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ABSTRACT: This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless
sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is
triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity
modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor
network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated
on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed
algorithm examines the person’s data over these 30days and automatically extracts the person’s daily pattern.
KeywordsHuman activity model–Spatiotemporal activity patterns–Sensor networks
Universal Access in the Information Society 04/2012; 10(2):125-138. · 0.33 Impact Factor
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Ninth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2011, 21-25 March 2011, Seattle, WA, USA, Workshop Proceedings; 01/2011
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Personal and Ubiquitous Computing. 01/2010; 14:473-487.
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Proceedings of the 8th International Conference on Embedded Networked Sensor Systems, SenSys 2010, Zurich, Switzerland, November 3-5, 2010; 01/2010
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INFOCOM 2010. 29th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies, 15-19 March 2010, San Diego, CA, USA; 01/2010
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Proceedings of the 9th International Conference on Information Processing in Sensor Networks, IPSN 2010, April 12-16, 2010, Stockholm, Sweden; 01/2010
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Proceedings of the 31st IEEE Real-Time Systems Symposium, RTSS 2010, San Diego, California, USA, November 30 - December 3, 2010; 01/2010
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UbiComp 2010: Ubiquitous Computing, 12th International Conference, UbiComp 2010, Copenhagen, Denmark, September 26-29, 2010, Proceedings; 01/2010
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Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010; 01/2010
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Proceedings of the 7th International Conference on Information Processing in Sensor Networks, IPSN 2008, St. Louis, Missouri, USA, April 22-24, 2008; 01/2008
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ABSTRACT: In this paper we demonstrate the application of a prob- abilistic grammar-based formulation to detect complex ac- tivities from simple sensor measurements. In particular, we present a grammar hierarchy for identifying "cooking ac- tivity" from low-level location measurements in an assisted living application. Using real data from a pilot network de- ployment, we show that our system can recognize complex behaviors in a manner that is invariant across multiple dif- ferent instances of the same activity. Our experiments also demonstrate that substantial data interpretation can take place at the node level, allowing the network to operate on compact symbolic representations.
Sensor Technologies and Applications, 2007. SensorComm 2007. International Conference on; 11/2007
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ABSTRACT: The paper explores an algorithm and methodology for realtime video compression and communication over sensor network. Video is encoded using the address-event representation (AER) and using custom image sensors capable of detecting intensity-differences (motion) information. The work focuses on keeping a constant low bit-rate over sensor network channels based on ZigBee radios, so that the motion information in an image can degrade gracefully when the resolution or the image content is varied over time. The paper shows that a 320x240 video stream can be compressed up to 50 times while being intelligible for interpreting motion patterns. This compression requires zero computation when the image is encoded with AER. The work will be applied to remote monitoring of home-care patients and surveillance
International Symposium on Circuits and Systems, New Orleans, LA, USA; 05/2007
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ABSTRACT: We propose a low-complexity algorithm and methodology for compression of surveillance video collected by a wireless sensor. The video is encoded using address-event representation (AER) by custom image sensors capable of detecting temporal differences. Temporal-differencing is usually the first processing step employed in artificial video processing.
A video monitoring a subject moving at low-speed recorded at 320x240 resolution can be compressed up to 50 times and remain intelligible for interpreting motion patterns. This compression requires zero computation, allowing low-power wireless sensor nodes to stream the video data over the network. The results of this work is to be applied to remote monitoring of home-care patients and surveillance.
Connecticut Symposium on Microelectronics and Optoelectronics, New Haven, CT, USA; 03/2007
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ABSTRACT: Node localization has attracted significant multidisciplinary research effort over the last few years. Despite these efforts,
there is still no clear consensus on a particular technology or approach that can be used in a wide variety of environments.
While this may be attributed to several reasons including cost, power and computation complexity, many researchers would agree
that physical layer issues impose the largest barrier in the wider deployment and use of node localization services.
12/2006: pages 105-134;
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ABSTRACT: Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network.
Information Processing in Sensor Networks, Nashville, Tennessee, USA; 04/2006
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3rd International Conference on Broadband Communications, Networks, and Systems (BROADNETS 2006), 1-5 October 2006, San José, California, USA; 01/2006
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Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN 2006, Nashville, Tennessee, USA, April 19-21, 2006; 01/2006
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Proceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN 2006, Nashville, Tennessee, USA, April 19-21, 2006; 01/2006
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International Symposium on Circuits and Systems (ISCAS 2006), 21-24 May 2006, Island of Kos, Greece; 01/2006
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Wireless Sensor Networks, Third European Workshop, EWSN 2006, Zurich, Switzerland, February 13-15, 2006, Proceedings; 01/2006
