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Body movements are an important communication medium through which affectivestates can be discerned. Movements that convey affect can also give machineslife-like attributes and help to create a more engaging human-machineinteraction. This paper presents an approach for automatic affective movementgeneration that makes use of two movement abstractions: 1) Laban movementanalysis (LMA), and 2) hidden Markov modeling. The LMA provides a systematictool for an abstract representation of the kinematic and expressivecharacteristics of movements. Given a desired motion path on which a targetemotion is to be overlaid, the proposed approach searches a labeled dataset inthe LMA Effort and Shape space for similar movements to the desired motion paththat convey the target emotion. An HMM abstraction of the identified movementsis obtained and used with the desired motion path to generate a novel movementthat is a modulated version of the desired motion path that conveys the targetemotion. The extent of modulation can be varied, trading-off between kinematicand affective constraints in the generated movement. The proposed approach istested using a full-body movement dataset. The efficacy of the proposedapproach in generating movements with recognizable target emotions is assessedusing a validated automatic recognition model and a user study. The targetemotions were correctly recognized from the generated movements at a rate of72% using the recognition model. Furthermore, participants in the user studywere able to correctly perceive the target emotions from a sample of generatedmovements, although some cases of confusion were also observed.

Affective Movement Generation using Laban Effort and Shape and Hidden Markov Models