CN111539327B - Gait information-based mild cognitive impairment recognition method and device - Google Patents
Gait information-based mild cognitive impairment recognition method and device Download PDFInfo
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Abstract
The application relates to a gait information-based mild cognitive impairment recognition method, which comprises the following steps: the method comprises the steps of collecting single task gait data and dual task gait data of a subject, and preprocessing the single task gait data and the dual task gait data. Based on a preset algorithm, according to the preprocessed single-task gait data and the dual-task gait data, obtaining difference features of the preprocessed single-task gait data and the preprocessed dual-task gait data, inputting the difference features into a pre-trained machine learning model, and obtaining a recognition result of the mild cognitive impairment of the subject. In the application, the flow is realized by a machine, and whether the subject has mild cognitive impairment is judged by a pre-trained machine learning classification model, so that the method is more convenient, quicker and more accurate than the prior art, and saves manpower and material resources.
Description
Technical Field
The application relates to the technical field of mild cognitive impairment recognition, in particular to a gait information-based mild cognitive impairment recognition method and device.
Background
Mild cognitive impairment (mild cognitive impairment, MCI) is a precursor to senile dementia and even alzheimer's disease, and if it can be screened or detected to some extent, effective early intervention can be performed on the patient. In the prior art, MCI is detected mainly through a form of a scale (MoCA, MMSE), and the scale calculates a score more complicated, takes a long time and consumes a great deal of manpower. The method of scale detection is only deployed in larger hospitals, which makes many potential patients unable to diagnose in time due to the influence of medical conditions, thereby missing the optimal treatment period.
Disclosure of Invention
In order to overcome the problems in the related art to at least a certain extent, the application provides a gait information-based mild cognitive impairment recognition method and device.
The scheme of the application is as follows:
according to a first aspect of an embodiment of the present application, there is provided a method for recognizing mild cognitive impairment based on gait information, including:
acquiring single task gait data and dual task gait data of a subject;
preprocessing the single task gait data and dual task gait data;
based on a preset algorithm, obtaining difference characteristics of the preprocessed single-task gait data and the preprocessed dual-task gait data according to the preprocessed single-task gait data and the preprocessed dual-task gait data;
and inputting the difference features into a pre-trained machine learning model to obtain a mild cognitive impairment recognition result of the subject.
Preferably, in one implementation manner of the present application, the collecting the single task gait data and the dual task gait data of the subject specifically includes:
acquiring joint point information of the subject during normal straight walking, and taking the joint point information as single-task gait data of the subject;
acquiring joint point information when the subject performs dual task straight walking, and taking the joint point information as dual task gait data of the subject;
the acquired joint point information of the subject when walking in a normal straight line and the acquired joint point information of the subject when walking in a straight line of a dual task are presented in a time sequence, and the units are frames.
Preferably, in one realisation of the application,
the collecting joint point information when the subject walks in a normal straight line comprises the following steps: collecting distances between each joint point and the sensor in the directions of an x axis, a y axis and a z axis when the subject walks normally and linearly;
the collecting joint point information when the subject performs dual task straight walking comprises the following steps: acquiring distances between each joint point and a sensor in the directions of an x axis, a y axis and a z axis when the subject performs dual task straight walking;
the data corresponding to each frame of the single task gait data and the dual task gait data comprise information of three spatial dimensions of each joint point.
Preferably, in an implementation manner of the present application, the preprocessing the single task gait data and the dual task gait data includes:
and detecting whether the single task gait data and the dual task gait data of the subject are missing information, and if the missing information is missing, filling the missing information by adopting a spline interpolation method.
Preferably, in an implementation manner of the present application, the preprocessing the single task gait data and the dual task gait data further includes:
moving the joint point information of each acquired frame to the same central position;
and according to a preset absolute distance value, carrying out standardized processing on joint point information of the single task gait data and the dual task gait data of the subject.
Preferably, in an implementation manner of the present application, the obtaining, based on a preset algorithm, the difference feature of the preprocessed single task gait data and the preprocessed dual task gait data according to the preprocessed single task gait data and the preprocessed dual task gait data includes:
and based on a DTW algorithm, obtaining a three-dimensional DTW distance of each joint point in the single-task gait data relative to a corresponding joint point in the dual-task gait data, and taking the three-dimensional DTW distance as the difference characteristic.
Preferably, in an implementation manner of the present application, the obtaining, based on the DTW algorithm, a three-dimensional DTW distance between each joint point in the single task gait data and a corresponding joint point in the dual task gait data includes:
based on a preset algorithm, obtaining initial frame numbers of the single task gait data and the dual task gait data according to a z-axis time sequence of the left heel joint point of the subject and a corresponding z-axis time sequence of the right heel joint point of the subject;
and according to the initial frame numbers of the single task gait data and the dual task gait data, keeping the starting stage and the walking amount of the single task gait data and the dual task gait data consistent.
Preferably, in an implementation manner of the present application, the obtaining, based on the DTW algorithm, a three-dimensional DTW distance between each joint point in the single task gait data and a corresponding joint point in the dual task gait data further includes:
constructing a matrix, wherein the matrix is used for representing paths of joint points in the single-task gait data relative to corresponding joint points in the dual-task gait data;
and determining the shortest path value from the upper left corner to the lower right corner of the matrix, wherein the shortest path value is the average value of total values of elements passing through, and taking the shortest path value as the three-dimensional DTW distance between a joint point in the single-task gait data and a corresponding joint point in the dual-task gait data.
Preferably, in one implementation manner of the present application, the method further includes:
acquiring single task gait data and dual task gait data of a historical subject, and taking difference characteristics of the single task gait data and the dual task gait data of the historical subject as sample data;
and training the sample data to obtain the machine learning model, wherein the machine learning model is used for representing the relationship between the difference characteristics and the recognition result of the mild cognitive impairment.
According to a second aspect of embodiments of the present application, there is provided a mild cognitive impairment recognition device based on gait information, comprising:
a processor and a memory;
the processor is connected with the memory through a communication bus:
the processor is used for calling and executing the program stored in the memory;
the memory is used for storing programs at least for executing the gait information-based mild cognitive impairment recognition method
The technical scheme provided by the application can comprise the following beneficial effects:
in the application, the single task gait data and the dual task gait data of a subject are acquired, and then the single task gait data and the dual task gait data are preprocessed. Based on a preset algorithm, according to the preprocessed single-task gait data and the dual-task gait data, obtaining difference features of the preprocessed single-task gait data and the preprocessed dual-task gait data, inputting the difference features into a pre-trained machine learning model, and obtaining a recognition result of the mild cognitive impairment of the subject. In the application, the flow is realized by a machine, and whether the subject has mild cognitive impairment is judged by a pre-trained machine learning classification model, so that the method is more convenient, quicker and more accurate than the prior art, and saves manpower and material resources.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a method for recognizing mild cognitive impairment based on gait information according to an embodiment of the present application;
fig. 2 is a schematic diagram of a joint point and corresponding number of a subject in a method for identifying mild cognitive impairment based on gait information according to an embodiment of the present application;
fig. 3 is a block diagram of a mild cognitive impairment recognition device based on gait information according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
Fig. 1 is a flowchart of a method for identifying mild cognitive impairment based on gait information according to an embodiment of the present application, and referring to fig. 1, a method for identifying mild cognitive impairment based on gait information includes:
s11: acquiring single task gait data and dual task gait data of a subject;
the method specifically comprises the following steps: collecting joint point information of a subject during normal straight walking, comprising: collecting distances between each joint point and the sensor in the directions of an x axis, a y axis and a z axis when a subject walks normally and linearly;
collecting joint point information when a subject performs dual task straight walking, comprising: collecting distances between each joint point and the sensor in the directions of an x axis, a y axis and a z axis when a subject performs dual task straight walking;
the acquired joint point information of the subject during normal straight walking and the joint point information of the subject during dual task straight walking are presented in a time sequence, the unit is a frame, and the data corresponding to each frame of single task gait data and dual task gait data comprises three spatial dimension information of each joint point.
In particular, joint point information for a single task gait and a dual task gait of a subject may be acquired using, but not limited to, a Kinect v2.0 sensor.
Kinect v2.0 is a 3D motion sensing camera developed by microsoft corporation that is capable of capturing 6 persons simultaneously and extracting their three-dimensional skeletal joint information. The single task gait refers to gait information generated by a subject during normal walking; the dual task gait refers to gait information generated by a subject attempting to complete another task (mathematical calculation, etc.) while walking. Studies have shown that the difference between the average velocities of the comparison of a single task gait and a dual gait is generally greater for MCI patients than for normal persons.
Single task gait data acquisition: and acquiring joint point information of a subject which normally walks along a straight line at a position which is 5 meters in front of the Kinect v2.0 sensor through the Kinect v2.0 sensor until the Kinect v2.0 sensor cannot acquire all bone joint points of the subject.
Dual task gait data acquisition: collecting joint point information of a subject which normally walks along a straight line and carries out arithmetical calculation in the heart at a position which is 5 meters in front of the Kinect v2.0 sensor through the Kinect v2.0 sensor until the Kinect v2.0 sensor cannot acquire all bone joint points of the subject
The acquired gait information is presented in time series in frames.
Preferably, 25 major nodes of the subject are selected as the nodes from which the acquisition is made. Figure 2 shows 25 joints of the subject collected by the Kinect v2.0 sensor and their corresponding numbering.
The data volume corresponding to each frame of the acquired gait information is 25 x 3, wherein the data volume comprises 25 articulation points, and each articulation point has three-dimensional (x-axis, y-axis and z-axis) information.
S12: preprocessing the single task gait data and the dual task gait data;
preprocessing the single task gait data and the dual task gait data, including:
detecting whether the single task gait data and the dual task gait data of the subject are missing information, and if so, filling the missing information by adopting a spline interpolation method.
The Kinect v2.0 sensor may lose part of the information when extracting bone joint information, and thus the lost information needs to be filled. The missing information can be filled in by adopting a spline interpolation method.
Preprocessing the single task gait data and the dual task gait data, and further comprising:
and moving the collected joint point information of each frame to the same center position.
The joint information acquired by the Kinect v2.0 sensor has three dimensions: the x-axis, y-axis, and z-axis represent distances of the three directional articulation points from the sensor, respectively. Different subjects have different degrees of offset from the sensor center at different times (values on the x, y axes are not uniform), so it is necessary to move each acquired frame of joint points to the same center position.
The method comprises the following steps:
let gait information of the subject be B epsilon R n×25×3 B (i, j, k) ε R is the value of the ith frame, the jth node, and the axis number k in meters. B (i, j) ∈R 3 The coordinates of the node point with j in the ith frame number are the coordinates of three coordinate axes of x, y and z, namely, B (i, j, 0), B (i, j, 1) and B (i, j, 2). The process of coordinate centering is to perform B (i, j,:) =b (i, j,:) -B (i, 0,:) for all the joint points, i.e., the joint point numbered 0 is taken as the origin of coordinates.
B (i, j,:) =b (i, j,:) -B (i, 0,:) represents an assignment operation, assigning a right-hand value to a left-hand variable.
Further, since different subjects have different heights, it is necessary to unify the heights of all subjects. The method comprises the following steps:
let the absolute value of the distance be d= |b (0, 1) -B (0, 1) |, B (i, j, k) is performed for each coordinate value of each node point =b (i, j, k)/d.
S13: based on a preset algorithm, obtaining difference characteristics of the preprocessed single-task gait data and the preprocessed dual-task gait data according to the preprocessed single-task gait data and the preprocessed dual-task gait data;
comprising the following steps: based on a DTW algorithm, obtaining a three-dimensional DTW distance of each joint point in the single-task gait data relative to a corresponding joint point in the dual-task gait data, and taking the three-dimensional DTW distance as a difference characteristic.
The method comprises the following steps: based on a preset algorithm, obtaining initial frame numbers of the single task gait data and the dual task gait data according to a z-axis time sequence of a left heel joint point of a subject and a z-axis time sequence of a corresponding right heel joint point of the subject;
referring to FIG. 2, the z-axis time series of the joint points numbered 14 (left heel joint points) is taken asThe z-axis time sequence of the articulation point (right heel articulation point) of the number 18 is taken as +.>
Column type
Will satisfyAnd->Can be used as the initial frame number for the gait data.
According to the initial frame numbers of the single task gait data and the dual task gait data, keeping the starting stage and the walking amount of the single task gait data and the dual task gait data consistent;
keeping the walking amount consistent:
let the gait data of the single task be B 1 ∈R n×25×3 Dual task gait data is B 2 ∈R m×25×3 The initial frame numbers found are i respectively 1 And i 2 . To ensure consistent walking time for both gait patterns, the formula h=min (n-i 1 ,m-i 2 ) The data length to be extracted for both gait data.
Let B 1 (i:j,:,:)∈R (j-i)×25×3 Is B 1 Data from the i-th frame to the j-1 th frame.
Then let B 1 :＝B 1 (i 1 :i 1 +h,:,:)，B 2 :＝B 2 (i 2 :i 2 +h,:,:)。
Constructing a matrix, wherein the matrix is used for representing paths of joint points in the single-task gait data relative to corresponding joint points in the dual-task gait data;
and determining the shortest path value from the upper left corner to the lower right corner of the matrix, wherein the shortest path value is the average value of total values of elements passing through, and taking the shortest path value as the three-dimensional DTW distance between the joint point in the single-task gait data and the corresponding joint point in the dual-task gait data.
Let the obtained single task gait data beDual task gait data is +.>Then for each joint point, a corresponding three-dimensional DTW distance can be calculated as one feature, for a total of 25 features.
Setting the single task gait data and the dual task gait data corresponding to the ith joint point as respectively
The three-dimensional DTW distance w can be calculated by a DTW algorithm i 。
The method comprises the following steps: construction of matrixWherein->
The value of the DTW distance is the shortest path value from the upper left corner of the matrix to the lower right corner of the matrix, where the shortest path value is the average of the total values of the elements passed.
Wherein the walking direction can only be rightward, downward and 45 degrees downward.
S14: and inputting the difference features into a pre-trained machine learning model to obtain a recognition result of the mild cognitive impairment of the subject.
The gait information-based mild cognitive impairment recognition method in some embodiments further comprises: acquiring single task gait data and dual task gait data of a historical subject, and taking difference characteristics of the single task gait data and the dual task gait data of the historical subject as sample data;
training the sample data to obtain a machine learning model, wherein the machine learning model is used for representing the relationship between the difference characteristics and the recognition result of the mild cognitive impairment.
The machine learning model may be, but is not limited to, a classifier, which may use a support vector machine, random forest, etc.
Fig. 3 is a block diagram of a mild cognitive impairment recognition device based on gait information according to an embodiment of the present application, and referring to fig. 3, a mild cognitive impairment recognition device based on gait information includes:
processor 21 and memory 22
The processor 21 is connected to the memory 22 via a communication bus:
wherein the processor 21 is used for calling and executing the program stored in the memory 22;
the memory 22 is configured to store a program for performing at least the gait information-based mild cognitive impairment recognition method according to any one of the above embodiments.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (4)
1. A method for recognizing mild cognitive impairment based on gait information, comprising:
acquiring single task gait data and dual task gait data of a subject;
preprocessing the single task gait data and dual task gait data;
based on a preset algorithm, obtaining difference characteristics of the preprocessed single-task gait data and the preprocessed dual-task gait data according to the preprocessed single-task gait data and the preprocessed dual-task gait data;
inputting the difference features into a pre-trained machine learning model to obtain a mild cognitive impairment recognition result of the subject;
the method for acquiring the single task gait data and the dual task gait data of the subject specifically comprises the following steps:
acquiring joint point information of the subject during normal straight walking, and taking the joint point information as single-task gait data of the subject;
acquiring joint point information when the subject performs dual task straight walking, and taking the joint point information as dual task gait data of the subject;
the method comprises the steps of acquiring joint point information of a subject when the subject walks normally and joint point information of the subject when the subject walks linearly in a dual task, wherein the joint point information and the joint point information are presented in a time sequence, and the units are frames;
the collecting joint point information when the subject walks in a normal straight line comprises the following steps: collecting the joint points of the subject when the subject walks in a normal straight line x A shaft(s), y Shaft and method for producing the same z The distances from the sensors in the axial direction are respectively;
the collecting joint point information when the subject performs dual task straight walking comprises the following steps: collecting the positions of all the joints when the subject performs the dual task straight walking x A shaft(s), y Shaft and method for producing the same z The distances from the sensors in the axial direction are respectively;
the data corresponding to each frame of the single task gait data and the dual task gait data comprise information of three spatial dimensions of each joint point;
the preprocessing of the single task gait data and the dual task gait data further comprises:
moving the joint point information of each acquired frame to the same central position;
according to a preset absolute distance value, carrying out standardized processing on joint point information of the single task gait data and the dual task gait data of the subject;
the method for obtaining the difference characteristics of the preprocessed single task gait data and the preprocessed dual task gait data based on the preset algorithm comprises the following steps:
based on a DTW algorithm, obtaining a three-dimensional DTW distance of each joint point in the single-task gait data relative to a corresponding joint point in the dual-task gait data, and taking the three-dimensional DTW distance as the difference characteristic;
based on a DTW algorithm, obtaining a three-dimensional DTW distance of each joint point in the single-task gait data relative to a corresponding joint point in the dual-task gait data comprises the following steps:
based on a preset algorithm, obtaining initial frame numbers of the single task gait data and the dual task gait data according to a z-axis time sequence of the left heel joint point of the subject and a corresponding z-axis time sequence of the right heel joint point of the subject;
according to the initial frame numbers of the single task gait data and the dual task gait data, keeping the starting stage and the walking amount of the single task gait data and the dual task gait data consistent;
constructing a matrix, wherein the matrix is used for representing paths of joint points in the single-task gait data relative to corresponding joint points in the dual-task gait data;
and determining the shortest path value from the upper left corner to the lower right corner of the matrix, wherein the shortest path value is the average value of total values of elements passing through, and taking the shortest path value as the three-dimensional DTW distance between a joint point in the single-task gait data and a corresponding joint point in the dual-task gait data.
2. The method of claim 1, wherein the preprocessing the single task gait data and dual task gait data comprises:
and detecting whether the single task gait data and the dual task gait data of the subject are missing information, and if the missing information is missing, filling the missing information by adopting a spline interpolation method.
3. The method as recited in claim 1, further comprising:
acquiring single task gait data and dual task gait data of a historical subject, and taking difference characteristics of the single task gait data and the dual task gait data of the historical subject as sample data;
and training the sample data to obtain the machine learning model, wherein the machine learning model is used for representing the relationship between the difference characteristics and the recognition result of the mild cognitive impairment.
4. A mild cognitive impairment recognition device based on gait information, comprising:
a processor and a memory;
the processor is connected with the memory through a communication bus:
the processor is used for calling and executing the program stored in the memory;
the memory for storing a program for performing at least the gait information-based mild cognitive impairment recognition method of any one of claims 1 to 3.
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