Towards Emotion Mapping for Multimodal Unobtrusive Stress Monitoring

Abstract

Recent advancements in AI-enabled healthcare technologies have brought attention to location-based emotion/stress mapping through smart wearables. This study explores the practicality and perceptions of using multimodal data for emotion detection and management, focusing on the role of individual geographical movement data as an additional modality for stress mapping. Our initial findings highlight that personalized, location-based feedback is key to adoption for stress monitoring, but many challenges remain to be addressed. This study also presents a discussion on utilizing stress mapping based on real-life location dataset. It lays the foundation for developing user-friendly, AI-driven solutions that leverage location data to enhance early awareness of stress-related emotions and improve accessibility to mental healthcare services.