TY - GEN
T1 - Daily Stress and Mood Recognition System Using Deep Learning and Fuzzy Clustering for Promoting Better Well-Being
AU - Lawanot, Worawat
AU - Inoue, Masahiro
AU - Yokemura, Taketoshi
AU - Mongkolnam, Pornchai
AU - Nukoolkit, Chakarida
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant number 15K000929.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/6
Y1 - 2019/3/6
N2 - Nowadays, the overall well-being is considered to be one of the most important issue. The company has been taking more and more consideration in improving their employees' well-being. The employees also have been taking several approaches to improve their current well-being status. However, the well-being is usually related to the daily activity and behavior, especially in the workplace where it affects stress and mood level. In other words, the quality of a person's well-being is affected by the behavior in a workplace. In this study, we proposed a well-being recognition system where we adopted a deep learning technique to provide a non-invasive monitoring system. We classified the well-being level using three features from two surveys, which covered both stress and mood. For this preliminary study, we trained the model for both generic classification and personalized classification. The personalized approach was taken as a step to provide a personalized health decision support system, which would help raise awareness in users and encourage them to improve their behavior and eventually contribute to a better well-being. We achieved the accuracy of 83% on generic model and 91% on a personalized model.
AB - Nowadays, the overall well-being is considered to be one of the most important issue. The company has been taking more and more consideration in improving their employees' well-being. The employees also have been taking several approaches to improve their current well-being status. However, the well-being is usually related to the daily activity and behavior, especially in the workplace where it affects stress and mood level. In other words, the quality of a person's well-being is affected by the behavior in a workplace. In this study, we proposed a well-being recognition system where we adopted a deep learning technique to provide a non-invasive monitoring system. We classified the well-being level using three features from two surveys, which covered both stress and mood. For this preliminary study, we trained the model for both generic classification and personalized classification. The personalized approach was taken as a step to provide a personalized health decision support system, which would help raise awareness in users and encourage them to improve their behavior and eventually contribute to a better well-being. We achieved the accuracy of 83% on generic model and 91% on a personalized model.
KW - Consumer Health
KW - Deep Learning
KW - Digital Healthcare
KW - Internet of Things
KW - Well-Being
UR - http://www.scopus.com/inward/record.url?scp=85063787111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063787111&partnerID=8YFLogxK
U2 - 10.1109/ICCE.2019.8661932
DO - 10.1109/ICCE.2019.8661932
M3 - Conference contribution
AN - SCOPUS:85063787111
T3 - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
BT - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019
Y2 - 11 January 2019 through 13 January 2019
ER -