Quantifying Personality in Human-Drone Interactions for Building Heat Loss Inspection with Virtual Reality Training

Reliable building energy audits are crucial for efficiency through heat loss detection. While drones assist inspections, they overlook the interplay between personality traits, stress management, and operational strategies expert engineers employ. This gap, combined with workforce shortages, necessitates effective knowledge transfer. This study proposes a VR-based training system for human-drone interaction in building heat loss inspection. Participants piloted a virtual drone with a thermographic monitor to identify defects. By analyzing flight patterns, stress adaptation, and inspection performance across diverse trainees, we found: (1) Flight Trajectories - Extraverts, Intuitives, Feelers, and Perceivers explored larger areas but showed higher misclassification rates, while Introverts, Sensors, Thinkers, and Judgers demonstrated methodical approaches. (2) Stress Adaptation - Heart rate variability revealed broader stress fluctuations among Extraverts, Intuitives, Feelers, and Perceivers, whereas Introverts, Sensors, Thinkers, and Judgers maintained steadier responses. Task complexity magnified these differences. (3) Inspection Performance - Extraverts, Intuitives, and Feelers achieved higher recall but over-identified defects. Introverts, Sensors, Thinkers, and Judgers made fewer random errors but risked overlooking subtle heat losses. These insights highlight the interplay among personality traits, stress management, and operational strategies in VR training for drone-assisted audits. The framework shows potential for addressing workforce shortages by facilitating knowledge transfer and optimizing human-drone collaboration.