Strategy to determine the foot plantar center of pressure of a person through deep learning neural networks

• Published in: Telkomnika 2020-08, Vol.18 (4), p.1954-1961
• Main Authors: Martínez, Henry Hernández, Ariza, Holman Montiel, Roa, Luz Andrea Gaviria
• Format: Article
• Language: English
• Subjects: Center of pressure; Computing costs; Cost analysis; Deep learning; Gait; Image processing; Image reconstruction; Machine learning; Neural networks; Pressure sensors; Sensors; Signal processing; Software; Strain gauges; Teaching methods; Video
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/telkomnika.v18i4.15200

[...]this paper proposes a partial solution to this problem by systematizing the experience of a specialist through a computational learning method. Analogously, in the analysis based on image and video processing, a signal associated with the movement of the subject is reconstructed from the capture of a sequence of images and identification of regions of interest, and in the same way it is compared with other data of control [9]. Unlike these techniques, the studies of the COP and COM are mostly not performed with image processing techniques, because they do not have a resolution that allows to measure the angles of deviation of the extremities. [...]techniques based on sensors are implemented, such as the balance platforms, pressure sensors or scales and scales. [...]this paper proposes a partial solution to the problem of obtaining and reproducibility of the results of such analysis, by systematizing the obtaining of POP behavior through a bio-inspired computational learning technique, since these techniques are relatively simple to implement and have a low computational cost compared to other mathematical or analytical techniques or models [15, 16].