Abstract
Human limb movement sensing is crucial in different areas of science. In this paper, a method for sensing human limb movement and the subsequent reconstruction in a 3-D plane is described. The sensors used in this task are four Microsoft Kinect, which has depth and RGB cameras. Depth images are processed by artificial vision algorithms to delimit an area where the movements will be performed. In the other hand, RGB images are processed by a Convolutional Neural Network to acquire a series of specific points which correspond to the human body’s joints. A comparison of the proposed algorithm performance is also described. The equations that relate the information in two dimensions are obtained by processing the four sensors are used to generate a skeleton in 3-D.
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Acknowledgements
The authors would like to thank the Instituto Politécnico Nacional for the support to carry out this research. H. Sossa appreciates the economic support received from the SIP-IPN and CONACYT under grants 20190007 and 65 (Frontiers of Science), respectively, to conduct this investigation. J. Cruz appreciates the economic support received from the SIP-IPN under grants 20195940 to conduct this investigation.
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Cruz-Silva, J.E., Montiel-Pérez, J.Y., Sossa-Azuela, H. (2019). 3-D Human Body Posture Reconstruction by Computer Vision. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_46
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