Learning inverse dynamics for human locomotion analysis

In this work, learning-based inverse dynamics algorithms are proposed for the analysis of human motion. Immeasurable joint torques and exterior contact forces are directly estimated from motions by machine learning techniques including deep neural networks, random forests and Ridge regression. A mul...

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Bibliographic Details
Published in:Neural computing & applications 2020-08, Vol.32 (15), p.11729-11743
Main Authors: Zell, Petrissa, Rosenhahn, Bodo
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Contact force
Data Mining and Knowledge Discovery
Datasets
Force measurement
Gait
Human motion
Human performance
Image Processing and Computer Vision
Inverse dynamics
Locomotion
Machine learning
Motion capture
Optimization
Original Article
Position measurement
Probability and Statistics in Computer Science
Regression analysis
Three dimensional motion
Torque
Walking
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Summary:In this work, learning-based inverse dynamics algorithms are proposed for the analysis of human motion. Immeasurable joint torques and exterior contact forces are directly estimated from motions by machine learning techniques including deep neural networks, random forests and Ridge regression. A multistage subclass approach is introduced. The method recovers occluded motion data and generates meaningful features, as well as gait phase labels to restrict and facilitate the regression of forces and moments. In contrast to the state-of-the-art inverse dynamics optimization, the learning-based methods are independent of ground reaction force measurements and the global position and orientation of the human body. These properties make the application to reconstructed poses from videos or inertial measurements possible, creating fast and simple access to the underlying dynamics of recorded human motions. The performance of the proposed methods is evaluated on a self-recorded data set including walking and running motions and on a publicly available gait data set by Fukuchi et al. (PeerJ 6:e4640, 2018). Furthermore, the applicability to reconstructed gait sequences taken from the well-known CMU database (Human motion capture database, 2014. http://mocap.cs.cmu.edu/ ) is investigated. Finally, the method is tested as a tool to detect abnormal torque distributions in gait, based on a reconstructed 3D motion of a limping subject.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04658-z