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Passive Sensing

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Encyclopedia of Behavioral Medicine

Definition

Passive sensing is a data collection method that requires no or minimal interactions from humans. Examples of passive sensing are location tracking, physical activity recognition, conversation detection, etc.

Description

Passive sensing is a data collection method that requires no or minimal interactions from humans. A familiar example of passive sensing is location tracking, where a mobile app continuously tracks an individual’s location in the background. Passive sensing, however, is not limited to location tracking. Other forms of passive sensing include tracking steps, physical activity (Lu et al. 2010), sleep (Lane et al. 2011), heart rate (Hovsepian et al. 2015), electrodermal response (Sano and Rosalind 2011), nonverbal speech patterns (Rabbi et al. 2011), app usage (Gordon et al. 2019), etc. Typically, passive sensing is done via smartphones, wearables (e.g., smartwatches), or implantable devices in the environment (e.g., Amazon Echo), but custom-made devices can...

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References and Further Reading

  • Arroyo-Gallego, T., et al. (2017). Detection of motor impairment in Parkinson’s disease via mobile touchscreen typing. IEEE Transactions on Biomedical Engineering, 64(9), 1994–2002.

    Article  Google Scholar 

  • Bae, S., et al. (2017). Detecting drinking episodes in young adults using smartphone-based sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2), 5.

    Article  Google Scholar 

  • Bater, J., et al. (2017). SMCQL: Secure querying for federated databases. Proceedings of the VLDB Endowment, 10(6), 673–684.

    Article  Google Scholar 

  • Cornet, V. P., & Holden, R. J. (2018). Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics, 77, 120–132.

    Article  Google Scholar 

  • Dwork, C. (2011). Differential privacy. Encyclopedia of cryptography and security (pp. 338–340).

    Google Scholar 

  • Gordon, M. L., et al. (2019). App usage predicts cognitive ability in older adults. In: Proceedings of the 2019 CHI conference on human factors in computing systems, ACM.

    Google Scholar 

  • Hovsepian, K., et al. (2015). cStress: Towards a gold standard for continuous stress assessment in the mobile environment. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, ACM.

    Google Scholar 

  • Lane, N. D., et al. (2011). Bewell: A smartphone application to monitor, model and promote wellbeing. In: 5th international ICST conference on pervasive computing technologies for healthcare.

    Google Scholar 

  • Lu, H., et al. (2010). The Jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM conference on embedded networked sensor systems, ACM.

    Google Scholar 

  • Michie, S. F., et al. (2014). ABC of behaviour change theories. London: Silverback Publishing.

    Google Scholar 

  • Nahum-Shani, I., et al. (2017). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446–462.

    Article  Google Scholar 

  • Rabbi, M., et al. (2011). Passive and in-situ assessment of mental and physical well-being using mobile sensors. In: Proceedings of the 13th international conference on ubiquitous computing, ACM.

    Google Scholar 

  • Rabbi, M., et al. (2018). Toward increasing engagement in substance use data collection: Development of the substance abuse research assistant app and protocol for a microrandomized trial using adolescents and emerging adults. JMIR Research Protocols, 7(7), e166.

    Article  Google Scholar 

  • Rahman, T., et al. (2016). Nutrilyzer: A mobile system for characterizing liquid food with photoacoustic effect. In: Proceedings of the 14th ACM conference on embedded network sensor systems CD-ROM, ACM.

    Google Scholar 

  • Saeb, S., et al. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), e175.

    Article  Google Scholar 

  • Sano, A., & Rosalind, W. (2011). Picard. Toward a taxonomy of autonomic sleep patterns with electrodermal activity. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, IEEE.

    Google Scholar 

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Correspondence to Mashfiqui Rabbi .

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Rabbi, M. (2020). Passive Sensing. In: Gellman, M. (eds) Encyclopedia of Behavioral Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6439-6_102004-1

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  • DOI: https://doi.org/10.1007/978-1-4614-6439-6_102004-1

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