Research in Sport Medicine and Technology

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In recent years, efforts have been made to use non-invasive methods to achieve these angles. The aim of this study was to investigate the capabilities and reliability of a non-invasive system based on surface data mining using infrared depth cameras. To do this, you must first use a number of mathematical relations to create a cloud of points of the surface and to extract the desired deviations by geometric analysis of the map and surface topography. In this study, after extraction of cloud of points, the gossip method based on the second surface derivative was used to identify anatomical landmarks. Based on this, the body surface area was divided into a number of convexity, convection and parabolic. Then, by mathematical analysis of the surface topography map, the angles of a number of backward anomalies were estimated. To test the repeatability of this method, intra class correlation coefficient and minimum detectable changes were used to evaluate the relative and absolute  reliability. The results of this study showed that it is possible to identify landmarks using the second derivative method with appropriate accuracy. The results of the reliability survey also showed acceptable and high values ​​for the studied angles. Thus, it can be said that the use of this method has a good introverted reliability and can be a good alternative to radiography in continuous evaluations.

Keywords: Surface Data Mining, Point Cloud, Spinal and Pelvic Abnormalities, Reliability.

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