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

SUBMITTER: Wouda FJ 

PROVIDER: S-EPMC5191118 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature