Complexity based pressure center nonlinear feature extraction method

Abstract

The invention discloses a complexity based pressure center nonlinear feature extraction method. Conventional COP (center of pressure) feature parameters cannot effectively describe nonlinear features of shaking of human bodies. The method is used for extracting COP nonlinear dynamical features by utilizing an established model based on neighborhood coarse graining two-dimensional Lempel-Ziv complexity and specifically includes step1, acquiring COP signals; step 2, performing sequence reconstructing based on neighborhood coarse graining; and step 3, calculating normalized Lempel-Ziv complexity of the COP signals based on a reconstructed sequence. The complexity based pressure center nonlinear feature extraction method effectively solves the problem about how to apply the Lempel-Ziv complexity to process the two-dimensional COP signals to extract nonlinear features of the COP signals, so that quantitative description can be performed to irregular degree of posture shaking of the human bodies.

Description

technical field [0001] The invention belongs to the field of biomechanics, and in particular relates to a method for extracting nonlinear features of a pressure center based on complexity. Specifically, it uses the two-dimensional Lempel-Ziv complexity based on neighborhood coarse-graining to extract the nonlinear dynamic characteristics of the center of pressure (COP) signal, and quantify the irregularity of the human body posture. described method. Background technique [0002] People often take their ability to stand still for granted. But in fact, maintaining body balance while standing upright is a complex task that needs to be completed by multi-level nerves in the posture control system including vestibular nucleus, brainstem reticular formation, spinal cord, cerebellum and cerebral cortex. The center integrates and processes the information obtained by the visual, proprioceptive and vestibular sensory organs. The musculoskeletal tissue related to motor function reg...

AI Technical Summary

Problems solved by technology

However, it is currently difficult to apply Lempel-Ziv complexity to COP signal analysis, mainly because the COP signal recorded by the force measuring platform and balance board is a two-dimensional signal. Although the one-dimensional complexity algorithm is very mature, the two-dimensional complex The complexity algorithm needs to be further studied, and there is no report on the application of Lempel-Ziv complexity to the analysis of COP signals.

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