Estimation of full-body poses using only five inertial sensors: an eager or lazy learning approach?

Frank Jasper Wouda, Matteo Giuberti, Giovanni Bellusci, Petrus H. Veltink

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    16 Citations (Scopus)
    298 Downloads (Pure)

    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.
    Original languageUndefined
    Pages (from-to)2138
    Number of pages17
    JournalSensors (Switserland)
    Volume16
    Issue number12
    DOIs
    Publication statusPublished - 15 Dec 2016

    Keywords

    • inertial motion capture
    • human movement
    • EWI-27528
    • reduced sensor set
    • IR-102654
    • Neural Networks
    • nearest neighbor search
    • orientation tracking
    • METIS-320916
    • Machine Learning

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