Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

Posada-Ordax, Jorge; Cosín-Matamoros, Julia; Iglesias, Marta Elena Losa; Becerro-de-Bengoa-Vallejo, Ricardo; Esteban‐Gonzalo, Laura; Martin-Villa, Carlos; Calvo‐Lobo, César

doi:10.3390/jcm10091804

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…The results of this study, however, show that, with the exception of hip joint RoM during FCWS (3.39°), the hip transverse plane motion produced RMSD and SEM values <3° which tend lower than data acquired with marker-based motion capture (McGinley et al, 2009 ). The SEM of spatiotemporal parameters reported here is similar to previous studies that have examined reliability using 3D motion capture, IMU sensors, or pressure sensing walkways (Paterson et al, 2008 ; Meldrum et al, 2014 ; Posada-Ordax et al, 2021 ).…”

Section: Discussionsupporting

confidence: 85%

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Riazati

McGuirk²,

Perry³

et al. 2022

Front. Hum. Neurosci.

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Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings.Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%.Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%.Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

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Section: Discussionsupporting

confidence: 85%

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Riazati

McGuirk²,

Perry³

et al. 2022

Front. Hum. Neurosci.

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“…Our results align with the accuracy of the ZeroWire® footswitch system, which has four piezoresistive pressure sensors on each foot and was used to record stance time in healthy participants; its accuracy ranged from an ICC of 0.86 to 0.97, depending on walking speed ( 24 ). Due to the increased variability and decreased walking speed that are often found in pathological gait, Inertial Measurement Units (IMU) often report reduced accuracy at low walking speeds of 0.4 m/s or severe deviations from normal human gait ( 40 , 41 ). This is reflected in the lower accuracy of IMU sensors used to detect stance duration in individuals with stroke ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Accuracy, concurrent validity, and test–retest reliability of pressure-based insoles for gait measurement in chronic stroke patients

Neumann,

Bauer,

Nastasi

et al. 2024

Front. Digit. Health

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IntroductionWearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test–retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke.MethodsPressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems.Results and discussionOver all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of −0.01 s). Bland–Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test–retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.

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“…Moreover, in contrast to the presented sensor setup, only one IMU at the pelvis was used for the gait analysis [ 56 ]. Furthermore, the effect of movement velocity needs to be considered in sensor-based gait analysis because inaccuracies were reported at low walking speeds of 0.4 m/s, probably due to the speed dependency of sensor-based metrics [ 57 ]. In line with these findings, algorithms for gait detection were reported to perform less accurately depending on the severity of deviations from normal human gait due to diseases such as stroke [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke

Schwarz

Husain

Einaudi

et al. 2023

Sensors

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The restoration of gait and mobility after stroke is an important and challenging therapy goal due to the complexity of the potentially impaired functions. As a result, precise and clinically feasible assessment methods are required for personalized gait rehabilitation after stroke. The aim of this study is to investigate the reliability and validity of a sensor-based gait analysis system in stroke survivors with different severities of gait deficits. For this purpose, 28 chronic stroke survivors (9 women, ages: 62.04 ± 11.68 years) with mild to moderate walking impairments performed a set of ambulatory assessments (3× 10MWT, 1× 6MWT per session) twice while being equipped with a sensor suit. The derived gait reports provided information about speed, step length, step width, swing and stance phases, as well as joint angles of the hip, knee, and ankle, which we analyzed for test-retest reliability and hypothesis testing. Further, test-retest reliability resulted in a mean ICC of 0.78 (range: 0.46–0.88) for walking 10 m and a mean ICC of 0.90 (range: 0.63–0.99) for walking 6 min. Additionally, all gait parameters showed moderate-to-strong correlations with clinical scales reflecting lower limb function. These results support the applicability of this sensor-based gait analysis system for individuals with stroke-related walking impairments.

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Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

Cited by 6 publications

References 49 publications

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Accuracy, concurrent validity, and test–retest reliability of pressure-based insoles for gait measurement in chronic stroke patients

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke

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