Affective Computing

Affective Computing (Emotion AI): Systems that recognize, interpret, and respond to human emotional states (e.g., frustration, joy) using sensors and machine learning.

This technology is the core of Emotion AI: systems built to recognize, interpret, process, and simulate human affect. The field originated at the MIT Media Lab in 1995 with founder Rosalind Picard's seminal work. Affective Computing leverages computer vision, natural language processing (NLP), and machine learning to analyze cues like facial expressions, vocal tone, and physiological signals (e.g., heart rate). Practical applications are already deployed: Empatica, a spin-off, uses its technology for FDA-cleared seizure monitoring, while other systems personalize education by detecting student confusion or optimize customer service by gauging user frustration.

https://www.media.mit.edu/groups/affect/overview/