Uncertainty Quantification in Machine Learning for Pervasive Systems: A Focus on Human Activity Recognition

This ArXiv article addresses a critical challenge in deploying machine learning models in real-world pervasive systems: the quantification of uncertainty. The focus on human activity recognition highlights the practical implications of understanding model confidence in applications like healthcare and smart homes.

• •Addresses the crucial need for uncertainty quantification in pervasive AI systems.
• •Specifically focuses on human activity recognition, a practical application area.
• •The research likely explores methods to improve model reliability and trustworthiness in real-world scenarios.