Abstract
We present a simple yet effective human action detection and counting scheme during physical exercise using video stream data. Counting human actions automatically is more meaningful for data analysis in sports centers, and for healthiness observations in rehabilitation centers. The identification of the action starts with the detection of crucial body regions, namely skeletal joints. We observed that hand-wrist, arm-elbow, and arm-shoulder points are crucial for human arm motion, whereas during leg motion ankle-knee, knee-waist, and waist-ankle points are critical. These body junctions get different angle values during physical exercise, which helps us track and count the action. We assumed a simple, cheap and effective solution for multi-tracking these joints, which are marked with a distinctive color. Color filtering and color-based tracking steps are then performed to detect and count the actions by tracing the angle variations between joints. The developed application and performance evaluation tests show that our technique provides a reasonable performance while providing a simple and cheap video setup.
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References
Alexander, G.L., Havens, T.C., Rantz, M., Keller, J., Abbott, C.C.: An analysis of human motion detection systems use during elder exercise routines. W. J. Nurs. Res. 32(2), 233–249 (2010)
Ar, I., Akgul, Y.S.: A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera. IEEE Trans. Neural Syst. Rehabil. Eng. 22(6), 1160–1171 (2014). doi:10.1109/TNSRE.2014.2326254
Dönderler, M.E., Şaykol, E., Arslan, U., Ulusoy, Ö., Güdükbay, U.: Bilvideo: Design and implementation of a video database management system. Multimedia Tools Appl. 27, 79–104 (2005)
Havens, T.C., Alexander, G.L., Abbott, C., Keller, J.M., Skubic, M., Rantz, M.: Contour tracking of human exercises. In: IEEE Workshop on Computational Intelligence for Visual Intelligence (CIVI 2009), pp. 22–28, Nashville, TN, USA (2009)
Kwak, H-M., Park, S-H., Yoon, Y-R.: An integrated system for body shape analysis and physical fitness test - HIMS. In: 27th Annual Conference of the IEEE Engineering in Medicine and Biology (EMBC), pp. 3742–3745, Shanghai, China (2005)
Lin, J.F.-S., Kulic, D.: Online segmentation of human motion for automated rehabilitation exercise analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 22(1), 168–180 (2014)
Rao, C., Yilmaz, A., Shah, M.: View-invariant representation and recognition of actions. Int. J. Comput. Vis. 50(2), 203–226 (2002)
Şaykol, E., Güdükbay, U., Ulusoy, Ö.: Scenario-based query processing for video surveillance archives. Eng. Appl. Artif. Intell. 23(3), 331–345 (2010)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)
Velloso, E., Bulling, A., Gellersen, H., Ugulino, W., Fuks, H.: Qualitative activity recognition of weight lifting exercises. In: Proceedings of the 4th Augmented Human International Conference (AH 2013), pp. 116–123. New York, NY, USA (2013)
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Özeroğlu, B., Şaykol, E. (2015). Counting Human Actions in Video During Physical Exercise. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_59
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DOI: https://doi.org/10.1007/978-3-319-26561-2_59
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