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Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns

Author

Listed:
  • Ivan R Felix
  • Luis A Castro
  • Luis-Felipe Rodriguez
  • Oresti Banos

Abstract

Collecting experimental data from multiple sensing devices has just recently become quite popular in behavioral and social sciences. Among existing devices, mobile phones stand out as they allow researchers to collect data from individuals in an unbiased, precise, unobtrusive, and timely manner. Current mobile sensing applications are typically developed from scratch, provide no reusable components, and frequently do not take advantage of the devices’ processing capabilities. In light of such limitations, this work presents a novel tool that leverages mobile phones not only to collect data via their sensors but also to process them on the device as soon as they are gathered. The tool provides researchers with easy-to-use services that allow them to configure the required processing routines on the mobile phones. This work proposes a new approach for rapid deployment of sensing campaigns targeted at scientists with basic technical knowledge and requiring low effort. We performed an evaluation aimed at determining whether there is a significant improvement in terms of user effectiveness and efficiency in the definition of new components. The results suggest that the proposed tool speeds up the time and reduces the effort taken for setting up and deploying a sensing campaign.

Suggested Citation

  • Ivan R Felix & Luis A Castro & Luis-Felipe Rodriguez & Oresti Banos, 2019. "Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719874186
    DOI: 10.1177/1550147719874186
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    Cited by:

    1. Oresti Banos & Joseph Rafferty & Luis A Castro, 2021. "Internet of things for health and well-being applications," International Journal of Distributed Sensor Networks, , vol. 17(3), pages 15501477219, March.
    2. Juyeon Son & Wonyoung Choi & Sang-Min Choi, 2020. "Trust information network in social Internet of things using trust-aware recommender systems," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

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