The SPAN lab develops inventions for wireless networks which improve their security, reliability, self-awareness, and sensing capabilities. Research applies statistical signal processing, networking, and radio propagation techniques. The innovations have application in localization and tracking, secret key generation for wireless networks, network design and deployment, modeling and analysis. The lab, directed by Neal Patwari, is a combination of the efforts of several graduate and undergraduate researchers.
Congratulations to Ossi Kaltiokallio, Hüseyin Yigitler, Riku Jäntti, and Neal Patwari! Their paper, "Catch a Breath: Non-invasive Respiration Rate Monitoring via Wireless Communication", was awarded the Best Paper Award at the 13th IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN 2014), held April 15-17 in Berlin, Germany. Ossi Kaltiokallio, Hüseyin Yigitler, and Riku Jäntti are affiliated with Aalto University in Espoo, Finland. Ossi was a visiting researcher in the SPAN lab in 2012.
Neal Patwari was the keynote speaker at the 2014 IEEE RFID Conference in Orlando, Florida, on April 10, 2014. He presented a talk, "Tracking Without Tags: Environmental Awareness Using RF Tomography", including a demonstration of the types of changes in signal strength experienced on a static link when a person walks through the link. The talk slides are now available.
Brad Mager presents his paper, "Fall Detection Using RF Sensor Networks", at the 24th Annual IEEE Symposium on Personal, Indoor, and Mobile Communications Conference (PIMRC '13) in London, on Tuesday September 3. His paper is the first presented paper in the "Event Sensing and Localization" Session at 11am - 12:40pm. His paper is co-authored by Neal Patwari and Maurizio Bocca.
Merrick McCracken presents his paper, Joint ultra-wideband and signal strength-based through-building tracking for tactical operations, at the 2013 IEEE Intl. Conf. on Sensing, Communications, and Networking, on Wednesday, June 26, in New Orleans. His paper, co-authored by Maurizio Bocca and Neal Patwari, explores combining ultra-wideband impulse radar with radio tomography in order to achieve accurate localization without having sensors on all sides of an area to be covered. The idea is particularly motivated by emergency response applications.
Brad Mager had a great night at the ECE Technical Open House Award Banquet. He was awarded "Outstanding Computer Engineering Student", and also awarded one of six "Best Presentation" awards for his talk on his senior thesis, "Fall Detection Using RF Sensor Networks". His talk was also mentioned by the keynote speaker and distinguished alumnus award winner Dr. John Sutherland as an example of useful research into the "internet of things". Congrats to Brad on his awards!
Dr. Yang Zhao presents Thursday, April 11, at the 12th ACM/IEEE International Conference on Information Processing in Sensor Networks in Philadelphia. Yang's talk is on his paper, Radio Tomographic Imaging and Tracking of Stationary and Moving People via Kernel Distance.
This article on Inc.com, "5 Ideas That Will Blow Your Mind" mentions Xandem's motion detection product as one of those five ideas. Having RF sensing mentioned along with "A Laser Printer for DNA" and "Software That Thinks Like a Human Brain"? Kinda blows our minds.
Ossi Kaltiokallio, Ph.D. student at Aalto University in Finland, and Maurizio Bocca and Neal Patwari, of the SPAN lab at the University of Utah, were awarded the "Best Paper Award" at the IEEE SenseApp workshop for their paper, "Follow @grandma: Long-Term Device-Free Localization for Residential Monitoring". Ossi Kaltiokallio presented the work today, 22 October 2012, in Clearwater, Florida. The work for the paper was conducted while Ossi was a visiting student in the SPAN lab. The best paper award was judged on quality of both the written paper and the oral presentation. IEEE SenseApp, an annual conference in its seventh year, is the short name of the IEEE International Workshop on Practical Issues in Building Sensor Network Applications. Only 20% of submissions were accepted as regular papers, and three were finalists for the best paper award.
Dr. Maurizio Bocca presented a talk Tuesday, October 9, at IEEE MASS 2012 in Las Vegas, on the paper:
For location and time, see the MASS 2012 program. A video from Dr. Bocca's presentation, posted on Youtube, shows the dramatic improvements possible when using multi-channel RSS measurements for the purpose of radio tomographic imaging.
A University of Utah team of researchers has won two awards at “EvAAL”, a prestigious international competition on tracking technologies. The competition, organized by the Ambient Assisted Living Open Association, involved ten research teams from around the world, including Canada, Spain, Germany, Switzerland, France, and the US. The University of Utah’s team is called the cyber-physical systems (CPS) Group, a team led by Dr. Maurizio Bocca, with participation from faculty from the School of Computing (Dr. Suresh Venkatasubramanian and Dr. Sneha K. Kasera) and Department of Electrical and Computer Engineering (Dr. Neal Patwari). The CPS Group won the 1st place award for tracking accuracy, and the 2nd place award overall. The accuracy of the team’s localization system was superior to all other teams, but took 2nd place in the overall score, which also includes scores given by a team of judges for “installation complexity” and “user acceptance”.
The first place award for tracking accuracy awarded to the CPS Group showed that their localization system had the best tracking performance of any of the systems of the participating teams. Each team developed and tested systems that locate and track a person while they are in their own home. The teams’ systems were tested, live, at the competition, held at the Smart House Living Lab in Universidad Politecnica de Madrid (UPM) in Madrid, Spain. During the test, a person walked around in a path that was unknown to any of the teams. Each team’s system continuously reported its best guess of where it believed the person was. Then the evaluators compared the estimates to the actual path; the difference was the team’s localization error. The team with the lowest localization error was the University of Utah’s team.
Localization and tracking of people in their home may sound like a big-brother surveillance technology, however, it is meant to be used only with the person’s permission, and only when a person requires it. One major application where people need such location sensing technologies is in ambient assisted living. The idea of “ambient” assisted living is ensure the health and well-being of a person who might not be otherwise able to live in their own home. For the very elderly or those who need long-term home-based care, the use of some technology may enable to them to stay in their home longer, rather than to needing move to assisted living facilities. Localization sensing is part of this -- a caretaker could be alerted if, for example, a person has fallen and hasn’t gotten up, or if a person has been laying in bed all day.
The Utah team developed a system that doesn’t require the person to wear what is called an active badge. An active badge has a radio transmitter that periodically sends the system a message, and the system locates where that message originates. However, the user must remember to wear the active badge -- and for elderly or infirm people, it is a disadvantage that the system only works when they remember to wear the active badge. The Utah team’s technology is based on transceivers deployed in the home that use radio tomography to determine where people are moving. Radio tomography (RT) is like computed tomography (CT) scans in medical environments, except that RT uses 2.4 GHz radio waves, just like WiFi devices, rather than x-rays, like medical CT scanners. This technology has been in development at the U. of U. for four years, and its accuracy has been improving with each new technological development at the lab. The team’s tests in an apartment here in Salt Lake City, prior to the competition, showed that a person could be located within 30 cm (1 foot). No other team used radio tomography (RT) as the basis for their sensing systems.
The Utah team’s localization system was highly accurate, significantly better than the other competitors. During the test, the evaluators were so surprised with the system’s accuracy that they stopped the test to make sure that there wasn’t something wrong. The evaluators re-ran the test when they realized that the system was just that accurate.
The “evaluating ambient assisted living technologies”, or EvAAL, competition, is an annual competition that draws competitors world-wide. The competition web site (http://evaal.aaloa.org/) describes in detail the localization and tracking competition rules and the teams participating in this year’s competition. The announcement was made Tuesday, Sept 25, 2012, at the AAL Forum, a conference and exposition of AAL technologies in Eindhoven, The Netherlands.
The CPS Group is funded by the U.S. National Science Foundation from Grant #1035565, “Enabling and Advancing Human and Probabilistic Context Awareness for Smart Facilities and Elder Care”.
This video shows the experimental results from a radio tomography (RT)-based two-person tracking experiment in an apartment. The people are not wearing any radio tags, instead, a wireless network of 33 (IEEE 802.15.4) transceivers deployed in the apartment measure changes in received signal strength (RSS), known as "the number of bars", caused by the people. Based on which links experience changes, the RT algorithm comes up with an image (shown at left) that has highest values (red) where it guesses that a person is located, and lowest values (blue) where it guesses that no person is located. A multi-target tracking algorithm developed by Dr. Maurizio Bocca at the University of Utah identifies from the image where the "blobs" are and what path they are taking through the apartment, using computer vision methods adapted to the RT problem. On the left, the video shows the apartment with black indicating walls, grey indicating furniture, white circle indicating the actual person location, and white X indicating the current estimate of the person. Dr. Bocca's algorithm track the two people to within an average error of about 30 cm (1 foot). Dr. Bocca and his University of Utah team used this system to compete in the EvAAL 2012 tracking competition and win 1st place for localization accuracy and 2nd place overall (overall score is an average of accuracy and other qualititative metrics from a panel of judges). (Video credit: Dr. Maurizio Bocca)