A Swarm of Wearable Sensors at the Edge of the Cloud for Robust Activity Recognition
Yashaswini Prathivadi, Carl Sechen, Roozbeh Jafari

Citation
Yashaswini Prathivadi, Carl Sechen, Roozbeh Jafari. "A Swarm of Wearable Sensors at the Edge of the Cloud for Robust Activity Recognition". Talk or presentation, 28, September, 2013; Poster from the First International Workshop on the Swarm at the Edge of the Cloud (SEC'13 @ ESWeek), Montreal.

Abstract
Wearable computers intelligently combine data from motion sensors placed at various locations on the body with the aim to recognize human activities for the applications of healthcare and wellness. Many activity recognition algorithms for wearable computers exist today. To ensure the effectiveness of the recognition algorithms, the sensors typically have to be worn with a known orientation, since patterns of interest or templates for signal processing would be generated for that orientation. If worn in a disparate orientation, activity recognition algorithms will likely fail. We propose a technique that enables the activity recognition algorithm to function properly irrespective of the orientation of the nodes. This will provide a unique opportunity to assure the effectiveness of the recognition algorithms even when the sensors accidentally move or are misplaced. More importantly, this will enable the notion of reusing data generated in the past potentially by other users, and when the sensors are worn differently. This will eliminate the need for training the system every time it is deployed on a new user for the first time. This feature will be extremely attractive for the swarm of wearable computers capable of generating vast amounts of data. The notion of data reuse will be empowered by performing the proposed technique in the cloud infrastructure or on the wearable computers in real-time.

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Citation formats  
  • HTML
    Yashaswini Prathivadi, Carl Sechen, Roozbeh Jafari. <a
    href="http://www.terraswarm.org/pubs/130.html"><i>A
    Swarm of Wearable Sensors at the Edge of the Cloud for
    Robust Activity Recognition</i></a>, Talk or
    presentation,  28, September, 2013; Poster from the <a
    href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
    >First International Workshop on the Swarm at the Edge of
    the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • Plain text
    Yashaswini Prathivadi, Carl Sechen, Roozbeh Jafari. "A
    Swarm of Wearable Sensors at the Edge of the Cloud for
    Robust Activity Recognition". Talk or presentation, 
    28, September, 2013; Poster from the <a
    href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
    >First International Workshop on the Swarm at the Edge of
    the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • BibTeX
    @presentation{PrathivadiSechenJafari13_SwarmOfWearableSensorsAtEdgeOfCloudForRobustActivity,
        author = {Yashaswini Prathivadi and Carl Sechen and Roozbeh
                  Jafari},
        title = {A Swarm of Wearable Sensors at the Edge of the
                  Cloud for Robust Activity Recognition},
        day = {28},
        month = {September},
        year = {2013},
        note = {Poster from the <a
                  href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
                  >First International Workshop on the Swarm at the
                  Edge of the Cloud (SEC'13 @ ESWeek)</a>, Montreal.},
        abstract = {Wearable computers intelligently combine data from
                  motion sensors placed at various locations on the
                  body with the aim to recognize human activities
                  for the applications of healthcare and wellness.
                  Many activity recognition algorithms for wearable
                  computers exist today. To ensure the effectiveness
                  of the recognition algorithms, the sensors
                  typically have to be worn with a known
                  orientation, since patterns of interest or
                  templates for signal processing would be generated
                  for that orientation. If worn in a disparate
                  orientation, activity recognition algorithms will
                  likely fail. We propose a technique that enables
                  the activity recognition algorithm to function
                  properly irrespective of the orientation of the
                  nodes. This will provide a unique opportunity to
                  assure the effectiveness of the recognition
                  algorithms even when the sensors accidentally move
                  or are misplaced. More importantly, this will
                  enable the notion of reusing data generated in the
                  past potentially by other users, and when the
                  sensors are worn differently. This will eliminate
                  the need for training the system every time it is
                  deployed on a new user for the first time. This
                  feature will be extremely attractive for the swarm
                  of wearable computers capable of generating vast
                  amounts of data. The notion of data reuse will be
                  empowered by performing the proposed technique in
                  the cloud infrastructure or on the wearable
                  computers in real-time.},
        URL = {http://terraswarm.org/pubs/130.html}
    }
    

Posted by Christopher Brooks on 1 Oct 2013.

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