SensPS: Sensing Personal Space Comfortable Distance between Human-Human Using Multimodal Sensors

TL;DR

This paper presents a multimodal sensor-based model using eye-tracking and physiological data to accurately estimate personal space preferences, enabling adaptive human-computer interactions and social space management.

Contribution

It introduces a novel sensor fusion approach with a Transformer model to predict personal space preferences, highlighting the significance of eye-tracking data over physiological signals.

Findings

Multimodal sensors effectively predict personal space preferences.

Eye-tracking features are more influential than wristband physiological data.

Transformer model achieves an F1 score of 0.87 in prediction accuracy.

Abstract

Personal space, also known as peripersonal space, is crucial in human social interaction, influencing comfort, communication, and social stress. Estimating and respecting personal space is essential for enhancing human-computer interaction (HCI) and smart environments. Personal space preferences vary due to individual traits, cultural background, and contextual factors. Advanced multimodal sensing technologies, including eye-tracking and wristband sensors, offer opportunities to develop adaptive systems that dynamically adjust to user comfort levels. Integrating physiological and behavioral data enables a deeper understanding of spatial interactions. This study develops a sensor-based model to estimate comfortable personal space and identifies key features influencing spatial preferences. Our findings show that multimodal sensors, particularly eye-tracking and physiological wristband data, can effectively predict personal space preferences, with eye-tracking data playing a more significant role. An experimental study involving controlled human interactions demonstrates that a Transformer-based model achieves the highest predictive accuracy (F1 score: 0.87) for estimating personal space. Eye-tracking features, such as gaze point and pupil diameter, emerge as the most significant predictors, while physiological signals from wristband sensors contribute marginally. These results highlight the potential for AI-driven personalization of social space in adaptive environments, suggesting that multimodal sensing can be leveraged to develop intelligent systems that optimize spatial arrangements in workplaces, educational institutions, and public settings. Future work should explore larger datasets, real-world applications, and additional physiological markers to enhance model robustness.