A regularization approach for identifying cumulative lagged effects in smart health applications

Karthik Srinivasan, Faiz Currim, Sudha Ram, Casey Lindberg, Esther Sternberg, Perry Skeath, Bijan Najafi, Javad Razjouyan, Hyo Ki Lee, Matthias R. Mehl, Davida Herzl, Reuben Herzl, Melissa Lunden, Nicole Goebel, Scott Andrews, Brian Gilligan, Judith Heerwagen, Kevin Kampschroer, Kelli Canada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent development1 of wearable sensor technologies have made it possible to capture concurrent data streams for ambient environment and instantaneous physiological stress response at a fine granularity. Characterizing the delay in physiological stress response time to each environment stimulus is as important as capturing the magnitude of the effect. In this paper, we discuss and evaluate a new regularization-based statistical method to determine the ideal lagged effect of five environmental factors - carbon dioxide, temperature, relative humidity, atmospheric pressure and noise levels on instantaneous stress response. Using this method, we infer that the first four environment variables have a cumulative lagged effect, of approximately 60 minutes, on stress response whereas noise level has an instantaneous effect on stress response. The proposed transformations to inputs result in models with better fit and predictive performance. This study not only informs the field of environment-wellbeing research about the cumulative lagged effects of the specified environmental factors, but also proposes a new method for determining optimal feature transformation in similar smart health studies.

Original languageEnglish (US)
Title of host publicationDH 2017 - Proceedings of the 2017 International Conference on Digital Health
PublisherAssociation for Computing Machinery
Pages99-103
Number of pages5
ISBN (Electronic)9781450352499
DOIs
StatePublished - Jul 2 2017
Event7th International Conference on Digital Health, DH 2017 - London, United Kingdom
Duration: Jul 2 2017Jul 5 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F128634

Other

Other7th International Conference on Digital Health, DH 2017
CountryUnited Kingdom
CityLondon
Period7/2/177/5/17

Keywords

  • Cumulative lag
  • Environment-wellbeing studies
  • Heart rate variability
  • Indoor environment quality
  • Smart health

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'A regularization approach for identifying cumulative lagged effects in smart health applications'. Together they form a unique fingerprint.

  • Cite this

    Srinivasan, K., Currim, F., Ram, S., Lindberg, C., Sternberg, E., Skeath, P., Najafi, B., Razjouyan, J., Lee, H. K., Mehl, M. R., Herzl, D., Herzl, R., Lunden, M., Goebel, N., Andrews, S., Gilligan, B., Heerwagen, J., Kampschroer, K., & Canada, K. (2017). A regularization approach for identifying cumulative lagged effects in smart health applications. In DH 2017 - Proceedings of the 2017 International Conference on Digital Health (pp. 99-103). (ACM International Conference Proceeding Series; Vol. Part F128634). Association for Computing Machinery. https://doi.org/10.1145/3079452.3079503