Smart Wearables: Real-Time Contextual Awareness through Prompt Engineering

Smart wearable devices have become an integral part of modern digital environments. These devices allow for continuous monitoring of physiological signals, user activities, and environmental parameters. These advancements exist in modern wearable devices; however, most wearable devices still utilise conventional rule-based approaches or machine learning methods. This is because of their inability to effectively interpret complex context information in real-time. With advancements in artificial intelligence, particularly large language models, prompt engineering is recognised as an effective approach to improve context interpretation. This paper proposes an extensive study of context-aware wearable devices. Additionally, it proposes an innovative architecture that utilises prompt engineering to improve context interpretation in wearable devices. The study also discusses various challenges faced by intelligent wearable devices. These challenges include response time, privacy concerns, and computational complexities. Further, it discusses future directions of intelligent wearable devices. The experimental analysis of various scenarios indicates that prompt-engineered inputs improve response accuracy significantly.