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Open AccessArticle

A Framework for Evaluating Stay Detection Approaches

Salzburg Research Forschungsgesellschaft mbH, Mobile and Web-Based Information Systems, Competence Field e-Health, Jakob Haringer Straße 5/3, 5020 Salzburg, Austria
Department of Geography and Anthropology, Louisiana State University, E-104 Howe-Russell Complex, Baton Rouge, LA 70803, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 315;
Received: 11 September 2017 / Revised: 3 October 2017 / Accepted: 16 October 2017 / Published: 19 October 2017
PDF [1179 KB, uploaded 19 October 2017]


In recent years, sensors of mobile devices are increasingly used in the research field of Active and Assisted Living (AAL), in particular, for movement analysis. Questions, such as where users typically stay (and for how long), where they have been or where they will most likely be going to, are of utmost importance for implementing smart AAL services. Due to the plethora of application scenarios and varying requirements, the challenge is the identification of an appropriate stay detection approach. Thus, this paper presents a comprehensive framework covering the entire process from data acquisition, pre-processing, parameterization to evaluation so that it can be applied to evaluate various stay detection methods. Additionally, ground truth data as well as application field data are used within the framework. The framework has been validated with three different spatio-temporal clustering approaches (time-based/incremental clustering, extended density based clustering, and a mixed method approach). Using the framework with ground truth data and data from the AAL field, it can be concluded that the time-based/incremental clustering approach is most suitable for this type of AAL applications. Furthermore, using two different datasets has proven successful as it provides additional data for selecting the appropriate method. Finally, the way the framework is designed it might be applied to other domains such as transportation, mobility, or tourism by adapting the pre-selection criteria. View Full-Text
Keywords: significant locations; location retrieval; Active and Assisted Living; AAL; process model significant locations; location retrieval; Active and Assisted Living; AAL; process model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Schneider, C.; Gröchenig, S.; Venek, V.; Leitner, M.; Reich, S. A Framework for Evaluating Stay Detection Approaches. ISPRS Int. J. Geo-Inf. 2017, 6, 315.

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