Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2016-12, Vol.16 (12), p.2138-2138
Main Authors: Wouda, Frank J, Giuberti, Matteo, Bellusci, Giovanni, Veltink, Peter H
Format: Article
Language:English
Subjects:
Adult
Algorithms
Biomechanical Phenomena
Female
Human Body
Human motion
human movement
Humans
inertial motion capture
Inertial platforms
Inertial sensing devices
Joints - physiology
Learning
Machine Learning
Magnetic disturbances
Male
Monitoring, Physiologic - instrumentation
Motion capture
Motion perception
Multisensor fusion
nearest neighbor search
neural networks
Neural Networks (Computer)
Orientation
orientation tracking
Position errors
Posture - physiology
reduced sensor set
Searching
Sensors
Time Factors
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Summary:Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 ∘ . Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances.
ISSN:1424-8220
1424-8220
DOI:10.3390/s16122138