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AWARE: Mobile Context Instrumentation Framework
Open accessAbstract
We present a mobile instrumentation toolkit, AWARE, an open-source effort to develop an extensible and reusable platform for capturing, inferring, and generating context on mobile devices. Mobile phones are sensor-rich but resource-constrained, and therefore several considerations need to be addressed when creating a research tool that ensures problem-free context collection. We demonstrate how AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications, with minimal battery impact. By encapsulating implementation details of sensor data retrieval and exposing the sensed context as higher-level abstractions, AWARE shifts the focus from software development to data analysis, both quantitative and qualitative. We have evaluated AWARE in several case studies and discuss its use, power consumption, and scalability.
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“In short, the AWARE app allows the collection of data on smartphone usage (e.g., usage time and frequency, GPS data, communication behavior) as well as ecological momentary assessments in form of questionnaires.”
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“Instrumenting smartphones and wearables to capture in situ, fine-grained, and moment-by-moment data sets with sensing apps [20-24] has made it possible to passively collect data sets in naturalistic settings.”
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Metadata
- Authors
- Ferreira, D., Kostakos, V., & Dey, A. K.
- Affiliations
- University of Oulu (FI)
- Carnegie Mellon University (US)
- Corresponding author
- Ferreira, Denzil
- Kostakos, Vassilis
- Venue
- Frontiers in ICT
- Publisher
- Frontiers Media SA
- Published
- 2015-04-20
- Volume
- 2
- ISSN
- 2297-198X (ISSN-L)
- OpenAlex
- W2078074240
- Cited by
- 409
- References
- 18
- Citations by year
- 2026: 24 · 2025: 30 · 2024: 49 · 2023: 35 · 2022: 42 · 2021: 48 · 2020: 39 · 2019: 31 · 2018: 48 · 2017: 35 · 2016: 25 · 2015: 3
- FWCI
- 31.88
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in ICT (publishedVersion, cc-by)
- Funders
- Academy of Finland (137736, 276786)
- Tekes (2932/31/2009)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- AWARE, context-aware systems, sensor data acquisition, battery consumption, resource-constrained mobile devices, context inference, open-source software toolkit, scalability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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