Automatic quantification of the degree of flexion on upper extremity through analysis of monocular video

José D. Ávila, Gerardo Ceballos, Valentín Molina, Hermann Dávila

Abstract


A method is introduced to obtain information on the movement of a human upper extremity based on automatic video analysis. This study used basic techniques of image segmentation and contour curvature calculation of the segmented extremity, as well as temporary dynamic alignment of sequences to track the two critical points that mark the elbow. Additionally, the median axes of the arm and forearm were calculated to then obtain curves of the extremity’s flexion angle over time. This methodology was used with seven individuals and the proposed automatic measurement was compared to the semi assisted measurement in which a physiatrist conducted measurements on images by using a computer. The results suggest that this methodology could serve as a support tool in medicine for the evaluation of different diseases that affect the mobility of the extremities.


Keywords


Temporary dynamic alignment; Flexion-extension angle; Quantification; Curvature of a signal; Segmentation

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References


Hu H., Tao Y. and Zhou H.(2005): Integration of Vision and Inertial Sensors for Homebased Rehabilitation, and Workshop on Integration of Vision and Inertial Sensors IEEE International Conference on Robotics and Automation, Barcelona, Spain.

American Academy of Child and Adolescent Psychiatry. Facts for Families. Technical Report 35, 2004.

T.A Zessiewicz and R.A Hauser. Medical treatment of motor and non-motor features of Parkinson’s disease. American Academy of Neurology, 3(1):12–38, 2007.

F. Martinez, F. Gómez, and E. Romero. Video analysis for estimation of human movement. rev.fac.med [online], 17(1):95–106, 2009.

M. Sezgin and B. Sankur. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1):146-168, 2004.

N. Otsu. A threshold selection method from gray level histograms. IEEE Trans.Systems, Man and Cybernetics, 9(1):62–66.

R. Fontoura R. and Marcondes R. (2001): Shape Analysis and Classification: Theory and Practice, CRC Press.

X. Bai, L. Lateki, and W. Liu. Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):449–462, 2007.

D. Xiao-Li, G. Cheng-Kui, and W. Zheng-Ou. A local segmented dynamic time warping distance measure algorithm for time series data mining. In Machine Learning and Cybernetics, pages 1247–1252, 2006.

W.K. Pratt. Digital Image Processing: PIKS Scientific Inside. Wiley, 2007.

Ceballos G., Paredes J., Hernandez L. (2008): Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain, Electrophoresis, 29: pp 2828-2840.

Molina Valentin, Ceballos Gerardo, Hermann Davila. Ecg Signal Analysis Using Temporary Dynamic Sequence Alignment. Revista Tecciencia, Escuela Colombiana de Carreras Industriales, vol 14 pp 11-16, 2013.




DOI: http://dx.doi.org/10.18180/tecciencia.2013.15.1

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