Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets - in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.
|Original language||English (US)|
|Title of host publication||UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|State||Published - Sep 7 2015|
|Event||3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan|
Duration: Sep 7 2015 → Sep 11 2015
|Name||UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing|
|Other||3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015|
|Period||9/7/15 → 9/11/15|
Bibliographical notePublisher Copyright:
Copyright © 2015 ACM.
- Mobile health (mHealth)
- Wearable sensors