A Dynamic Architecture Linking Autonomic Activity to Emotional Dimensions: Real‐Time Estimation From Photoplethysmography

The present study introduces a novel, real-time emotion (or generalized behavioral) tracking model capable of independently estimating valence, motivation (approach-avoidance), and activation (arousal) through estimations of parasympathetic (PNS), α-adrenergic (α-SNS), and β-adrenergic (β-SNS) sympathetic nervous system activity. These separate autonomic systems correspond to distinct emotional dimensions: valence to the PNS, motivation (approach-avoidance) to the α-SNS, and activation to the β-SNS. This framework enables continuous, real-time, and interpretable estimation of emotion from a single, wearable-compatible signal through photoplethysmography (PPG). The tested model utilizes inter-beat interval (IBI) and pulse wave or pulse volume amplitude (PVA), which are tracked using an extended Kalman filter (EKF) to extract frequencies (VLF-UHF) and standard heart rate variability metrics (HRV). These features were mapped onto emotional dimensions using supervised partial least squares (PLS) regressions from behavioral validation measures: facial electromyograph (EMG) for valence, a joystick for motivation, and eye-tracking (activation). The dimensional predictions reached within-subject accuracy levels comparable to those of traditional physiological models. Additionally, these emotional dimensions can be used to produce reasonable, discrete emotion probabilities based solely on theory (without requiring training data). These findings support a new model of emotion based on separate autonomic systems and dimensions that functionally define emotions in real-time. Such an approach enables dynamic emotional inference or generalized behavior across various contexts, including experimental design, clinical monitoring, and ambulatory assessment, utilizing low-cost, wearable technology.