TY - BOOK

T1 - Learning-based inverse dynamics for human motion analysis

AU - Zell, Petrissa

PY - 2022

Y1 - 2022

N2 - 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

AB - 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

KW - Maschinelles Lernen

KW - menschliche Bewegung

KW - Künstliche Neuronale Netze

KW - inverse Dynamik

KW - self-supervised learning

KW - inverse dynamics

KW - selbstüberwachtes Lernen

KW - Machine Learning

KW - gait analysis

KW - artificial neural networks

KW - human motion

KW - joint moments

KW - Gelenkmomente

KW - Ganganalyse

U2 - 10.51202/9783186877109

DO - 10.51202/9783186877109

M3 - Monograph

SN - 9783183877102

T3 - Fortschritt-Berichte VDI

BT - Learning-based inverse dynamics for human motion analysis

CY - Düsseldorf

ER -