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arXiv · 1705.06224

Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions

Kleomenis Katevas·Ilias Leontiadis·Martin Pielot·Joan Serrà
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Abstract

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.

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Kleomenis Katevas, Ilias Leontiadis, Martin Pielot, Joan Serrà. 2017-05-17. Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions. https://doi.org/10.1145/3089801.3089802

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