Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3089801.3089802 | Proceedings of the 1st International Workshop on Deep Learning for Mobile Systems and Applications |
DocType | Volume | ISSN |
Conference | abs/1705.06224 | DeepMobile Workshop, MobileHCI 2017 |
Citations | PageRank | References |
5 | 0.40 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kleomenis Katevas | 1 | 39 | 5.89 |
| Ilias Leontiadis | 2 | 761 | 44.38 |
| Martin Pielot | 3 | 768 | 50.22 |
| Joan Serrà | 4 | 379 | 34.66 |