CN110600132A - Digital twin intelligent health prediction method and device based on vibration detection - Google Patents
Digital twin intelligent health prediction method and device based on vibration detection Download PDFInfo
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Abstract
The application provides a digital twin intelligent health prediction method and device based on vibration detection. A digital twin intelligent health prediction method based on vibration detection comprises the following steps: when a health prediction instruction is received, acquiring a motion video of a user; identifying a stressed part corresponding to the current action of the user based on the motion video of the user; judging whether the stressed part is positioned in the shooting range of the mobile terminal; if the stressed part is located in the shooting range of the mobile terminal, obtaining a vibration video of the stressed part according to the motion video; determining first vibration information of the stressed part according to the vibration video of the stressed part; acquiring a first stress condition of a stress part from a digital twin intelligent health prediction model according to first vibration information; according to the first stress condition, a first risk degree score of the current action of the user is determined. According to the technical scheme, the risk degree of the current action of the user during fitness is monitored in real time.
Description
Technical Field
The application relates to the technical field of internet, in particular to a digital twin intelligent health prediction method and device based on vibration detection.
Background
With the continuous development of the internet, the internet plus is a new business state for the development of the internet under innovation 2.0, and in a popular way, the internet plus is the internet plus all traditional industries, but the internet and the traditional industries are deeply fused by using an information communication technology and an internet platform instead of being simply added, so that a new development ecology is created. The method represents a new social form, namely, the optimization and integration of the Internet in social resource configuration are fully exerted, the innovation achievements of the Internet are deeply integrated in all the fields of economy and society, the innovation power and the productivity of the whole society are improved, and a wider new economic development form taking the Internet as infrastructure and realizing tools is formed.
Traditional health monitoring mechanism generally adopts localized check out test set, if the user wears the bracelet in the motion process, the bracelet can gather user's in the motion process body data, carry out localized vibration detection through the bracelet, and health prediction etc., but need purchase the bracelet that has the vibration detection function like this, and because the bracelet can only be worn on hand, can't detect other positions of health, detection area is little, be difficult to satisfy the intelligent health prediction demand of increasing user in various motion scenes.
Disclosure of Invention
The embodiment of the application provides a digital twin intelligent health prediction method and device based on vibration detection, wherein the vibration detection is carried out on the body building action of a user, and the risk degree score of the current action of the user is obtained through a digital twin intelligent health prediction model, so that the risk degree of the current action of the user during body building is monitored in real time.
Specifically, the data transmission flow in the vibration detection method disclosed in the embodiment of the present application may be based on the internet + technology, so as to form a local + cloud or server distributed intelligent vibration detection system, on one hand, the local may perform accurate original image acquisition and preprocessing through an acquisition device, on the other hand, the cloud or server may predict the health condition of the detected target based on the acquired distributed data by combining various special health prediction models obtained through statistical analysis of big data technology, so as to implement deep fusion of the internet and the traditional health monitoring industry, improve the intelligence and accuracy of health monitoring, and meet the increasing intelligent health prediction requirements in various motion scenes.
The application provides a digital twin intelligent health prediction method based on vibration detection, which is applied to a mobile terminal, wherein the mobile terminal comprises a camera, and the method comprises the following steps:
when a health prediction instruction is received, acquiring a motion video of a user;
identifying a stressed part corresponding to the current action of the user based on the motion video of the user;
judging whether the stressed part is positioned in the shooting range of the mobile terminal;
if the stressed part is located within the shooting range of the mobile terminal, obtaining a vibration video of the stressed part according to the motion video;
determining first vibration information of the stress part according to the vibration video of the stress part;
acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulation vibration information of different body parts of the user under different stress conditions;
and determining a first risk score of the current action of the user according to the first stress condition.
A second aspect of the present application provides a digital twin intelligent health prediction apparatus based on vibration detection, the apparatus comprising:
the acquisition unit is used for acquiring a motion video of the user when receiving the health prediction instruction;
the identification unit is used for identifying a stress part corresponding to the current action of the user based on the motion video of the user;
the judging unit is used for judging whether the stressed part is positioned in the shooting range of the mobile terminal;
the first acquisition unit is used for acquiring a vibration video of the stressed part according to the motion video if the stressed part is positioned in the shooting range of the mobile terminal;
the first determining unit is used for determining first vibration information of the stress part according to the vibration video of the stress part;
the second acquisition unit is used for acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, and the digital twin intelligent health prediction model stores simulated vibration information of different body parts of the user under different stress conditions;
and the second determining unit is used for determining a first risk degree score of the current action of the user according to the first stress condition.
A third aspect of the application provides a mobile terminal comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps of any of the methods described above.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform some or all of the steps described in any of the methods above.
According to the digital twin intelligent health prediction method and device based on vibration detection, when a health prediction instruction is received, a motion video of a user is collected, a stress part corresponding to the current action of the user is identified based on the motion video of the user, a vibration video of the stress part is obtained according to the motion video, first vibration information of the stress part is determined according to the vibration video of the stress part, a first stress condition of the stress part is obtained from a digital twin intelligent health prediction model according to the first vibration information, and a first risk score of the current action of the user is determined according to the first stress condition. Therefore, when the user exercises, the motion video of the user can be acquired in real time through the mobile terminal, the mobile terminal processes the motion video of the user to obtain the risk degree score of the current action of the user, the risk degree of the current action of the user during exercise can be monitored in real time, and when the user is detected through the mobile terminal, the detection area is large, and the health prediction requirements of the user in various motion scenes can be met.
Drawings
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 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 without creative efforts.
FIG. 1a is a schematic diagram of a digital twin intelligent health prediction system based on vibration detection according to an embodiment of the present application;
FIG. 1b is a flowchart of a digital twin intelligent health prediction method based on vibration detection according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a digital twin intelligent health prediction device based on vibration detection according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a part mechanics model provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application;
fig. 5 is a schematic diagram of an elbow motion of a user according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a digital twin intelligent health prediction method and device based on vibration detection, wherein the vibration detection is carried out on the body building action of a user, and the risk degree score of the current action of the user is obtained through a digital twin intelligent health prediction model, so that the risk degree of the current action of the user during body building is monitored in real time.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The following provides a detailed description of examples of the present application.
According to the digital twin intelligent health prediction method and device based on vibration detection, when a user exercises, the vibration video of a stressed part of the user in the exercise process is collected through the mobile terminal, the vibration video of the stressed part is processed to obtain vibration information, and the risk score of the current action of the user is obtained through the digital twin intelligent health prediction model. Therefore, when the user exercises, the motion video of the user can be acquired in real time through the mobile terminal, the mobile terminal processes the motion video of the user to obtain the risk degree score of the current action of the user, the risk degree of the current action of the user during exercise can be monitored in real time, and when the user is detected through the mobile terminal, the detection area is large, and the health prediction requirements of the user in various motion scenes can be met.
Referring to fig. 1a, fig. 1a is a schematic diagram of a digital twin intelligent health prediction system based on vibration detection according to an embodiment of the present application, where the health prediction system includes a mobile terminal and a user, the mobile terminal may be a mobile phone, a tablet computer, a notebook computer, a mobile internet device, or other types of terminal devices with cameras, the user may perform various fitness actions, and the mobile terminal includes a camera, and the mobile terminal may photograph real-time actions of the user through the camera. In the health prediction system 100 shown in fig. 1a, the mobile terminal 101 is a mobile phone, the mobile phone includes a rear-facing camera, the rear-facing camera has a certain shooting range, and the exercise of the user 102 is taken as a push-up, where:
the mobile terminal 101 is used for collecting a motion video of the user 102 when receiving the health prediction instruction; identifying a stress part corresponding to the current action of the user 102 based on the motion video of the user 102; judging whether the stressed part is positioned in the shooting range of the mobile terminal 101; if the stressed part is located within the shooting range of the mobile terminal 101, obtaining a vibration video of the stressed part according to the motion video; determining first vibration information of the stress part according to the vibration video of the stress part; acquiring a first stress condition of the stressed part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulated vibration information of different body parts of the user 102 under different stress conditions; determining a first risk score for a current action of the user 102 based on the first stress condition.
Specifically, as shown in fig. 1a, when the mobile terminal 101 receives the health prediction instruction, the motion video of the user 102 is collected through the camera, that is, the motion video of the user 102 performing push-up is collected, the mobile terminal 101 recognizes that the current action of the user 102 is push-up based on the video of the user 102 performing push-up, and further determines that the stress part corresponding to the current action of the user 102 is an elbow joint, the mobile terminal 101 determines that the elbow joint is located in the shooting range, and further obtains the vibration video of the elbow joint, because the elbow joint of the two arms is involved when the user 102 performs push-up, the mobile terminal 101 respectively obtains the vibration video of the elbow joint of the two arms, and then determines the risk score of the current push-up action of the user 102, if the frequency of the user 102 performing push-up is too high, the vibration frequency of the elbow joint is also too high, and therefore the obtained risk score is high, the mobile terminal 101 may alert or prompt the user 102 in time, prompting the user 102 to decrease the frequency, or temporarily stop the motion. Therefore, when the user exercises, the motion video of the user can be acquired in real time through the mobile terminal, the mobile terminal processes the motion video of the user to obtain the risk degree score of the current action of the user, the risk degree of the current action of the user during exercise can be monitored in real time, and when the user is detected through the mobile terminal, the detection area is large, and the health prediction requirements of the user in various motion scenes can be met.
Referring to fig. 1b, fig. 1b is a flowchart of a digital twin intelligent health prediction method based on vibration detection according to an embodiment of the present application, where the digital twin intelligent health prediction method based on vibration detection according to the embodiment of the present application is applied to a mobile terminal, and the mobile terminal includes a camera. As shown in fig. 1b, a digital twin intelligent health prediction method based on vibration detection according to an embodiment of the present application may include:
101. when the health prediction instruction is received, a motion video of the user is collected.
Specifically, when the user is in fitness, the health prediction function of the mobile terminal can be started, and when the mobile terminal receives a health prediction instruction, the camera is started to collect the motion video of the user.
The mobile terminal can be a mobile phone, a tablet computer, a notebook computer, a mobile internet device, or other types of terminal devices with cameras.
102. And identifying a stressed part corresponding to the current action of the user based on the motion video of the user.
The method comprises the steps that a user carries out real-time image acquisition on the user through a mobile terminal in the movement process, the current action of the user is identified based on the acquired movement video of the user, and therefore the stressed part corresponding to the current action of the user is determined.
Specifically, when the mobile terminal is a mobile phone, the mobile phone interface displays an entry of the body-building pull prevention function, after a user clicks to enter the body-building pull prevention function, the mobile phone starts a camera to acquire a body-building image of the user in real time, and the mobile phone identifies a stressed part corresponding to the action based on the current action of the user in the body-building process. For example, when the current movement of the user is push-up, the force-receiving portion is an arm, and when the current movement of the user is sit-up, the force-receiving portion is an abdomen.
103. And judging whether the stressed part is positioned in the shooting range of the mobile terminal.
Specifically, because the mobile terminal is placed at different positions or the mobile terminal has different angles for shooting the user, the situation that the mobile terminal does not shoot the stressed part of the user exists, and therefore, whether the stressed part is located in the shooting range of the mobile terminal needs to be judged.
In one possible example, the method for determining whether the stressed part is located within the shooting range of the mobile terminal may be:
and comparing the characteristic points of the stressed part with the characteristic points of each body part of the user in the image frame of the motion video, if the comparison is successful, indicating that the stressed part is positioned in the shooting range of the mobile terminal, and if the comparison is failed, indicating that the stressed part is not positioned in the shooting range of the mobile terminal.
104. And if the stressed part is positioned in the shooting range of the mobile terminal, acquiring a vibration video of the stressed part according to the motion video.
Specifically, if the stressed part is located within the shooting range of the mobile terminal, the position of the stressed part is determined in the motion video, and then the vibration video of the stressed part is selected from the motion video.
105. And determining first vibration information of the stress part according to the vibration video of the stress part.
Specifically, the Lagrange motion amplification method is adopted to process the vibration video of the stressed part to obtain a vibration amplification video, and the vibration amplification video is processed to obtain first vibration information of the stressed part.
106. And acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulation vibration information of different body parts of the user under different stress conditions.
The digital twin technology is another technology wind direction besides artificial intelligence, machine learning, AR/VR and block chain as a core technology for realizing interactive fusion between a manufactured physical world and an information world. The digital twin technology dynamically presents past and present behaviors or flows of a certain physical entity in a digital form. As a technology that makes full use of data, is intelligent, and integrates multiple disciplines, the digital twin technology provides more real-time, efficient, and intelligent services in practicing intelligent manufacturing concepts and goals.
Specifically, the digital twin intelligent health prediction model and the mobile terminal run synchronously, the motion condition of the human body is simulated through the digital twin intelligent health prediction model, whether the motion part is easy to be pulled and injured in the body building process of the human body can be predicted in time, the space pattern processing and the digital twin technology are combined through the digital twin intelligent health prediction method based on vibration detection, the combination of the computer vision technology and artificial intelligence is achieved, and the motion condition of the human body is judged through vibration detection. The digital twin intelligent health prediction model needs to be constructed in advance, and as for different users, the height, the weight, the sex, the age, the physical condition, the environment and the like of the user may be different, when the digital twin intelligent health prediction model is constructed in advance, firstly, the human body parameters of the user need to be obtained through the mobile terminal, the human body parameters of the user are input to generate the digital twin intelligent health prediction model, after the construction is completed, the digital twin intelligent health prediction model simulates vibration information of different body parts of the user under different stress conditions, and the simulated vibration information is stored in the digital twin intelligent health prediction model.
107. And determining a first risk score of the current action of the user according to the first stress condition.
Specifically, different stress parts of the user have different stress ranges and stress frequencies, and if the stress is too large or the stress frequency is too high, the stress parts of the user are likely to be pulled or the body of the user is likely to be damaged, so that the risk degree score of the current action of the user can be determined according to the first stress condition.
For example, when a user is doing push-up, if the frequency of arm movement is too high, arm muscles are easily pulled; for another example, when a user is doing a dynamic hip bridge, if the hip lifting height is too high, the waist muscle is easily damaged.
Therefore, according to the digital twin intelligent health prediction method based on vibration detection provided by the embodiment of the application, when a health prediction instruction is received, a motion video of a user is collected, a stress part corresponding to the current action of the user is identified based on the motion video of the user, a vibration video of the stress part is obtained according to the motion video, first vibration information of the stress part is determined according to the vibration video of the stress part, a first stress condition of the stress part is obtained from a digital twin intelligent health prediction model according to the first vibration information, and a first risk score of the current action of the user is determined according to the first stress condition. Therefore, when the user exercises, the motion video of the user can be acquired in real time through the mobile terminal, the mobile terminal processes the motion video of the user to obtain the risk degree score of the current action of the user, the risk degree of the current action of the user during exercise can be monitored in real time, and when the user is detected through the mobile terminal, the detection area is large, and the health prediction requirements of the user in various motion scenes can be met.
Another embodiment of the present application provides another digital twin intelligent health prediction method based on vibration detection, which may include:
201. when the health prediction instruction is received, a motion video of the user is collected.
Specifically, when the user is in fitness, the health prediction function of the mobile terminal can be started, and when the mobile terminal receives a health prediction instruction, the camera is started to collect the motion video of the user.
The mobile terminal can be a mobile phone, a tablet computer, a notebook computer, a mobile internet device, or other types of terminal devices with cameras.
202. And identifying a stress part corresponding to the current action of the user based on the motion video of the user.
The method comprises the steps that a user carries out real-time image acquisition on the user through a mobile terminal in the movement process, the current action of the user is identified based on the acquired movement video of the user, and therefore the stressed part corresponding to the current action of the user is determined.
Specifically, when the mobile terminal is a mobile phone, the mobile phone interface displays an entry of the body-building pull prevention function, after a user clicks to enter the body-building pull prevention function, the mobile phone starts a camera to acquire a body-building image of the user in real time, and the mobile phone identifies a stressed part corresponding to the action based on the current action of the user in the body-building process.
203. And judging whether the stressed part is positioned in the shooting range of the mobile terminal.
Specifically, because the mobile terminal is placed at different positions or the mobile terminal has different angles for shooting the user, the situation that the mobile terminal does not shoot the stressed part of the user exists, and therefore, whether the stressed part is located in the shooting range of the mobile terminal needs to be judged.
In one possible example, the method for determining whether the stressed part is located within the shooting range of the mobile terminal may be: and comparing the characteristic points of the stressed part with the characteristic points of each body part of the user in the image frame of the motion video, if the comparison is successful, indicating that the stressed part is positioned in the shooting range of the mobile terminal, and if the comparison is failed, indicating that the stressed part is not positioned in the shooting range of the mobile terminal.
204. And if the stressed part is positioned in the shooting range of the mobile terminal, acquiring a vibration video of the stressed part according to the motion video.
Specifically, if the stressed part is located within the shooting range of the mobile terminal, the position of the stressed part is determined in the motion video, and then the vibration video of the stressed part is selected from the motion video.
The vibration video of the stressed part comprises the motion process of the stressed part, and the motion process is very small and needs to be amplified for extracting the subsequent vibration information. By adopting the Lagrange motion amplification method, the amplification of the micro motion can be realized by tracking the motion trail and clustering the target characteristic points in the video.
205. And calibrating the image frame of the vibration video of the stressed part to obtain a plurality of stable motion characteristic points.
In one possible example, the method for calibrating the image frames of the vibration video of the force-receiving portion to obtain the plurality of motion feature points of the temperature may be: selecting at least one frame of image in the vibration video of the stressed part; determining a reference characteristic point which is in a relatively static state in the vibration video of the stressed part in the video acquisition process according to the at least one frame of image; intercepting each frame image included in the at least one frame image according to N different circle centers to obtain N basic circular partitions, wherein N is an integer greater than 3; selecting a target circular partition from the N basic circular partitions, wherein the target circular partition comprises a relative motion characteristic point, and the relative motion characteristic point is a motion characteristic point for performing relative motion on the reference characteristic point; intercepting the target circular subarea according to a preset window to obtain a plurality of intercepted subareas, wherein the size and the shape of the preset window are determined according to the muscle form of the stressed part; sequentially acquiring relative motion characteristic points with motion distances within a preset range from the plurality of intercepting subareas, and accumulating the numerical values of the acquired relative motion characteristic points, wherein the preset range is the vibration amplitude range of the stressed part in a normal stress state; and when the accumulated numerical value is not less than the preset numerical value, determining the acquired relative motion characteristic points as the stable motion characteristic points.
Specifically, the lagrangian amplification method is adopted to amplify the vibration video, a plurality of stable motion characteristic points, namely micro-motion points, in the vibration video need to be obtained firstly, so as to be distinguished from static points (background points) and violent motion points in the vibration video, for the vibration video, besides the vibration image is shot, some relatively static background images are included, for example, when a user is making a lead body upwards, a cross bar supported by the user is a relatively static background image, and for example, when the user is making a push-up, the ground is a relatively static background image. And acquiring points on the object in a relatively static state as reference characteristic points, and extracting a plurality of stable motion characteristic points in the vibration video according to a preset motion characteristic point extraction strategy.
It can be seen that for at least one frame of image in the vibration video, not every region contains a moving point, and if each region in the image frame is examined one by one, a lot of time is consumed to obtain a stable moving feature point. Then, the motion feature points can be extracted by adopting a proper partitioning strategy to improve the efficiency.
206. And processing the stable multiple motion characteristic points to obtain a first vibration amplification video.
In one possible example, the method of processing the stable plurality of motion feature points to obtain the first vibration-amplified video may be: tracking the plurality of motion characteristic points to obtain track vectors of the plurality of motion characteristic points; clustering the track vectors of the plurality of motion characteristic points by adopting a clustering algorithm to obtain K-type motion layers; obtaining a target motion layer needing to be amplified from the K-type motion layer; multiplying the offset distance of the motion characteristic point in the target motion layer by a magnification factor for amplification to obtain an amplified motion layer; rendering the enlarged motion layer to obtain the first vibro-enlarged video.
Specifically, a plurality of motion characteristic points are tracked to obtain corresponding track vectors, and the track vectors describe the motion direction, the motion distance, the brightness change and the like of the motion characteristic points by numerical values; and clustering the track vectors of the plurality of motion characteristic points by adopting a clustering algorithm to obtain K-type motion layers, and dividing the K-type motion layers according to the correlation and the similarity of the track vectors, so that different motion layers contain different types of motion, and a motion layer corresponding to micro motion in the K-type motion layers is selected for amplification processing to obtain an amplified motion layer. Finally, because the enlargement of the motion layer causes some blank areas to be included in the image frame corresponding to the target video, the image frame needs to be rendered and filled.
It can be seen that some vibrations are tiny in the user motion process, accuracy rate is reduced if vibration information is directly extracted, and the accuracy rate of vibration information extraction can be improved by amplifying the vibrations so as to facilitate subsequent processing.
207. And obtaining an image frame corresponding to the first vibration amplification video, and calculating the image frame by adopting a phase correlation algorithm to obtain a first cross power spectrum between image frames.
The formula for calculating the image frame by adopting the phase correlation algorithm is as follows:
wherein, FaIs the fourier transform of the image of the a-frame,for the conjugate signal of the fourier transform of the b frame image, the lower side of the divisor is the modulus of the correlation product of the two fourier transformed signals. And R is the cross-power spectrum of the calculation result in the step.
After the cross-power spectrum is obtained, the cross-power spectrum contains frequency domain noise, so that the cross-power spectrum can be subjected to filtering processing, and the signal to noise ratio is improved, so that the accuracy of subsequently extracted vibration information is improved.
Optionally, after the image frames are calculated by using a phase correlation algorithm to obtain a cross-power spectrum between the image frames, the method further includes:
acquiring one or more correlation peaks in a cross-power spectrum, wherein the correlation peaks are frequency domain signals; determining a filtering strategy corresponding to each correlation peak according to the corresponding position of each correlation peak in the vibration video and the frequency band of each correlation peak; and carrying out filtering processing on each correlation peak according to a filtering strategy corresponding to each correlation peak.
208. And performing inverse Fourier transform on the first cross-power spectrum to obtain first vibration information.
Specifically, the cross-power spectrum reflects the vibration information in the frequency domain, and the vibration information needs to be analyzed in the time domain, so that an inverse fourier transform (or an inverse fourier transform) is needed. The formula adopted for performing the inverse fourier transform is as follows:
wherein,and performing inverse Fourier transform on the cross power spectrum, wherein R' is the cross power spectrum obtained after filtering, and R is vibration information of pixels in the vibration video.
209. And acquiring a first stress condition of the stress part from the digital twin intelligent health prediction model according to the first vibration information.
The digital twin intelligent health prediction model stores simulation vibration information of different body parts of a user under different stress conditions.
In one possible example, before obtaining the first stress condition of the stress part from the digital twin intelligent health prediction model according to the first vibration information, the method further includes: acquiring human body parameters of a user, wherein the human body parameters comprise any combination of age, sex, height, weight, body fat rate, heart rate and blood pressure; m part mechanical models corresponding to M body parts of a user are constructed according to human body parameters, wherein M is a positive integer, and the M body parts correspond to the M part mechanical models one by one; respectively applying radial force to M designated positions in the M part mechanical models, wherein the M part mechanical models correspond to the M designated positions one by one, and the radial force is the stress of the simulated M body parts; determining M moving distances of M part mechanical models, wherein the M part mechanical models correspond to the M moving distances one to one; and inputting the M moving distances into a preset dynamics algorithm for calculation so as to obtain M pieces of simulated vibration information of the M part mechanics models.
In particular, the part mechanics model is a virtual body part, that is, the virtualization of the user's body part is achieved by employing the structural part mechanics model. Further, a part mechanics model is constructed by three-dimensionally scanning the body part of the user.
In the step of applying radial force to M designated positions in the M part mechanical models, the radial force is a friction force of radial movement, and the radial force y is uniformly applied to the designated positions of the part mechanical models in the radial direction, so that vibrations from the radial forces are cancelled, and the friction force is assumed to move only in the radial direction.
When a radial force is applied to the part-force model, the radial force needs to be calculated.
For example, referring to fig. 3, fig. 3 is a schematic diagram of a part mechanics model according to an embodiment of the present application. Let the unit length h, the inlet radius be R1 and the outlet radius be R2. Accordingly, the import and export areas thereofLet t be a unit vector acting on this axial element and n1 and n2 be the normal unit vectors of the inlet and outlet, respectively. Assuming that the angle between n1 and n2 is sufficiently small, the part of the mechanical model is considered to be a cone or a cylinder. Let v1 and v2 be the average of the port velocities, respectively. The formula for calculating the radial force borne by the part mechanical model is as follows:
wherein,
this force, calculated in each cell unit of the site, forms all the forces on the site due to the cell movement. The relative position of the designated location at the location relative to the location of the axis is determined by a scanned Computer Aided Design (CAD) model, and the friction force is correlated to the vibration model.
Wherein, in the step of inputting the M moving distances into a preset dynamics algorithm for calculation, the preset dynamics algorithm is as follows:
wherein Is an inertia matrix, Ks Is a rigidity matrix, Cs Is a connection matrix, and Mb Is a moment matrix.
Wherein the radial force is represented by the formulaCalculating three components of force which can be decomposed into three axes parallel to the three axes, determining moments M1, M2 and M3 on the three axes at the specified positions, and generating a matrix Mb＝[M1M2M3]TSubstituting the angular displacement into the preset dynamics algorithm for calculation to obtain angular displacement, further converting the calculated angular displacement into linear displacement by using the fixed distance between the detected area and the designated position, and projecting the linear displacement onto a two-dimensional (Y-Z) plane by using a trigonometric relation.
The simulation vibration information stored in the digital twin intelligent health prediction model comprises a vibration oscillogram, a modal graph and a thermodynamic graph.
It can be seen that the human body contains many body parts, and it is too complicated to analyze the health conditions of all body parts, so that a single mechanical model can be adopted to analyze the movement conditions of all body parts under different health conditions, and the mutual influence caused by the movement of the body parts is not considered, but the energy generated by the joint action of the body parts is simulated by measuring the movement conditions of the body parts under different conditions. Since the body parts involved in the movement of the human body are substantially fixed, only those body parts that are mainly involved in the movement are analyzed, thereby reducing the complexity of the modeling.
210. According to the first stress condition, a first risk degree score of the current action of the user is determined.
Specifically, different stress parts of the user have different stress ranges and stress frequencies, and if the stress is too large or the stress frequency is too high, the stress parts of the user are likely to be pulled or the body of the user is likely to be damaged, so that the risk degree score of the current action of the user can be determined according to the first stress condition.
In one possible example, after determining the first risk score of the current action of the user according to the first stress condition, the method further includes: when the first risk score is the muscle strain possibility score, judging whether the muscle strain possibility score exceeds a preset possibility score; if the muscle strain possibility score exceeds the preset possibility score, sending a first early warning message, wherein the first early warning message is used for prompting a user to adjust the movement posture or stop the movement so as to avoid strain of the stressed part; when the first risk score is the physical function score, judging whether the physical function score is lower than a preset score; and if the physical function score is lower than the preset score, sending a second early warning message, wherein the second early warning message is used for prompting the user to slow down the movement frequency or stop moving so as to avoid the physical damage of the user.
Therefore, when the user is in fitness, the motion video of the user can be acquired in real time through the mobile terminal, the mobile terminal processes the motion video of the user to obtain the risk degree score of the current action of the user, the risk degree of the current action of the user is monitored in real time when the user is in fitness, and early warning prompt is carried out on the user.
211. And if the stressed part is not positioned in the shooting range of the mobile terminal, determining an associated part which is associated with the stressed part and positioned in the shooting range of the mobile terminal.
In one possible example, the method of determining the associated part associated with the force-receiving part and located within the shooting range of the mobile terminal may be: determining at least one association site associated with the force-receiving site; judging whether a first associated part located in the shooting range of the mobile terminal exists in the at least one associated part; if the first relevant part exists, determining that the first relevant part is a relevant part which is relevant to the stressed part and is located in the shooting range of the mobile terminal; if the second relevant part is not in the shooting range of the mobile terminal, starting a wide-angle shooting mode so that the second relevant part in the at least one relevant part exists; and determining the second associated part as an associated part which is associated with the stressed part and is positioned in the shooting range of the mobile terminal.
It can be seen that when the stressed portion is not located within the shooting range of the mobile terminal, the associated portion with the stressed portion located within the shooting range can be determined, or the associated portion with the stressed portion located within the shooting range can be determined by starting the wide-angle shooting mode, so that the vibration information of the stressed portion can be determined subsequently according to the vibration information of the associated portion.
In another possible example, if the stressed portion is within the shooting range of the mobile terminal, the clothing characteristics of the user need to be identified, so as to judge whether the vibration video of the stressed portion can be accurately acquired, if the clothing characteristics of the user at the stressed portion cover the vibration characteristics of the stressed portion, the vibration video of the stressed portion cannot be accurately acquired, the associated portion which is related to the stressed portion and can accurately acquire the vibration video needs to be determined again within the shooting range of the mobile terminal, and the vibration video of the associated portion is acquired based on the position of the associated portion.
212. And acquiring a vibration video of the associated part according to the motion video.
213. And determining the vibration information of the relevant part according to the vibration video of the relevant part.
214. And obtaining second vibration information of the stressed part according to the vibration information of the associated part.
215. And acquiring a second stress condition of the stress part from the digital twin intelligent health prediction model according to the second vibration information.
216. And determining a second risk degree score of the current action of the user according to the second stress condition.
Therefore, in the embodiment of the application, the motion video of the user in the body building process is obtained through the mobile terminal, the stressed part is confirmed, different processing is carried out according to whether the stressed part is within the shooting range of the mobile terminal, the vibration information of the stressed part is obtained, the vibration information of the stressed part is compared with the simulated vibration information of the stressed part, stored in the digital twin intelligent health prediction model, of the stressed part under the unstressed condition, the stressed condition of the stressed part is determined, the danger degree score of the current action of the user is determined, and the user is warned according to the danger degree score. Therefore, when the body-building action amplitude of the user is too large, the frequency is too high or the posture is wrong, the alarm prompt can be carried out on the user, so that the risk degree of the current action of the user during body building is detected in real time, the body-building action of the user is normalized, and the negative influence on the body health is avoided.
Referring to fig. 5, fig. 5 is a schematic diagram of an elbow movement of a user according to an embodiment of the present application. As shown in fig. 5, when a user does elbow motion, for example, when the user exercises biceps brachii muscle, an elbow is in a repeated process of a bending state and a straightening state, when the elbow of the user is straightened, the elbow joint part is from the elbow 1 shown in fig. 5, during the motion process, the elbow joint part is from the elbow 2 and the elbow 3 shown in fig. 5, when the elbow of the user is bent, the elbow joint part is from the elbow 4 shown in fig. 5, when the user does elbow motion, the elbow joint part regularly vibrates, so that a vibration video of the elbow joint part can be acquired through a terminal such as a mobile phone of the user, and the vibration video is analyzed to judge whether the exercise motion of the user is standard or not and whether elbow joint injury can be caused or not.
Referring to fig. 2, fig. 2 is a schematic diagram of a digital twin intelligent health prediction apparatus based on vibration detection according to another embodiment of the present application. As shown in fig. 2, another embodiment of the present application provides a digital twin intelligent health prediction apparatus based on vibration detection, which may include:
the acquisition unit 201 is used for acquiring a motion video of a user when receiving the health prediction instruction;
the identification unit 202 is configured to identify a stressed portion corresponding to the current action of the user based on the motion video of the user;
a judging unit 203, configured to judge whether the stressed portion is located within a shooting range of the mobile terminal;
a first obtaining unit 204, configured to obtain a vibration video of the stressed portion according to the motion video if the stressed portion is within a shooting range of the mobile terminal;
a first determining unit 205, configured to determine first vibration information of the force-receiving portion according to the vibration video of the force-receiving portion;
a second obtaining unit 206, configured to obtain a first stress condition of the stressed portion from a digital twin intelligent health prediction model according to the first vibration information, where the digital twin intelligent health prediction model stores simulated vibration information of different body portions of the user under different stress conditions;
a second determining unit 207, configured to determine a first risk score of the current action of the user according to the first stress condition.
For specific implementation of the digital twin intelligent health prediction device based on vibration detection in the embodiment of the present application, reference may be made to each embodiment of the digital twin intelligent health prediction method based on vibration detection, which is not described herein again.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application. As shown in fig. 4, a mobile terminal provided in an embodiment of the present application may include:
a processor 401, such as a CPU.
The memory 402 may alternatively be a high speed RAM memory or a stable memory such as a disk memory.
A communication interface 403 for implementing connection communication between the processor 401 and the memory 402.
Those skilled in the art will appreciate that the configuration of the mobile terminal shown in fig. 4 is not intended to be limiting of the mobile terminal and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 4, the memory 402 may include an operating system, a network communication module, and programs for health prediction. The operating system is a program that manages and controls the hardware and software resources of the mobile terminal, a program that supports health prediction, and the execution of other software or programs. The network communication module is used to enable communication between the various components within the memory 402, as well as with other hardware and software in the mobile terminal.
In the mobile terminal shown in fig. 4, a processor 401 is configured to execute a program of health prediction stored in a memory 402, implementing the following steps:
when a health prediction instruction is received, acquiring a motion video of a user;
identifying a stressed part corresponding to the current action of the user based on the motion video of the user;
judging whether the stressed part is positioned in the shooting range of the mobile terminal;
if the stressed part is located within the shooting range of the mobile terminal, obtaining a vibration video of the stressed part according to the motion video;
determining first vibration information of the stress part according to the vibration video of the stress part;
acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulation vibration information of different body parts of the user under different stress conditions;
and determining a first risk score of the current action of the user according to the first stress condition.
For specific implementation of the mobile terminal provided in the embodiment of the present application, reference may be made to each embodiment of the digital twin intelligent health prediction method based on vibration detection, which is not described herein again.
Another embodiment of the present application provides a computer-readable storage medium storing a computer program for execution by a processor to perform the steps of:
when a health prediction instruction is received, acquiring a motion video of a user;
identifying a stressed part corresponding to the current action of the user based on the motion video of the user;
judging whether the stressed part is positioned in the shooting range of the mobile terminal;
if the stressed part is located within the shooting range of the mobile terminal, obtaining a vibration video of the stressed part according to the motion video;
determining first vibration information of the stress part according to the vibration video of the stress part;
acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulation vibration information of different body parts of the user under different stress conditions;
and determining a first risk score of the current action of the user according to the first stress condition.
For specific implementation of the computer-readable storage medium of the present application, reference may be made to the above embodiments of the digital twin intelligent health prediction method based on vibration detection, which are not described herein again.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application. In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above 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; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A digital twin intelligent health prediction method based on vibration detection is applied to a mobile terminal, wherein the mobile terminal comprises a camera, and the method comprises the following steps:
when a health prediction instruction is received, acquiring a motion video of a user;
identifying a stressed part corresponding to the current action of the user based on the motion video of the user;
judging whether the stressed part is positioned in the shooting range of the mobile terminal;
if the stressed part is located within the shooting range of the mobile terminal, obtaining a vibration video of the stressed part according to the motion video;
determining first vibration information of the stress part according to the vibration video of the stress part;
acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, wherein the digital twin intelligent health prediction model stores simulation vibration information of different body parts of the user under different stress conditions;
and determining a first risk score of the current action of the user according to the first stress condition.
2. The method of claim 1, wherein the determining the first vibration information of the force-receiving portion according to the vibration video of the force-receiving portion comprises:
calibrating the image frame of the vibration video of the stressed part to obtain a plurality of stable motion characteristic points;
tracking the plurality of motion characteristic points to obtain track vectors of the plurality of motion characteristic points;
clustering the track vectors of the plurality of motion characteristic points by adopting a clustering algorithm to obtain K-type motion layers;
obtaining a target motion layer needing to be amplified from the K-type motion layer;
multiplying the offset distance of the motion characteristic points in the target motion layer by a magnification factor for amplification to obtain an amplified motion layer;
rendering the amplified motion layer to obtain the first vibration amplification video;
acquiring an image frame corresponding to the first vibration amplification video, and calculating the image frame by adopting a phase correlation algorithm to obtain a first cross power spectrum between image frames;
and performing inverse Fourier transform on the first cross power spectrum to obtain the first vibration information.
3. The method of claim 2, wherein the calibrating the image frames of the vibration video of the force-receiving portion to obtain a plurality of stable motion feature points comprises:
selecting at least one frame of image in the vibration video of the stress part;
determining a reference characteristic point which is in a relatively static state in the vibration video of the stressed part in the video acquisition process according to the at least one frame of image;
intercepting each frame image included in the at least one frame image according to N different circle centers to obtain N basic circular partitions, wherein N is an integer greater than 3;
selecting a target circular partition from the N basic circular partitions, wherein the target circular partition comprises a relative motion characteristic point, and the relative motion characteristic point is a motion characteristic point for performing relative motion on the reference characteristic point;
intercepting the target circular subarea according to a preset window to obtain a plurality of intercepted subareas, wherein the size and the shape of the preset window are determined according to the muscle form of the stressed part;
sequentially acquiring relative motion characteristic points with motion distances within a preset range from the plurality of intercepting subareas, and accumulating numerical values of the acquired relative motion characteristic points, wherein the preset range is a vibration amplitude range of the stressed part in a normal stressed state;
and when the accumulated numerical value is not less than a preset numerical value, determining the acquired relative motion characteristic points as the stable motion characteristic points.
4. The method according to any one of claims 1 to 3, further comprising:
if the stressed part is not located in the shooting range of the mobile terminal, determining an associated part which is associated with the stressed part and is located in the shooting range of the mobile terminal;
acquiring a vibration video of the associated part according to the motion video;
determining vibration information of the associated part according to the vibration video of the associated part;
obtaining second vibration information of the stressed part according to the vibration information of the associated part;
acquiring a second stress condition of the stress part from a digital twin intelligent health prediction model according to the second vibration information;
and determining a second risk score of the current action of the user according to the second stress condition.
5. The method according to claim 4, wherein the determining of the associated part associated with the force-bearing part and located within the shooting range of the mobile terminal comprises:
determining at least one association site associated with the force-receiving site;
judging whether a first associated part located in the shooting range of the mobile terminal exists in the at least one associated part;
if the first relevant part exists, determining that the first relevant part is a relevant part which is relevant to the stressed part and is located in the shooting range of the mobile terminal;
if the at least one relevant part does not exist, starting a wide-angle shooting mode so that a second relevant part located in a shooting range of the mobile terminal exists in the at least one relevant part;
and determining the second associated part as an associated part which is associated with the stressed part and is positioned in the shooting range of the mobile terminal.
6. The method according to claim 1, wherein before obtaining the first stress condition of the stress site from the digital twin intelligent health prediction model according to the first vibration information, the method further comprises:
acquiring human body parameters of the user, wherein the human body parameters comprise any combination of age, sex, height, weight, body fat rate, heart rate and blood pressure;
m part mechanical models corresponding to M body parts of the user are constructed according to the human body parameters, wherein M is a positive integer, and the M body parts correspond to the M part mechanical models one by one;
respectively applying radial force to M designated positions in the M part mechanical models, wherein the M part mechanical models correspond to the M designated positions one by one, and the radial force is used for simulating the stress of the M body parts;
determining M moving distances of the M part mechanics models, wherein the M part mechanics models correspond to the M moving distances one to one;
and inputting the M moving distances into a preset dynamics algorithm for calculation so as to obtain M pieces of simulated vibration information of the M part mechanics models.
7. The method of claim 1, wherein after determining a first risk score for a current action of the user based on the first stress condition, the method further comprises:
when the first risk score is the muscle strain possibility score, judging whether the muscle strain possibility score exceeds a preset possibility score;
if the muscle strain possibility score exceeds the preset possibility score, sending a first early warning message, wherein the first early warning message is used for prompting the user to adjust the movement posture or stop the movement so as to avoid strain of the stressed part;
when the first risk score is a physical function score, judging whether the physical function score is lower than a preset score;
and if the physical function score is lower than the preset score, sending a second early warning message, wherein the second early warning message is used for prompting the user to slow down the movement frequency or stop moving so as to avoid the physical damage of the user.
8. A digital twin intelligent health prediction apparatus based on vibration detection, the apparatus comprising:
the acquisition unit is used for acquiring a motion video of the user when receiving the health prediction instruction;
the identification unit is used for identifying a stress part corresponding to the current action of the user based on the motion video of the user;
the judging unit is used for judging whether the stressed part is positioned in the shooting range of the mobile terminal;
the first acquisition unit is used for acquiring a vibration video of the stressed part according to the motion video if the stressed part is positioned in the shooting range of the mobile terminal;
the first determining unit is used for determining first vibration information of the stress part according to the vibration video of the stress part;
the second acquisition unit is used for acquiring a first stress condition of the stress part from a digital twin intelligent health prediction model according to the first vibration information, and the digital twin intelligent health prediction model stores simulated vibration information of different body parts of the user under different stress conditions;
and the second determining unit is used for determining a first risk degree score of the current action of the user according to the first stress condition.
9. A mobile terminal, characterized in that the mobile terminal comprises a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method according to any one of claims 1 to 7.
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