CN111035393B - Three-dimensional gait data processing method, system, server and storage medium - Google Patents
Three-dimensional gait data processing method, system, server and storage medium Download PDFInfo
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Abstract
The application is suitable for the technical field of computers, and provides a three-dimensional gait data acquisition method, which comprises the following steps: after a gait information acquisition instruction is monitored, original three-dimensional gait data acquired by each somatosensory sensor in a preset somatosensory sensor array within preset time is synchronously acquired, the original three-dimensional gait data are sent to a data processing server, the original three-dimensional gait data are acquired by each somatosensory sensor in the preset somatosensory sensor array within preset time, and the gait data can be efficiently and accurately acquired.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, a system, a server, and a storage medium for processing three-dimensional gait data.
Background
Gait data refers to the movement data of each part of the human body when the human walks in a natural state, and is a complex human behavior characteristic. Since the gait data provides important basis for studying the health condition and living habits of the person, how to efficiently and accurately acquire the gait data becomes more and more important.
Disclosure of Invention
In view of this, embodiments of the present application provide a three-dimensional gait data processing method, system, server and storage medium to efficiently and accurately acquire different data.
A first aspect of an embodiment of the present application provides a three-dimensional gait data processing method, which is applied to an acquisition server, and the three-dimensional gait data acquisition method includes:
after a gait information acquisition instruction is monitored, original three-dimensional gait data of a monitored object are synchronously acquired, wherein the original three-dimensional gait data comprise three-dimensional gait data of the monitored object acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time length;
and sending the original three-dimensional gait data to a data processing server, and processing the original three-dimensional gait data by the data processing server to obtain the three-dimensional gait data of the monitored object.
A second aspect of the embodiments of the present application provides a three-dimensional gait data processing method, which is applied to a data processing server, and the three-dimensional gait data processing method includes:
acquiring original three-dimensional gait data sent by an acquisition server, wherein the original three-dimensional gait data comprise three-dimensional gait data acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time;
determining overlapped three-dimensional gait data based on timestamp information of the original three-dimensional gait data, wherein the original three-dimensional gait data comprise human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
and carrying out fusion processing on the multiple frames of overlapped human body bone joint point data according to the spatial distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data.
In an optional implementation manner, before the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data, the method further includes:
determining abnormal bone joint point data in the human body bone joint point data according to a preset reference distance between all bone joint points in a human body bone structure;
performing data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data;
correspondingly, the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
In an optional implementation manner, the determining abnormal bone joint point data in the human bone joint point data according to a preset reference distance between bone joint points in a human bone structure includes:
sequencing the data of the human skeleton joint points collected by each somatosensory sensor in the somatosensory sensor array according to the timestamp information of the original three-dimensional gait data to respectively obtain multiple frames of data of the human skeleton joint points with time sequences;
respectively calculating the distance between any two adjacent skeletal joint point data in each frame of human skeletal joint point data;
and determining abnormal bone joint point data in the human body bone joint point data according to the distance and a preset reference distance between all bone joint points in the human body bone structure.
In an optional implementation, before the determining the plurality of frames of the overlapped human bone joint point data in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data, the method includes:
according to the inclination angle of each somatosensory sensor in the somatosensory sensor array, coordinate conversion is carried out on the corrected skeleton joint point data respectively to obtain standard skeleton joint point data under the same preset coordinate system;
correspondingly, the determining the human bone joint point data of the plurality of frames overlapping in the bone joint point data based on the time stamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the standard bone joint point data based on the time stamp information of the original three-dimensional gait data.
In an optional implementation manner, before the coordinate conversion of the corrected bone joint point data according to the inclination angle of each motion sensing sensor in the motion sensing sensor array, the method further includes:
acquiring human body bone joint point data in the original three-dimensional gait data;
and performing least square fitting processing on the human skeleton joint point data to obtain the inclination angle of each somatosensory sensor.
A third aspect of an embodiment of the present application provides a three-dimensional gait data processing system, including: the motion sensing system comprises an acquisition server, a motion sensing sensor array in communication connection with the acquisition server and a data processing server in communication connection with the acquisition server;
the motion sensing sensor array comprises a plurality of motion sensing sensors which are arranged in a preset arrangement mode, and the motion sensing sensors are mutually independent;
the acquisition server is used for synchronously acquiring original three-dimensional gait data after monitoring a gait information acquisition instruction, wherein the original three-dimensional gait data comprises three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time length, and the original three-dimensional gait data is sent to the data processing server;
the data processing server is used for acquiring the original three-dimensional gait data sent by the acquisition server and determining overlapped three-dimensional gait data based on timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
and carrying out fusion processing on the multiple frames of overlapped human body bone joint point data according to the spatial distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data.
A fourth aspect of the embodiments of the present application provides a three-dimensional gait data processing method, including:
after monitoring a gait information acquisition instruction, an acquisition server synchronously acquires original three-dimensional gait data, wherein the original three-dimensional gait data comprises three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time length, and the original three-dimensional gait data is sent to a data processing server;
the data processing server acquires the original three-dimensional gait data sent by the acquisition server, and determines overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
and carrying out fusion processing on the multiple frames of overlapped human body bone joint point data according to the spatial distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data.
A fifth aspect of the embodiments of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the three-dimensional gait data acquisition method according to the first aspect of the above embodiments when executing the computer program, or implements the steps of the three-dimensional gait data processing method according to the second aspect of the above embodiments when executing the computer program.
A sixth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the three-dimensional gait data acquisition method according to the first aspect of the embodiments, or the processor, when executing the computer program, implements the steps of the three-dimensional gait data processing method according to the second aspect of the embodiments. Compared with the prior art, the three-dimensional gait data acquisition method provided by the first aspect of the embodiment of the application has the advantages that after a gait information acquisition instruction is monitored, original three-dimensional gait data acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time period are synchronously acquired, the original three-dimensional gait data are sent to a data processing server, the original three-dimensional gait data are acquired by each somatosensory sensor in the preset somatosensory sensor array within the preset time period, and the gait data can be efficiently and accurately acquired.
Compared with the prior art, the second aspect to the sixth aspect of the embodiment of the present application have the same beneficial effects as the first aspect of the embodiment of the present application has, and are not described herein again.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a system configuration diagram of a three-dimensional gait data processing system according to a first embodiment of the present application;
FIG. 2 is an array diagram of the body sensor array of FIG. 1;
fig. 3 is a flowchart of an implementation of a three-dimensional gait data acquisition method provided in the second embodiment of the present application;
fig. 4 is a flowchart of an implementation of a three-dimensional gait data processing method according to a third embodiment of the present application;
fig. 5 is a flowchart of an implementation of a three-dimensional gait data processing method according to a fourth embodiment of the present application;
FIG. 6 is a flowchart illustrating an implementation of S502 in FIG. 5;
fig. 7 is a flowchart of an implementation of a three-dimensional gait data processing method according to a fifth embodiment of the present application;
fig. 8 is a flowchart of an implementation of a three-dimensional gait data processing method according to a sixth embodiment of the present application;
fig. 9 is a schematic structural diagram of an acquisition server provided in an embodiment of the present application;
fig. 10 is a schematic structural diagram of a data processing server provided in an embodiment of the present application;
fig. 11 is a schematic diagram of a server provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be noted that the gait data is a complex behavior characteristic, which is the movement state of each bone of the body when the human being walks in a natural state. The gait data is different from the biological characteristics which can be acquired only by near contact, such as fingerprints, facial appearance, heart rate, electroencephalogram and the like, and the gait data is used as a remote non-contact biological characteristic and plays an important role in the field of human health and living habits.
At present, common gait data acquisition methods include a gait information acquisition method based on wearable sensor equipment and a gait information acquisition method based on a color image; the gait information acquisition method based on the wearable sensor device has the advantages that on one hand, the device is complex and heavy to wear, and can affect the gait of the acquisition object, and on the other hand, the acquisition device is expensive and is not beneficial to large-scale data acquisition. The gait information acquisition based on the color image is limited by the influence of ambient light and the like, and the anti-light and same-color-system interference capability of the color image color extraction mode is extremely poor, so that the distance between different objects is difficult to separate in principle.
In order to solve the above problems, the present application provides a three-dimensional gait data processing method, system, server and storage medium, which aims to acquire human body motion data under natural gait, and mainly perform three-dimensional somatosensory test to acquire and test the tested natural gait. The somatosensory sensor is a sensor which detects and senses a human body structure through a 3D shooting or measuring technology. The 3D photographing or measuring technique is actually a relatively mature technique with various solutions, such as single color camera, dual color camera, optical interference, ultrasound, structured light speckle, and TOF (measuring time of flight of light), etc. At present, a relatively mature somatosensory sensor is a Kinect product of MicroSoft. The device has a powerful function of extracting human skeleton joint points, and is applied as a tool for collecting joint point motion information in many academic researches in the gait field.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples. Fig. 1 is a system configuration diagram of a three-dimensional gait data processing system according to a first embodiment of the present application. As can be seen from fig. 1, the three-dimensional gait data processing system 10 provided in the embodiment of the present application includes: the system comprises an acquisition server 101, a somatosensory sensor array 102 in communication connection with the acquisition server 101 and a data processing server 103 in communication connection with the acquisition server 101; wherein,
the motion sensing sensor array 102 comprises a plurality of motion sensing sensors 1021 arranged according to a preset arrangement mode, and the motion sensing sensors 1021 are mutually independent.
It should be noted that, the arrangement manner of the motion sensing sensor array 102 is deployed by a tester according to experience and test requirements in advance, and there is no fixed requirement for the distance between each of the motion sensing sensors.
For example, in an alternative implementation, as shown in FIG. 2, is an array diagram of the array of body sensors of FIG. 1. As shown in fig. 2, in this embodiment, the entire somatosensory sensor array 102 includes a preset number (for example, 6) of somatosensory sensors 1021, a distance between every two adjacent somatosensory sensors 1021 is 2.6 meters, all the somatosensory sensors 1021 are disposed on a ceiling boom, and a distance from the ceiling boom to the ground is greater than a preset height distance of a human body, for example, 2.65 meters.
Through the analysis, the three-dimensional gait data of the human body in the preset range are collected in the natural motion state by the somatosensory sensor array which is pre-deployed in the human body motion space, the limitation of the measurement range of a single sensor is overcome, and the three-dimensional gait data are collected under the condition that a user is not disturbed.
In this embodiment, each motion sensing sensor is a Kinect sensor, and it should be noted that each motion sensing sensor may also be another motion sensing sensor such as an Intel real sense.
The acquisition server 101 is configured to, after monitoring a gait information acquisition instruction, synchronously acquire original three-dimensional gait data of a monitored object, where the original three-dimensional gait data includes three-dimensional gait data of the monitored object acquired by each motion sensor in the motion sensor array within a preset time period, and send the original three-dimensional gait data to the data processing server to instruct the data processing server to process the original three-dimensional gait data to obtain three-dimensional gait data of the monitored object;
the data processing server 102 is configured to acquire the original three-dimensional gait data sent by the acquisition server, and determine overlapped three-dimensional gait data based on timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
and carrying out fusion processing on the multiple frames of overlapped human body bone joint point data according to the spatial distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data.
According to the analysis, after a gait information acquisition instruction is monitored by an acquisition server, three-dimensional gait data acquired by each somatosensory sensor in a pre-deployed somatosensory sensor array within a preset time length is synchronously acquired, the original three-dimensional gait data is sent to a data processing server, and the data processing server determines multi-frame overlapped human body skeletal joint point data acquired by any two adjacent somatosensory sensors in the somatosensory sensor array based on timestamp information of the original three-dimensional gait data; and according to the spatial distance between any two adjacent frames of the human body bone joint point data, carrying out fusion processing on the multiple frames of the overlapped human body bone joint point data to obtain target three-dimensional gait data. The human body three-dimensional gait data can be rapidly and accurately acquired.
Fig. 3 is a flowchart illustrating an implementation of a three-dimensional gait data acquisition method according to a second embodiment of the present application. The embodiment of the application is realized by hardware or software execution of the acquisition server. The details are as follows:
s301, after monitoring a gait information acquisition instruction, acquiring original three-dimensional gait data of a monitored object synchronously, wherein the original three-dimensional gait data comprises three-dimensional gait data of the monitored object acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time.
It can be understood that the gait information acquisition instruction can be triggered by an operation interface provided by the acquisition server by a worker, or can be triggered by other terminal devices or servers. For example, in an optional implementation manner, the gait information acquisition instruction is triggered by a control server, the control server sends the gait information acquisition instruction to the acquisition server, and the acquisition server monitors the different information acquisition instructions and then synchronously acquires three-dimensional gait data acquired by each somatosensory sensor in the preset somatosensory sensor array within a preset time period. The gait information acquisition instruction comprises the preset duration.
It should be noted that in some application scenarios, the three-dimensional gait data acquired by each somatosensory sensor in the preset time period may be more, and more three-dimensional gait data cannot be acquired simultaneously by one acquisition server, so that the three-dimensional gait data may be acquired by a plurality of acquisition servers. For example, the three-dimensional gait data acquired by one corresponding somatosensory sensor is acquired by one acquisition server, so that the data acquisition efficiency can be improved, and the failure probability of the acquisition server is reduced.
And S302, sending the original three-dimensional gait data to a data processing server to instruct the data processing server to process the original three-dimensional gait data to obtain the three-dimensional gait data of the monitored object.
It can be understood that the acquisition server and the somatosensory sensor array are usually arranged in an acquisition place, the data processing server can be arranged in any place, and after the acquisition server acquires the original three-dimensional gait data, the original three-dimensional gait data is sent to the data processing server for subsequent use.
Through the analysis, the three-dimensional gait data acquisition method provided by the embodiment of the application can be used for synchronously acquiring the original three-dimensional gait data acquired by each somatosensory sensor in the preset somatosensory sensor array within the preset time after monitoring the gait information acquisition instruction, sending the original three-dimensional gait data to the data processing server, and acquiring the original three-dimensional gait data within the preset time by means of each somatosensory sensor in the preset somatosensory sensor array, so that the gait data can be efficiently and accurately acquired.
Fig. 4 is a flowchart illustrating an implementation of a three-dimensional gait data processing method according to a third embodiment of the present application. The embodiment of the application is realized by hardware or software of the data processing server. The details are as follows:
s401, acquiring original three-dimensional gait data sent by an acquisition server, wherein the original three-dimensional gait data comprise three-dimensional gait data acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time length.
The original three-dimensional gait data is the three-dimensional gait data acquired by each somatosensory sensor within a preset time length, the three-dimensional gait data acquired by each somatosensory sensor at different acquisition moments are different, and the three-dimensional gait data acquired by each somatosensory sensor at the corresponding acquisition moment has timestamp information.
S402, determining overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data, wherein the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises multi-frame overlapped human skeleton joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array.
Specifically, based on the timestamp information of the original three-dimensional gait data, the multi-frame overlapped human body bone joint point data in the human body bone joint point data collected by any two adjacent somatosensory sensors can be positioned.
And S403, performing fusion processing on the multiple frames of overlapped human body bone joint point data according to the space distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data.
It can be understood that each frame of human body bone joint point data includes all the human body bone joint point data, the spatial distance between any two adjacent frames of human body bone joint point data in the multiple frames of overlapped human body bone joint point data is calculated, the multiple frames of overlapped human body bone joint point data can be filtered according to the spatial distance, the overlapped human body joint point data is filtered, the process of fusing the multiple frames of human body bone joint point data is realized, and the target three-dimensional gait data is obtained.
As can be seen from the above analysis, in this embodiment, based on the timestamp information of the original three-dimensional gait data, multiple frames of overlapped human body bone joint point data are determined, and according to the spatial distance between any two adjacent frames of the human body bone joint point data, the multiple frames of overlapped human body bone joint point data are subjected to fusion processing, so as to obtain target three-dimensional gait data. The method can further filter out coincident three-dimensional gait data from the original three-dimensional gait data, and fuse the original three-dimensional gait data into complete data acquired within a preset time length, so that more complete and accurate three-dimensional gait data can be obtained.
Fig. 5 is a flowchart illustrating an implementation of a three-dimensional gait data processing method according to a fourth embodiment of the present application. As can be seen from fig. 5, in this embodiment, compared with the embodiment shown in fig. 4, the specific implementation processes of S501 and S401 and S505 and S403 are the same, but the difference is that S502 to S503 are further included before S504, and the specific implementation processes of S504 and S402 are different. S501 and S502 are in a sequential execution relationship. The details are as follows:
s502, determining abnormal bone joint point data in the human body bone joint point data according to the preset reference distance between the bone joint points in the human body bone structure.
It is understood that the distance between the bone joint points in the human bone structure is usually within a certain preset range, and the present embodiment presets the reference distance between the bone joint points in the human bone structure, which is the average value of the distances between the bone joint points in the human body obtained according to a large amount of experimental data.
In an optional implementation manner, the human skeleton joint point data of the consecutive multiple frames may be selected, the distance between each piece of bone joint point data in each frame of human skeleton joint point data is calculated, the calculated distances are compared with the reference distance between each piece of bone joint point in the preset human skeleton structure, and when the absolute value of the difference between the distance between the same piece of bone joint point data and other pieces of bone joint point data in the human skeleton joint point data corresponding to the consecutive multiple frames and the reference distance between each piece of bone joint point in the preset human skeleton structure is greater than the preset distance threshold, the piece of bone joint point data is indicated as abnormal bone joint point data.
Specifically, as shown in fig. 6, it is a flowchart of a specific implementation of S502 in fig. 5. As can be seen from fig. 6, S502 includes S5021 to S5023. The details are as follows:
and S5021, sequencing the human skeleton joint point data collected by each somatosensory sensor in the somatosensory sensor array according to the timestamp information of the original three-dimensional gait data, and respectively obtaining multi-frame human skeleton joint point data with a time sequence.
The method can improve the accuracy and efficiency of multi-frame human skeletal joint data analysis by sequencing based on the timestamp information.
And S5022, respectively calculating the distance between any two adjacent bone joint point data in each frame of human bone joint point data.
It is to be understood that, in general, the rate of change of the distance between two adjacent pieces of bone joint point data with the change in human body motion is the smallest, and therefore, abnormal bone joint point data can be determined by calculating the distance between any two adjacent pieces of bone joint point data.
And S5023, determining abnormal bone joint point data in the human body bone joint point data according to the distance and the reference distance between the preset bone joint points in the human body bone structure.
Specifically, when the absolute values of the differences between the distance between the bone joint point data and two adjacent bone joint point data and the reference distance are both greater than the preset distance threshold, it is determined that the current bone joint point data is abnormal bone joint point data.
And S503, performing data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data.
It should be noted that the abnormal bone joint point data is processed according to a preset denoising and compensating method, for example, the preset denoising and compensating method is a gaussian filtering processing method, the abnormal bone joint point data is denoised through the gaussian filtering processing method, the abnormal bone joint point data is eliminated and the data is smoothed, so as to obtain corrected bone joint point data.
S504, determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
Through the analysis, the abnormal data in the original three-dimensional gait data are denoised and compensated to obtain the corrected bone joint point data, so that the acquired distorted data caused by the somatosensory sensor can be effectively relieved.
Fig. 7 is a flowchart illustrating an implementation of a three-dimensional gait data processing method according to a fifth embodiment of the present application. As can be seen from fig. 7, in this embodiment, compared with the embodiment shown in fig. 5, the specific implementation processes of S701 to S703 are the same as those of S501 to S503 and the specific implementation processes of S706 and S505, but the difference is that the specific implementation processes of S704 and S705 and S504 are different before S705. S703 and S704 are in a sequential execution relationship. The details are as follows:
and S704, respectively performing coordinate conversion on the corrected bone joint point data according to the inclination angle of each somatosensory sensor in the somatosensory sensor array to obtain standard bone joint point data under the same preset coordinate system.
It can be understood that, because the installation the in-process of feeling the sensor, the error that is difficult to avoid and the body is felt the error that the sensor produced in process of production, every body is felt the sensor and all has slight difference with the angle on ground. In this embodiment, an angle between each of the motion sensing sensors and the ground is referred to as the tilt angle. And the original bone joint point data acquired by each somatosensory sensor is based on the spatial coordinate system corresponding to the somatosensory sensor, so that coordinate conversion is performed according to the inclination angle, the corrected bone joint point data is converted into standard bone joint point data under the same preset coordinate system, and more accurate data basis can be provided for follow-up research.
S705, determining the human body bone joint point data overlapped by multiple frames in the standard bone joint point data based on the time stamp information of the original three-dimensional gait data.
As can be seen from the above analysis, in this embodiment, the corrected bone joint point data is subjected to coordinate conversion according to the inclination angle of each somatosensory sensor, so as to obtain standard bone joint point data in the same preset coordinate system, thereby obtaining more accurate three-dimensional gait data.
Fig. 8 is a flowchart illustrating an implementation of a three-dimensional gait data processing method according to a sixth embodiment of the present application. As can be seen from fig. 8, in this embodiment, compared with the embodiment shown in fig. 7, the specific implementation processes of S801 to S803 and S701 to S703, and S806 to S808 and S704 to S706 are the same, but S804 to S805 are further included before S806. S804 and S803 are in a parallel execution relationship. The details are as follows:
s804, acquiring human body bone joint point data in the original three-dimensional gait data.
And S805, performing least square fitting processing on the human skeleton joint point data to obtain the inclination angle of each somatosensory sensor.
The application proposes that the human body bone joint point data is relatively stable, for example, SpineBase joint point data is relatively stable in the advancing process after a large amount of data analysis. And fitting a straight line y-k x + b by using a least square method according to the z-axis data and the y-axis data of the relatively stable joint point data, wherein the slope k of the fitted straight line is the tangent value of the inclination angle corresponding to the relatively stable joint point data.
According to the analysis, the human body skeleton joint point data are fitted and processed through the least square method, and the inclination angle of each somatosensory sensor is obtained. Errors caused by installation of each somatosensory sensor can be conveniently and quickly solved.
Fig. 9 is a schematic structural diagram of an acquisition server according to an embodiment of the present application. As can be seen from fig. 9, the acquisition server 9 provided in this embodiment includes:
the acquiring module 901 is configured to acquire original three-dimensional gait data synchronously after monitoring a gait information acquiring instruction, where the original three-dimensional gait data includes three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time.
A sending module 902, configured to send the original three-dimensional gait data to a data processing server.
Fig. 10 is a schematic structural diagram of a data processing server according to an embodiment of the present application. As can be seen from fig. 10, the data processing server 10 according to the embodiment of the present application includes:
a first determining module 1001, configured to acquire the original three-dimensional gait data sent by the acquisition server, and determine overlapped three-dimensional gait data based on timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
a first obtaining module 1002, configured to perform fusion processing on multiple frames of overlapped human body bone joint point data according to a spatial distance between any two adjacent frames of human body bone joint point data, so as to obtain target three-dimensional gait data.
In an optional implementation manner, the method further includes:
the second determination module is used for determining abnormal bone joint point data in the human body bone joint point data according to the reference distance between all the bone joint points in the preset human body bone structure;
the second obtaining module is used for carrying out data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data;
correspondingly, the first determining module is specifically configured to:
and determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
In an optional implementation manner, the second determining module includes:
the sequencing unit is used for sequencing the human skeleton joint point data acquired by each somatosensory sensor in the somatosensory sensor array according to the timestamp information of the original three-dimensional gait data to respectively obtain multiple frames of human skeleton joint point data with time sequences;
the computing unit is used for respectively computing the distance between any two adjacent skeletal joint point data in each frame of human skeletal joint point data;
and the determining unit is used for determining abnormal bone joint point data in the human body bone joint point data according to the distance and a reference distance between all bone joint points in a preset human body bone structure.
In an optional implementation manner, the method further includes:
the conversion module is used for respectively performing coordinate conversion on the corrected skeleton joint point data according to the inclination angle of each somatosensory sensor in the somatosensory sensor array to obtain standard skeleton joint point data under the same preset coordinate system;
correspondingly, the first determining module is specifically configured to:
and determining the human body bone joint point data overlapped by multiple frames in the standard bone joint point data based on the time stamp information of the original three-dimensional gait data.
In an optional implementation manner, the method further includes:
the acquisition module is used for acquiring human body bone joint point data in the original three-dimensional gait data;
and the processing module is used for performing least square fitting processing on the human skeleton joint point data to obtain the inclination angle of each somatosensory sensor.
Fig. 11 is a schematic diagram of a server provided in an embodiment of the present application. As shown in fig. 11, the server 2 of this embodiment includes: a processor 20, a memory 21 and a computer program 22 stored in the memory 21 and executable on the processor 20, such as a program of a remote control terminal. The processor 20, when executing the computer program 22, implements the steps in the various three-dimensional gait data acquisition method embodiments or the steps in the three-dimensional gait data processing method embodiments described above, such as steps 301 to 302 shown in fig. 3 or steps 401 to 403 shown in fig. 4.
Illustratively, the computer program 22 may be divided into one or more modules/units, which are stored in the memory 21 and executed by the processor 20 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 22 in the server 2. For example, the computer program 22 may be divided into an acquisition module and a transmission module (module in the virtual device), and the specific functions of each module are as follows:
the system comprises an acquisition module, a synchronization module and a processing module, wherein the acquisition module is used for acquiring original three-dimensional gait data synchronously after monitoring a gait information acquisition instruction, and the original three-dimensional gait data comprises three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time;
and the sending module is used for sending the original three-dimensional gait data to a data processing server.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of communication units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (8)
1. A three-dimensional gait data processing method is applied to a data processing server, and comprises the following steps:
acquiring original three-dimensional gait data sent by an acquisition server, wherein the original three-dimensional gait data comprise three-dimensional gait data acquired by each somatosensory sensor in a preset somatosensory sensor array within a preset time;
determining overlapped three-dimensional gait data based on timestamp information of the original three-dimensional gait data, wherein the original three-dimensional gait data comprises human body skeletal joint point data; the overlapped three-dimensional gait data comprise a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
according to the spatial distance between any two adjacent frames of the human body bone joint point data, carrying out fusion processing on the multiple frames of the human body bone joint point data which are overlapped to obtain target three-dimensional gait data;
before the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data, further comprising:
determining abnormal bone joint point data in the human body bone joint point data according to a preset reference distance between all bone joint points in a human body bone structure;
performing data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data;
correspondingly, the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
2. A three-dimensional gait data processing method according to claim 1, wherein the determining abnormal bone joint point data in the human body bone joint point data according to a preset reference distance between bone joint points in a human body bone structure comprises:
sequencing the data of the human skeleton joint points collected by each somatosensory sensor in the somatosensory sensor array according to the timestamp information of the original three-dimensional gait data to respectively obtain multiple frames of data of the human skeleton joint points with time sequences;
respectively calculating the distance between any two adjacent skeletal joint point data in each frame of human skeletal joint point data;
and determining abnormal bone joint point data in the human body bone joint point data according to the distance and a preset reference distance between all bone joint points in the human body bone structure.
3. A three-dimensional gait data processing method according to claim 1, characterized in that before said determining the plurality of frames of overlapping human skeletal joint point data in the corrected skeletal joint point data based on time stamp information of the original three-dimensional gait data, it comprises:
according to the inclination angle of each somatosensory sensor in the somatosensory sensor array, coordinate conversion is carried out on the corrected skeleton joint point data respectively to obtain standard skeleton joint point data under the same preset coordinate system;
correspondingly, the determining the human bone joint point data of the plurality of frames overlapping in the bone joint point data based on the time stamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the standard bone joint point data based on the time stamp information of the original three-dimensional gait data.
4. The method of processing three-dimensional gait data according to claim 3, further comprising, before said coordinate transforming each of said corrected bone joint point data according to a tilt angle of each of said somatosensory sensors in said array of somatosensory sensors, respectively:
acquiring human body bone joint point data in the original three-dimensional gait data;
and performing least square fitting processing on the human skeleton joint point data to obtain the inclination angle of each somatosensory sensor.
5. A three-dimensional gait data processing system, comprising: the motion sensing system comprises an acquisition server, a motion sensing sensor array in communication connection with the acquisition server and a data processing server in communication connection with the acquisition server;
the motion sensing sensor array comprises a plurality of motion sensing sensors which are arranged in a preset arrangement mode, and the motion sensing sensors are mutually independent;
the acquisition server is used for synchronously acquiring original three-dimensional gait data after monitoring a gait information acquisition instruction, wherein the original three-dimensional gait data comprises three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time length, and the original three-dimensional gait data is sent to the data processing server;
the data processing server is used for acquiring the original three-dimensional gait data sent by the acquisition server and determining overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
the data processing server is also used for carrying out fusion processing on the multiple frames of overlapped human body bone joint point data according to the space distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data;
before the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data, further comprising:
determining abnormal bone joint point data in the human body bone joint point data according to a preset reference distance between all bone joint points in a human body bone structure;
performing data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data;
correspondingly, the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
6. A three-dimensional gait data processing method is characterized by comprising the following steps:
after monitoring a gait information acquisition instruction, an acquisition server synchronously acquires original three-dimensional gait data, wherein the original three-dimensional gait data comprises three-dimensional gait data acquired by each somatosensory sensor in the somatosensory sensor array within a preset time length, and the original three-dimensional gait data is sent to a data processing server;
the data processing server acquires the original three-dimensional gait data sent by the acquisition server, and determines overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data; the original three-dimensional gait data comprises human body bone joint point data; the overlapped three-dimensional gait data comprises a plurality of frames of overlapped human body skeletal joint point data collected by any two adjacent somatosensory sensors in the somatosensory sensor array;
the data processing server performs fusion processing on the multiple frames of overlapped human body bone joint point data according to the spatial distance between any two adjacent frames of human body bone joint point data to obtain target three-dimensional gait data;
before the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data, further comprising:
determining abnormal bone joint point data in the human body bone joint point data according to a preset reference distance between all bone joint points in a human body bone structure;
performing data compensation and denoising processing on the abnormal bone joint point data to obtain corrected bone joint point data;
correspondingly, the determining the overlapped three-dimensional gait data based on the timestamp information of the original three-dimensional gait data comprises:
and determining the human body bone joint point data overlapped by multiple frames in the corrected bone joint point data based on the time stamp information of the original three-dimensional gait data.
7. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the three-dimensional gait data processing method according to any of claims 1 to 4.
8. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the three-dimensional gait data processing method according to any one of claims 1 to 4.
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