SuperAD

Abstract

Activity recognition (AR) has become an essential component of many applications present in our everyday lives such as life-logging, fitness tracking, health and wellbeing monitoring. To build an AR system, one needs to first identify a set of activities of interest and collect labeled training data for these activities. However, activities of interest are not often known in advance. For example, a system designed to monitor a user's life style for potential diabetes risk needs to recognize all physical activities a user performs in her daily life. Given the large number of possible human activities, many of them cannot be foreseen during the model training time. In this work, we study the problem of discovering these unknown activities after the system is deployed by asking users to provide additional labels. Our goal is to discover all the unknown activities (i.e., obtain at least one label per class) while minimizing the amount of labels a user needs to provide. We propose SuperAD (Supervised Activity Discovery) approach, which combines active learning, semi-supervised learning and generative modeling to discover new unknown activities. We show that the proposed approach is especially effective when discovering activities with imbalance class distribution.