Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks
Name
IEEE_Conference_MIT_ML_Terumi.pdf
Description
Accepted version
Size
1.11 MB
Format
Unknown
Checksum (MD5)
8e9a471917f3d863cbaaa349ddf398fe
Author(s)
Umematsu, Terumi
Sano, Akane
Taylor, Sarah E.
Picard, Rosalind W.
Date Issued
2019
Journal
2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Umematsu, Terumi, Sano, Akane, Taylor, Sara and Picard, Rosalind W. 2019. "Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks." 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Accurately forecasting stress may enable people to make behavioral changes that could improve their future health. For example, accurate stress forecasting might inspire people to make changes to their schedule to get more sleep or exercise, in order to reduce excessive stress tomorrow night. In this paper, we examine how accurately the previous N-days of multi-modal data can forecast tomorrow evening's high/low binary stress levels using long short-Term memory neural network models (LSTM), logistic regression (LR), and support vector machines (SVM). Using a total of 2,276 days, with 1,231 overlapping 8-day sequences of data from 142 participants (including physiological signals, mobile phone usage, location, and behavioral surveys), we find the LSTM significantly outperforms LR and SVM with the best results reaching 83.6% using 7 days of prior data. Using time-series models improves the forecasting of stress even when considering only subsets of the multi-modal data set, e.g., using only physiology data. In particular, the LSTM model reaches 81.4% accuracy using only objective and passive data, i.e., not including subjective reports from a daily survey.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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/BHI.2019.8834624
