The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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.”
In the citing paper
“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.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dey, A. K., Abowd, G. D., & Salber, D. (2001) · Human-Computer Interaction · doi:10.1207/s15327051hci16234_02 · Cited by 2,943
Self-citationSensing meets mobile social networks
Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., & Campbell, A. T. (2008) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1460412.1460445 · Cited by 1,033
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
Raento, M., Oulasvirta, A., Petit, R., & Toivonen, H. T. T. (2005) · IEEE Pervasive Computing · doi:10.1109/mprv.2005.29 · Cited by 483
A framework for developing mobile, context-aware applications
Biegel, G., & Cahill, V. (2004) · doi:10.1109/percom.2004.1276875 · Cited by 309
Understanding Human-Smartphone Concerns: A Study of Battery Life
Ferreira, D., Dey, A. K., & Kostakos, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21726-5_2 · Cited by 240
Self-citationFactors influencing quality of experience of commonly used mobile applications
İckin, S., Wac, K., Fiedler, M., Janowski, L., Hong, J.-H., & Dey, A. K. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6178833 · Cited by 225
Self-citationDey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J.-H., & Ramos, J. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030135 · Cited by 197
Self-citationContextual experience sampling of mobile application micro-usage
Ferreira, D., Gonçalves, J., Kostakos, V., Barkhuus, L., & Dey, A. K. (2014) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2628363.2628367 · Cited by 168
Self-citationCarter, S., Mankoff, J. C., & Heer, J. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240644 · Cited by 103
Dickerson, R. F., Gorlin, E. I., & Stankovic, J. A. (2011) · doi:10.1145/2077546.2077552 · Cited by 103
Flagged first, then by citations
Research works printed but not linked: 2
- Dickerson, R. F., Gorlin, E. I., and Stankovic, J. A. (2011). "Empath: a continuous remote emotional health monitoring system for depressive illness," in Proceedings of the 2nd Conference on Wireless Health (San Diego, CA: ACM), 5:1-5:10.
- Falaki, H., Mahajan, R., and Estrin, D. (2011). "SystemSens: a tool for monitoring usage in smartphone research deployments," in Proceedings of the Sixth Interna- tional Workshop on Mobiarch (Washington, DC: ACM), 25-30.
Cited by
Sorted by citations
Items 401–405 of 405
A Reproducible Analytics Workflow for Longitudinal Healthcare Digital Twins
Rehani, K., & Matawie, K. M. (2026) · IEEE International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) · doi:10.1109/ccgridw69005.2026.00054
Shi, F., Jiang, M., Ding, J., & Ning, H. (2026) · Adolescents · doi:10.3390/adolescents6050084
Caproni, B., Dreier, M. J., Fedor, J., Low, C. A., & Hamilton, J. L. (2026) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-026-00711-6
Yang, Y., Ihara, K., Garrido, F. A. S., Hirata, K., Jiang, T., Lan, H., Wu, H., Yu, H., Tamada, Y., Murashita, K., et al. (2026) · Sensors · doi:10.3390/s26196093
Zhu, Y., Yang, Y., & Thompson, R. J. (2026) · JMIR mhealth and uhealth · doi:10.2196/90970
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 ›
Are you an author, or do you see a problem with this page? Request a review
