Abstract / truncated to 115 words (read the full abstract)

Human motion capture is important for a wide variety of applications, e.g., biomechanical analysis, virtual reality and character animation. Current human motion capture solutions require a large number of markers/sensors to be placed on the body. In this work, it is shown that this can be reduced by using data-driven approaches. First a comparison of the use of lazy and eager learning methods for estimation of full-body movements from a minimal sensor set is done, which shows that both learning approaches lead to similar estimation accuracy. Next, improvements of the time coherency of output poses of the previously developed eager learning method are introduced by using a stacked input neural network. Results show that these ... toggle 5 keywords

sensor fusion – biomechanics – inertial sensing – machine learning – minimal sensing

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