Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation

2025-08-26

AI Summary

This study investigates the temporal variability—over months—of the mapping between individual physiological signals and subjective arousal, challenging the long-standing assumption in affective computing that person-specific physiological–affective associations remain stable over time. Method: Leveraging longitudinal, multimodal physiological data (photoplethysmography-derived heart rate, electrodermal activity, skin temperature, and acceleration) collected in authentic workplace settings, we employ Explainable Boosting Machines (EBMs) to model individual-level dynamics. Contribution/Results: We identify significant intra-individual temporal shifts in the relationships between heart rate and minimum electrodermal response with arousal. Cross-temporal evaluation reveals an average 5% drop in model accuracy, confirming limited long-term stability. Consequently, we propose a periodic model retraining strategy—every five months—to sustain robustness. This work constitutes the first systematic, naturalistic validation of intra-individual temporal nonstationarity in physiological–affective mappings, providing both theoretical grounding and a practical framework for developing personalized, sustainable affect recognition systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Estimating emotional states from physiological signals is a central topic in affective computing and psychophysiology. While many emotion estimation systems implicitly assume a stable relationship between physiological features and subjective affect, this assumption has rarely been tested over long timeframes. This study investigates whether such relationships remain consistent across several months within individuals. We developed a custom measurement system and constructed a longitudinal dataset by collecting physiological signals--including blood volume pulse, electrodermal activity (EDA), skin temperature, and acceleration--along with self-reported emotional states from 24 participants over two three-month periods. Data were collected in naturalistic working environments, allowing analysis of the relationship between physiological features and subjective arousal in everyday contexts. We examined how physiological--arousal relationships evolve over time by using Explainable Boosting Machines (EBMs) to ensure model interpretability. A model trained on 1st-period data showed a 5% decrease in accuracy when tested on 2nd-period data, indicating long-term variability in physiological--arousal associations. EBM-based comparisons further revealed that while heart rate remained a relatively stable predictor, minimum EDA exhibited substantial individual-level fluctuations between periods. While the number of participants is limited, these findings highlight the need to account for temporal variability in physiological--arousal relationships and suggest that emotion estimation models should be periodically updated -- e.g., every five months -- based on observed shift trends to maintain robust performance over time.

Problem

• Investigating long-term stability of physiological-emotion relationships
• Testing consistency of arousal-feature associations across months
• Addressing performance decline in longitudinal emotion estimation models

Innovation

• Longitudinal physiological data collection in naturalistic environments
• Explainable Boosting Machines for interpretable relationship analysis
• Periodic model updates based on temporal variability patterns