Predictors of Life Satisfaction based on Daily Activities from Mobile Sensor Data

In recent years much research work has been dedicated to detecting user activity patterns from sensor data such as location, movement and proximity. However, how daily activities are correlated to people's happiness (such as their satisfaction from work and social lives) is not well explored. In this work, we propose an approach to investigate the relationship between users' daily activity patterns and their life satisfaction level. From a well-known longitudinal dataset collected by mobile devices, we extract various activity features through location and proximity information, and compute the entropies of these data to capture the regularities of the behav-ioral patterns of the participants. We then perform component analysis and structural equation modeling to identify key behavior contributors to self-reported satisfaction scores. Our results show that our analytical procedure can identify meaningful assumptions of causality between activities and satisfaction. Particularly, keeping regularity in daily activities can significantly improve the life satisfaction.

Published in:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 497-500
Presented at:
the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), Toronto, Canada, April 26 – May 1

 Record created 2015-08-24, last modified 2018-09-13

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