SwePub
Sök i SwePub databas

  Extended search

Träfflista för sökning "WFRF:(Bleser Gabriele) "

Search: WFRF:(Bleser Gabriele)

  • Result 11-12 of 12
Sort/group result
   
EnumerationReferenceCoverFind
11.
  • Reiss, Attila, et al. (author)
  • Activity Recognition Using Biomechanical Model Based Pose Estimation
  • 2010
  • In: Smart Sensing and Context, 2010. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642169816 - 9783642169823 ; , s. 42-55
  • Conference paper (peer-reviewed)abstract
    • In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.
  •  
12.
  • Weber, Markus, et al. (author)
  • Unsupervised model generation for motion monitoring
  • 2011
  • In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC). - : IEEE. - 9781457706523
  • Conference paper (peer-reviewed)abstract
    • This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.
  •  
Skapa referenser, mejla, bekava och länka
  • Result 11-12 of 12

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view