CARDIO-Affect: A Hamiltonian-Variability Framework for Spatio-Temporal Emotional Pattern Recognition with Manifold-Based Individual and Group Profiling

A theoretical framework named CARDIO-Affect has been developed for the analysis of long-term emotional dynamics within defined social groups. This framework addresses the complexities of naturalistic emotion, which often includes multi-stable attractors, weak chaos, long-range memory, and sparse heterogeneous coupling, features not captured by conventional short-clip facial emotion analysis. It models individual emotion as a multi-stable nonlinear stochastic dynamical system and group emotion as an emergent property of a sparsely coupled network.

Understanding complex, long-term emotional dynamics within groups is critical for fields requiring nuanced human interaction analysis, such as organizational behavior, public safety, and consumer sentiment. This framework offers a scientific approach to quantifying and predicting these dynamics, moving beyond superficial, short-term analyses to provide deeper insights into group cohesion, stability, and potential emergent behaviors.

CARDIO-Affect is a complex-systems theoretical framework for long-term emotional dynamics in bounded social groups.

Forward consideration, not a verified fact.