A Wavelet Based Low Power Architecture for Periodic Activity Monitoring
Mohammad-Mahdi Bidmeshki, Roozbeh Jafari

Citation
Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. "A Wavelet Based Low Power Architecture for Periodic Activity Monitoring". SRC Techcon, 2013, Austin, TX, 20, June, 2013.

Abstract
Continuous and real-time monitoring of human activities has numerous applications in health-care and wellness domains. Light-weight wearable computers utilizing inertial sensors are ideal for real-time monitoring. However, power requirement is a major obstacle for miniaturization of wearable systems, due to the need for sizable batteries. In this paper, we propose a low-power programmable signal processing architecture for activity monitoring applications. The significant power reduction is achieved by performing signal processing in a tiered-fashion and removing the signals that are not of interest as early as possible. This architecture uses wavelet decomposition and is suitable for the discrimination of periodic activities. The experimental results show that this architecture achieves 75.7% power saving while maintaining 96.9% sensitivity, compared to the scenario where the signal processing is not performed in tiered-fashion.

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Citation formats

• HTML

  Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. <a
  href="http://www.terraswarm.org/pubs/96.html"
  >A Wavelet Based Low Power Architecture for Periodic
  Activity Monitoring</a>, SRC Techcon, 2013, Austin,
  TX, 20, June, 2013.

• Plain text

  Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. "A Wavelet
  Based Low Power Architecture for Periodic Activity
  Monitoring". SRC Techcon, 2013, Austin, TX, 20, June,
  2013.

• BibTeX

  @inproceedings{BidmeshkiJafari13_WaveletBasedLowPowerArchitectureForPeriodicActivityMonitoring,
      author = {Mohammad-Mahdi Bidmeshki and Roozbeh Jafari},
      title = {A Wavelet Based Low Power Architecture for
                Periodic Activity Monitoring},
      booktitle = {SRC Techcon, 2013, Austin, TX},
      day = {20},
      month = {June},
      year = {2013},
      abstract = {Continuous and real-time monitoring of human
                activities has numerous applications in
                health-care and wellness domains. Light-weight
                wearable computers utilizing inertial sensors are
                ideal for real-time monitoring. However, power
                requirement is a major obstacle for
                miniaturization of wearable systems, due to the
                need for sizable batteries. In this paper, we
                propose a low-power programmable signal processing
                architecture for activity monitoring applications.
                The significant power reduction is achieved by
                performing signal processing in a tiered-fashion
                and removing the signals that are not of interest
                as early as possible. This architecture uses
                wavelet decomposition and is suitable for the
                discrimination of periodic activities. The
                experimental results show that this architecture
                achieves 75.7% power saving while maintaining
                96.9% sensitivity, compared to the scenario where
                the signal processing is not performed in
                tiered-fashion.},
      URL = {http://terraswarm.org/pubs/96.html}
  }
  

Posted by Christopher Brooks on 14 Aug 2013.