Transcript Wireless Sensor Localization Decoding Human Movement

Wireless Sensor Localization
Decoding Human Movement
Michael Baswell
CS526 Semester Project,
Spring 2006
5/1/2006
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Introduction & Background
Goal: to measure human body
movement and, ultimately, to
create a formal language
describing this motion.
●Not a new idea, but new technologies may allow better/more accurate results
●Wireless sensors are small enough to be wearable;
can they be useful in this research?
●This presentation focuses on sensor localization; if
we can localize with high precision, we can measure
movement
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Motion Tracking Technologies
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Markers on joints – LotR/Gollum
Markers can be
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Visual (cameras track movement)
Electromagnetic
Inertial sensors
Drawbacks:
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Line-of-sight
Surrounding environment can cause
interference & errors
COST! Proprietary Systems can run
$30-40 thousand or more.
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Current Wireless Location
Systems
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GPS – outside only, accuracy in meters to 10's
of meters
ActiveBadge – indoor, IR-based. Locates
badge to the current room only.
Wireless motes
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have been simulated to locate to within meters
(Rupp, Sinha, etc.)
Work via RSS (Radio Signal Strength)
approximations; signal attenuates over distance &
due to obstacles
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Mote Localization & RSSI
(continued)
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Drawbacks:
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RSSI provides, at best, approximate distance info
from broadcaster to recipient
Obstructions cause further attenuation, again this
can only be approximated
Empirical measurements at Berkeley, using Mica2
mote sensor network:
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Outdoors, flat field, no obstruction: 3-meter resolution
Indoors, lab environment: no distance information
Clearly, if the environment is nonstatic,
approximations will be even further off
This is not good enough!
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Cricket Indoor Location System
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MIT project
Indoor location system
“fine-grained location
information”
accuracy 1-3 cm
Currently on 2nd version;
ongoing development &
research
Based on Mica2 platform,
but adds ultrasound
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Cricket v2 (continued)
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Cricket motes can be configured as either a
“beacon” or as a “listener” (or can be
configured to do both)
Beacons broadcast an RF indentifier signal,
and at the same time emit an ultrasonic “chirp”
Passive listeners measure the time lapse
between the two, and compute distance to that
beacon
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RF propagates at speed of light
Ultrasound propagates at speed of sound
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Cricket Advantages
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Because listeners are passive, the system
scales well.
Good resolution – possibly good enough
already for our purposes
Inexpensive - ~$225 / mote
Distance-finding research at Berkeley has
found similar degrees of accuracy:
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Varying accuracy due to distance from beacons
Also varies by frequency of ultrasonic pulse
Further research could increase accuracy
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Cricket Config Screen
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Cricket Limitations
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Too low, at least in default config (avg 1 sec /
broadcast)
Accuracy of 1-3 cm is good, but is it good
enough?
Due to limited range from beacons, large
movements may not be capturable (think
about a ballet leap)
Due to these limitations, additional sensors
such as flex sensors or inertial sensors, may
need to be integrated into the system as well
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Cricket In Action
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Videos online at Cricket web site
http://cricket.csail.mit.edu/
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Tracking a moving train
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Auto-configuring robots (Roomba video)
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Summary
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For the goal of this project, we need highly
accurate, quick measurements
Cricket is good, but there is room for
improvement still
May need to use a hybrid system:
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cricket sensors plus cameras/markers?
Flex sensors?
May need to focus on smaller movements or
individual body parts
Further development of this platform may
remove some of the limitations
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References
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http://cricket.csail.mit.edu/
http://www.cs.berkeley.edu/%7Ekamin/localization.html
Yifei Wang, “Human movement tracking using a wearable wireless
sensor network,” Masters Thesis, Iowa State University, 2005
Cricket v2 User Manual, Cricket Project, MIT Computer
Science and Artificial Intelligence Lab, January 2005
Hari Balakishnan, Roshan Baliga, Dorothy Curtis, Michel
Goraczko, Allen Miu, Bodhi Priyantha, Adam Smith, Ken
Steele, Seth Teller, Kevin Wang, “ Lessons from Developing
and Deploying the Cricket Indoor Location System,” MIT
Computer Science and Artificial Intelligence Laboratory
(CSAIL), November 2003
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