LAURA –  Localization And Ubiquitous monitoRing of pAtients for health care support Politecnico di Milano A dvanced  N etw...
The LAURA project  <ul><li>Partnership between </li></ul><ul><ul><li>Politecnico di Milano </li></ul></ul><ul><ul><li>Fond...
The LAURA system <ul><li>Patient Localization and Tracking ( PLTS ) </li></ul><ul><ul><li>Localize patients </li></ul></ul...
The LAURA system Alessandro Redondi 2 novembre 2010
Related work – indoor localization Alessandro Redondi 2 novembre 2010 <ul><li>Signal type </li></ul><ul><ul><li>RF-based  ...
RSSI Fingerprinting <ul><li>During a training phase, a database of fingerprints is collected </li></ul><ul><li>A client no...
Propagation model <ul><li>Parametric methods explicitly assume a  model  that relates RSSI and inter-node distance </li></...
RSSI-based localization <ul><li>Deploy  N   anchor nodes  </li></ul><ul><li>Initialization:  </li></ul><ul><ul><li>Collect...
Self-calibration [Lim et al., 2010]  <ul><li>Anchor node positions </li></ul><ul><li>RSSI measurements  between anchor nod...
Self-calibration [Lim et al., 2010]  <ul><li>The following model is adopted in [Lim et al., 2010] </li></ul><ul><li>Where ...
Self-calibration [Lim et al., 2010]  <ul><li>Matrix T  can be estimated solving  N   least squares  problems </li></ul><ul...
RSSI-based localization Alessandro Redondi 2 novembre 2010 <ul><li>In order to increase  robustness  of the solution </li>...
Localization <ul><li>Localization algorithm </li></ul><ul><ul><li>Collect  RSSI measurements  between client node and anch...
Tracking Alessandro Redondi 2 novembre 2010 <ul><li>Particle filter tracking </li></ul><ul><ul><li>State-space  representa...
Tracking <ul><li>Particle filter tracking </li></ul><ul><ul><li>Check for  wall crossing </li></ul></ul><ul><ul><li>Check ...
Monitoring  [Karatonis et al., 2006] Alessandro Redondi 2 novembre 2010 <ul><li>Human Movement Classifier Using a  Biaxial...
Monitoring  [Karatonis et al., 2006] Alessandro Redondi 2 novembre 2010 <ul><li>Classifier output: </li></ul><ul><ul><li>U...
Network Architecture Alessandro Redondi 2 novembre 2010 <ul><li>Hierarchical Addressing Tree routing protocol  (L. Borsani...
Network Architecture <ul><li>Localization requires  </li></ul><ul><ul><li>Construction and maintenance of  RSSI matrix  (t...
Performance evaluation <ul><li>Anchor and client nodes:  MicaZ  and  IRIS  motes </li></ul><ul><li>Localization/tracking  ...
Performance – static nodes Alessandro Redondi 2 novembre 2010
Performance – moving nodes Alessandro Redondi 2 novembre 2010
LAURA system overview Alessandro Redondi 2 novembre 2010 GATEWAY  / LOCALIZATION ENGINE CLIENT 1 CLIENT 2 CLIENT N
Graphical user interface Alessandro Redondi 2 novembre 2010
Graphical user interface Alessandro Redondi 2 novembre 2010
Ongoing work <ul><li>Ongoing work: </li></ul><ul><ul><li>Energy-aware optimizations </li></ul></ul><ul><ul><li>Distributed...
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LAURA - LocAlization and Ubiquitous monitoRing of pAtients for health care support

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This works illustrates the LAURA system, which performs localization, tracking and monitoring of patients hosted at nursing institutes by exploiting a wireless sensor network based on the IEEE 801.15.4 (Zigbee) standard. We focus on the indoor personal localization module, which leverages a method based on received signal strength measurements, together with a particle filter to perform tracking of moving patients. We discuss the implementation and dimensioning of the localization and tracking system using commercial hardware, and we test the LAURA system in real environment, both with static and moving patients, achieving an average localization error lower than 2 m in 80% of the cases.

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LAURA - LocAlization and Ubiquitous monitoRing of pAtients for health care support

  1. 1. LAURA – Localization And Ubiquitous monitoRing of pAtients for health care support Politecnico di Milano A dvanced N etwork T echnologies Lab oratory A. Redondi, M. Tagliasacchi, M. Cesana, L. Borsani, F.Salice and P. Tarrìo Speaker: Alessandro Redondi 2 novembre 2010
  2. 2. The LAURA project <ul><li>Partnership between </li></ul><ul><ul><li>Politecnico di Milano </li></ul></ul><ul><ul><li>Fondazione Eleonora e Lidia </li></ul></ul><ul><ul><ul><li>a small-size nursing institute </li></ul></ul></ul><ul><ul><ul><ul><li>approx. 35 patients </li></ul></ul></ul></ul><ul><ul><ul><ul><li>2000 m 2 </li></ul></ul></ul></ul><ul><ul><ul><li>assistance of people with a broad range of pathologies (cognitive and perceptual disorders, Down’s syndrome, epilepsy, etc.) </li></ul></ul></ul><ul><li>Goals: </li></ul><ul><ul><li>Design and the implementation of a lightweight system based on wireless sensor networks ( WSNs ) for the automatic supervision of patients </li></ul></ul>Alessandro Redondi 2 novembre 2010
  3. 3. The LAURA system <ul><li>Patient Localization and Tracking ( PLTS ) </li></ul><ul><ul><li>Localize patients </li></ul></ul><ul><ul><li>Detect intrusions </li></ul></ul><ul><ul><li>Signal alerts </li></ul></ul><ul><li>Patient Monitoring ( PMS ) </li></ul><ul><ul><li>Report the patient’s status </li></ul></ul><ul><ul><li>Patient-dependent monitoring (heart beat, breath, proximity to other patients, etc.) </li></ul></ul><ul><li>Network architecture ( NA ) </li></ul><ul><ul><li>Wireless sensor network </li></ul></ul><ul><ul><li>Deliver the collected information to a central controller </li></ul></ul><ul><ul><li>Low cost / low power (IEEE 802.15.4 – Zigbee) </li></ul></ul>Alessandro Redondi 2 novembre 2010
  4. 4. The LAURA system Alessandro Redondi 2 novembre 2010
  5. 5. Related work – indoor localization Alessandro Redondi 2 novembre 2010 <ul><li>Signal type </li></ul><ul><ul><li>RF-based (WiFi, ZigBee , UWB) </li></ul></ul><ul><ul><li>Audio (audible range / ultrasound) </li></ul></ul><ul><ul><li>Infrared </li></ul></ul><ul><li>Input data </li></ul><ul><ul><li>RSSI </li></ul></ul><ul><ul><li>TDOA, TOA, RTT </li></ul></ul><ul><li>Signal model </li></ul><ul><ul><li>Parametric (e.g. model based) </li></ul></ul><ul><ul><li>Non-parametric (e.g. fingerprinting ) </li></ul></ul>
  6. 6. RSSI Fingerprinting <ul><li>During a training phase, a database of fingerprints is collected </li></ul><ul><li>A client node forms a vector of RSSI measurements and that is compared with the pre-computed fingerprints in the database </li></ul>Alessandro Redondi 2 novembre 2010 <ul><li>PROS : simplicity, accuracy </li></ul><ul><li>CONS : non-negligible setup cost, non robust. </li></ul>
  7. 7. Propagation model <ul><li>Parametric methods explicitly assume a model that relates RSSI and inter-node distance </li></ul><ul><ul><li>RSSI measured btw nodes d 0 meters apart </li></ul></ul><ul><ul><li>path loss exponent </li></ul></ul><ul><ul><li>noise term representing shadow-fading effects </li></ul></ul><ul><li>Propagation model might also incorporate knowledge of absorbing surfaces between anchor nodes </li></ul>Alessandro Redondi 2 novembre 2010
  8. 8. RSSI-based localization <ul><li>Deploy N anchor nodes </li></ul><ul><li>Initialization: </li></ul><ul><ul><li>Collect RSSI measurements between anchor nodes </li></ul></ul><ul><ul><li>Estimate model parameters </li></ul></ul><ul><li>Deploy n client nodes </li></ul><ul><ul><li>Collect RSSI measurements between client nodes and anchor nodes </li></ul></ul><ul><ul><li>Estimate the location of client nodes (e.g. using least squares or ML) </li></ul></ul><ul><li>Issue: a fixed propagation model is unsuitable in indoor environments </li></ul>Alessandro Redondi 2 novembre 2010
  9. 9. Self-calibration [Lim et al., 2010] <ul><li>Anchor node positions </li></ul><ul><li>RSSI measurements between anchor nodes </li></ul><ul><li>Inter-node distances </li></ul><ul><li>is the Euclidean distance between anchors i and j </li></ul>Alessandro Redondi 2 novembre 2010
  10. 10. Self-calibration [Lim et al., 2010] <ul><li>The following model is adopted in [Lim et al., 2010] </li></ul><ul><li>Where is a N x N matrix defining signal-to-distance mapping </li></ul>Alessandro Redondi 2 novembre 2010
  11. 11. Self-calibration [Lim et al., 2010] <ul><li>Matrix T can be estimated solving N least squares problems </li></ul><ul><li>which lead to the solution </li></ul>Alessandro Redondi 2 novembre 2010
  12. 12. RSSI-based localization Alessandro Redondi 2 novembre 2010 <ul><li>In order to increase robustness of the solution </li></ul><ul><ul><li>Use SVD-based regularization to compute pseudo-inverse </li></ul></ul><ul><ul><li>Use ell-1 norm regularization to impose sparsity </li></ul></ul>
  13. 13. Localization <ul><li>Localization algorithm </li></ul><ul><ul><li>Collect RSSI measurements between client node and anchor nodes </li></ul></ul><ul><ul><li>Estimate distances </li></ul></ul><ul><ul><li>Estimate the client node location </li></ul></ul>Alessandro Redondi 2 novembre 2010
  14. 14. Tracking Alessandro Redondi 2 novembre 2010 <ul><li>Particle filter tracking </li></ul><ul><ul><li>State-space representation: position + velocity </li></ul></ul><ul><ul><li>Prediction step  kinematic model </li></ul></ul><ul><ul><li>Update step  based on estimated distances </li></ul></ul>
  15. 15. Tracking <ul><li>Particle filter tracking </li></ul><ul><ul><li>Check for wall crossing </li></ul></ul><ul><ul><li>Check if particles are “ trapped ” in a room </li></ul></ul><ul><ul><li>Sequential Importance Resampling (SIR) </li></ul></ul>Alessandro Redondi 2 novembre 2010
  16. 16. Monitoring [Karatonis et al., 2006] Alessandro Redondi 2 novembre 2010 <ul><li>Human Movement Classifier Using a Biaxial Accelerometer </li></ul><ul><li>Threshold based binary tree algorithm </li></ul><ul><li>Implemented onboard </li></ul>
  17. 17. Monitoring [Karatonis et al., 2006] Alessandro Redondi 2 novembre 2010 <ul><li>Classifier output: </li></ul><ul><ul><li>Upright </li></ul></ul><ul><ul><li>Lying (prone/supine) </li></ul></ul><ul><ul><li>Walking </li></ul></ul><ul><ul><li>Possible Fall / Fall Alarm </li></ul></ul><ul><li>Accuracy </li></ul>
  18. 18. Network Architecture Alessandro Redondi 2 novembre 2010 <ul><li>Hierarchical Addressing Tree routing protocol (L. Borsani, S. Guglielmi, A. Redondi, M.Cesana, Tree-Based Routing Protocol for Mobile Wireless Sensor Networks, accepted for publication at WONS2011) </li></ul><ul><ul><li>Maintained with dynamic association policies </li></ul></ul><ul><ul><li>Rooted at PAN coordinator node, collecting data from the tree </li></ul></ul>
  19. 19. Network Architecture <ul><li>Localization requires </li></ul><ul><ul><li>Construction and maintenance of RSSI matrix (to capture changes in RF propagation) </li></ul></ul><ul><ul><li>Collection of RSSI samples measured at client node </li></ul></ul><ul><li>Beaconing (period 200ms) </li></ul><ul><ul><li>Each client (anchor) node stores 3 (5) RSSI samples </li></ul></ul><ul><ul><li>Median value is computed and buffered </li></ul></ul><ul><ul><li>Averaged medians are sent to PAN node every 1 (20) seconds </li></ul></ul><ul><ul><li>Used also to maintain the sensor networks </li></ul></ul>Alessandro Redondi 2 novembre 2010
  20. 20. Performance evaluation <ul><li>Anchor and client nodes: MicaZ and IRIS motes </li></ul><ul><li>Localization/tracking refresh rate : 1Hz </li></ul><ul><li>Node density : 0.02 – 0.15 nodes / m 2 </li></ul>Alessandro Redondi 2 novembre 2010
  21. 21. Performance – static nodes Alessandro Redondi 2 novembre 2010
  22. 22. Performance – moving nodes Alessandro Redondi 2 novembre 2010
  23. 23. LAURA system overview Alessandro Redondi 2 novembre 2010 GATEWAY / LOCALIZATION ENGINE CLIENT 1 CLIENT 2 CLIENT N
  24. 24. Graphical user interface Alessandro Redondi 2 novembre 2010
  25. 25. Graphical user interface Alessandro Redondi 2 novembre 2010
  26. 26. Ongoing work <ul><li>Ongoing work: </li></ul><ul><ul><li>Energy-aware optimizations </li></ul></ul><ul><ul><li>Distributed and cooperative localization </li></ul></ul><ul><ul><li>Wide area / multi floor support </li></ul></ul><ul><ul><li>Software reengineering: web server + DB </li></ul></ul><ul><li>Resources: </li></ul><ul><ul><li>Real datasets available for download at </li></ul></ul><ul><ul><li>www.laura.como.polimi.it/old/Home.html </li></ul></ul>Alessandro Redondi 2 novembre 2010

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