Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks

Skurowski, Przemysław; Pawlyta, Magdalena

doi:10.3390/s21186115

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…, [95], [96], [97], [98] The fundamental distinction among the different moving average filters is the weighting function. Whereas in SMA filters the information extrapolated from each frame of the window is weighted equally, weighted moving average (WMA) filters assign different weights to different frames.…”

Section: A Moving Averagementioning

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: A Moving Averagementioning

confidence: 99%

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

Martini,

Calanca,

Bombieri

2024

Preprint

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“…Approaches include interpolation [14], fusion of weak regressors [15], inverse kinematics [16], skeletal model [17], and inter-marker correlations [18,19]. Nowadays, deep neural networks are hot topics) [20][21][22]. However, such approaches usually require a lot of training data, which might not be available-every new marker configuration, new type of activity, or even individual actor might require retraining the network.…”

Section: Previous Workmentioning

confidence: 99%

Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

Skurowski

Pawlyta

2022

Sensors

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Optical motion capture systems are prone to errors connected to marker recognition (e.g., occlusion, leaving the scene, or mislabeling). These errors are then corrected in the software, but the process is not perfect, resulting in artifact distortions. In this article, we examine four existing types of artifacts and propose a method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology, and loose predictor. The tests involved multiple simulations using synthetically-distorted sequences, performance comparisons to human operators (concerning real life data), and an applicability analysis for the distortion removal.

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“…Se trabajó con un conjunto de datos simulados que incluían 720 adquisiciones elaborados a través de los datos experimentales de un sujeto sin impedimentos físicos o patologías siguiendo a (Skurowski & Pawlyta, 2021). Se colocaron 30 marcadores reflectivos sobre la piel del participante en distintos puntos anatómicos.…”

Section: Datos Experimentalesunclassified

Limpieza y etiquetado de datos de marcha: un algoritmo automático basado en el análisis de la trayectoria de los marcadores

Sganga

Ritacco²,

Ravera³

2022

AJEA

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La captura de movimiento (MoCap) óptico es un proceso a través del cual graba la ubicación de marcadores reflectivos colocados sobre la piel del paciente para analizar su biomecánica a través de diferentes modelos. La captura de estos marcadores puede presentar oclusiones y ruidos que hace que la limpieza de los datos crudos y su etiquetado no pueda ser de forma automática. Este proceso le demanda al operador mucho tiempo. No existe en la bibliografía una solución que aborde ambos problemas en simultáneo. En este trabajo proponemos un algoritmo que limpia los marcadores fantasmas y espurios, completa los gaps de estos y los etiqueta en función de una plantilla cargada previamente. Se estudio su robustez con datos simulados aumentando el ruido de las adquisiciones y los gaps con falta de información. Los resultados obtenidos son muy promisorios, demostrando la robustez del algoritmo y su factible aplicación en un entorno clínico disminuyendo los tiempos de procesamiento.

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Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks

Cited by 6 publications

References 38 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

Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

Limpieza y etiquetado de datos de marcha: un algoritmo automático basado en el análisis de la trayectoria de los marcadores

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