Toward context-aware just-in-time information
Anuruddha Hettiarachchi, Anuruddha Premalal, Dileeka Días, Suranga Chandima Nanayakkara · 2014
Transferring computational tasks from user-worn devices to everyday objects allows users to freely focus on their regular non-computing tasks. Identifying micro-activities (short-repetitive-activities that compose the macro-level behavior) enables the understanding of subtle behavioral changes and providing just-in-time information without explicit user input. In this paper, we propose the concept of micro-activity recognition of augmented-everyday-objects and evaluate the applicability of machine learning algorithms that are previously used for macro-level activity recognition. We outline a few proof-of-concept application scenarios that provide micro-activity-aware just-in-time information.