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

TL;DR

This paper introduces a novel method for generating affective human movements by combining Laban movement analysis with hidden Markov models, enabling the creation of expressive motions that can be recognized and perceived by humans.

Contribution

It proposes an integrated approach that uses Laban movement analysis and HMMs to generate emotionally expressive movements from desired motion paths.

Findings

Target emotions recognized at 72% accuracy by an automatic model.

Participants correctly perceived emotions in user study.

The method allows adjustable modulation between kinematic and affective constraints.

Abstract

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.