Toward Culturally Sensitive Emotion Estimation through Multimodal Sensing and Everyday Behavioral Cues

Abstract

This paper aims to provide new insights for the advancement of emotion estimation technologies through a comprehensive analysis that integrates model comparison, behavioral correlation analysis, and a multicultural perspective. First, we conduct a comparative review of representative emotion estimation models, including Russell’s two-dimensional circumplex model and Plutchik’s three-dimensional emotion model. Their theoretical foundations, expressive capacities, and applicability to computational emotion estimation are examined to clarify their respective strengths and limitations in practical implementations.Based on this theoretical comparison, we construct an emotion estimation model using physiological and behavioral sensing. Heart rate data acquired from a heart rate sensor are combined with pressure data obtained from pressure sensors embedded in the environment. In addition to these sensor signals, behavioral features such as the degree of leaning against a chair and the timing of beverage consumption are extracted as observable indicators of user behavior. These multimodal data are integrated to estimate users’ emotional states, enabling an analysis that goes beyond physiological signals alone.We then investigate the relationship between estimated emotional states and behavioral data in detail. Statistical analyses reveal that specific behavioral patterns exhibit meaningful correlations with particular emotional dimensions, suggesting that behavioral data can serve as complementary information for emotion estimation. The results indicate that incorporating behavior-based features can enhance robustness and interpretability, especially in situations where physiological signals are noisy or insufficient.Furthermore, this study examines emotion estimation from a multicultural perspective by considering differences in emotional expression across cultural backgrounds. We analyze how cultural factors influence both observable behaviors and physiological responses, and how these differences affect the performance of emotion estimation models. The findings demonstrate that models developed within a single cultural context may not generalize well to others without adaptation, highlighting the necessity of incorporating cultural sensitivity into model design and evaluation.Overall, this research contributes to the field of affective computing by demonstrating the value of combining established emotion models with multimodal sensing and behavioral analysis while explicitly addressing cultural diversity. The proposed approach provides a foundational framework for improving the accuracy and applicability of emotion estimation systems in real-world settings. By integrating theoretical model comparison, empirical sensor-based analysis, and multicultural considerations, this study broadens the scope of emotion estimation research and supports the development of more inclusive and reliable emotion-aware technologies for future applications.In addition, the implications of this work extend to human–computer interaction, social robotics, and adaptive systems that respond to users’ internal states. By leveraging everyday behaviors that naturally occur during interaction, the proposed framework reduces reliance on intrusive measurements and supports more seamless deployment in daily environments. The methodological insights presented in this paper can inform the design of future studies, including longitudinal experiments and real-time adaptive systems. Ultimately, this research underscores the importance of interdisciplinary approaches that integrate psychology, sensing technology, and cultural studies, and it lays the groundwork for scalable emotion estimation systems capable of supporting personalized and context-aware interactions across diverse user populations.These contributions collectively advance emotion-aware system design for ethically responsible global technological innovation.

Keywords: Emotion Estimation, Multimodal Sensing, Behavioral Cues, Cultural Differences, Affective Computing, Human–Computer Interaction

DOI: 10.54941/ahfe1008109