Loca2005

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Loca2005

  1. 1. Where am I: Recognizing On-Body Position of Wearable Sensors Paul Lukowicz, Holger Junker, Gerhard Tröster und Kai Kunze
  2. 2. Overview • Motivation  Vision • Approach • Procedure • Experimental Setup • Results • Ongoing/Future Work
  3. 3. Motivation • Current State of Context Recognition:  Dedicated sensors at predefined positions • Fixed number of sensors • Known orientation  Limited to specific recognition tasks
  4. 4. Motivation • Context Recognition Vision:  Self-organizing body network of sensors  Placement of sensors in body worn appliances Part of EU funded ‘RELATE’ Project Partners: TECO Karlsruhe, Lancaster University, TU Delft
  5. 5. Motivation • Issues:  Appliances need to recognize their location on the body.  They need to exchange information (privacy concerns)  Context Recognition algorithms need to cope with varying numbers of sensors/appliances.
  6. 6. Apporach Vertical Axis Wrist Vertical Axis Head
  7. 7. Approach Vertical Axis Pocket Walking Pocket Walking Head
  8. 8. Procedure Raw Data Extract Features (1 sec. sliding window)  Only accelerometer data Frame-by-Frame Walking Classification  Using absolute sum Majority Decision (10 sec. jumping window)  Walk Classification with penalty on false positives. No Walking? Yes Frame-by-Frame Location Classification Majority Decision (10 sec. jumping window)
  9. 9. Procedure  Features: 19 computed, 6 finally used • RMS • 75% Percentile • Inter Quartile Range • Frequency Range Power • Frequency Entropy • Sums Power Wave Det Coefficient
  10. 10. Experimental Setup Measurement Course: • Working on a desk • Walking along a corridor • Making coffee • Walking • Giving a ’presentation’. • Walking. • Walking up and down a staircase. Sensor Placement: • (optional) working at desk. • Right side of head (above eyes) • Left breast • Left wrist • Right trouser pocket 18 data sets recorded (12-15 min.) (6 subjects 3 trials each).
  11. 11. Results
  12. 12. Results Reference labeled frame-by-frame Frame-by-frame with walk smoothing Event based approach: Both with smoothing 100 % location recognition
  13. 13. Results Confusion Matrix for the mean of C4.5 over all data sets of both smoothed walking and location (98% correctly classified) a b c d Classified as 865 2 31 2 a = Head 0 847 4 49 b = Trousers 42 0 883 1 c = Breast 17 68 10 921 d = Wrist
  14. 14. Future Work • Currently: Mathlab scripts /Java, Batch processing  Shift to use the Sensor Toolbox  Real-time • Analysis of more data (containing also different body locations)  ETH  Georgia Tech students • Bootstrapping the Body-Network • Collaboration of devices containing the sensors with ‘intelligent objects’ in the environment.
  15. 15. Future Work Recognize Bootstrapping Determine On-Body + Communication Relative Positioning Position

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