Multimodal Emotion AI

Cross-cultural algorithms interpreting affect from multiple biosignals and expressions.

BACK TO SOMA VIEW INTERACTIVE VERSION

Multimodal Emotion AI

Machine learning systems detecting emotions through facial micro-expressions, voice prosody, gesture, physiological signals (HRV, GSR, respiration), and contextual cues, trained on globally diverse datasets to account for cultural variation in affective expression.

TRL

7/9Operational

Impact

5/5

Investment

5/5

Category

Software

Connections

Software

Cross-Cultural Affective Models

Frameworks that adapt affect recognition to cultural contexts and norms.

Hardware

Neuro-Affective Headsets

EEG/fNIRS devices tuned to detect emotional states.

Software

Affect-Adaptive Dialogue Models

Conversational agents that track and respond to long-term emotional trajectories.

Hardware

Physiological Computing Sensors

Multi-parameter biosignal devices tracking HRV, GSR, respiration, and muscle tension.

Hardware

Ambient Affective Sensing Grids

Distributed sensor meshes that infer group mood and social dynamics in spaces.

Envisioning is an emerging technology research institute and advisory.

2011 — 2026