Bootstrapping Personalised Human Activity Recognition Models Using Online Active Learning

Abstract—In Human Activity Recognition (HAR) supervised and semi-supervised training are important tools for devising parametric activity models. For the best modelling performance, typically large amounts of annotated sample data are required. Annotating often represents the bottleneck in the overall mod-elling process as it usually involves retrospective analysis of experimental ground truth, like video footage. These approaches typically neglect that prospective users of HAR systems are themselves key sources of ground truth for their own activities. We therefore propose an Online Active Learning framework to collect user-provided annotations and to bootstrap personalized human activity models. We evaluate our framework on existing benchmark datasets and demonstrate how it outperforms stan-dard, more naive annotation methods. Furthermore, we enact a user study where participants provide annotations using a mobile app that implements our framework. We show that Online Active Learning is a viable method to bootstrap personalized models especially in live situations without expert supervision. I