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IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

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Distributed, Ambient and Pervasive Interactions (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12203))

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Abstract

The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.

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Notes

  1. 1.

    https://developer.apple.com/support/app-store/.

  2. 2.

    https://developer.android.com/about/dashboards/index.html.

  3. 3.

    https://developer.apple.com/library/archive/documentation/Performance/Conceptual/EnergyGuide-iOS/WorkLessInTheBackground.html.

  4. 4.

    https://developer.apple.com/documentation/uikit/app_and_environment/managing_your_app_s_life_cycle.

  5. 5.

    https://developer.apple.com/documentation/usernotifications/setting_up_a_remote_notification_server/pushing_background_updates_to_your_app.

  6. 6.

    https://developer.apple.com/app-store/review/guidelines/.

  7. 7.

    https://github.com/tetujin/aware-push.

  8. 8.

    https://developer.apple.com/documentation/usernotifications/setting_up_a_remote_notification_server/generating_a_remote_notification.

  9. 9.

    https://github.com/tetujin/AWAREFramework-iOS.

  10. 10.

    https://cocoapods.org/.

  11. 11.

    https://github.com/tetujin/AWAREFramework-iOS/tree/master/Screenshots/esms.

  12. 12.

    https://apps.apple.com/app/aware-client-v2/id1455986181.

  13. 13.

    https://github.com/tetujin/aware-client-ios-v2.

References

  1. Center, P.R.: Smartphone ownership is growing rapidly around the world, but not always equally, February 2019

    Google Scholar 

  2. Lane, N., Miluzzo, E., Choudhury, T., Campbell, A., et al.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)

    Article  Google Scholar 

  3. Wang, R., Chen, F., Chen, Z., Li, T., Campbell, A.T., et al.: StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–14 (2014)

    Google Scholar 

  4. Rachuri, K.K., Aucinas, A., et al.: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing - Ubicomp 2010, p. 281 (2010)

    Google Scholar 

  5. Ferreira, D., Kostakos, V., Dey, A.K.: AWARE: mobile context instrumentation framework. Front. ICT 2, 6 (2015)

    Article  Google Scholar 

  6. Katevas, K., Haddadi, H., Tokarchuk, L.: SensingKit: evaluating the sensor power consumption in iOS devices. Proceedings - 12th International Conference on Intelligent Environments, IE 2016, pp. 222–225 (2016)

    Google Scholar 

  7. Xiong, H., Gerber, M.S., et al.: Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 415–426. ACM Press, New York (2016)

    Google Scholar 

  8. Statcounter: Mobile Vendor Market Share. https://gs.statcounter.com/vendor-market-share/mobile/japan

  9. Bae, S., Chung, T., Ferreira, D., Dey, A.K., Suffoletto, B.: Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: implications for just-in-time adaptive interventions. Addict. Behav. 83, 42–47 (2018)

    Article  Google Scholar 

  10. Doryab, A., et al.: Identifying behavioral phenotypes of loneliness and social isolation with passive sensing: statistical analysis, data mining and machine learning of smartphone and fitbit data. JMIR mHealth uHealth 7(7), e13209 (2019)

    Article  Google Scholar 

  11. Kuosmanen, E., Kan, V., Ferreira, D., et al.: Challenges of Parkinson’s disease: user experiences with stop. In: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019. Association for Computing Machinery, New York (2019)

    Google Scholar 

  12. Wang, R., Harari, G., Campbell, A.T., et al.: SmartGPA: how smartphones can assess and predict academic performance of college students. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, pp. 295–306. Association for Computing Machinery, New York (2015)

    Google Scholar 

  13. Hormuth, S.E.: The sampling of experiences in situ. J. Pers. 54(1), 262–293 (1986)

    Article  Google Scholar 

  14. Froehlich, J., Chen, M.Y., Consolvo, S., Harrison, B., Landay, J.A.: MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones (2007)

    Google Scholar 

  15. Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Ann. Rev. Clin. Psychol. 4, 1–32 (2008)

    Article  Google Scholar 

  16. Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: The social fMRI: measuring, understanding, and designing social mechanisms in the real world. In: Proceedings of the 13th International Conference on Ubiquitous Computing - UbiComp 2011, p. 445. ACM Press, New York (2011)

    Google Scholar 

  17. Place, S., Nierenberg, A., Azarbayejani, A., et al.: Behavioral indicators on a mobile sensing platform predict clinically validated psychiatric symptoms of mood and anxiety disorders. J. Med. Internet Res. 19(3), e75 (2017)

    Article  Google Scholar 

  18. Wu, P., Zhu, J., Zhang, J.Y.: MobiSens: a versatile mobile sensing platform for real-world applications. Mob. Netw. Appl. 18(1), 60–80 (2013)

    Article  Google Scholar 

  19. Trossen, D., Pavel, D.: AIRS: a mobile sensing platform for lifestyle management research and applications. In: Borcea, C., Bellavista, P., Giannelli, C., Magedanz, T., Schreiner, F. (eds.) MOBILWARE 2012. LNICST, vol. 65, pp. 1–15. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36660-4_1

    Chapter  Google Scholar 

  20. Lane, N., Choudhury, T., Campbell, A., et al.: BeWell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare (2011)

    Google Scholar 

  21. ilumivu: mEMA. https://ilumivu.com/

  22. Pollak, J.P., Adams, P., Gay, G.: PAM: a photographic affect meter for frequent, in situ measurement of affect. In: CHI (2011)

    Google Scholar 

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number JP18K11274.

Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Yuuki Nishiyama & Kaoru Sezaki

  2. University of Oulu, Oulu, Finland

    Denzil Ferreira

  3. Keio University, Tokyo, Japan

    Yusaku Eigen, Wataru Sasaki, Tadashi Okoshi & Jin Nakazawa

  4. University of Washington, Seattle, USA

    Anind K. Dey

Authors
  1. Yuuki Nishiyama
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  2. Denzil Ferreira
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  3. Yusaku Eigen
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  4. Wataru Sasaki
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  5. Tadashi Okoshi
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  6. Jin Nakazawa
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  7. Anind K. Dey
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  8. Kaoru Sezaki
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Corresponding author

Correspondence to Yuuki Nishiyama .

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Editors and Affiliations

  1. Smart Future Initiative, Frankfurt/Main, Germany

    Norbert Streitz

  2. Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

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Nishiyama, Y. et al. (2020). IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform. In: Streitz, N., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. HCII 2020. Lecture Notes in Computer Science(), vol 12203. Springer, Cham. https://doi.org/10.1007/978-3-030-50344-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-50344-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50343-7

  • Online ISBN: 978-3-030-50344-4

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