Learning-based inverse dynamics for human motion analysis / Petrissa Zell, M. Sc., Hannover

By:

Zell, Petrissa, 1989- [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Fortschritt-Berichte VDI. Reihe 10Informatik/Kommunikation ; Nr. 877Publisher: Düsseldorf : VDI Verlag GmbH, [2022]Description: 1 Online-Ressource (XIII, 145 Seiten) : IllustrationenISBN:

9783186877109

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Hochschulschrift

Additional physical formats: 9783183877102 | Erscheint auch als: Learning-based inverse dynamics for human motion analysis. Druckausgabe Düsseldorf : VDI Verlag GmbH, 2022. XIII, 145 SeitenRVK: RVK: ST 300DOI: DOI: 10.51202/9783186877109Online resources:

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Dissertation note: Dissertation - Gottfried Wilhelm Leibniz Universität Hannover, 2021 Summary: This dissertation deals with machine learning techniques for inverse dynamics of human motion. Inverse dynamics refers to the derivation of acting forces and moments from the motion of a kinematic model. More precisely, the objective is to estimate joint torques, ground reaction forces and ground reaction moments at both feet based on the three-dimensional input motion of a skeletal model. The problem is solved using a data-driven machine learning approach, proposing several regression models that are particularly suitable with respect to limited data availability. The goal is to exploit the inherent strengths of machine learning, such as fast and noiseresistant data analysis. The described methods are able to predict underlying joint torques and exterior forces with high precision (on gait sequences: relative root mean squared errors of 7.0 %, 16.1 % and 11.9 % for reaction forces, reaction moments and joint moments which correspond to Pearson‘s correlation coefficients of 0.91, 0.83 and 0.82), while reducing computation times by two orders of magnitude compared to traditional optimization.PPN: PPN: 1801153639Package identifier: Produktsigel: ZDB-281-VDG | ZDB-281-VDI | GBV-18-NDS

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