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Wireless Sensor Localization Decoding Human Movement

Michael Baswell CS526 Semester Project, Spring 2006. Wireless Sensor Localization Decoding Human Movement. Goal: to measure human body movement and, ultimately, to create a formal language describing this motion. Not a new idea, but new tech-

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Wireless Sensor Localization Decoding Human Movement

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  1. Michael Baswell CS526 Semester Project, Spring 2006 Wireless Sensor LocalizationDecoding Human Movement

  2. Goal: to measure human body movement and, ultimately, to create a formal language describing this motion. Not a new idea, but new tech- nologies 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 Introduction & Background

  3. Motion Tracking Technologies • Markers on joints – LotR/Gollum • Markers can be • Visual (cameras track movement) • Electromagnetic • Inertial sensors • Drawbacks: • Line-of-sight • Surrounding environment can cause interference & errors • COST! Proprietary Systems can run $30-40 thousand or more.

  4. Current Wireless Location Systems • GPS – outside only, accuracy in meters to 10's of meters • ActiveBadge – indoor, IR-based. Locates badge to the current room only. • Wireless motes • 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

  5. Mote Localization & RSSI (continued) • Drawbacks: • 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: • 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!

  6. Cricket Indoor Location System • 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

  7. Cricket v2 (continued) • 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 • RF propagates at speed of light • Ultrasound propagates at speed of sound

  8. Cricket Advantages • 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: • Varying accuracy due to distance from beacons • Also varies by frequency of ultrasonic pulse • Further research could increase accuracy

  9. Cricket Config Screen

  10. Cricket Limitations • 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

  11. Cricket In Action • Videos online at Cricket web site • http://cricket.csail.mit.edu/ • Tracking a moving train • Auto-configuring robots (Roomba video)

  12. Summary • 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: • 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

  13. References • 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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