Network-Level Power-Performance Trade-Off in Wearable Activity Recognition: A Dynamic Sensor Selection Approach

  • Authors:
  • Piero Zappi;Daniel Roggen;Elisabetta Farella;Gerhard Tröster;Luca Benini

  • Affiliations:
  • University of California, San Diego;ETH Zurich;University of Bologna;ETH Zurich;University of Bologna

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2012

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Abstract

Wearable gesture recognition enables context aware applications and unobtrusive HCI. It is realized by applying machine learning techniques to data from on-body sensor nodes. We present an gesture recognition system minimizing power while maintaining a run-time application defined performance target through dynamic sensor selection. Compared to the non managed approach optimized for recognition accuracy (95% accuracy), our technique can extend network lifetime by 4 times with accuracy 90% and by 9 times with accuracy 70%. We characterize the approach and outline its applicability to other scenarios.