Improving Experience Sampling with Multi-view User-driven Annotation Prediction

Publication Year: 2019 Publication Type : ConferenceProceeding


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.


    author = {Jonathan Liono and Flora D. Salim and Niels van Berkel and Vassilis Kostakos and A. Kai Qin},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    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 = {},
    year = {2019}


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