Supervised Activity Discovery

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. Ubicomp/ISWC’15 Adjunct , September 07 11, 2015, Osaka, Japan Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3575-1/15/09$15.00 DOI: http://dx.doi.org/10.1145/2800835.2801617 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.