CN111178338A - Method for establishing database and standardized model in gait analysis system - Google Patents
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Abstract
The invention discloses a method for establishing a database and a standardized model in a gait analysis system, which comprises the steps of selecting a plurality of testees meeting tested conditions, and acquiring human body gait data of each tester by a three-dimensional motion capture system; removing the defect data and the error data to obtain effective human gait data; carrying out data standardization processing on the obtained effective human body gait data; and dividing gait cycles, matching the human body gait data of each gait cycle to establish a standard database, and simulating and establishing a standardized model by adopting the standard database. The method takes a three-dimensional gait analysis system as a technical platform, collects relevant data of kinematics and motion biomechanics when preselected healthy subjects, such as the gait of children, establishes a database and a standardized model, and can be used for researching abnormal gait of birth defects of children.
Description
Technical Field
The invention relates to a method for establishing a database and a standardized model in a gait analysis system.
Background
For the human body, motor coordination refers to the ability to maintain a cyclic relationship, either anteroposterior or phase-related, between different parts or joints of the body, both in space and time. Gait is a complex cyclic process involving coordinated movement among many joints, and coordination of gait is achieved by controlling the movement of the relevant joints via the central nervous system, reducing the freedom of movement of the joints and thereby achieving coordination of the limbs. In normal gait of a healthy person, a high degree of movement coordination exists in the relevant limbs and joints, and the movement coordination of a person with abnormal gait is disrupted.
Gait analysis is a branch of biomechanics, and is used for performing kinematic observation and dynamic analysis on the activities of limbs and joints when a human body walks, and providing a series of time, geometry, mechanics and other parameter values and curves, so that the gait function of the human body is objectively and quantitatively evaluated.
The three-dimensional gait analysis system is an advanced function evaluation technology, and can be used for analyzing abnormal gait, evaluating treatment, determining a surgical scheme, evaluating surgery and assisting the use of orthotics. Compared with clinical measurement and visual gait analysis, the three-dimensional gait analysis system has better reliability. Many experimental studies have also demonstrated that clinical measurements are less accurate and objective than three-dimensional gait analysis systems. In addition, due to the reliability and accuracy of the three-dimensional gait analysis system, the three-dimensional gait analysis system has been used as an evaluation standard in scientific research to evaluate the reliability and effectiveness of other evaluation methods.
The three-dimensional gait analysis system is an analysis tool which analyzes objective data of biodynamics, kinematics and muscle activity conditions without being influenced by experience and subjective factors of testers, so that the evaluation of the motion function of the children is more objective and more comprehensive compared with the traditional scale evaluation method and visual evaluation, the further treatment of the motion dysfunction of the children is more targeted, the individual treatment can be more accurately carried out, and the motion function of the children is better improved.
At present, in the motion function evaluation and rehabilitation treatment of the birth defect children, the analysis of abnormal gait is more and more emphasized, particularly, along with the massive application of the quantification and visualization analysis of a three-dimensional gait analysis system, the damage of the nervous system and the skeletal muscle system of a child patient can be more accurately diagnosed and evaluated, the kinematic parameters, the dynamic parameters, the space-time parameters and the like in the abnormal gait caused by the damage can be quantitatively analyzed, the general gait is visually analyzed, and the gait reconstruction and the shape correction are facilitated.
Document 1: a method for establishing a Wenity practical gait database and extracting and characterizing gait features.
Document 1 proposes (1) a method for establishing a practical gait database, which designs an establishment method of a gait feature database, a data structure, a method for acquiring gait information, and a method for determining height query conditions in detail, and establishes a multi-information gait database of a hundred-person scale. (2) A gait recognition method based on a database technology is designed by combining a relational database technology, so that the gait recognition can utilize the high efficiency of relational database query to quickly reduce the recognition range and improve the operation efficiency and accuracy. And the good identity recognition effect of the gait recognition method based on the database technology is verified through experiments. (3) The gait feature weak periodicity representation method based on the space-time energy graph is provided, the gait feature weak periodicity and the definition of the space-time energy graph of a gait sequence are provided, and the feature representation is proved to be insensitive to noise interference in a statistical analysis mode. The method is combined with several gait feature extraction algorithms proposed by thesis, and experimental results show that the method greatly simplifies the preprocessing process of gait recognition, reduces the data storage space of gait features, reduces the dependency of feature extraction on specific conditions of factors such as pace, camera sampling frequency and the like, and meanwhile, the recognition performance also has certain practical reference value. (4) The method is characterized in that the advantages of local characteristics of signals are analyzed and represented by wavelet multiresolution, a gait characteristic representation and recognition algorithm based on wavelet analysis and mutual information entropy is provided by combining criteria of mutual information entropy, similar subgraphs obtained by wavelet transformation and all detail subgraphs with local direction characteristics are utilized in combined gait characteristics, corresponding combined characteristic parameter sets are obtained when the maximum mutual information entropy is obtained, the parameters highlight detail differences of walking habits in gait, extracted gait characteristics are more concentrated on key information points which are beneficial to classification, and therefore the gait recognition process is closer to the intelligent recognition process of human vision. An improved method for mutual information measurement is provided in the gait recognition stage, namely, the effective thinking of energy analysis and mutual information measurement is continued, and the recognition speed is improved.
Document 2: in chinese patent CN201310516264.8 human gait database and its establishment method, a method for establishing human gait database is disclosed, which comprises the following steps:
(1) collecting human body gait data, wherein the human body gait data comprises movement data, human body EMG data and plantar pressure data;
(2) the human body gait data are stored in a classified mode through person identity recognition;
(3) designing a human body gait database interface, wherein the database interface is connected with the gait data, and extracting different types of gait data according to the operation requirements of a user on the database interface.
(4) The motion data are collected through an infrared motion capture system, the human body EMG data are collected through a wireless electromyography collection system, and the plantar pressure data are collected through a three-dimensional force measuring platform.
The patent discloses a gait data acquisition method and a method for identifying a human body through three-dimensional gait data, but does not describe in detail how to process a large amount of three-dimensional gait data to form a standardized data scheme.
Document 3: zhangfeng, Zhang Shuizu and the like, a gait feature extraction method based on a human walking model is researched [ J ]. computer application and software, 2009,5:198-207.
Document 3 discloses a three-dimensional gait analysis model that can abstract a human body into interconnected rigid bodies and is composed of a plurality of key points and a plurality of joints. Meanwhile, a feature extraction method is also disclosed, which can collect the kinematic data based on the three-dimensional gait model when the human body gait is in a gait by a motion capture system, and specifies the gait cycle feature division method, the gait footprint feature calculation mode and the like.
In the prior art, the three-dimensional gait analysis system is applied to the evaluation of the motion function of clinical children at home and has a plurality of problems. The existing three-dimensional gait analysis system is lack of a normal database of domestic children, and the currently used databases are all provided by western three-dimensional gait analysis system companies. However, the gaits of the western person and the eastern person are not completely the same, and it is not completely accurate to measure and evaluate the gaits of the eastern person by using the normal value range of the western person. Therefore, there is a need for a method to build a database and standardized models that can be used in new three-dimensional gait analysis systems.
The human body modeling method based on the database has various choices, domestic software for human body motion simulation mainly comprises LifeMOD, AnyBody, ANSYS and the like, and the software has the defects of inaccuracy in muscle control, high price and the like. In order to better solve the problem of human motion simulation, the Stanford university develops OpenSim, which is an open-source free software applied to human musculoskeletal model development, simulation and motion analysis.
The human body modeling theory of OpenSim is mainly derived from Hill equations and Hill muscle triad models. The whole simulation process mainly comprises four steps of model scaling (scaling), Inverse Kinematics (IK), Residual Reduction (RRA) and muscle calculation control (CMC).
OpenSim often uses height, weight data and muscle characteristic data of a certain person to build a general model, and the general model needs to be scaled to obtain an individualized model. The model scaling is based on laboratory test mark point data, and the length and quality of each link are scaled according to the proportion between the experimental data and human body ring nodes in the general model. During the scaling process, the error between the marker point in the experiment and the theoretical point in the model is controlled by the least square method.
In view of the foregoing, there is a need for a method that enables the creation of new databases and standardized models by collecting three-dimensional gait data of local personnel, particularly for groups of children.
Disclosure of Invention
The invention aims to provide a method for establishing a database and a standardized model in a gait analysis system, which can acquire three-dimensional gait data of local or national health personnel and is used for establishing a domestic database and a standardized model which meet clinical requirements.
To achieve the above object, an embodiment of the present invention provides a method for establishing a database and a standardized model in a gait analysis system, including:
(1) selecting a plurality of testees meeting the tested conditions, exposing and detecting joint points by the testees, marking and fixing the joint points according to a whole body mode, and acquiring human body gait data of each tester by a three-dimensional motion capture system; the human gait data comprises a kinematics data group, a dynamics data group and a surface electromyography data group, and each data group comprises a plurality of parameters;
(2) each subject uses the same three-dimensional motion capture system to collect human gait data for a plurality of times, a group of human gait data is obtained each time, defect data and error data are removed, and effective human gait data are obtained;
(3) carrying out data standardization processing on the obtained effective human body gait data, wherein each parameter in each data set is represented by mean number +/-standard deviation, and obtaining standardized human body gait data;
(4) and (4) dividing gait cycles based on the human body gait data acquired in the step (3), matching the human body gait data of each gait cycle to establish a standard database, and simulating and establishing a standardized model by adopting the standard database.
Preferably, the test group is children, and the children are 3-9 years old and can listen to the children instructing normal walking.
Preferably, the parameters included in the kinematic data set include a maximum dorsiflexion angle of the ankle joint, a maximum plantar flexion angle of the ankle joint, and a maximum extension angle of the knee joint; parameters included in the dynamic data comprise maximum forward ground reaction force, backward ground reaction force and vertical ground reaction force; the parameters included in the surface electromyography data set are surface electromyography signal intensities.
4. A method of establishing as claimed in claim 1, characterized by: the three-dimensional motion capture system comprises a plantar pressure system and a surface myoelectricity detection system.
Preferably, the human gait data analyzed by the three-dimensional motion capture system further includes pace speed, step length, stride frequency, total support phase time, swing phase time, and initial double support phase time.
In summary, the invention has the following advantages:
the invention takes a three-dimensional gait analysis system as a technical platform, collects relevant data of kinematics and motion biomechanics when preselected healthy subjects such as the gait of children, establishes a database and a standardized model, prepares a standardized database for further researching abnormal gait of birth defects of children, and is convenient to realize the conversion process from laboratory to clinic and from scientific research to practical application.
Differences between subjects, and between each trial, will occur with respect to the time of each gait cycle. The method of the invention collects and measures gait parameters of the subjects, and can reduce the difference among the subjects for standardizing the parameters of all the subjects.
Detailed Description
The invention provides a method for establishing a database and a standardized model in a gait analysis system, which comprises the following steps:
(1) selecting a plurality of testees meeting the tested conditions, exposing and detecting joint points by the testees, marking and fixing the joint points according to a whole body mode, and acquiring human body gait data of each tester by a three-dimensional motion capture system; the human body gait data analyzed and obtained by the three-dimensional motion capture system further comprises pace speed, step length, stride, step frequency, total support phase time, swing phase time and initial double support phase time. The human gait data comprises a kinematics data set, a dynamics data set and a surface electromyography data set, wherein each data set comprises a plurality of parameters.
The invention takes establishing a Fujian children database as an example; a three-dimensional gait analysis system is used as a technical platform, the relevant data of kinematics and motion biomechanics of healthy children in Fujian province during gait are collected, a database and a standardized model are established, and standardized database preparation is further carried out for the study of abnormal gait of the children with birth defects.
The selection method of the tested population comprises the following steps:
the random sampling mode is adopted in Fuzhou urban areas to collect gait data of children and pupils in kindergarten.
Grouping standard:
A. selecting 100 children between the ages of 3-9 years;
B. using a Wechsler scale for preschool and preschool children and using a Wechsler scale for 7-9 year old children, and measuring the total IQ to be more than 60 for the children of 3-6 years old;
C. can be matched with doctors to carry out testing and walk naturally according to instructions.
D. Exclusion criteria: children with acute and chronic diseases affecting gait, such as heart, lung, nerve, bone, muscle, etc.
(2) Each child subject uses the same three-dimensional motion capture system to collect human gait data for 6-12 times, a group of human gait data is obtained each time, defect data and error data are removed, and 600-800 groups of effective human gait data are obtained.
Data collected in the three-dimensional gait analysis system:
the parameters included in the kinematics data set comprise the maximum dorsiflexion angle of the ankle joint, the maximum plantar flexion angle of the ankle joint and the maximum extension angle of the knee joint of the support phase; the data is output after the signal is collected by a computer after the mark is captured by the three-dimensional motion capture system.
Parameters included in the dynamic data comprise maximum forward ground reaction force, backward ground reaction force and vertical ground reaction force; the data are obtained by detecting a plantar pressure system matched with a three-dimensional motion capture system, and the data are output after the signals are collected by a computer.
The parameters included in the surface electromyography data set comprise surface electromyography signal intensity; the data are obtained by detecting a matched surface myoelectricity detection system in a three-dimensional gait analysis system, and the data are output after being collected by a computer.
(3) Carrying out data standardization processing on the obtained effective human body gait data, wherein each parameter in each data set is represented by mean number +/-standard deviation, and obtaining standardized human body gait data;
(4) and (4) dividing gait cycles based on the human body gait data acquired in the step (3), matching the human body gait data of each gait cycle to establish a standard database, and simulating and establishing a standardized model by adopting the standard database.
The gait cycle refers to the time that elapses between the time that one heel lands and the time that the other heel lands again during walking. Each gait cycle is divided into a support phase and a step phase; the support phase accounts for approximately 60% of the gait cycle; the swing period accounts for about 40% of the swing period.
Staging and time of gait cycle:
(1) the first touchdown: the initial points of the gait cycle and the support phase; the moment when the heel or other parts of the sole of the foot first contact the ground. The first landing mode for normal people walking is heel landing.
(2) A load-bearing reaction period: the foot heel touches the ground and the sole of the foot touches the ground for a period of time.
(3) In the middle standing period: when the finger is lifted from the lower limb at the opposite side to the trunk right above the leg at the side; at the moment, the center of gravity is located right above the supporting surface, and the gait cycle is 15% -40%.
(4) And (4) at the end stage of standing: the gait cycle is 40% -50% from the time when the heel is supported off the ground to the time when the heel of the lower limb of the opposite side is landed.
(5) In the early stage of stepping: the fingers are in a 50% to 60% gait cycle from heel strike of the contralateral lower limb to a period of time before toe off support.
(6) In the initial step: 60% -70% gait cycle from the support leg off the ground to the time when the knee joint of the leg reaches the maximum flexion.
(7) In the middle of the step: and when the knee joint swings from the maximum flexion to the state that the lower leg is vertical to the ground, the gait cycle is 70% -85%.
(8) And (4) at the end of the step: the lower leg perpendicular to the ground swings forwards until the heel touches the ground again, and the gait cycle is 85% -100%.
After the collected gait data are standardized according to the dividing method, the gait track of a period of time can be divided into appropriate intervals so as to establish a database at the later stage.
Claims (5)
1. A method for establishing a database and a standardized model in a gait analysis system is characterized by comprising the following steps:
(1) selecting a plurality of testees meeting the tested conditions, exposing and detecting joint points by the testees, marking and fixing the joint points according to a whole body mode, and acquiring human body gait data of each tester by a three-dimensional motion capture system; the human gait data comprises a kinematics data group, a dynamics data group and a surface electromyography data group, and each data group comprises a plurality of parameters;
(2) each subject uses the same three-dimensional motion capture system to collect human gait data for a plurality of times, a group of human gait data is obtained each time, defect data and error data are removed, and effective human gait data are obtained;
(3) carrying out data standardization processing on the obtained effective human body gait data, wherein each parameter in each data set is represented by mean number +/-standard deviation, and obtaining standardized human body gait data;
(4) and (4) dividing gait cycles based on the human body gait data acquired in the step (3), matching the human body gait data of each gait cycle to establish a standard database, and simulating and establishing a standardized model by adopting the standard database.
2. The method of establishing according to claim 1, wherein: the test population is children, and the children are 3-9 years old and can listen to the children instructing normal walking.
3. The method of establishing according to claim 1, wherein: the parameters included in the kinematics data set comprise a maximum dorsiflexion angle of a support phase ankle joint, a maximum plantar flexion angle of an ankle joint and a maximum extension angle of a knee joint; the parameters included in the dynamic data comprise maximum forward ground reaction force, backward ground reaction force and vertical ground reaction force; the parameters included in the surface electromyography data set include surface electromyography signal intensity.
4. The method of establishing according to claim 1, wherein: the three-dimensional motion capture system comprises a plantar pressure system and a surface myoelectricity detection system.
5. The method of establishing according to claim 1, wherein: the human body gait data analyzed and obtained by the three-dimensional motion capture system further comprises pace speed, step length, stride, step frequency, total support phase time, swing phase time and initial double support phase time.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111898574A (en) * | 2020-08-05 | 2020-11-06 | 复旦大学附属中山医院 | Standing walking test analysis system and method |
CN113642506A (en) * | 2021-08-26 | 2021-11-12 | 北京复数健康科技有限公司 | Method and system for image annotation based on data matching |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103514302A (en) * | 2013-10-28 | 2014-01-15 | 深圳先进技术研究院 | Human body gait database and establishment method thereof |
CN106175778A (en) * | 2016-07-04 | 2016-12-07 | 中国科学院计算技术研究所 | A kind of method setting up gait data collection and gait analysis method |
US20170238846A1 (en) * | 2016-02-19 | 2017-08-24 | Xerox Corporation | Computer vision system for ambient long-term gait assessment |
CN108209924A (en) * | 2018-01-16 | 2018-06-29 | 北京大学第三医院 | The analysis method of gait feature after a kind of Anterior Cruciate Ligament Ruptures |
-
2020
- 2020-03-18 CN CN202010189116.XA patent/CN111178338A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103514302A (en) * | 2013-10-28 | 2014-01-15 | 深圳先进技术研究院 | Human body gait database and establishment method thereof |
US20170238846A1 (en) * | 2016-02-19 | 2017-08-24 | Xerox Corporation | Computer vision system for ambient long-term gait assessment |
CN106175778A (en) * | 2016-07-04 | 2016-12-07 | 中国科学院计算技术研究所 | A kind of method setting up gait data collection and gait analysis method |
CN108209924A (en) * | 2018-01-16 | 2018-06-29 | 北京大学第三医院 | The analysis method of gait feature after a kind of Anterior Cruciate Ligament Ruptures |
Non-Patent Citations (1)
Title |
---|
董良超 等: "三维运动捕捉***用于国内学龄期及***健康发育儿童步态分析的研究", 《精准医学杂志》 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111898574A (en) * | 2020-08-05 | 2020-11-06 | 复旦大学附属中山医院 | Standing walking test analysis system and method |
CN113642506A (en) * | 2021-08-26 | 2021-11-12 | 北京复数健康科技有限公司 | Method and system for image annotation based on data matching |
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