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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