We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.
@article{arxiv.1705.06224,
title = {Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions},
author = {Kleomenis Katevas and Ilias Leontiadis and Martin Pielot and Joan Serrà},
journal= {arXiv preprint arXiv:1705.06224},
year = {2017}
}