IS = { zkontrolovano 01 Jan 2015 },
  UPDATE  = { 2014-12-16 },
  key =         {obdrzalek12accuracy},
  author =      {Obdr{\v z}{\' a}lek, {\v S}t{\v e}p{\' a}n and
                  Kurillo, Gregorij and Ofli, Ferda  and Bajcsy, Ruzena 
                  and Seto, Edmund  and Jimison, Holly  and Pavel, Michael },
  authorship =  {50-15-15-5-5-5-5},
  title =       {Accuracy and robustness of Kinect pose estimation in
                  the context of coaching of elderly population},
  year =        {2012},
  pages =       {1188-1193},
  booktitle =   {EMBC 2012: Proceedings of the Annual International
                  Conference of the IEEE Engineering in Medicine and
                  Biology Society},
  publisher =   {IEEE},
  address =     {Piscataway,  USA},
  isbn =        {978-1-4244-4119-8},
  book_pages =  {6808},
  month =       {August-September},
  day = {28--1},
  venue =       {Hilton Bayfront Hotel in San Diego, California, USA},
  organization ={IEEE},
  annote =      {The Microsoft Kinect camera is becoming increasingly
                  popular in many areas as ide from entertainment,
                  including human activity monitoring and
                  rehabilitation. Many people, h owever, fail to
                  consider the reliability and accuracy of the Kinect
                  human pose estimation when they depend on it as a
                  measuring system. In this paper we compare the
                  Kinect pose estimation (skeletonization) with more
                  established techniques for pose estimation from
                  motion capture dat a, examining the accuracy of
                  joint localization and robustness of pose estimation
                  with respect to the orientation and occlusions. We
                  have evaluated six physical exercises aimed at
                  coaching of elderly population. Experimental results
                  present pose estimation accuracy rates and corres
                  ponding error bounds for the Kinect system.},
  keywords =    {Computer assisted coaching, marker-less motion capture, Kinect, accuracy eval
  prestige =    {international},
  project =     {National Science Foundation (NSF) grants 1111965, HHS 90TR0003/01 },
  doi =         {10.1109/EMBC.2012.6346149},