Conference Proceedings

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

Abstract

© 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..

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