Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?
AbstractHuman 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
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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.
Wouda FJ, Giuberti M, Bellusci G, Veltink PH. Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach? Sensors. 2016; 16(12):2138.Chicago/Turabian Style
Wouda, Frank J.; Giuberti, Matteo; Bellusci, Giovanni; Veltink, Peter H. 2016. "Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?" Sensors 16, no. 12: 2138.
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