The representation of human movements for recognition and synthesis is important in many application fields such as: surveillance, human-computer interaction, motion capture, and humanoid robots. Hidden Markov models (HMMs) are a common statistical framework in this context, since they are generative and are able to deal with the intrinsic dynamic variation of movements performed by humans. In this work we argue that many human movements are parametric, i.e., a parametric variation of the movements in dependence of, e.g., a position a person is pointing at. The parameter is part of the semantic of a movement. And while classic HMMs treat them as noise, we will use parametric HMMs (PHMMs) [6,19] to model the parametric variability of human movements explicitly. In this work, we discuss both types of PHMMs, as introduced in  and , and we will focus our considerations on the recognition and synthesis of human arm movements. Furthermore, we will show in various experiments the use of PHMMs for the control of a humanoid robot by synthesizing movements for relocating objects at arbitrary positions. In vision-based interaction experiments, PHMM are used for the recognition of pointing movements, where the recognized parameterization conveys to a robot the important information which object to relocate and where to put it. Finally, we evaluate the accuracy of recognition and synthesis for pointing and grasping arm movements and discuss that the precision of the synthesis is within the natural uncertainty of human movements.
Lecture Notes in Computer Science, 2009, p. 148-168