A Framework for Evaluating Stay Detection Approaches

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...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 6; no. 10; p. 315
Main Authors Schneider, Cornelia, Gröchenig, Simon, Venek, Verena, Leitner, Michael, Reich, Siegfried
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2017
Subjects
AAL
Active and Assisted Living
Clustering
Data
Data acquisition
Data processing
Detection
Electronic devices
Evaluation
Frameworks
Ground truth
location retrieval
Methods
Parameterization
process model
significant locations
Tourism
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi6100315