Gesture spotting using wrist worn microphone and 3-axis accelerometer

  • Authors:
  • Jamie A. Ward;Paul Lukowicz;Gerhard Tröster

  • Affiliations:
  • Swiss Federal Institute of Technology (ETH), Zurich;University of Health Sciences, Tirol, Austria;Swiss Federal Institute of Technology (ETH), Zurich

  • Venue:
  • Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
  • Year:
  • 2005

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Abstract

We perform continuous activity recognition using only two wrist-worn sensors - a 3-axis accelerometer and a microphone. We build on the intuitive notion that two very different sensors are unlikely to agree in classification of a false activity. By comparing imperfect, jumping window classifications from each of these sensors, we are able discern activities of interest from null or uninteresting activities. Where one sensor alone is unable to perform such partitioning, using comparison we are able to report good overall system performance of up to 70% accuracy. In presenting these results, we attempt to give a more-in depth visualization of the errors than can be gathered from confusion matrices alone.