Integrating wireless EEGs into medical sensor networks

Sarikaya, Behçet; Alim, Md. Abdul; Rezaei, Siamak

doi:10.1145/1143549.1143823

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…The Tmote series wireless sensor platform such as the Tmote Mini, Tmote Sky, and Tmote Invent have been implemented in several real life mobile sensing test beds including the BikeNet project that represents the mobile networked sensing system for bikes [5], medical sensor network that acts as a human Brain Computer Interface [6], and an online person tracking system [7]. iBadge [8] is a lightweight power-aware sensor badge wearable by a human being, which can capture human interactions and send the captured data wirelessly to a base station.…”

Section: Related Workmentioning

confidence: 99%

Data visualization: From body sensor network to social networks

Rahman

Saddik

Gueaieb

2009

2009 IEEE International Workshop on Robotic and Sensors Environments

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Sensors can capture very sensitive and valuable information without human intervention and send it to remote location. However, capturing sensory data from a Body Sensor Network (BSN) and sending it to social networks is a challenging task. This is because it requires a number of distributed networks to work together seamlessly. The task becomes more challenging when both the BSN and the social networks are mobile. It requires a framework which can handle the mobility of both the BSN and members of social networks and can send the sensory data to the social networks for real-time visualization. In this paper, we propose an open source framework, named SenseFace, which seamlessly incorporates a four-tier network including a BSN, cellular network, Internet and an overlay network consisting of social networks, to pass sensory data from a mobile BSN to the overlay network. The overlay network can intelligently manage one's social network and produce different data visualization formats suitable for email, fax, voicemail, SMS, MMS, APRS network, IM networks such as hotmail, gmail, yahoo, and existing social networks such as Facebook, YouTube, LinkedIn, delicious, Wordpress etc. Finally, we present the framework design and the hardware and software that have been used for the implementation of the framework.

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Section: Related Workmentioning

confidence: 99%

Data visualization: From body sensor network to social networks

Rahman

Saddik

Gueaieb

2009

2009 IEEE International Workshop on Robotic and Sensors Environments

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“…The classical example of uncertain data modelled by probability density functions (PDFs) are sensor networks. In numerous applications, sensor networks are collecting data to monitor complex systems, for example the behavior of a certain species in its natural habitat [13], the environmental conditions in a region [12], the medical conditions of a patient [16], or the production of a chemical reactor [21]. A common characteristic of all sensor networks is that the individual sensors collect uncertain data.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic skyline queries

Böhm¹,

Fiedler²,

Oswald³

et al. 2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

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The ability to deal with uncertain information is becoming increasingly important for modern database applications. Whereas a conventional (certain) object is usually represented by a vector from a multidimensional feature space, an uncertain object is represented by a multivariate probability density function (PDF). This PDF can be defined either discretely (e.g. by a histogram) or continuously in parametric form (e.g. by a Gaussian Mixture Model). For a database of uncertain objects, the users expect similar data analysis techniques as for a conventional database of certain objects. An important analysis technique for certain objects is the skyline operator which finds maximal or minimal vectors with respect to any possible attribute weighting. In this paper, we propose the concept of probabilistic skylines, an extension of the skyline operator for uncertain objects. In addition, we propose efficient and effective methods for determining the probabilistic skyline of uncertain objects which are defined by a PDF in parametric form (e.g. a Gaussian function or a Gaussian Mixture Model). To further accelerate the search, we elaborate how the computation of the probabilistic skyline can be supported by an index structure for uncertain objects. An extensive experimental evaluation demonstrates both the effectiveness and the efficiency of our technique.

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“…CodeBlue [6] extends wireless sensor networks applications for emergency medical services in disasters. In [7], we have designed a wireless medical sensor network for recognizing mental states based on Electroencephalograms (EEG) recordings.…”

Section: Wsn For Medical Monitoringmentioning

confidence: 99%

“…The IEEE 802.15.4 radio supports a minimum data rate of 20 kbps and a maximum data rate of 250 kbps and can be used to transport EEG data. In [7], we have designed a combined software and hardware platform for medical sensor networks like the CodeBlue [10]. We have designed EEG sensors with Tmote Sky [11] motes, which employ the IEEE 802.15.4 radio and 12-bit analog to digital conversion hardware.…”

Integrating wireless EEGs into medical sensor networks

Cited by 12 publications

References 10 publications

Data visualization: From body sensor network to social networks

Data visualization: From body sensor network to social networks

Probabilistic skyline queries

A security architecture for IPv6 enabled wireless medical sensor networks.

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