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.

Electronic downloads

• ASwarmofWearableSensorsattheEdgeoftheCloudforRobustActivityRecognition_Prathivadi_SEC13_Montreal.pdf · application/pdf · 927 kbytes
• prathivadi_swarmwearable_edge.pdf · application/pdf · 183 kbytes. (Abstract)

• 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.