Effects of low-pass filter combinations on lower extremity joint moments in distance running

Mai, Patrick; Willwacher, Steffen

doi:10.1016/j.jbiomech.2019.08.005

Cited by 48 publications

(28 citation statements)

References 27 publications

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“…Indeed, several studies using OpenPose data have reported heavily filtering the joint centre trajectories using either a Butterworth low-pass filter (cut-off frequency-2 Hz [17]) or a weighted moving average filter [37]. Butterworth low-pass filters are widely used by biomechanics researchers [38] and demonstrated the ability to reduce the magnitudes of error in the OpenPose data in this study (Tables 2 and 4, Figure 2). However, the Butterworth low-pass filter did demonstrate sensitivities to large outliers for example, Figure 2 (lower) between approximately 1.5 and 2 m on the x-axis.…”

Section: Discussionmentioning

confidence: 80%

Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Needham

Evans

Cosker

et al. 2021

Sensors

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The ability to accurately and non-invasively measure 3D mass centre positions and their derivatives can provide rich insight into the physical demands of sports training and competition. This study examines a method for non-invasively measuring mass centre velocities using markerless human pose estimation and Kalman smoothing. Marker (Qualysis) and markerless (OpenPose) motion capture data were captured synchronously for sprinting and skeleton push starts. Mass centre positions and velocities derived from raw markerless pose estimation data contained large errors for both sprinting and skeleton pushing (mean ± SD = 0.127 ± 0.943 and −0.197 ± 1.549 m·s−1, respectively). Signal processing methods such as Kalman smoothing substantially reduced the mean error (±SD) in horizontal mass centre velocities (0.041 ± 0.257 m·s−1) during sprinting but the precision remained poor. Applying pose estimation to activities which exhibit unusual body poses (e.g., skeleton pushing) appears to elicit more erroneous results due to poor performance of the pose estimation algorithm. Researchers and practitioners should apply these methods with caution to activities beyond sprinting as pose estimation algorithms may not generalise well to the activity of interest. Retraining the model using activity specific data to produce more specialised networks is therefore recommended.

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Section: Discussionmentioning

confidence: 80%

Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Needham

Evans

Cosker

et al. 2021

Sensors

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“…This was to avoid tracking oscillatory net joint moments, which cannot be produced by muscles as they are unable to activate/deactivate instantaneously. However, in this approach the cut-off frequency to filter the GRF is too low based on a residual analysis, which leads to artificially extending the ground contact phase (Mai & Willwacher, 2019;Robertson & Dowling, 2003). Thus, the GRF onset/offset timing differences, in addition to the right lower-limb extension, can potentially be explained by the tracking of the filtered GRF.…”

Section: Notementioning

confidence: 99%

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Haralabidis

Serrancolí

Colyer

et al. 2021

PeerJ

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Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

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“…101 points). The choice of appropriate cut-off frequency ranges widely in the literature, 20 Hz seems as a good trade-off between reducing noise and attaining as much physiological frequency content as possible 19 . The interested reader may also refer to [ref.…”

Section: Data Recording and Testing Protocolmentioning

confidence: 99%

GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

et al. 2020

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The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. therefore, we present GaitRec, a comprehensive and completely annotated large-scale dataset containing bilateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bilateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

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Effects of low-pass filter combinations on lower extremity joint moments in distance running

Cited by 48 publications

References 27 publications

Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

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