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Sensors 2016, 16(12), 2138; doi:10.3390/s16122138

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

1
Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands
2
Xsens Technologies B.V., Pantheon 6a, Enschede 7521 PR, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Kamiar Aminian
Received: 30 September 2016 / Revised: 4 December 2016 / Accepted: 8 December 2016 / Published: 15 December 2016
(This article belongs to the Special Issue Body Worn Behavior Sensing)
View Full-Text   |   Download PDF [606 KB, uploaded 15 December 2016]   |  

Abstract

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. View Full-Text
Keywords: inertial motion capture; orientation tracking; machine learning; neural networks; nearest neighbor search; human movement; reduced sensor set inertial motion capture; orientation tracking; machine learning; neural networks; nearest neighbor search; human movement; reduced sensor set
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MDPI and ACS Style

Wouda, F.J.; Giuberti, M.; Bellusci, G.; Veltink, P.H. Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach? Sensors 2016, 16, 2138.

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