title: Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?
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.
authors.affiliation: Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands. f.j.wouda@utwente.nl.
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Wouda
authors.affiliation: Xsens Technologies B.V., Pantheon 6a, Enschede 7521 PR, The Netherlands. Matteo.Giuberti@xsens.com.
authors.forename: Matteo
authors.initials: M
authors.lastname: Giuberti
authors.affiliation: Xsens Technologies B.V., Pantheon 6a, Enschede 7521 PR, The Netherlands. Giovanni.Bellusci@xsens.com.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Bellusci
authors.affiliation: Institute for Biomedical Technology and Technical Medicine (MIRA), University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands. p.h.veltink@utwente.nl.
authors.forename: Peter H
authors.initials: PH
authors.lastname: Veltink
mesh_terms: D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D005260:Female; D018594:Human Body; D006801:Humans; D007596:Joints / Q000502:physiology; D000069550:Machine Learning; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D016571:Neural Networks, Computer; D009949:Orientation; D011187:Posture / Q000502:physiology*; D013997:Time Factors
keywords: human movement; inertial motion capture; machine learning; nearest neighbor search; neural networks; orientation tracking; reduced sensor set