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Tracking Exercise Motions of Older Adults Using Contours

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercises — treadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.
Rocznik
Strony
21--42
Opis fizyczny
Bibliogr. 28 poz., rys.
Twórcy
autor
  • Department of Electrical and Computer Engineering
  • Sinclair School of Nursing
autor
  • School of Health Professions University of Missouri, Columbia, MO 65211, USA
autor
  • Department of Electrical and Computer Engineering
autor
  • Department of Electrical and Computer Engineering
autor
  • Sinclair School of Nursing
Bibliografia
  • 1. Aggarwal, J. K. and Cai, Q. (1999). Human motion analysis: A review. Computer Vision and Image Understanding, 73:90–102.
  • 2. Alexander, G., Havens, T., Rantz, M., Keller, J., and Abbott, C. (2009). An analysis of human motion detection systems use during elder exercise routines. in press, Western Journal of Nursing Research.
  • 3. Anderson, D., Keller, J., Skubic, M., Chen, X., and He, Z. (2006). Recognizing falls from silhouettes. In Proc. IEEE EMBS, pages 6388–6391, New York, NY.
  • 4. Clerc, M. and Kennedy, J. (2002). The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. On Evolut. Comput., 6(1).
  • 5. Collins, R., Lipton, A., Fujiyoshi, H., and Kanade, T. (2001). Algorithms for cooperative multi-sensor surveillance. Proc. of the IEEE, 89(10).
  • 6. Fisher, N., Pendergast, D., and Calkins, E. (1991). Muscle rehabilitation in impaired elderly nursing home residents. Arch. Phys. Med. Rehabil., 72:181–185.
  • 7. Fletcher, B., Gulanick, M., and Braun, L. (2005). Physical activity and exercise for elders with cardiovascular disease. Medsurg Nursing, 14(2):101– 109.
  • 8. Gonzalez, R. and Woods, R. (2002). Digital Image Processing. Prentice Hall, Upper Saddle River, NJ, 2 edition. Halloy et al., J. (2007). Social integration of robots into groups of cockroaches to control self-organizined choices. Science, 318.
  • 9. Havens, T., Alexander, G., Abbott, C., Keller, J., Skubic, M., and Rantz, M. (2009). Contour tracking of human exercises. In Proc. IEEE CIVI, Nashville, TN.
  • 10. Havens, T., Spain, C., Salmon, N., and Keller, J. (2008). Roach infestation optimization. In Proc. SIS, pages 1–7, St. Louis, MO.
  • 11. Heath, J. and Stuart, M. (2002). Prescribing exercise for frail elders. J. Am. Board Fam. Prac., 15(3):218–228.
  • 12. Jeanson, R., Rivault, C., Deneubourg, J., Blancos, S., Fournier, R., Jost, C., and Theraulaz, G. (2005). Self-organized aggregation in cockroaches. Animal Behaviour, 69:169–180.
  • 13. Kennedy, J. and Eberhardt, R. (1995). Particle swarm optimization. In Proceedings of the IEEE Int. Conf. on Neural Networks, pages 1942–1948, Piscataway, NJ.
  • 14. McMurdo, M. and Rennie, L. (1993). A controlled trial of exercise by residents of old people’s homes. Age Ageing, 22:11–15.
  • 15. Mubarak, S. (2003). Understanding human behavior from motion imagery. Machine Vision and Applications, 14(4):210–214.
  • 16. Pardas, M. and Sayrol, E. (2000). A new approach to tracking with active contours. In Int. Conf. Image Proc., volume 2, pages 259–262, Vancouver, BC.
  • 17. Rosenfeld, A. and Pfaltz, J. (1968). Distance functions on digital pictures. Pattern Recognition, 1(1):33–61.
  • 18. Sauvage Jr., L., Myklebust, B., and Crow-Pan, et al., J. (1992). A clinical trial of strengthening and aerobic exercise to improve gait and balance in elderly male nursing home residents. Am. J. Phys. Med. Rehabil., 71:333–342.
  • 19. Schnelle, J., MacRae, P., Ouslander, J., Simmons, S., and Nitta, M. (1995). Functional incidental training (ftt), mobility performance and incontinence care with nursing home residents. J. Am. Geriatric Soc., 43:1356–1362.
  • 20. Staggers, N. (1991). Human factors: The missing element in computer technology. Computers in Nursing, 9:47–49.
  • 21. Staggers, N. (2003). Human factors: Imperative concepts for information systems in critical care. AACN Clinical Issues, 14:310–319.
  • 22. Tanaka, H. (2009). Habitual exercise for the elderly. Family and Community Health, 32(1):S57–S65.
  • 23. Thayananthan, A., Torr, P., and Cipolla, R. (2004). Likelihood models for template matching. In British Machine Vision Conference, pages 949–958.
  • 24. Wang, L., Hu, W., and Tan, T. (2003). Recent developments in human motion analysis. Pattern Recognition, 36(3):586–601.
  • 25. Watanabe, H. and Mizunami, M. (2007). Pavolv’s cockroach: Classical conditioning of salivation in an insect. PLoS ONE, 2(6):e529.
  • 26. Weinger, M., Pantiskas, C., Wiklund, M., and Carstensen, P. (1998). Incorporating human factors into the design of medical devices. J. Am. Med. Assoc., 280:1484.
  • 27. Wildenauer, P., Blauensteiner, A., and Kampel, M. (2006). Motion detection using an improved colour model. Advances in Visual Computing, pages 607– 616.
  • 28. Zijlstra, F. (2007). Silhouette-based human pose analysis for feedback during physical exercises. In 6th Twente Student Conf. on IT, Netherlands.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca1b2169-4605-4204-ae88-b80270d7b7bf
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