GymSkill: A personal trainer for physical exercises

Andreas Moller, Luis Roalter, Stefan Diewald, Johannes Scherr, Matthias Kranz, Nils Hammerla, Patrick Olivier, Thomas Plotz

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    41 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publication2012 IEEE International Conference on Pervasive Computing and Communications, PerCom 2012
    Number of pages8
    Publication statusPublished - 4 Jun 2012
    EventIEEE International Conference on Pervasive Computing and Communications 2012 - Lugano, Switzerland
    Duration: 19 Mar 201223 Mar 2012
    Conference number: 10th (Proceedings)


    ConferenceIEEE International Conference on Pervasive Computing and Communications 2012
    Abbreviated titlePerCom 2012
    Internet address


    • Activity recognition
    • Health
    • Mobile
    • Quantitative time-series analysis
    • Skill assessment

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