Improving experience sampling with multi-view user-driven annotation prediction
J Liono, FD Salim, N Van Berkel, V Kostakos, AK Qin
2019 IEEE International Conference on Pervasive Computing and Communications (PerCom | IEEE | Published : 2019
© 2019 IEEE. A fundamental challenge in real-time labelling of activity data is user burden. The Experience Sampling Method (ESM) is widely used to obtain such labels for sensor data. However, in an in-situ deployment, it is not feasible to expect users to precisely label the start and end time of each event or activity. For this reason, time-point based experience sampling (without an actual start and end time) is prevalent. We present a framework that applies multi-instance and semi-supervised learning techniques to perform to predict user annotations from multiple mobile sensor data streams. Our proposed framework estimates users' annotations in ESM-based studies progressively, via an int..View full abstract
Jonathan Liono is supported by the Australian Government Research Training Program Scholarship from the RMIT University. This research was partially supported by Microsoft Research.