Joint movement intelligent scoring method and knee joint movement intelligent grading scoring method

Abstract

The invention relates to a joint movement intelligent scoring method, comprising the following steps: collecting three-dimensional six-degree-of-freedom movement data of a joint of a tested person; calculating similarity scores of movement of each degree of freedom in the six-degree-of-freedom movement data; utilizing a DTW algorithm to compare the similarity of movement data of each degree of freedom of the joint of the tested person with average movement data of the corresponding degree of freedom of similar normal people, so as to obtain accumulated distance values of degree-of-freedom movement data of the joint of the tested person; mapping the accumulated distance values to calculate similarity scores of the degree-of-freedom movement of the joint of the tested person; and according to the similarity score of movement of each degree of freedom of the joint of the tested person, calculating the total score of six-degree-of-freedom movement of the joint of the tested person. Three-dimensional six-degree-of-freedom movement data of the joint are converted into the corresponding similarity scores, movement of each degree of freedom of the joint can be evaluated objectively, visually and accurately based on the scores, and comprehensive evaluation of three-dimensional movement of the joint can be realized through learning of the score of each degree of freedom.

Description

technical field [0001] The invention relates to the technical field of joint motion data processing, in particular to a joint motion intelligent scoring method and a knee joint motion intelligent grading scoring method. Background technique [0002] Since the emergence of three-dimensional motion analysis of the human body, the quantitative evaluation of patient three-dimensional motion data has always been the focus and difficulty of sports medicine research. In foreign countries, the recognized quantitative gait index mainly includes Normalcy Index (NI), which is now also known as Gillette gait index (GGI). Gait deviation index (GDI) is a new evaluation index further developed from GGI. In addition, quantitative indexes such as MDP are used to evaluate the gait data of patients. [0003] The above quantitative indicators need to be combined with multi-joint gait data such as hip joints, knee joints, and ankle joints to obtain and compare the above-mentioned gait index, an...

AI Technical Summary

Problems solved by technology

[0003] The above quantitative indicators need to be combined with multi-joint gait data such as hip joints, knee joints, and ankle joints to obtain and compare the above-mentioned gait index, and these quantitative indicators only evaluate the overall function, and it is difficult to specify specific parameters for specific joints. have a more accurate evaluation

Moreover, obtaining multi-joint gait data requires a large-scale test environment and the assistance of multiple people for testing, which is time-consuming and labor-intensive, and is not suitable for clinical use.

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