TY - GEN
T1 - Understanding ambulatory and wearable data for health and wellness
AU - Sano, Akane
AU - Picard, Rosalind W.
PY - 2014
Y1 - 2014
N2 - In our research, we aim (1) to recognize human internal states and behaviors (stress level, mood and sleep behaviors etc), (2) to reveal which features in which data can work as predictors and (3) to use them for intervention. We collect multi-modal (physiological, behavioral, environmental, and social) ambulatory data using wearable sensors and mobile phones, combining with standardized questionnaires and data measured in the laboratory. In this paper, we introduce our approach and some of our projects.
AB - In our research, we aim (1) to recognize human internal states and behaviors (stress level, mood and sleep behaviors etc), (2) to reveal which features in which data can work as predictors and (3) to use them for intervention. We collect multi-modal (physiological, behavioral, environmental, and social) ambulatory data using wearable sensors and mobile phones, combining with standardized questionnaires and data measured in the laboratory. In this paper, we introduce our approach and some of our projects.
UR - http://www.scopus.com/inward/record.url?scp=84904907174&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904907174&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84904907174
SN - 9781577356547
T3 - AAAI Spring Symposium - Technical Report
SP - 59
EP - 60
BT - Big Data Becomes Personal
PB - AI Access Foundation
T2 - 2014 AAAI Spring Symposium
Y2 - 24 March 2014 through 26 March 2014
ER -