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 interactive pipeline of co-training and active learning. We evaluate our work using data collected from an in-the-wild data collection.
@inproceedings{DBLP:conf/percom/LionoSBKQ19,
author = {Jonathan Liono and
Flora D. Salim and
Niels van Berkel and
Vassilis Kostakos and
A. Kai Qin},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/percom/LionoSBKQ19.bib},
booktitle = {2019 IEEE International Conference on Pervasive Computing and Communications,
PerCom, Kyoto, Japan, March 11-15, 2019},
doi = {10.1109/PERCOM.2019.8767394},
pages = {1--11},
publisher = {IEEE},
timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
title = {Improving Experience Sampling with Multi-view User-driven Annotation
Prediction},
url = {https://doi.org/10.1109/PERCOM.2019.8767394},
year = {2019}
}
© 2021 Flora Salim - CRUISE Research Group.