From trajectories to activities

Xie, Kexin; Deng, Ke; Zhou, Xiaofang

doi:10.1145/1629890.1629897

Cited by 49 publications

(10 citation statements)

References 20 publications

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“…GPS is the most widely used technology for inference of stay points and user context [7–14], followed by Wi-Fi [5, 15–17] and a combination of several technologies, including cell IDs (CIDs), Bluetooth, accelerometer, sound, and camera [6, 18–20]. The main advantage of GPS is its high positioning accuracy and good coverage in the open outdoor environment, while Wi-Fi is mostly available in indoor and urban environments.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Wi-Fi-Based Approach for Extraction of User Places and Their Context

Vidmar

Štular

Kos

et al. 2015

International Journal of Distributed Sensor Networks

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With the analysis of various sensor data from the mobile devices, it is possible to extract user situations, so-called user context. This is needed for the development of modern, user-friendly services. Therefore, we developed a simple, nonintrusive, and automatic method based on the Wi-Fi fingerprints and GPS. The method finds user stay points, aggregates them into meaningful stay regions, and assigns them four general user contexts: home, work, transit, and free time. We evaluated its performance on the real traces of six different users who annotated their contexts over eight days. The method determined the stay mode of the users with accuracy, precision, and recall of above 96%. In combination with the novel approach for aggregation, all regions relevant to the users were determined. Among the tested aggregation schemes, the fingerprint similarity approach worked the best. The context of the determined stay regions was on average accurately inferred in 98% of the time. For the contexts home, work, and free time, the precision and recall exceeded 86%. The results indicate that the method is robust and can be deployed in various fields where context awareness is desired.

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Section: Related Workmentioning

confidence: 99%

An Automatic Wi-Fi-Based Approach for Extraction of User Places and Their Context

Vidmar

Štular

Kos

et al. 2015

International Journal of Distributed Sensor Networks

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“…HITS and PageRank) [6] [34]. In addition, [28] designs a semantic spatio-temporal join method to infer activities from trajectories, based on a small set of predefined activity hotspots. [16] mines periodic behaviors in trajectories, focusing on brief semantics like home/office.…”

Section: Related Workmentioning

confidence: 99%

“…This is based on a hidden Markov model algorithm that we designed, considering varying spatial densities of possible POIs for heterogeneous (sparse as well as densely populated) geographies. Overlapping ROIs and dense POIs have been traditionally ignored in spatio-temporal data mining [1] [28]. The annotations from the three layers are combined to produce the annotated trajectory SST .…”

Section: System Architecturementioning

confidence: 99%

“…Unlike most other algorithms to identify POIs [1] [28], an unique novelty of our approach is that it works for densely populated area with many possible POI candidates for annotation, thus catering to heterogeneous people and vehicle trajectories. It also enables identifying the activity (behavior) behind the stop, thus annotating the trajectory with such information.…”

Section: Annotation With Semantic Pointsmentioning

confidence: 99%

“…The 3rd party sources have a high density of POIs in this area. Traditional one-to-one match methods like [28] are not suitable here. Our Semantic Point Annotation layer enriches the stops computed from the GPS tracks of the private cars and extracts the most probable POI category for each stop.…”

Section: Vehicle Trajectoriesmentioning

confidence: 99%

See 2 more Smart Citations

SeMiTri

Yan

Chakraborty

Parent

et al. 2011

Proceedings of the 14th International Conference on Extending Database Technology

129

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GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For application purposes the stream is transformed into finite subsequences called trajectories. Existing knowledge extraction algorithms defined for trajectories mainly assume a specific context (e.g. vehicle movements) or analyze specific parts of a trajectory (e.g. stops), in association with data from chosen geographic sources (e.g. points-of-interest, road networks). We investigate a more comprehensive semantic annotation framework that allows enriching trajectories with any kind of semantic data provided by multiple 3rd party sources.This paper presents SeMiTri -the framework that enables annotating trajectories for any kind of moving objects. Doing so, the application can benefit from a "semantic trajectory" representation of the physical movement. The framework and its algorithms have been designed to work on trajectories with varying data quality and different structures, with the objective of covering abstraction requirements of a wide range of applications. Performance of SeMiTri has been evaluated using many GPS datasets from multiple sourcesincluding both fast moving objects (e.g. cars, trucks) and people's trajectories (e.g. with smartphones). These two kinds of experiments are reported in this paper.

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Semantics in Mobile Sensing

Yan

Chakraborty

2014

Synthesis Lectures on Data, Semantics, and Knowledge

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From trajectories to activities

Cited by 49 publications

References 20 publications

An Automatic Wi-Fi-Based Approach for Extraction of User Places and Their Context

An Automatic Wi-Fi-Based Approach for Extraction of User Places and Their Context

SeMiTri

Semantics in Mobile Sensing

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