research-article

My smartphone knows i am hungry

Authors:

Fanglin Chen,

Rui Wang,

Xia Zhou,

Andrew T. CampbellAuthors Info & Claims

WPA '14: Proceedings of the 2014 workshop on physical analytics

Pages 9 - 14

https://doi.org/10.1145/2611264.2611270

Published: 11 June 2014 Publication History

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Abstract

Can a smartphone learn our eating habits without the user being in the loop? Clearly, the phone could use checkins based on location to infer that if you were in a cafe, for example, there is a good possibility you might eat or drink something. In this paper, we use inferred behavioral data and location history to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input. In this paper, we report on a simple model that predicts the food purchases of a group of undergraduate college students (N=25) using inferred behavioral and location data from smartphones. The 10-week study uses the dining related purchase records from student college cards as ground-truth to validate our prediction model. Initial results show that we can predict food and drink purchases with an accuracy of 74% using three weeks of training data.

References

[1]

Dartmouth dining service. http://www.dartmouth.edu/dining/plans/.

Google Scholar

[2]

Amft, O., and Tröster, G. Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine 42, 2 (2008), 121--136.

Digital Library

Google Scholar

[3]

Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. Classification and regression trees. CRC press, 1984.

Google Scholar

[4]

Feunekes, G. I., de Graaf, C., Meyboom, S., and van Staveren, W. A. Food choice and fat intake of adolescents and adults: associations of intakes within social networks. Preventive medicine 27, 5 (1998), 645--656.

Google Scholar

[5]

Flegal, K. M., Carroll, M. D., Ogden, C. L., and Johnson, C. L. Prevalence and trends in obesity among us adults, 1999--2000. Jama 288, 14 (2002), 1723--1727.

Crossref

Google Scholar

[6]

Hebden, L., Cook, A., van der Ploeg, H. P., and Allman-Farinelli, M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Research Protocols 1, 2 (2012).

Crossref

Google Scholar

[7]

Lowry, R., Galuska, D. A., Fulton, J. E., Wechsler, H., Kann, L., and Collins, J. L. Physical activity, food choice, and weight management goals and practices among us college students. American Journal of Preventive Medicine 18, 1 (2000), 18--27.

Crossref

Google Scholar

[8]

Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., and Campbell, A. T. The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (2010), ACM, pp. 71--84.

Digital Library

Google Scholar

[9]

Menze, B. H., Kelm, B. M.,Masuch, R., Himmelreich, U., Bachert, P., Petrich,W., and Hamprecht, F. A. A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC bioinformatics 10, 1 (2009), 213.

Google Scholar

[10]

Rabbi, M., Ali, S., Choudhury, T., and Berke, E. Passive and in-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th international conference on Ubiquitous computing (2011), ACM, pp. 385--394.

Digital Library

Google Scholar

[11]

Reddy, S., Parker, A., Hyman, J., Burke, J., Estrin, D., and Hansen, M. Image browsing, processing, and clustering for participatory sensing: lessons from a dietsense prototype. In Proceedings of the 4th workshop on Embedded networked sensors (2007), ACM, pp. 13--17.

Digital Library

Google Scholar

[12]

Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., and Campbell, A. T. StudentLife: Assessing mental well-being, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM Conference on Ubiquitous Computing (2014), ACM.

Google Scholar

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https://doi.org/10.1109/ACIIW59127.2023.10388089
Santillán Cooper MArmentano M(2022)Predicting future sedentary behaviour using wearable and mobile devicesInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10310459:6Online publication date: 1-Nov-2022
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Index Terms

My smartphone knows i am hungry
1. Information systems
  1. Information systems applications

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Published In

WPA '14: Proceedings of the 2014 workshop on physical analytics

June 2014

54 pages

ISBN:9781450328258

DOI:10.1145/2611264

Program Chairs:
Deborah Estrin
Cornell Tech, USA
,
Venkat Padmanabhan
Microsoft Research, India

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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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2014

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Conference

MobiSys'14

Sponsor:

SIGMOBILE

MobiSys'14: The 12th Annual International Conference on Mobile Systems, Applications, and Services

June 16, 2014

New Hampshire, Bretton Woods, USA

Acceptance Rates

WPA '14 Paper Acceptance Rate 6 of 8 submissions, 75%;

Overall Acceptance Rate 11 of 17 submissions, 65%

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Cited By

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Bonafonte IBustos CLarrazolo ALuna GArenas ABaró XTourgeman IBalcells MLapedriza A(2023)Analyzing the contribution of different passively collected data to predict Stress and Depression2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388089(1-4)Online publication date: 10-Sep-2023
https://doi.org/10.1109/ACIIW59127.2023.10388089
Santillán Cooper MArmentano M(2022)Predicting future sedentary behaviour using wearable and mobile devicesInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10310459:6Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.103104
Abo-Tabik MBenn YCosten N(2021)Are Machine Learning Methods the Future for Smoking Cessation Apps?Sensors10.3390/s2113425421:13(4254)Online publication date: 22-Jun-2021
https://doi.org/10.3390/s21134254
Epstein DTyburski MKowalczyk WBurgess-Hull APhillips KCurtis BPreston K(2020)Prediction of stress and drug craving ninety minutes in the future with passively collected GPS datanpj Digital Medicine10.1038/s41746-020-0234-63:1Online publication date: 4-Mar-2020
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Trifan AOliveira MOliveira J(2019)Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible LimitationsJMIR mHealth and uHealth10.2196/126497:8(e12649)Online publication date: 23-Aug-2019
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Zhao YMorris AMarais BPardo AScott K(2019)Exploring how medical students learn during clinical rotations: a pilot study with a mobile applicationHealth and Technology10.1007/s12553-019-00305-89:3(257-267)Online publication date: 27-Feb-2019
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De Choudhury MKumar MWeber ILee CPoltrock SBarkhuus LBorges MKellogg W(2017)Computational Approaches Toward Integrating Quantified Self Sensing and Social MediaProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998219(1334-1349)Online publication date: 25-Feb-2017
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