Effect of gap-filling technique and gap location on linear and nonlinear calculations of motion during locomotor activities

Gonabadi, Arash Mohammadzadeh; Cesar, Guilherme M.; Buster, Thad W.; Burnfield, Judith M.

doi:10.1016/j.gaitpost.2022.02.025

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Misplacements, occlusions and dropped markers are also a common source of noise in marker-based mocap systems [8]. The combination of such noise sources, along with temporary de-synchronizations between the optics and the elaboration software, leads to time periods during which the human pose information is even missing [9].…”

Section: Introductionmentioning

confidence: 99%

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Martini,

Calanca,

Bombieri

2024

Preprint

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Software platforms used for human motion analysis are increasingly being utilized in various fields, ranging from Healthcare to Industry 5.0. However, the inherent inaccuracy of these platforms often results in noisy descriptions of human poses or time periods during which the information is even missing. As a consequence, data filtering for denoising or completion is a common yet fundamental step before data analysis. Over the years, different techniques have been proposed, from generalpurpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis, with a focus on 3D positional data extrapolated through marker-based or markerless motion capture systems. It proposes a concise taxonomy based on the filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from literature. The survey also includes implementation codes or links to the authors’ original codes, allowing readers to easily reproduce all the algorithms in different experimental settings (https://github.com/PARCO-LAB/mocap-refinement).

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Section: Introductionmentioning

confidence: 99%

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Martini,

Calanca,

Bombieri

2024

Preprint

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Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities

Mohammadzadeh Gonabadi,

Buster,

Cesar

et al. 2024

Journal of Applied Biomechanics

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This study investigated how data series length and gaps in human kinematic data impact the accuracy of Lyapunov exponents (LyE) calculations with and without cubic spline interpolation. Kinematic time series were manipulated to create various data series lengths (28% and 100% of original) and gap durations (0.05–0.20 s). Longer gaps generally resulted in significantly higher LyE% error values in each plane in noninterpolated data. During cubic spline interpolation, only the 0.20-second gap in frontal plane data resulted in a significantly higher LyE% error. Data series length did not significantly affect LyE% error in noninterpolated data. During cubic spline interpolation, sagittal plane LyE% errors were significantly higher at shorter versus longer data series lengths. These findings suggest that not interpolating gaps in data could lead to erroneously high LyE values and mischaracterization of movement variability. When applying cubic spline, a long gap length (0.20 s) in the frontal plane or a short sagittal plane data series length (1000 data points) could also lead to erroneously high LyE values and mischaracterization of movement variability. These insights emphasize the necessity of detailed reporting on gap durations, data series lengths, and interpolation techniques when characterizing human movement variability using LyE values.

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Joint Angle Variability Is Altered in Patients with Peripheral Artery Disease after Six Months of Exercise Intervention

Fallahtafti

Salamifar

Hassan

et al. 2022

Entropy

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Supervised exercise therapy (SET) is a conservative non-operative treatment strategy for improving walking performance in patients with peripheral artery disease (PAD). Gait variability is altered in patients with PAD, but the effect of SET on gait variability is unknown. Forty-three claudicating patients with PAD underwent gait analysis before and immediately after a 6-month SET program. Nonlinear gait variability was assessed using sample entropy, and the largest Lyapunov exponent of the ankle, knee, and hip joint angle time series. Linear mean and variability of the range of motion time series for these three joint angles were also calculated. Two-factor repeated measure analysis of variance determined the effect of the intervention and joint location on linear and nonlinear dependent variables. After SET, walking regularity decreased, while the stability remained unaffected. Ankle nonlinear variability had increased values compared with the knee and hip joints. Linear measures did not change following SET, except for knee angle, in which the magnitude of variations increased after the intervention. A six-month SET program produced changes in gait variability toward the direction of healthy controls, which indicates that in general, SET improved walking performance in individuals with PAD.

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Effect of gap-filling technique and gap location on linear and nonlinear calculations of motion during locomotor activities

Cited by 3 publications

References 28 publications

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Denoising and Completion Filters for Human Motion Software: a Survey with Code

Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities

Joint Angle Variability Is Altered in Patients with Peripheral Artery Disease after Six Months of Exercise Intervention

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