Low Power Programmable Architecture for Periodic Activity Monitoring
Mohammad-Mahdi Bidmeshki, Roozbeh Jafari

Citation
Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. "Low Power Programmable Architecture for Periodic Activity Monitoring". ACM/IEEE 4th International Conference on Cyber-Physical System, 8, April, 2013.

Abstract
Body sensor networks (BSNs) are considered a great example for cyber-physical systems due to their close coupling with human body. Activity monitoring is one of the numerous applications of BSNs. Continuous and real-time monitoring of human activities has many applications in healthcare and wellness domains. BSNs utilizing light-weight wearable computers and equipped with inertial sensors are highly suitable for real-time activity monitoring. However, power requirement is a major obstacle for miniaturization of these wearable systems, due to the need for sizable batteries, and also limits the life time of the system. Authors in this paper propose a low-power programmable signal processing architecture for dynamic and periodic activity monitoring applications which utilizes the properties of physical world (i.e., human body movements) to reduce the power consumption of the system. 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. The proposed architecture uses wavelet decomposition and is favorable for the discrimination of periodic activities. The experimental results show that the proposed architecture achieves 75.7% power saving while maintaining 96.9% sensitivity in the detection of target actions, compared with the scenario where the signal processing is not performed in tiered-fashion. This creates opportunities to enable the next generation of self-powered wearable computers.

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Citation formats

• HTML

  Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. <a
  href="http://www.terraswarm.org/pubs/70.html"
  >Low Power Programmable Architecture for Periodic
  Activity Monitoring</a>, ACM/IEEE 4th International
  Conference on Cyber-Physical System, 8, April, 2013.

• Plain text

  Mohammad-Mahdi Bidmeshki, Roozbeh Jafari. "Low Power
  Programmable Architecture for Periodic Activity
  Monitoring". ACM/IEEE 4th International Conference on
  Cyber-Physical System, 8, April, 2013.

• BibTeX

  @inproceedings{BidmeshkiJafari13_LowPowerProgrammableArchitectureForPeriodicActivityMonitoring,
      author = {Mohammad-Mahdi Bidmeshki and Roozbeh Jafari},
      title = {Low Power Programmable Architecture for Periodic
                Activity Monitoring},
      booktitle = {ACM/IEEE 4th International Conference on
                Cyber-Physical System},
      day = {8},
      month = {April},
      year = {2013},
      abstract = {Body sensor networks (BSNs) are considered a great
                example for cyber-physical systems due to their
                close coupling with human body. Activity
                monitoring is one of the numerous applications of
                BSNs. Continuous and real-time monitoring of human
                activities has many applications in healthcare and
                wellness domains. BSNs utilizing light-weight
                wearable computers and equipped with inertial
                sensors are highly suitable for real-time activity
                monitoring. However, power requirement is a major
                obstacle for miniaturization of these wearable
                systems, due to the need for sizable batteries,
                and also limits the life time of the system.
                Authors in this paper propose a low-power
                programmable signal processing architecture for
                dynamic and periodic activity monitoring
                applications which utilizes the properties of
                physical world (i.e., human body movements) to
                reduce the power consumption of the system. 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. The proposed architecture
                uses wavelet decomposition and is favorable for
                the discrimination of periodic activities. The
                experimental results show that the proposed
                architecture achieves 75.7% power saving while
                maintaining 96.9% sensitivity in the detection of
                target actions, compared with the scenario where
                the signal processing is not performed in
                tiered-fashion. This creates opportunities to
                enable the next generation of self-powered
                wearable computers.},
      URL = {http://terraswarm.org/pubs/70.html}
  }
  

Posted by Christopher Brooks on 17 May 2013.
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