Daytime Data and LSTM can Forecast Tomorrow’s Stress, Health, and Happiness
Name
EMBC2019_MIT_Terumi.pdf
Description
Accepted version
Size
284.19 KB
Format
Unknown
Checksum (MD5)
813058db143fd25e55917578d6320f38
Author(s)
Umematsu, Terumi
Sano, Akane
Picard, Rosalind W.
Date Issued
July 2019
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Umematsu, Terumi, Sano, Akane and Picard, Rosalind W. 2019. "Daytime Data and LSTM can Forecast Tomorrow’s Stress, Health, and Happiness." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2019.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing on stress, health, and happiness) from static models (support vector machine and logistic regression) and time-series models (long short-term memory neural network models (LSTM)) using the previous seven days of physiological, mobile phone, and behavioral survey data. We especially examine how using only a portion of the day's data (e.g. just night-time, or just daytime) influences the forecasting accuracy. The results show that accuracy is improved, across every condition tested, by using an LSTM instead of using static models. We find that daytime-only physiology data from wearable sensors, using an LSTM, can provide an accurate forecast of tomorrow's well-being using students' daily life data (stress: 80.4%, health: 86.0%, and happiness: 79.1%), achieving the same accuracy as using data collected from around the clock. These findings are valuable steps toward developing a practical and convenient well-being forecasting system.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/EMBC.2019.8856862
