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Towards qualitative assessment of weight lifting exercises using body-worn sensors

Published:17 September 2011Publication History

ABSTRACT

Sports exercises are beneficial for general health and fitness. Some exercises such as weight lifting are particularly error-prone and using incorrect techniques can result in serious injuries. The current work aims to develop a weight lifting assistant that relies on motion sensors mounted on the body and integrated into gym equipment that provides qualitative feedback on the user's performance. We believe that by comparing motion data recorded from different parts of the body with a mathematical model of the correct technique, we will be able to qualitatively assess the user's performance, and provide a score and suggestions for improvement.

References

  1. G. O'Donovan et al., "The ABC of Physical Activity for Health: A consensus statement from the British Association of Sport and Exercise Sciences," Journal of Sports Sciences, vol. 28, no. 6, pp. 573--591, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  2. M. Gallagher, "Ten Most Common Causes of Training Injury," Muscle & Fitness, Jun-1996.Google ScholarGoogle Scholar
  3. Z. Y. Kerr, C. L. Collins, and R. D. Comstock, "Epidemiology of Weight Training-Related Injuries Presenting to United States Emergency Departments, 1990 to 2007," The American Journal of Sports Medicine, vol. 38, no. 4, pp. 765--771, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Beetz, B. Kirchlechner, and M. Lames, "Computerized real-time analysis of football games," IEEE Pervasive computing, p. 33--39, 200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. F. Michahelles and B. Schiele, "Sensing and monitoring professional skiers," IEEE Pervasive Computing, p. 40--46, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Hey and S. Carter, "Pervasive computing in sports training," Pervasive Computing, IEEE, vol. 4, no. 3, p. 54, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Anderson1, A. J. Harrison, and G. M. Lyons, "Accelerometry Based Ipsative Biofeedback to Improve Kinematic Consistency and Performance in Rowing."Google ScholarGoogle Scholar
  8. M. Baechlin, K. Foerster, J. Schumm, D. Breu, J. Germann, and G. Troester, "An automatic parameter extraction method for the 7x50m Stroke Efficiency Test," in Third International Conference on Pervasive Computing and Applications, 2008, vol. 1, p. 442--44.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. Baechlin and G. Troester, "Pervasive computing in swimming: A model describing acceleration data of body worn sensors in crawl swimming," in Joint Conference on Pervasive Computing (JCPC), 2009, p. 293--298.Google ScholarGoogle Scholar
  10. K. Chang, M. Chen, and J. Canny, "Tracking free-weight exercises," UbiComp 2007: Ubiquitous Computing, p. 19--37, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 1 Bannach, D., Amft, O., Lukowicz, P.: "Rapid Prototyping of Activity Recognition Applications". In: IEEE Pervasive Computing. Vol 7:2, 2008, 22--31. ISSN: 1536-1. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Towards qualitative assessment of weight lifting exercises using body-worn sensors

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