Analyzing group interaction on networks of face-to-face proximity using wearable sensors

Atzmueller, Martin; Thiele, Lisa; Stumme, Gerd; Kauffeld, Simone

doi:10.1109/fiot.2018.8325593

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…The results of Study 2 are in line with previous studies on the overlap between RFID data and self-report data on social interactions (Thiele, Atzmueller, Kauffeld, & Stumme, 2014;Atzmueller et al, 2018). Together with our results, these findings indicate that there is a large but not perfect overlap between RFID measures and self-reports, indicating that each method still measures something unique that the other does not capture.…”

Section: Discussion Studysupporting

confidence: 92%

The validity of RFID badges measuring face-to-face interactions

Elmer¹,

Chaitanya²,

Purwar³

et al. 2018

Preprint

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Face-to-face interactions are important for a variety of individual behaviors and outcomes. In recent years, a number of human sensor technologies have been proposed to incorporate direct observations in behavioral studies of face-to-face interactions. One of the most promising emerging technologies is the application of active Radio Frequency Identification (RFID) badges. They are increasingly applied in behavioral studies because of their low costs, straightforward applicability, and moderate ethical concerns. However, despite the attention that RFID badges have recently received, there is a lack of systematic tests on how valid RFID badges are in measuring face-to-face interactions. With two studies, we aim to fill this gap. Study 1 (N = 11) compares how data assessed with RFID badges correspond with video data of the same interactions (construct validity) and how this fit can be improved using straightforward data processing strategies. The analyses show that the RFID badges have a sensitivity of 50%, which can be enhanced to 65% when flickering signals with gaps of less than 75 s are interpolated. The specificity is relatively less affected by this interpolation process (before interpolation 97%, after interpolation 94.7%)-resulting in an improved accuracy of the measurement. In Study 2 (N = 73) we show that self-report data of social interactions correspond highly with data gathered with the RFID badges (criterion validity).

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Section: Discussion Studysupporting

confidence: 92%

The validity of RFID badges measuring face-to-face interactions

Elmer¹,

Chaitanya²,

Purwar³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, the SocioPattern sensors have been used in several ubiquitous and social environments, e. g., regarding educational/university contexts including scientific conferences [17,7,33,23] and schools [41]. Further applications also relate to student freshman weeks, e. g., [13], where the matching of subjective questionnaire data to face-to-face contact networks have been investigated.…”

Section: Observing and Analyzing Physical Interactionsmentioning

confidence: 99%

“…With respect to the accuracy of the applied Sociopattern proximity tags, we refer to the results of Cattuto et al [17] when analyzing face-to-face interations; they confirm that if the tags are worn on the chest, then very few false positive contacts are observed; face-to-face proximity can be observed with a probability of over 99% using the interval of 20 seconds for a minimal contact duration. We refer to [17,13,24] for a detailed discussion.…”

Section: Observing Social Interaction Using Wearable Sensorsmentioning

confidence: 99%

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Behavioral Link Analytics on Heterogeneous Human Interaction Networks

et al. 2020

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For designing and modeling Artificial Intelligence (AI) systems in the area of human-machine interaction, suitable approaches for user modeling are important in order to both capture user characteristics. Using multimodal data, this can be performed from various perspectives. Specifically, for modeling user interactions in human interaction networks, appropriate approaches for capturing those interactions, as well as to analyze them in order to extract meaningful patterns are important. Specifically, for modeling user behavior for the respective AI systems, we can make use of diverse heterogeneous data sources. This paper investigates face-to-face as well as socio-spatial interaction networks for modeling user interactions from three perspectives: We analyze preferences and perceptions of human social interactions in relation to the interactions observed using wearable sensors, i. e., face-to-face as well as socio-spatial interactions fo the respective actors. For that, we investigate the correspondence of according networks, in order to identify conformance, exceptions, and anomalies. The analysis is performed on a real-world dataset capturing networks of proximity interactions coupled with self-report questionnaires about preferences and perception of those interactions. The different networks, and according perspectives then provide different options for user modeling and integration into AI systems modeling such user behavior.

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“…These devices provide an immeasurable amount of information, which allows to collect meaningful details about a person's routine. With these sensors it is possible to monitor health [1], locate a person at a certain time [2], the activity carried out at that moment [3], to determine if the person was anxious [4], and to detect group social interaction [5]. However, works that rely on accelerometers or GPS sensors only acquire partial information missing the whole context of the event.…”

Section: Introductionmentioning

confidence: 99%

Behavioural pattern discovery from collections of egocentric photo-streams

Menchon,

Talavera,

Massa

et al. 2020

Preprint

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The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person's patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.

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Analyzing group interaction on networks of face-to-face proximity using wearable sensors

Cited by 12 publications

References 39 publications

The validity of RFID badges measuring face-to-face interactions

The validity of RFID badges measuring face-to-face interactions

Behavioral Link Analytics on Heterogeneous Human Interaction Networks

Behavioural pattern discovery from collections of egocentric photo-streams

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