Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software

Kuryło, Piotr; Pivarčiová, Elena; Cyganiuk, Joanna; Frankovský, Peter

doi:10.2478/msr-2019-0001

Abstract

This paper discusses the problem of possibilities for applying a machine vision in the measurement of the trajectory of the upper limb movement in rehabilitation exercises. The fundamental presumption of designed system was to get the image from the camera’s CCD processor, possibilities of measuring and its processing. As a result of the application system, it is possible to dynamically determine the radius between the shoulder and forearm, and also the angle between the shoulder and the chest of the man, as function of the limb motion. The created system gives the possibility to use a non-invasive method of measurement, allows visualization and full analysis of the rehabilitation progress and also allows keeping electronic records of patients.

References

[1] Meyer-Baese, A. (2004). Pattern Recognition for Medical Imaging. Academic Press.
Search in Google Scholar Back to article
[2] Russ, J.C. (2007). The Image Processing Handbook. CRC Press.10.1201/9780203881095
Search in Google Scholar Back to article
[3] Woźnicki, J. (1996). Podstawowe techniki przetwarzania obrazu. Warsaw, Poland: Wydawnictwo Komunikacji i Łączności (Transport and Communication Publishers). (in Polish)
Search in Google Scholar Back to article
[4] Poppe, R. (2007). Vision-based human motion analysis: An overview. Computer Vision and Image Understanding, 108 (1–2), 4–18.10.1016/j.cviu.2006.10.016
Search in Google Scholar Back to article
[5] Najarian, K., Splinter, R. (2005). Biomedical Signal and Image Processing. CRC Press.10.1201/9781420058079
Search in Google Scholar Back to article
[6] Rangayyan, R.M. (2004). Biomedical Image Analysis. CRC press.10.1201/9780203492543
Search in Google Scholar Back to article
[7] Acton, S., Ray, N. (2005). Biomedical Image Analysis: Tracking. Morgan & Claypool Publishers.10.1007/978-3-031-02237-1
Search in Google Scholar Back to article
[8] Tadeusiewicz, R., Ogiela, M. (2008). Modern Computational Intelligence Methods for the Interpretation of Medical Images. Springer.
Search in Google Scholar Back to article
[9] Nagesh, Y., Bleakley, C., Lennon, O. (2010). Wearable absolute 6 DOF exercise training system for post stroke rehabilitation. In Proceedings of the Fourth Irish Human Computer Interaction Conference. Dublin City University.
Search in Google Scholar Back to article
[10] Kuryło, P., Cyganiuk, J., Tertel, E., Frankovský, P. (2016). Machine vision investigate the trajectory of the motion human body – review of the methods. Acta Mechatronica, 1 (2), 7–13.
Search in Google Scholar Back to article
[11] Deutscher, J., Blake, A., Reid, I. (2000). Articulated body motion capture by annealed particle filtering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 126–133.10.1109/CVPR.2000.854758
Search in Google Scholar Back to article
[12] Schmidt, J., Fritsch, J., Kwolek, B. (2006). Kernel particle filter for real-time 3D body tracking in monocular color images. In Proceedings of IEEE Conference on Automatic Face and Gesture Recognition. IEEE, 567–572.10.1109/FGR.2006.69
Search in Google Scholar Back to article
[13] Ivekovič, Š., Trucco, E., Petillot, Y.R. (2008). Human body pose estimation with particle swarm optimisation. Evolutionary Computation, 16 (4), 509–528.10.1162/evco.2008.16.4.50919053497
Search in Google Scholar Back to article
[14] Zalewski, A., Cegiełka, R. (1999). Matlab - Obliczenia numeryczne i ich zastosowanie. Warsaw, Poland: Scientific and Technical Publishers. (in Polish)
Search in Google Scholar Back to article
[15] Rosales, R., Siddiqui, M., Alon, J., Sclaroff, S. (2001). Estimating 3D body pose using uncalibrated cameras. In Proceedings in IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 821–827.10.1109/CVPR.2001.990566
Search in Google Scholar Back to article
[16] Sigal, L., Bhatia, S., Roth, S., Black, M.J., Isard, M. (2004). Tracking loose-limbed people. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 15–48.10.1109/CVPR.2004.1315063
Search in Google Scholar Back to article
[17] Sidenbladh, H., Black, M., Fleet, D. (2000). Stochastic tracking of 3D human figures using 2D image motion. In Proceedings of Computer Vision – ECCV 2000 : 6th Europen Conference on Computer Vision. Springer, 702–718.10.1007/3-540-45053-X_45
Search in Google Scholar Back to article
[18] Szczechowicz, J. (2011). Pomiary kątowe zakresu ruchu: zapisy pomiarów, metoda SFTR. Krakow, Poland: University School of Physical Education in Krakow. (in Polish)
Search in Google Scholar Back to article
[19] Wróbel, Z., Koprowski, R. (2004). Praktyka przetwarzania obrazów w programie Matlab. Akademicka Oficyna Wydawnicza EXIT. (in Polish)
Search in Google Scholar Back to article
[20] Tomczak, L., Mosorov, V., Sankowski, D., Nowakowski, J. (2007). Image defect detection methods for visual inspection systems. In 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics. IEEE, 454–456.10.1109/CADSM.2007.4297617
Search in Google Scholar Back to article
[21] Liu, J., Wang, X. (2012). Advanced Sliding Mode Control for Mechanical Systems: Design, Analysis and MATLAB Simulation. Springer.10.1007/978-3-642-20907-9_3
Search in Google Scholar Back to article
[22] Shaoqiang, Y., Zhong, L., Xingshan, L. (2008). Modeling and simulation of robot based on Matlab/SimMechanics. In 27th Chinese Control Conference. IEEE, 161–165.10.1109/CHICC.2008.4604913
Search in Google Scholar Back to article
[23] Ostertagová, E. (2012). Aplikovaná štatistika v počítačovom prostredí MATLABu. Košice, Slovakia: Equilibria. (in Slovak)
Search in Google Scholar Back to article
[24] Sudharsan, J., Karunamoorthy, L. (2016). Path Planning and co-simulation control of 8 DOF anthropomorphic robotic arm. International Journal of Simulation Modelling, 15 (2), 302–312.10.2507/IJSIMM15(2)9.339
Search in Google Scholar Back to article
[25] Cuesta, E., Mantaras, D.A., Luque, P., Alvarez, B.J., Muina, D. (2015). Dynamic deformations in coordinate measuring arms using virtual simulation. International Journal of Simulation Modelling, 14 (4), 609–620.10.2507/IJSIMM14(4)4.311
Search in Google Scholar Back to article
[26] Russ, J.C., Matey, J.R., Mallinckrodt, A.J., McKay, S. (1994). The image processing handbook. Computers in Physics, 8 (2), 177–178.10.1063/1.4823282
Search in Google Scholar Back to article
[27] Lozhkin, A., Abramov, I., Bozek, P., Nikitin, Y. (2014). The issue of calculating elliptic trajectories. Manufacturing Technology, 14 (4), 561–566.10.21062/ujep/x.2014/a/1213-2489/MT/14/4/561
Search in Google Scholar Back to article
[28] Zheng, F., Shao, L., Racic, V., Brownjohn, J. (2016). Measuring human-induced vibrations of civil engineering structures via vision-based motion tracking. Measurement, 83, 44–56.10.1016/j.measurement.2016.01.015
Search in Google Scholar Back to article
[29] Harťanský, R., Smieško, V., Rafaj, M. (2017). Modifying and accelerating the method of moments calculation. Computing and Informatics, 36 (3), 664–682.10.4149/cai_2017_3_664
Search in Google Scholar Back to article
[30] Daponte, P., De Vito, L., Riccio, M., Sementa, C. (2014). Design and validation of a motion-tracking system for ROM measurements in home rehabilitation. Measurement, 55, 82–96.10.1016/j.measurement.2014.04.021
Search in Google Scholar Back to article

Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software

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