Joint Optimization of Kinematics and Anthropometrics for Human Motion Denoising

Zhou, Le; Lannan, Nate; Fan, Guoliang

doi:10.1109/jsen.2022.3144946

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…Employing methods such as Kalman filtering and particle filters to impose additional constraints on human motion data is a common practice [36,37]. Several studies have modified the Kalman filter to achieve better human motion restoration effects [38]. While filtering methods are classic, they also have many limitations, and learning-based approaches have garnered more attention.…”

Section: Human Motion Enhancement Methodsmentioning

confidence: 99%

“…We selected three different architectural enhancement methods as our baseline for comparison. These include TPE-DE, which employs a Tobit particle filter for motion enhancement [38]; BRA-P, an autoencoder model based on LSTM [41]; and a STRNN, an RNN model that models human motion by learning human flow dynamics [39]. These methods represent the mainstream frameworks for processing human motion data.…”

Section: Motion Enhancement Evaluationmentioning

confidence: 99%

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Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning

He,

Yang,

Konomi

2024

Sensors

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Human motion capture technology, which leverages sensors to track the movement trajectories of key skeleton points, has been progressively transitioning from industrial applications to broader civilian applications in recent years. It finds extensive use in fields such as game development, digital human modeling, and sport science. However, the affordability of these sensors often compromises the accuracy of motion data. Low-cost motion capture methods often lead to errors in the captured motion data. We introduce a novel approach for human motion reconstruction and enhancement using spatio-temporal attention-based graph convolutional networks (ST-ATGCNs), which efficiently learn the human skeleton structure and the motion logic without requiring prior human kinematic knowledge. This method enables unsupervised motion data restoration and significantly reduces the costs associated with obtaining precise motion capture data. Our experiments, conducted on two extensive motion datasets and with real motion capture sensors such as the SONY mocopi, demonstrate the method’s effectiveness in enhancing the quality of low-precision motion capture data. The experiments indicate the ST-ATGCN’s potential to improve both the accessibility and accuracy of motion capture technology.

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Section: Human Motion Enhancement Methodsmentioning

confidence: 99%

Section: Motion Enhancement Evaluationmentioning

confidence: 99%

Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning

He,

Yang,

Konomi

2024

Sensors

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“…The KF state is then updated with the adjusted measurement. Other combinations of state observer and evolutionary algorithm are [136], [137].…”

Section: A State Observer and Evolutionary Algorithmmentioning

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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“…In [20] , the authors propose the Bettle antennae algorithm (BAA) in wireless sensor networks for jamming attack points. The authors [21] focus on a new approach, deployed for human motion-denoising via a joint optimisation of kinematic and anthropometric constraints. This is considered as noisy-wave skeleton data meant for depth-sensor-based motion capture (D-Mocap).…”

Section: Literature Reviewmentioning

confidence: 99%

“…(16) . This leads to joint kinematics optimization described [21] .