Gait analysis method for assisting in screening meniscus injuries

An analysis method and meniscus technology, applied in the medical field, can solve the problems of closed examination space, patients unable to cooperate to complete the examination, and long scanning time, so as to improve the accuracy and save the cost and time of detection.

Inactive Publication Date: 2017-05-10
张余
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Problems solved by technology

But there are certain defects in both of them, for example, the cost of both is relatively expensive; arthroscopic surgery belongs to invasive detection; patients with cardiac pacemakers or some metal foreign bodies cannot be examined by MRI; The examination space of MRI equipment is relatively closed, and the scanning time is relatively long, some patients cannot cooperate to complete the examination due to fear

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  • Gait analysis method for assisting in screening meniscus injuries
  • Gait analysis method for assisting in screening meniscus injuries
  • Gait analysis method for assisting in screening meniscus injuries

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specific Embodiment approach

[0071] For the 11 people who are the data acquisition objects of the test set to be detected in the embodiment of the present invention, collect the knee joint gait characteristic data of the patient with meniscus injury, and extract the gait characteristic variable, the knee joint gait of the collected meniscus injury patient State feature data to form a test set; then use the constant value neural network Construct a set of dynamic estimators, embed the nonlinear gait system dynamics knowledge corresponding to patients with meniscus injury and healthy normal people in the training gait pattern library into the dynamic estimator, and use the gait characteristic data of patients with meniscus injury The difference with this group of dynamic estimators is made to form a group of classification error results, and the abnormal gait of patients with meniscus injury is calculated according to the minimum error. The specific implementation of the above-mentioned method of operation ...

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Abstract

The invention discloses a gait analysis method for assisting in screening meniscus injuries. The gait analysis method includes the steps that gait feature data of meniscus injury patients and healthy able-bodied persons is collected and subjected to neural network modeling and training; proper gait features are selected through kinematic and dynamic analysis of body gait, and gait dynamic knowledge is obtained; then the gait feature data of the meniscus injury patients serves as a test set. In this way, screening is accurately and rapidly assisted, it is avoided that non-invasive diagnosis is carried out under MRI and an arthroscopy, the accuracy of preoperative diagnosis is greatly improved, and the detection cost and the detection time are saved. The gait analysis method for assisting in screening meniscus injuries can be widely applied to the field of medical treatment.

Description

technical field [0001] The invention relates to the medical field, in particular to a gait analysis method for assisting screening of meniscus damage. Background technique [0002] The meniscus is a fibrocartilaginous plate, one on the inside and one on the outside, in the shape of a half moon. It is located between the tibial plateau and the inside and outside of the femur. The meniscus has two edges, one front and one back. Meniscal injury is a knee joint disease characterized by soft legs or knee joint locking in some patients, quadriceps atrophy, and localized tenderness with fixed knee joint space. Meniscus injuries are mostly caused by torsional external forces. When one leg bears weight and the calf is fixed in a semi-flexed and abducted position, the body and thigh suddenly rotate internally, and the medial meniscus is subjected to rotational pressure between the femur and tibia, resulting in meniscus tear. crack. The upper surface of the meniscus is concave, the l...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16H50/20G16H50/30
Inventor 张余曾炜马立敏
Owner 张余
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