SURFtogether: Towards Context Proximity Detection Using Visual Features

Maier, Marco Johannes; Marouane, Chadly; Klette, Manuel; Dorfmeister, Florian; Marcus, Philipp; Linnhoff‐Popien, Claudia

doi:10.4108/icst.iccasa.2014.257255

Cited by 1 publication

(2 citation statements)

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“…We perform our co-presence detection for the best encounter times of 55 different parameter sets, including proximity verification sets, sensor data, user groups, and device groups. We use multiple proximity periods ∈ [5,10,15,20,25,30] min to evaluate the time granularity of user's co-presence. To verify the user's proximity, two user devices have to encounter the same wireless device, e.g., access point or BLE beacon, within the proximity time window.…”

Section: B Results Of Co-presence Detectionmentioning

confidence: 99%

“…Our work can be positioned within the field of proximity detection or co-presence detection (used interchangeably in this paper). A number of approaches have been proposed for co-presence detection using the similarity of Bluetooth signals [7], Wi-Fi signals [8], ambient sound [9], images [10], and accelerometer data [11]. Some works concentrate on the estimation of face-to-face interaction among users up to 1.5 m using Bluetooth signals [12], proximity sensors [13], or comparing magnetometer readings to link devices in close proximity of a few centimeters [14].…”

Section: Related Workmentioning

confidence: 99%

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Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity

Haus

Ding

Ott

2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Pervasive computing environments are characterized by a plethora of sensing and communication-enabled devices that diffuse themselves among different users. Built-in sensors and telecommunication infrastructure allow co-presence detection. In turn, co-presence detection enables context-aware applications, like those for social networking among close-by users, and for modeling human behavior. We aim to support developers building better context-aware applications by a deepened understanding of which set of context information is appropriate for co-presence detection. We have gathered a multimodal dataset for proximity sensing, including several proximity verification sets, like Bluetooth, Wi-Fi, and GSM encounters, to be able to associate sensor's data with a spatial granularity. We show that sensor modalities are suitable to recognize the spatial adjacency of users with different spatio-temporal granularity. We find that individual user mobility has only a minor, negligible effect on co-presence detection. In contrast, the heterogeneity of device's sensor hardware has a major negative impact on co-presence detection. To reveal energy pitfalls with respect to usability, we perform an energy analysis pertaining to the usage stemming from different sensors for co-presence detection.

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Section: B Results Of Co-presence Detectionmentioning

confidence: 99%