Comm2Sense: Detecting proximity through smartphones

Carreras, Iacopo; Matic, Aleksandar; Saar, Piret; Osmani, Venet

doi:10.1109/percomw.2012.6197489

Cited by 25 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The WiFi interface on COTS smartphones cannot operate simultaneously in discovering and discoverable mode as opposed to WiFi Direct. For that reason, the authors [Carreras et al 2012] were forced to modify the firmware of the devices to switch between the two modes. As an improvement, several works tried to estimate the interpersonal distance of users, to infer the existence of social interactions.…”

Section: Single Modalitymentioning

confidence: 99%

“…Features utilised to train the classifier were average and maximum values of a 10-sample window. Finally, Comm2Sense [Carreras et al 2012], followed the same process for training a classifier on 20-sample window that determines in which interaction zone people socialise.…”

Section: A Survey On Mobile Social Signal Processing A:19mentioning

confidence: 99%

“…Also, when using a large window of samples (e.g. 20 samples [Carreras et al 2012]), the data may be outdated leading to erroneous inference results. 5.5.2.…”

Section: A Survey On Mobile Social Signal Processing A:19mentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Mobile Social Signal Processing

et al. 2016

View full text Add to dashboard Cite

Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices.In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.

show abstract

Section: Single Modalitymentioning

confidence: 99%

Section: A Survey On Mobile Social Signal Processing A:19mentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Mobile Social Signal Processing

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The resolution of imputation of transmission will likely be dependent on the richness of the social networks involved: mapping transmission in an isolated hospital ward is easier than in a subway system. And while it will always be difficult to prove that an unknown third person was not the basis of transmission, additional ubiquitous digital data sources may fill in the missing information (40,41).…”

Section: Liable For the Futurementioning

confidence: 99%

Societal Implications of the Internet of Pathogens

Greninger

2019

J Clin Microbiol

View full text Add to dashboard Cite

The growth of pathogen genomics shows no signs of abating. Wholegenome sequencing of clinical viral and bacterial isolates continues to grow in nearly exponential bounds. Reductions in cost driven by new technology have created a seamless environment for generating, sharing, and analyzing pathogen genomes. The high-resolution view of infectious disease transmission dynamics offered by analyzing whole genomes from pathogens, coupled with the genomicist ethic of widespread data sharing, has created a veritable Internet of pathogens, which inadvertently produces new threats to patient privacy and protected heath information. The health care system, and society more generally, have yet to explore the farreaching privacy concerns raised by readily accessible pathogen genomic data. The recent use of human genomic databases, the existence of freely available alternative data and metadata sources, and lax regulation of collecting publicly available genomes to identify individuals in a criminal context raise concerning parallels about what is possible with pathogen genomics. The growing ability to ascertain culpability for infectious disease transmission at a nearly individual level could change our perspective on disease outbreaks from one based on public health to one based on individual liability. These technological breakthroughs in the absence of an understanding of potential privacy and liability issues lead to questions about the dominant paradigm of better living through pathogen genomics.

show abstract

“…The subjects' devices must remain in Bluetooth-discoverable state, which raises a number of security and privacy concerns, as described in the Introduction. There has been some developments in substituting Bluetooth with WiFi, an approach in which one of the phones acts as a hotspot and is sensed by others [6]. In controlled test environments this approach appears to offer a distance estimation resolution of 0.5m [33], providing a better understanding of the nature of the contacts [19].…”

Section: Location and Mobilitymentioning

confidence: 99%

Inferring Person-to-person Proximity Using WiFi Signals

Sapieżyński

Stopczynski

Wind

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches-including RFID badges or Bluetooth scanning-offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply.

show abstract

Comm2Sense: Detecting proximity through smartphones

Cited by 25 publications

References 7 publications

A Survey on Mobile Social Signal Processing

A Survey on Mobile Social Signal Processing

Societal Implications of the Internet of Pathogens

Inferring Person-to-person Proximity Using WiFi Signals

Contact Info

Product

Resources

About