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Loca2005 Loca2005 Presentation Transcript

  • Where am I: Recognizing On-Body Position of Wearable Sensors Paul Lukowicz, Holger Junker, Gerhard Tröster und Kai Kunze
  • Overview • Motivation  Vision • Approach • Procedure • Experimental Setup • Results • Ongoing/Future Work
  • Motivation • Current State of Context Recognition:  Dedicated sensors at predefined positions • Fixed number of sensors • Known orientation  Limited to specific recognition tasks View slide
  • 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 View slide
  • 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.
  • Apporach Vertical Axis Wrist Vertical Axis Head
  • Approach Vertical Axis Pocket Walking Pocket Walking Head
  • 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)
  • Procedure  Features: 19 computed, 6 finally used • RMS • 75% Percentile • Inter Quartile Range • Frequency Range Power • Frequency Entropy • Sums Power Wave Det Coefficient
  • 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).
  • Results
  • Results Reference labeled frame-by-frame Frame-by-frame with walk smoothing Event based approach: Both with smoothing 100 % location recognition
  • 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
  • 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.
  • Future Work Recognize Bootstrapping Determine On-Body + Communication Relative Positioning Position