• DocumentCode
    2085121
  • Title

    GymSkill: A personal trainer for physical exercises

  • Author

    Möller, Andreas ; Roalter, Luis ; Diewald, Stefan ; Scherr, Johannes ; Kranz, Matthias ; Hammerla, Nils ; Olivier, Patrick ; Plötz, Thomas

  • Author_Institution
    Tech. Univ. Munchen, Munich, Germany
  • fYear
    2012
  • fDate
    19-23 March 2012
  • Firstpage
    213
  • Lastpage
    220
  • Abstract
    We present GymSkill, a personal trainer for ubiquitous monitoring and assessment of physical activity using standard fitness equipment. The system records and analyzes exercises using the sensors of a personal smartphone attached to the gym equipment. Novel fine-grained activity recognition techniques based on pyramidal Principal Component Breakdown Analysis (PCBA) provide a quantitative analysis of the quality of human movements. In addition to overall quality judgments, GymSkill identifies interesting portions of the recorded sensor data and provides suggestions for improving the individual performance, thereby extending existing work. The system was evaluated in a case study where 6 participants performed a variety of exercises on balance boards. GymSkill successfully assessed the quality of the exercises, in agreement with the professional judgment provided by a physician. User feedback suggests that GymSkill has the potential to serve as an effective tool for motivating and supporting lay people to overcome sedentary, unhealthy lifestyles. GymSkill is available in the Android Market as `VMI Fit´.
  • Keywords
    gesture recognition; medical computing; mobile computing; principal component analysis; smart phones; Android market; GymSkill; PCBA; VMI fit; fine-grained activity recognition techniques; gym equipment; personal smartphone; personal trainer; physical activity; physical exercises; pyramidal principal component breakdown analysis; standard fitness equipment; ubiquitous monitoring; user feedback; Algorithm design and analysis; Electric breakdown; Humans; Monitoring; Sensors; Servers; Training; activity recognition; health; mobile; quantitative time-series analysis; skill assessment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on
  • Conference_Location
    Lugano
  • Print_ISBN
    978-1-4673-0256-2
  • Electronic_ISBN
    978-1-4673-0257-9
  • Type

    conf

  • DOI
    10.1109/PerCom.2012.6199869
  • Filename
    6199869