TY - GEN
T1 - A regularization approach for identifying cumulative lagged effects in smart health applications
AU - Srinivasan, Karthik
AU - Currim, Faiz
AU - Ram, Sudha
AU - Lindberg, Casey
AU - Sternberg, Esther
AU - Skeath, Perry
AU - Najafi, Bijan
AU - Razjouyan, Javad
AU - Lee, Hyo Ki
AU - Mehl, Matthias R.
AU - Herzl, Davida
AU - Herzl, Reuben
AU - Lunden, Melissa
AU - Goebel, Nicole
AU - Andrews, Scott
AU - Gilligan, Brian
AU - Heerwagen, Judith
AU - Kampschroer, Kevin
AU - Canada, Kelli
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - Cumulative lag
KW - Environment-wellbeing studies
KW - Heart rate variability
KW - Indoor environment quality
KW - Smart health
UR - http://www.scopus.com/inward/record.url?scp=85025475310&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025475310&partnerID=8YFLogxK
U2 - 10.1145/3079452.3079503
DO - 10.1145/3079452.3079503
M3 - Conference contribution
AN - SCOPUS:85025475310
T3 - ACM International Conference Proceeding Series
SP - 99
EP - 103
BT - DH 2017 - Proceedings of the 2017 International Conference on Digital Health
PB - Association for Computing Machinery
T2 - 7th International Conference on Digital Health, DH 2017
Y2 - 2 July 2017 through 5 July 2017
ER -