Journal of Multimedia Processing and Technologies Pit Pichappan 17 3 2026 https://doi.org/10.6025/jmpt/2026/17/3/95-123 https://www.dline.info/jmpt/fulltext/v17n3/jmptv17n3_1.pdf Understanding the temporal evolution of human emotions is critical for developing empathetic and adaptive Human Robot Interaction (HRI) systems. While existing research predominantly focuses on instantaneous emotion classification, it often overlooks the longitudinal dynamics and underlying latent affective structures. This study introduces the Temporal Emotion Dynamics and Latent Regime Framework (TEDLRF) to model the evolution of emotions as a stochastic temporal process. Utilizing a pre trained Vision Transformer for facial emotion recognition, we analyzed longitudinal sequences of five basic emotions through Markov Chains, Hidden Markov Models (HMM), and Dynamic Bayesian Networks. Our analysis reveals that emotional states exhibit remarkable temporal inertia, with an Emotional Stability Index of 0.991 and self transition probabilities exceeding 97%. Furthermore, HMM state discovery identifies three distinct latent affective regimes: isolated anger, persistent sadness, and a flexible positive neutral subsystem. Survival analysis further demonstrates significant heterogeneity in emotional persistence, where sadness forms highly stable, long duration attractors, whereas surprise remains highly transient. Emotional evolution is characterized by quasi stationary phases punctuated by discrete regime shifts rather than continuous drift. These findings indicate that observable facial expressions are manifestations of deeper, unobservable psychological states. Consequently, we propose that next generation adaptive robots must transition from reactive, mimetic responses to proactive, temporally aware architectures. By inferring latent emotional regimes and anticipating affective transitions via change point detection, robots can deliver context sensitive, empathetic behaviors, ultimately fostering more natural and effective human AI collaboration.