Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning

Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning

Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and/or sufficie...

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Bibliographic Details
Main Authors: Frank J. Wouda, Matteo Giuberti, Nina Rudigkeit, Bert-Jan F. van Beijnum, Mannes Poel, Peter H. Veltink
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
Subjects:
inertial motion capture
machine learning
neural networks
deep learning
LSTM
time coherence
human movement
reduced sensor set
pose estimation
Online Access:https://www.mdpi.com/1424-8220/19/17/3716

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