Abstract
In recent years, non-invasive human activity recognition (HAR) has gathered huge momentum using locomotive sensors. However, for effective HAR, there is a need for a significant volume of annotated data. Typically, the conventional practices for gathering HAR annotations have relied on human annotators. Nevertheless, the growing volume of data often leads to the collection of shallow annotations, which in most cases ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, try to develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures. To achieve this, in the backend, AmicroN applies change-point detection for the precise detection of activity boundaries followed by zero-shot learning with verb attributes to identify the unseen micro-activities without any external supervision. Rigorous evaluation on a publicly available Kitchen dataset shows that AmicroN can identify the micro-activities with a median F1-score of \geq \mathbf{0.75}≥0.75 for all the subjects, which can help develop novel pervasive applications.