Details
| Original language | English |
|---|---|
| Pages (from-to) | 11729-11743 |
| Number of pages | 15 |
| Journal | Neural Computing and Applications |
| Volume | 32 |
| Issue number | 15 |
| Publication status | Published - 23 Dec 2019 |
Abstract
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.
Keywords
- Deep neural networks, Gait phase detection, Human locomotion, Inverse dynamics
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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In: Neural Computing and Applications, Vol. 32, No. 15, 23.12.2019, p. 11729-11743.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning inverse dynamics for human locomotion analysis
AU - Zell, Petrissa
AU - Rosenhahn, Bodo
PY - 2019/12/23
Y1 - 2019/12/23
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - Gait phase detection
KW - Human locomotion
KW - Inverse dynamics
UR - http://www.scopus.com/inward/record.url?scp=85077148319&partnerID=8YFLogxK
U2 - 10.1007/s00521-019-04658-z
DO - 10.1007/s00521-019-04658-z
M3 - Article
AN - SCOPUS:85077148319
VL - 32
SP - 11729
EP - 11743
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 15
ER -