and the relationship between potential and kinetic energy oscillations of the center-of-mass. Based on the literature, we further incorporated a total of 93 gait characteristics (including the above-mentioned ones) and employed multivariate statistics comprising principal component analysis for data compression and hierarchical clustering for classification.

RESULTS

While the ability to run including a flight phase increased with age, the flight phase did not reach 20% of the gait cycle. It seems that children use a walk-run-strategy when learning to run. Yet, the correlation strength between potential and kinetic energies saturated and so did the amount of recovered mechanical energy. Clustering the set of gait characteristics allowed for classifying gait in more detail. This defines a metric for maturity in terms of deviations from adult gait, which disagrees with chronological age.

CONCLUSIONS

The degree of gait maturity estimated statistically using various gait characteristics does not always relate directly to the chronological age of the child.

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical Modelling

Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3 mm and the average inter-subject RMS error of 23.7 mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.

An Asian-centric human movement database capturing activities of daily living

Assessment of human movement performance in activities of daily living (ADL) is a key component in clinical and rehabilitation settings. Motion capture technology is an effective method for objective assessment of human movement. Existing databases capture human movement and ADL performance primarily in the Western population, and there are no Asian databases to date. This is despite the fact that Asian anthropometrics influence movement kinematics and kinetics. This paper details the protocol in the first phase of the largest Asian normative human movement database. Data collection has commenced, and this paper reports 10 healthy participants. Twelve tasks were performed and data was collected using Qualisys motion capture system, force plates and instrumented table and chair. In phase two, human movement of individuals with stroke and knee osteoarthritis will be captured. This can have great potential for benchmarking with the normative human movement captured in phase one and predicting recovery and progression of movement for patients. With individualised progression, it will offer the development of personalised therapy protocols in rehabilitation.

Filtering affects the calculation of the largest Lyapunov exponent

The calculation of the largest Lyapunov exponent (LyE) requires the reconstruction of the time series in an N-dimensional state space. For this, the time delay (Tau) and embedding dimension (EmD) are estimated using the Average Mutual Information and False Nearest Neighbor algorithms. However, the estimation of these variables (LyE, Tau, EmD) could be compromised by prior filtering of the time series evaluated. Therefore, we investigated the effect of filtering kinematic marker data on the calculation of Tau, EmD and LyE using several different computational codes. Kinematic marker data were recorded from 37 subjects during treadmill walking and filtered using a low pass digital filter with a range of cut-off frequencies (23.5-2Hz). Subsequently, the Tau, EmD and LyE were calculated from all cut-off frequencies. Our results demonstrated that the level of filtering affected the outcome of the Tau, EmD and LyE calculations for all computational codes used. However, there was a more consistent outcome for cut-off frequencies above 10 Hz which corresponded to the optimal cut-off frequency that could be used with this data. This suggested that kinematic data should remain unfiltered or filtered conservatively before calculating Tau, EmD and LyE.

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

Background

Walking patterns can provide important indications of a person’s health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist.

Methods

The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups.

Results

Several parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180° turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups.

Conclusion

Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject’s functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder.

Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

BACKGROUND

Understanding the effects of gait speed on biomechanical variables is fundamental for a proper evaluation of alterations in gait, since pathological individuals tend to walk slower than healthy controls. Therefore, the aim of the study was to perform a systematic review of the effects of gait speed on spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces in healthy children, young adults, and older adults.

METHODS

A systematic electronic search was performed on PubMed, Embase, and Web of Science databases to identify studies published between 1980 and 2019. A modified Quality Index was applied to assess methodological quality, and effect sizes with 95% confidence intervals were calculated as the standardized mean differences. For the meta-analyses, a fixed or random effect model and the statistical heterogeneity were calculated using the I² index.

RESULTS

Twenty original full-length studies were included in the final analyses with a total of 587 healthy individuals evaluated, of which four studies analyzed the gait pattern of 227 children, 16 studies of 310 young adults, and three studies of 59 older adults. In general, gait speed affected the amplitude of spatiotemporal gait parameters, joint kinematics, joint kinetics, and ground reaction forces with a decrease at slow speeds and increase at fast speeds in relation to the comfortable speed. Specifically, moderate-to-large effect sizes were found for each age group and speed: children (slow, - 3.61 to 0.59; fast, - 1.05 to 2.97), young adults (slow, - 3.56 to 4.06; fast, - 4.28 to 4.38), and older adults (slow, - 1.76 to 0.52; fast, - 0.29 to 1.43).

CONCLUSIONS

This review identified that speed affected the gait patterns of different populations with respect to the amplitude of spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces. Specifically, most of the values analyzed decreased at slower speeds and increased at faster speeds. Therefore, the effects of speed on gait patterns should also be considered when comparing the gait analysis of pathological individuals with normal or control ones.

Which Gait Parameters and Walking Patterns Show the Significant Differences Between Parkinson's Disease and Healthy Participants?

This study investigated the difference in the gait of patients with Parkinson’s disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using inertial measurement units (IMU). Group differences between the mean and variance of the gait parameters (stride interval, stance interval, swing interval and double support interval) for the three groups were calculated and statistical significance was tested. The results showed that the variance in each of the four gait parameters of PD patients was significantly higher compared with the controls, irrespective of the three walking patterns. This study showed that the variance of any of the gait interval parameters obtained using IMU during any of the walking patterns could be used to differentiate between the gait of PD and control people.

Normal and pathological gait classification LSTM model

Computer vision-based clinical gait analysis is the subject of permanent research. However, there are very few datasets publicly available; hence the comparison of existing methods between each other is not straightforward. Even if the test data are in an open access, existing databases contain very few test subjects and single modality measurements, which limit their usage. The contributions of this paper are three-fold. First, we propose a new open-access multi-modal database acquired with the Kinect v.2 camera for the task of gait analysis. Second, we adapt to use the skeleton joint orientation data to calculate kinematic gait parameters to match golden-standard MOCAP systems. We propose a new set of features based on 3D low-limbs flexion dynamics to analyze the symmetry of a gait. Third, we design a Long-Short Term Memory (LSTM) ensemble model to create an unsupervised gait classification tool. The results show that joint orientation data provided by Kinect can be successfully used in an inexpensive clinical gait monitoring system, with the results moderately better than reported state-of-the-art for three normal/pathological gait classes.

Transcranial Direct Current Stimulation in Pediatric Motor Disorders: A Systematic Review and Meta-analysis

OBJECTIVE

To systematically examine the safety and effectiveness of transcranial direct current stimulation (tDCS) interventions in pediatric motor disorders.

DATA SOURCES

PubMed, EMBASE, Cochrane, CINAHL, Web of Science, and ProQuest databases were searched from inception to August 2018.

STUDY SELECTION

tDCS randomized controlled trials (RCTs), observational studies, conference proceedings, and dissertations in pediatric motor disorders were included. Two authors independently screened articles based on predefined inclusion criteria.

DATA EXTRACTION

Data related to participant demographics, intervention, and outcomes were extracted by 2 authors. Quality assessment was independently performed by 2 authors.

DATA SYNTHESIS

A total of 23 studies involving a total of 391 participants were included. There was no difference in dropout rates between active (1 of 144) and sham (1 of 144) tDCS groups, risk difference 0.0, 95% confidence interval (-.05 to .04). Across studies, the most common adverse effects in the active group were tingling (17.2%), discomfort (8.02%), itching (6.79%), and skin redness (4%). Across 3 studies in children with cerebral palsy, tDCS significantly improved gait velocity (MD=.23; 95% confidence interval [0.13-0.34]; P<.0005), stride length (MD=0.10; 95% confidence interval [0.05-0.15]; P<.0005), and cadence (MD=15.7; 95% confidence interval [9.72-21.68]; P<.0005). Mixed effects were found on balance, upper extremity function, and overflow movements in dystonia.

CONCLUSION

Based on the studies reviewed, tDCS is a safe technique in pediatric motor disorders and may improve some gait measures and involuntary movements. Research to date in pediatric motor disorders shows limited effectiveness in improving balance and upper extremity function. tDCS may serve as a potential adjunct to pediatric rehabilitation; to better understand if tDCS is beneficial for pediatric motor disorders, more well-designed RCTs are needed.

Gait and functional status analysis before and after total knee arthroplasty

BACKGROUND

Among the procedures for severe gonarthrosis, total knee arthroplasty (TKA) is considered a successful method patient satisfaction and functional improvement; however, TKA is commonly associated with incompletely recovered gait function. The aim of this study was to evaluate the influence of TKA and physiotherapy programmes on gait features and patient-reported functional status and the relationship between them, leading to broader knowledge of the origins of long-term gait disturbances.

METHODS

Walking speed, step length and single support time were analysed by GAITRite system in 60 healthy controls and 21 TKA patients analysed at four time points: one day before and five days after surgery and before and after a three-week rehabilitation (12 and 15 weeks after surgery). Functional status was assessed using the Western Ontario and McMaster Osteoarthritis Index (WOMAC).

RESULTS

At all time points, the TKA subjects walked significantly slower than the controls, but walking speed continuously increased after surgery. Gait asymmetries were observed in single support time (before surgery) and step length (after surgery). Partial restoration of gait function was observed 12 weeks after surgery and completion of the rehabilitation programme. An indirect correlation between gait velocity and function WOMAC subscores was found.

CONCLUSIONS

Patients after TKA were characterised by significant improvements in self-reported functionality and progressive reduction of gait abnormalities, probably related to pain reduction. However, at 15 weeks after surgery, patients exhibited step length asymmetry, which could be considered as an effect of habits of three-point crutch gait in the early postoperative period.

Lower limb angular velocity during walking at various speeds

BACKGROUND

Although it is well established that lower limb joint angles adapt to walking at various speeds, limited research has examined the modifications in joint angular velocity. There is currently no normative dataset for joint angular velocity during walking, which would be useful to allow comparisons for patient cohorts. Additionally, understanding normal joint angular velocity may assist clinical assessment and treatment procedures to incorporate methods that replicate the movement speed of the lower limb joints during walking.

RESEARCH QUESTION

This study aimed to examine lower limb joint angles and angular velocities in a healthy population walking at various gait speeds.

METHODS

Thirty-six healthy adult participants underwent three-dimensional gait analysis while walking at various speeds during habitual and slowed walking. The peak joint angles and angular velocities during important phases of the gait cycle were examined for the hip, knee and ankle in the sagittal plane. Data were grouped in 0.2 m/s increments from a walking speed of 0.4 m/s to 1.6 m/s to represent the range of walking speeds reported in studies of people with gait impairments.

RESULTS

For joint angles and angular velocities, the shape of the gait traces were consistent regardless of the walking speed. However as walking speed increased, so did the peak joint angles and angular velocities for the hip, knee and ankle. The largest angular velocity occurred when the knee joint extended at the terminal swing phase of gait. For the ankle and hip joints, the largest angular velocity occurred during the push-off phase.

SIGNIFICANCE

This study examined how lower limb joint angular velocities change with various walking speeds. These data can be used as a comparator for data from clinical cohorts, and has the potential to be used to match clinical assessment and treatment methods to joint angular velocity during walking.