Figure 4 Sparsity analysis of human motion.
Improved measurement of human motion, using an optical motion capture system or a depth sensor, allows robots to recognize human actions from superficial motion data, such as camera images containing human actions or positions of human bodies.
In this work, a novel, motion-based nonparametric approach to the problem of human motion analysis is presented.
The synchronized motion sequences are utilized to learn a model of human motion and to extract signal statistics.
In Stage 2 a set of adaptive filters is used to learn the characteristic features of human motion that can easily be classified into various human motion types.
All of Nexi's cues were derived from examples of human motion, to make them as authentic as possible.
Intensive research on imitation learning of human motions has been performed for the robots that can recognize human activity and synthesize human-like motions, and this research is subsequently extended to integration of motions and language.
First, we discuss the influence of our human motion features.
Although human biological motion involved more complex movements than non-biological motion, both the form (the configuration of dots) and motion information of human biological motion were changed from medaka biological motion.
Feature selection of the human motions and music pieces is also needed for more accurate extraction of the relationship between human motions and music pieces.
