research-article

Predictors of life satisfaction based on daily activities from mobile sensor data

Authors:

Onur Yürüten,

Jiyong Zhang,

Pearl H.Z. PuAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 497 - 500

https://doi.org/10.1145/2556288.2557147

Published: 26 April 2014 Publication History

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Abstract

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 behavioral 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.

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Cited By

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Gao NYu ZXu YYu CWang YSalim FShi YKostakos VKay JHoang T(2024)Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior ModelingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678423(729-735)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678423
Peng ZMa XYang DTsang KGuo QKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Effects of Support-Seekers’ Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health CommunitiesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445446(1-12)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445446
Mehmood RYang HKim SBalinsky ATan ATarng W(2019)Predictive human emotion recognition system using deep functional affective state modelingProceedings of the 1st International Conference on Advanced Information Science and System10.1145/3373477.3373706(1-5)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3373477.3373706
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Index Terms

Predictors of life satisfaction based on daily activities from mobile sensor data
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2014

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CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

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CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

View all

Gao NYu ZXu YYu CWang YSalim FShi YKostakos VKay JHoang T(2024)Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior ModelingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678423(729-735)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678423
Peng ZMa XYang DTsang KGuo QKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Effects of Support-Seekers’ Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health CommunitiesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445446(1-12)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445446
Mehmood RYang HKim SBalinsky ATan ATarng W(2019)Predictive human emotion recognition system using deep functional affective state modelingProceedings of the 1st International Conference on Advanced Information Science and System10.1145/3373477.3373706(1-5)Online publication date: 15-Nov-2019
https://dl.acm.org/doi/10.1145/3373477.3373706
Kim BJo S(2018)Deep Physiological Affect Network for the Recognition of Human EmotionsIEEE Transactions on Affective Computing10.1109/TAFFC.2018.2790939(1-1)Online publication date: 2018
https://doi.org/10.1109/TAFFC.2018.2790939
Hsieh YJeng Y(2018)Development of Home Intelligent Fall Detection IoT System Based on Feedback Optical Flow Convolutional Neural NetworkIEEE Access10.1109/ACCESS.2017.27713896(6048-6057)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2017.2771389
Skatova AShipp VSpacagna LBedwell BBeltagui ARodden TBegole BKim JInkpen KWoo W(2015)DatawearProceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems10.1145/2702613.2725450(323-326)Online publication date: 18-Apr-2015
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Kuni RPrathivadi YWu JBennett TJafari R(2015)Exploration of interactions detectable by wearable IMU sensors2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2015.7299394(1-6)Online publication date: Jun-2015
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Wang SZhou G(2015)A review on radio based activity recognitionDigital Communications and Networks10.1016/j.dcan.2015.02.0061:1(20-29)Online publication date: Feb-2015
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