Chromatophores are the pigment-containing cells in the skins of animals such as fish and cephalopods which have chromomorphic (colour-changing) and controllable goniochromic (iridescent-changing) properties. These animals control the optical properties of their skins for camouflage and, it is speculated, for communication. The ability to replicate these properties in soft artificial skin structures opens up new possibilities for active camouflage, thermal regulation and active photovoltaics. This paper presents the design and implementation of soft and compliant artificial chromatophores based on the cutaneous chromatophores in fish and cephalopods. We demonstrate artificial chromatophores that are actuated by electroactive polymer artificial muscles, mimicking the radially orientated muscles found in natural chromatophores. It is shown how bio-inspired chromomorphism may be achieved using both areal expansion of dielectric elastomer structures and by the hydrostatic translocation of pigmented fluid into an artificial dermal melanophore.

We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users' experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures.

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