  Body movements are an important communication medium through which affective
states can be discerned. Movements that convey affect can also give machines
life-like attributes and help to create a more engaging human-machine
interaction. This paper presents an approach for automatic affective movement
generation that makes use of two movement abstractions: 1) Laban movement
analysis (LMA), and 2) hidden Markov modeling. The LMA provides a systematic
tool for an abstract representation of the kinematic and expressive
characteristics of movements. Given a desired motion path on which a target
emotion is to be overlaid, the proposed approach searches a labeled dataset in
the LMA Effort and Shape space for similar movements to the desired motion path
that convey the target emotion. An HMM abstraction of the identified movements
is obtained and used with the desired motion path to generate a novel movement
that is a modulated version of the desired motion path that conveys the target
emotion. The extent of modulation can be varied, trading-off between kinematic
and affective constraints in the generated movement. The proposed approach is
tested using a full-body movement dataset. The efficacy of the proposed
approach in generating movements with recognizable target emotions is assessed
using a validated automatic recognition model and a user study. The target
emotions were correctly recognized from the generated movements at a rate of
72% using the recognition model. Furthermore, participants in the user study
were able to correctly perceive the target emotions from a sample of generated
movements, although some cases of confusion were also observed.
