research-article

How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

Authors:

Christoph Klebsattel,

Matthias Budde,

Markus Reichert,

Philip Santangelo,

Ulrich Ebner-PriemerAuthors Info & Claims

UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers

Pages 693 - 702

https://doi.org/10.1145/2800835.2804394

Published: 07 September 2015 Publication History

Abstract

We present MoA², a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable sensing. In a formative study, we evaluated the usability and unobtrusiveness of our mood assessment. A median SUS score of 90 shows a high usability. Subjects reported an easy, fast and intuitive use. The mood recognition was evaluated in terms of classification accuracy. First, we analyzed which features are best for the recognition. Spatio-temporal attributes, i.e. daytime, day of week and location, correlate most with the monitored mood. Based on the identified attributes, we trained personalized classifiers using Naïve Bayes and applied ten-fold-cross validation. The average recognition accuracy was 0.76 which is comparable to related work.

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Cited By

Turner WBrühl ABöker HSchulze BMarschall KLa Marca RPfaff MRussmann TSchmidt-Trucksäss A(2024)Heart rate vARiability and physical activity in inpatient treatMent of burnOut and DepressIon (HARMODI): protocol of a cross-sectional study with up to 8-week follow upBMJ Open10.1136/bmjopen-2023-08129914:6(e081299)Online publication date: 25-Jun-2024
https://doi.org/10.1136/bmjopen-2023-081299
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
Sarsenbayeva ZFleming CTag BWithana Avan Berkel NMcEwan A(2023)A Review on Mood Assessment Using SmartphonesHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_22(385-413)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42283-6_22
Show More Cited By

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How to use smartphones for less obtrusive ambulatory mood assessment and mood recognition

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Published In

cover image ACM Conferences

UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers

September 2015

1626 pages

ISBN:9781450335751

DOI:10.1145/2800835

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 ACM.

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 ACM 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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Yahoo! Japan: Yahoo! Japan
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
FX Palo Alto Laboratory, Inc.: FX Palo Alto Laboratory, Inc.
Google Inc.
ACM: Association for Computing Machinery
Rakuten Institute of Technology: Rakuten Institute of Technology
Microsoft: Microsoft
Bell Labs: Bell Laboratories
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
KDDI
Panasonic: Panasonic Corporation
NTT DoCoMo
Telefónica: Telefónica
ISTC-PC: Intel Science and Technology Center for Pervasive Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

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UbiComp '15

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Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Turner WBrühl ABöker HSchulze BMarschall KLa Marca RPfaff MRussmann TSchmidt-Trucksäss A(2024)Heart rate vARiability and physical activity in inpatient treatMent of burnOut and DepressIon (HARMODI): protocol of a cross-sectional study with up to 8-week follow upBMJ Open10.1136/bmjopen-2023-08129914:6(e081299)Online publication date: 25-Jun-2024
https://doi.org/10.1136/bmjopen-2023-081299
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
Sarsenbayeva ZFleming CTag BWithana Avan Berkel NMcEwan A(2023)A Review on Mood Assessment Using SmartphonesHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_22(385-413)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42283-6_22
Aalbers GHendrickson AVanden Abeele MKeijsers L(2022)Smartphone-tracked Digital Markers of Momentary Subjective Stress in College Students: An Idiographic Machine Learning Analysis (Preprint)JMIR mHealth and uHealth10.2196/37469Online publication date: 22-Feb-2022
https://doi.org/10.2196/37469
Wampfler RKlingler SSolenthaler BSchinazi VGross MHolz C(2022)Affective State Prediction from Smartphone Touch and Sensor Data in the WildProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501835(1-14)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501835
Fiedler JSeiferth CEckert TWoll AWunsch K(2022)Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth interventionSchlafqualität, Valenz, energetische Erregung und Ruhe als Prädiktoren für gerätegestützt gemessene körperliche Aktivität während einer dreiwöchigen mHealth-InterventionGerman Journal of Exercise and Sport Research10.1007/s12662-022-00809-y52:2(237-247)Online publication date: 14-Apr-2022
https://doi.org/10.1007/s12662-022-00809-y
Barbosa NWang GUr BWang Y(2021)Who Am I?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781225:3(1-32)Online publication date: 14-Sep-2021
https://dl.acm.org/doi/10.1145/3478122
Kołakowska ASzwoch WSzwoch M(2020)A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone SensorsSensors10.3390/s2021636720:21(6367)Online publication date: 8-Nov-2020
https://doi.org/10.3390/s20216367
Torkamaan HZiegler J(2020)Mobile Mood TrackingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322074:4(1-30)Online publication date: 18-Dec-2020
https://dl.acm.org/doi/10.1145/3432207
Wampfler RKlingler SSolenthaler BSchinazi VGross MBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch DataProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376504(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376504
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