Wireless Sensor Localization Decoding Human Movement Michael Baswell CS526 Semester Project, Spring 2006

Introduction & Background ● 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

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.

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

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!

Cricket Indoor Location System ● MIT project ● Indoor location system ● “fine-grained location information” ● accuracy 1-3 cm ● Currently on 2 nd version; ongoing development & research ● Based on Mica2 platform, but adds ultrasound

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

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

Cricket Config Screen

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

Cricket In Action ● Videos online at Cricket web site ● ● Tracking a moving train ● Auto-configuring robots (Roomba video)

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

References ● ● ● 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