Comparison of programs for determining temporal-spatial gait variables from instrumented walkway data: PKmas versus GAITRite.
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ABSTRACT:
SUBMITTER: Egerton T
PROVIDER: S-EPMC4150969 | biostudies-literature | 2014 Aug
REPOSITORIES: biostudies-literature
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Project description:Walking ability is frequently assessed with the 10-meter walking test (10MWT), which may be instrumented with multiple Kinect v2 sensors to complement the typical stopwatch-based time to walk 10 meters with quantitative gait information derived from Kinect's 3D body point's time series. The current study aimed to evaluate a multi-Kinect v2 set-up for quantitative gait assessments during the 10MWT against a gold-standard motion-registration system by determining between-systems agreement for body point's time series, spatiotemporal gait parameters and the time to walk 10 meters. To this end, the 10MWT was conducted at comfortable and maximum walking speed, while 3D full-body kinematics was concurrently recorded with the multi-Kinect v2 set-up and the Optotrak motion-registration system (i.e., the gold standard). Between-systems agreement for body point's time series was assessed with the intraclass correlation coefficient (ICC). Between-systems agreement was similarly determined for the gait parameters' walking speed, cadence, step length, stride length, step width, step time, stride time (all obtained for the intermediate 6 meters) and the time to walk 10 meters, complemented by Bland-Altman's bias and limits of agreement. Body point's time series agreed well between the motion-registration systems, particularly so for body points in motion. For both comfortable and maximum walking speeds, the between-systems agreement for the time to walk 10 meters and all gait parameters except step width was high (ICC ≥ 0.888), with negligible biases and narrow limits of agreement. Hence, body point's time series and gait parameters obtained with a multi-Kinect v2 set-up match well with those derived with a gold standard in 3D measurement accuracy. Future studies are recommended to test the clinical utility of the multi-Kinect v2 set-up to automate 10MWT assessments, thereby complementing the time to walk 10 meters with reliable spatiotemporal gait parameters obtained objectively in a quick, unobtrusive and patient-friendly manner.
| S-EPMC4603795 | biostudies-literature
Project description:BackgroundParkinson's disease (PD) presents with motor symptoms such as bradykinesia, rigidity, and tremor that can affect gait. To monitor changes associated with disease progression or medication use, quantitative gait assessment is often performed during clinical visits. Conversely, vision-based solutions have been proposed for monitoring gait quality in non-clinical settings.MethodsWe use three 2D human pose-estimation libraries (AlphaPose, Detectron, OpenPose) and one 3D library (ROMP) to calculate gait features from color video, and correlate them with those extracted by a Zeno instrumented walkway in older adults with PD. We calculate video-based gait features using a manual and automated heel-strike detection algorithm, and compare the correlations when the participants walk towards and away from the camera separately.ResultsBased on analysis of 67 bidirectional walking bouts from 25 adults with PD, moderate to strong positive correlations were identified between the number of steps, cadence, as well as the mean and coefficient of variation of step width calculated from Zeno and video using 2D pose-estimation libraries. We noted that our automated heel-strike annotation method struggled to identify short steps.ConclusionGait features calculated from 2D joint trajectories are more strongly correlated with the Zeno than analogous gait features calculated from ROMP. Based on our analysis, videos processed with 2D pose-estimation libraries can be used for longitudinal gait monitoring in individuals with PD. Future work will seek to improve the prediction of gait features using a comprehensive machine learning model to predict gait features directly from color video without relying on intermediate extraction of joint trajectories.
| S-EPMC9252334 | biostudies-literature
Project description:BackgroundFalls are common following stroke and are frequently related to deficits in balance and mobility. This study aimed to investigate the predictive strength of gait and balance variables for evaluating post-stroke falls risk over 12?months following rehabilitation discharge.MethodsA prospective cohort study was undertaken in inpatient rehabilitation centres based in Australia and Singapore. A consecutive sample of 81 individuals (mean age 63?years; median 24?days post stroke) were assessed within one week prior to discharge. In addition to comfortable gait speed over six metres (6mWT), a depth-sensing camera (Kinect) was used to obtain fast-paced gait speed, stride length, cadence, step width, step length asymmetry, gait speed variability, and mediolateral and vertical pelvic displacement. Balance variables were the step test, timed up and go (TUG), dual-task TUG, and Wii Balance Board-derived centre of pressure velocity during static standing. Falls data were collected using monthly calendars.ResultsOver 12?months, 28% of individuals fell at least once. The faller group had increased TUG time and reduced stride length, gait speed variability, mediolateral and vertical pelvic displacement, and step test scores (P?<?0.001-0.048). Significant predictors, when adjusted for country, prior falls and assistance (i.e., physical assistance and/or gait aid use) were stride length, step length asymmetry, mediolateral pelvic displacement, step test and TUG scores (P?<?0.040; IQR-odds ratio(OR)?=?1.37-7.85). With comfortable gait speed as an additional covariate, to determine the additive benefit over standard clinical assessment, only mediolateral pelvic displacement, TUG and step test scores remained significant (P?=?0.001-0.018; IQR-OR?=?5.28-10.29).ConclusionsReduced displacement of the pelvis in the mediolateral direction during walking was the strongest predictor of post-stroke falls compared with other gait variables. Dynamic balance measures, such as the TUG and step test, may better predict falls than gait speed or static balance measures.
| S-EPMC6322221 | biostudies-literature
Project description:BACKGROUND:Assessing the risk of disability in older adults is important for developing prevention and intervention strategies to decrease potential disability and dependency. The aim of this study was to examine the association between spatio-temporal gait variables and disability among older adults. METHODS:We conducted a prospective study in a community setting. We collected data from 4121 subjects (≥ 65 years, mean age: 71.9 years). Gait speed, cadence, stride length, and stride length variability were measured at baseline. Participants were instructed to walk at their usual pace along a 6.4 m straight and flat path on which an electronic gait measuring device was mounted at mid 2.4 m. Subsequent disability was confirmed from long-term care insurance records. RESULTS:During follow-up duration (mean: 49.6 months), 425 participants had incident disability. The cut-off value to detect high or low function in each gait variable was determined using the Youden index. Cox proportional hazard analysis adjusted for covariates showed that disability was significantly predicted by low function in each gait variable using the cut-off values: gait speed (hazard ratio [95% confidential intervals]: 2.06 [1.65-2.57]), stride length (2.17 [1.72-2.73]), cadence (1.49 [1.20-1.86], and stride length variability (1.46 [1.19-1.80]). The number of gait variables that scored in the low function category were also cumulatively related to subsequent disability (p < .001). CONCLUSIONS:This study revealed that spatio-temporal gait variables had a significant predictive value for incident disability. Multifaceted and quantitative gait analysis can contribute to disability risk assessment.
| S-EPMC6993362 | biostudies-literature
Project description:In-shoe systems and pressure plates are used to assess plantar pressure during gait, but additional tools are employed to evaluate other gait parameters. The GAITRite® system is a clinical gait evaluation tool. Extensive literature is available for spatiotemporal parameters, but it is scarce for relative plantar pressure data. Therefore, we investigated whether, when controlling for age, the GAITRite® system is able to distinguish the effects of walking velocity on plantar pressure parameters in six plantar regions in a large sample of adults. Participants (83 women and 87 men, aged 18-85 years) walked at three self-selected velocities (slow, preferred, fast) on a 6-m long GAITRite® walkway. Relative peak pressure, pressure-time integral, peak time and contact area were computed for six zones (lateral and medial heel, mid- and forefoot). The impact of age (covariate), sex, side, velocity, pressure zone and their interactions on pressure variables was evaluated. Velocity affected peak pressure, pressure-time integral, peak time and contact area (p < 0.001). With increasing self-selected gait velocity, medial forefoot peak pressure and pressure-time integral increased (p < 0.001), while heel and lateral forefoot regions displayed a nonlinear plantar pressure evolution. These results suggest lower (heel strike) or more equally distributed (push-off) loads at preferred gait velocity.
| S-EPMC9781928 | biostudies-literature
Project description:ImportanceRegaining the ability to walk safely is a high priority for adults with vestibular loss. Thus, practitioners need comprehensive knowledge of vestibulopathic gait to design, provide, and/or interpret outcomes of interventions. To date, few studies have characterized the effects of vestibular loss on gait.ObjectivesTo investigate the use of an instrumented 2-minute walk test in adults with vestibular loss, to further characterize vestibulopathic gait, and to assess whether those with chronic vestibular loss have enduring gait deficits.Design, setting, and participantsThis cross-sectional study, conducted between April 3, 2018, and June 27, 2019, recruited adults 20 to 79 years of age from an academic, tertiary, hospital-based, ambulatory care setting who were healthy or had confirmed unilateral or bilateral vestibular hypofunction. Of the 43 adults who were screened from convenience and referred samples, 2 declined, and 7 were excluded because of health conditions.ExposuresThe main exposure was the instrumented 2-minute walk test, which was conducted with participants using wearable inertial measurement units while they walked a 10-m path at their self-selected speed and turned 180° in their self-selected direction at either end.Main outcomes and measuresThe primary measures were spatiotemporal gait metrics (eg, stride length [SL] and peak whole-body turning velocity). Multivariate analysis of variance was used to assess between-group differences. Validity was assessed using the area under the curve from receiver operator characteristic analyses.ResultsData from 17 healthy adults (mean [SD] age, 39.27 [11.20] years; 13 [76%] female) and 13 adults with vestibular loss (mean [SD] age, 60.50 [10.81] years; 6 [46%] female) were analyzed. Very large between-group differences were found for SL (left) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.47 [0.04] m vs 1.31 [0.04] m; Cohen d, 1.35; 95% CI, 0.18-2.52), SL (right) (estimated marginal mean [SE] for healthy vs vestibular groups, 1.46 [0.04] m vs 1.29 [0.04] m; Cohen d, 1.44; 95% CI, 0.25-2.62), and peak turn velocity (estimated marginal mean [SE] for healthy vs vestibular groups, 240.17 [12.78]°/s vs 189.74 [14.70]°/s; Cohen d, 1.23; 95% CI, 0.07-2.40). The area under the curve was 0.79 (95% CI, 0.62-0.95) for SL (left), 0.81 (95% CI, 0.64-0.97) for SL (right), and 0.86 (95% CI, 0.72-0.99) for peak turn velocity.Conclusions and relevanceIn this cross-sectional study, instrumented gait analysis had good discriminative validity and revealed persistent deficits in gait stability in those with chronic vestibular loss. The findings of this study suggest that these clinically and functionally meaningful deficits could be targets for vestibular rehabilitation.
| S-EPMC8251650 | biostudies-literature
Project description:BackgroundCongenital disorders of glycosylation (CDG) are genetic diseases caused by impaired synthesis of glycan moieties linked to glycoconjugates. Phosphomannomutase 2 deficiency (PMM2-CDG), the most frequent CDG, is characterized by prominent neurological involvement. Gait disturbance is a major cause of functional disability in patients with PMM2-CDG. However, no specific gait assessment for PMM2-CDG is available. This study analyses gait-related parameters in PMM2-CDG patients using a standardized clinical assessment and instrumented gait analysis (IGA).ResultsSeven adult patients with a molecular diagnosis of PMM2-CDG were followed-up from February 2021 to December 2022 and compared to a group of healthy control (HC) subjects, matched for age and sex. Standardized assessment of disease severity including ataxia and peripheral neuropathy along with isometric muscle strength and echo-biometry measurements at lower limbs were performed. IGA spatiotemporal parameters were obtained by means of a wearable sensor in basal conditions. PMM2-CDG patients displayed lower gait speed, stride length, cadence and symmetry index, compared to HC. Significant correlations were found among the used clinical scales and between disease severity (NCRS) scores and the gait speed measured by IGA. Variable reduction of knee extension strength and a significant decrease of lower limb muscle thickness with conserved echo intensity were found in PMM2-CDG compared to HC.ConclusionsThe study elucidates different components of gait disturbance in PMM2-CDG patients and shows advantages of using wearable sensor-based IGA in this frame. IGA parameters may potentially serve as quantitative measures for follow-up or outcome quantification in PMM2-CDG.
| S-EPMC10837865 | biostudies-literature
Project description:Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as "knowledge engines" in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality-especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.
| S-EPMC6119462 | biostudies-literature
Project description:Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N=57 patients during levodopa challenge tests. The proposed model was able to identify kinematic features associated with each FOG severity level that were highly consistent with the features that movement disorders specialists are trained to identify as characteristic of freezing. In this work, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.
| S-EPMC9882551 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) is a common problem in the older population. To reduce pain and stress in the affected knee joint compartment, a functional knee brace is often prescribed by physicians to protect it from high loads.ObjectivesAn instrumented gait analysis should evaluate how the 4-point knee orthosis for varus or valgus load relief (M.4s OA) changes the kinematics of the knee, especially in the frontal plane.Methods17 healthy participants took part and were analyzed with an inertial sensor system (MyoMotion) giving continuous, objective information on the anatomical angles. The measurements were made both without wearing a knee brace and with the brace in different settings.ResultsThe results show a significant reduction in the maximum knee abduction and raised knee adduction. The knee brace, with a strong adjustment in varus or valgus orientation, caused a shift of maximum ab-/adduction in the proposed direction in 69% and 75% of the dynamic tests, respectively. The knee motion in the frontal plane shows individual movement patterns.ConclusionThe use of the brace leads to significant changes in the knee's movement. Patient-specific movement patterns may explain different effects of functional knee braces on individual persons. Inertial sensors have been shown to be a low-cost, easy-to-use option for individual movement analysis and further personalized therapy.
| S-EPMC7482934 | biostudies-literature