CN111208508A - Physical activity measurement method, device and electronic equipment - Google Patents
Physical activity measurement method, device and electronic equipment Download PDFInfo
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Abstract
本申请涉及一种运动量测量方法、装置及电子设备，其中方法包括：通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号；根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与运动状态对应的持续时间、与所述运动状态对应的运动距离；将所述运动量信息通过终端设备展示给目标用户。本申请无需人体随身携带可穿戴式测量设备，就能够是实现对人体运动量的非接触式测量，并且能准确测量出用户的运动量，且不会给被测用户带来不便和不舒适的感受，提升用户体验。
The present application relates to a method, device and electronic device for measuring exercise quantity, wherein the method includes: obtaining environmental information of an area where a target user is located through a radar signal, wherein the radar signal is obtained after a millimeter-wave radar sends a detection signal to the target user The returned signal; the exercise amount information of the target user is determined according to the environmental information of the area where the target user is located, wherein the exercise amount information includes the duration corresponding to the exercise state of the target user, and the corresponding exercise state of the target user. Movement distance; the movement amount information is displayed to the target user through the terminal device. The present application does not require the human body to carry a wearable measuring device, and can realize the non-contact measurement of the human body movement, and can accurately measure the user's movement without inconvenience and discomfort to the measured user, Improve user experience.
Description
Technical Field
The present disclosure relates to the field of electromagnetic wave detection technologies, and in particular, to a method and an apparatus for measuring motion quantity, and an electronic device.
Background
With the improvement of living standard of people, more and more attention is paid to health problems, the amount of exercise has gradually become an important index for measuring whether a person is healthy, and the level of health of a user is analyzed and judged by detecting the daily amount of exercise of the user, which has gradually become an important way for current auxiliary medical treatment. With the continuous improvement of the scientific and technical level, a large number of portable and wearable auxiliary medical devices for measuring the amount of human motion are available, such as: cell-phone, intelligent bracelet etc.. These devices calculate their amount of exercise by monitoring data such as the number of steps taken by a person, the time of walking, the time of sleeping, etc., and then determine their health status. However, both portable and wearable measurement devices use contact measurement methods, which require a person to carry a sensor with him or her to be able to complete the measurement. Therefore, some limitations are encountered in some portable scenarios, such as: analyzing the amount of exercise in the basketball player's field, affecting the sleeping posture during sleeping, etc.
Disclosure of Invention
In order to solve the technical problem that a wearable measuring device is inconvenient to use to measure the amount of motion of a user in a part of scenes, the application provides a motion amount measuring method and device and electronic equipment.
In a first aspect, the present application provides a motion amount measuring method, including:
acquiring environmental information of an area where a target user is located through a radar signal, wherein the radar signal is a signal returned after a millimeter wave radar sends a detection signal to the target user;
determining the motion amount information of the target user according to the environment information of the area where the target user is located, wherein the motion amount information comprises the duration time corresponding to the motion state and the motion distance corresponding to the motion state of the target user;
and displaying the motion amount information to a target user through terminal equipment.
Optionally, the step of determining the motion amount information of the target user according to the environment information of the area where the target user is located includes:
determining identity information and position change information of the target user according to environment information of an area where the target user is located, wherein the position change information is displacement and consumed time of the target user moving from a first position to a second position;
acquiring target Doppler wave data generated by the target user in different motion states, and determining the motion state of the target user according to the target Doppler wave data;
and determining the motion amount information of the target user according to the position change information and the motion state of the target user.
Optionally, the step of determining the identity information and the location change information of the target user according to the environment information of the area where the target user is located includes:
determining objects and positions of the objects in the environmental information according to a K-means clustering algorithm;
acquiring Doppler wave data and position change information generated by a moving object in the environment information;
and obtaining Doppler characteristic data in the Doppler wave data, and inputting the Doppler characteristic data corresponding to each moving object into an identity discrimination classifier to obtain identity information and position change information of the target user.
Optionally, the step of obtaining doppler feature data in the doppler wave data, inputting the doppler feature data corresponding to each moving object into an identity discrimination classifier, and training to obtain identity information and position change information of the target user includes:
training by taking Doppler feature data of different users with known identities as identity discrimination samples to obtain the identity discrimination classifier;
and respectively inputting each acquired Doppler feature data into the identity discrimination classifier to obtain a user identity corresponding to a sample with the highest correlation in the identity discrimination classifier, and determining identity information and position change information of the target user.
Optionally, the step of acquiring target doppler wave data generated by the target user in different motion states, and determining the motion state of the target user according to the target doppler wave data includes:
and inputting the Doppler characteristic data of the target user into a motion state classifier model to obtain the motion state of the target user in different time periods.
Optionally, before inputting the doppler feature data of the target user into a motion state classifier model to obtain the motion state of the target user in different time periods, the method further includes:
and inputting the target Doppler wave data serving as a motion state sample into a support vector machine classifier for training to obtain the motion state classifier model.
Optionally, the step of determining the motion amount information of the target user according to the position change information and the motion state of the target user includes:
acquiring position change information of the target user at different time;
and determining the movement time and the corresponding movement distance of the target user in each movement state according to the position change information of the target user at different times and the movement state, wherein the movement time is the accumulated time length of the target user in the corresponding movement state.
In a second aspect, the present application provides a motion amount measuring device comprising:
the environment information acquisition module is used for acquiring environment information of an area where a target user is located through a radar signal, wherein the radar signal is a signal returned after a millimeter wave radar sends a detection signal to the target user;
the data processing module is used for determining the motion amount information of the target user according to the environment information of the area where the target user is located, wherein the motion amount information comprises the duration time corresponding to the motion state and the motion distance corresponding to the motion state of the target user;
and the data display module is used for displaying the motion amount information to a target user through terminal equipment.
In another aspect, the present application provides an electronic device including a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the method when executing the program.
In another aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the method and the device provided by the embodiment of the application, the non-contact measurement of the amount of motion of the human body can be realized without carrying a wearable measuring device with the human body, the amount of motion of the user can be accurately measured, inconvenience and uncomfortable feeling can not be brought to the user to be measured, and the user experience is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for measuring amount of exercise according to an embodiment of the present disclosure;
fig. 2 is a diagram illustrating statistics of a motion amount according to an embodiment of the present application;
fig. 3 is a diagram illustrating statistics of another motion amount according to an embodiment of the present application;
fig. 4 is a schematic view of a motion quantity measuring device according to an embodiment of the present application;
fig. 5 is a schematic view of an internal structure of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some 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.
Fig. 1 is a flowchart of a method for measuring a quantity of motion according to an embodiment of the present application, where the method includes:
s11, acquiring environmental information of the area where the target user is located through radar signals, wherein the radar signals are returned after the millimeter wave radar sends detection signals to the target user.
Specifically, the millimeter wave radar is used for detecting targets in a certain space range, the space is a fixed space, such as indoor places, basketball courts and other places, the millimeter wave radar is installed at a certain position of the space, for example, the millimeter wave radar can be installed in an indoor air conditioner or installed on a ceiling of the basketball courts, electromagnetic wave signals are transmitted to the fixed space through the millimeter wave radar, a lot of information of users can be determined through Doppler wave signals reflected by the users in the space, interference in the fixed space, particularly the indoor space, is small, and accuracy of results measured by the millimeter wave radar is high. The millimeter wave radar transmits electromagnetic wave signals to the space, all objects contacting the space generate reflected waves, and the millimeter wave radar receives the reflected waves and sends information contained in the received time and waveform to a subsequent data analysis module for processing.
The millimeter wave radar is used for acquiring the motion amount information of the user in the fixed space, the user is not influenced, the acquired information is high in precision, and the measurement result is accurate.
And S12, determining the motion amount information of the target user according to the environment information of the area where the target user is located, wherein the motion amount information comprises the duration corresponding to the motion state and the motion distance corresponding to the motion state of the target user.
Specifically, after the reflected wave information of the object in the space is acquired, a digital signal filter is adopted to filter the received reflected wave, so as to filter noise signals and abnormal points. The analysis of the data in the filtered signal first requires finding the target user measuring the amount of motion from the signal.
In this embodiment, a K clustering algorithm is selected, clustering is performed according to point cloud data detected in a spatial environment in a return signal, each class can represent an object, and can identify the position and height attributes of the object, and determine whether each object is stationary or moving according to the position change information of the object.
After the morphological characteristics of each object are acquired, the user identity needs to be identified from each moving object, for example, in a basketball court, a plurality of users are playing a basketball game, wherein the moving object includes a basketball besides a user, only the motion amount information of the user needs to be measured, and the motion amount information of the basketball does not need to be measured, so that the determination of the user identity is very important. The millimeter wave radar transmits and receives millimeter wave signals in real time, sends object related information carried in millimeter waves to a background for information processing, tracks and captures each moving object in real time according to a particle filtering and tracking algorithm, obtains position information of each moving object and Doppler wave data sent by the object, and extracts Doppler characteristic data of each moving object from the Doppler wave data.
Before the user identity is identified, Doppler wave information of each user is collected in advance as sample data which comprises state data of height, weight, posture and the like of the user, the sample data of each user is respectively input into a relevant filter (CF) to be trained to obtain different identity discrimination relevant filter classifiers (identity discrimination classifiers for short), the sample data of one user can be trained to obtain an identity discrimination classifier for detecting the identity of the user, then the Doppler characteristic data of each moving object obtained in the embodiment is sequentially correlated with each identity discrimination classifier, the correlation between the Doppler characteristic data of each moving object and which identity discrimination classifier is obtained according to the principle that the correlation score or confidence coefficient of the identity discrimination classifier is the highest, and the moving object can be judged to be the user corresponding to the identity discrimination classifier with the highest correlation, the identity information of the user can be determined, and after the identity information of the user is determined, the position information, the speed information and the like of the user at different times can be determined.
For example, the existing three users respectively provide respective sample data, three identity discrimination classifiers A, B, C are obtained through training, correlation calculation is performed on the obtained doppler feature data of each moving object and an identity discrimination classifier A, B, C, wherein the correlations between the first moving object and the A, B, C three identity discrimination classifiers are respectively 30%, 90% and 40%; the relativity of the second moving object to A, B, C three identity discrimination classifiers is respectively 80%, 20% and 30%; the correlations between the third moving object and A, B, C, namely, the three identity discrimination classifiers are respectively 40%, 30% and 85%, so that it can be determined that the correlation between the first moving object and the identity discrimination classifier B is the highest, the user identity corresponding to the identity discrimination classifier B can be determined to be the first moving object to be detected, similarly, the user identity corresponding to the identity discrimination classifier a is the first moving object to be detected, and the user identity corresponding to the identity discrimination classifier C is the first moving object to be detected. Therefore, the identity of each user can be obtained, the identity of the user to be detected is selected as the identity of the target user, Doppler characteristic data corresponding to the identity of the target user is obtained, and position information, speed information and the like at different times are obtained.
After the identity information of the target user is determined and the doppler feature data of the target user is obtained, the motion state and the motion time of the target user need to be determined.
Firstly, acquiring target Doppler wave data generated by a target user in different motion states, inputting the target Doppler wave data into a support vector machine classifier as a training sample for training to obtain a motion state classifier model as Doppler spectrums generated by people in different motion states are different, wherein the motion state classifier model can be used for judging the motion state of the target user and comprises the following steps: sleeping, sitting, lying, walking, jogging, running and the like.
It should be noted that the support vector machine classifier can also be replaced by a machine learning model such as a neural network model, a random forest, a decision tree, and the like, and the classifier capable of classifying the motion state of the user is obtained by training.
After a motion state classifier model is obtained through training, real-time position information and Doppler wave data information of a target user are obtained, then the real-time Doppler wave data information of the user is input into the motion state classifier model to obtain the real-time motion state information of the target user, and finally the motion distance of the target user in each motion state is calculated according to the duration time of each motion state, wherein the motion time in any motion state is the accumulation of the time length in the motion state, and the motion distance is the accumulation of the motion distance in the motion state. For example, the target user has jog training at 6-8 am and 7-9 pm, and when the user's jog state is identified by the motion state classifier model, the sum of the time in the jog state, i.e., two hours in the morning 6-8 plus two hours in the evening 7-9, is calculated for four hours, and the jog distance of the user is calculated from the change in the user's position in these four hours.
User information acquired by the millimeter wave radar is calculated through a training model, accurate exercise amount information of various motion states of a user to be detected can be obtained, the required time is short, the calculation accuracy is high, and the user can accurately know the own exercise amount.
And S13, displaying the motion amount information to the target user through the terminal equipment.
Specifically, after the exercise amount information of the user in different exercise states is obtained through measurement, the exercise amount is counted in a form of a graph, fig. 2 is a schematic diagram of exercise amount statistics provided by the embodiment of the present application, as shown in fig. 2, the exercise amount of the user in a day time can be counted by taking a day time as a boundary during the statistics, a ratio of the exercise amount of each exercise state to a total exercise amount in the day time is calculated, meanwhile, a statistical diagram of the exercise amount of the user every day is stored, and an exercise suggestion is provided to the user according to comparison between historical statistics and current statistics, for example, the exercise amount of the user from monday to friday is not much, the exercise amount of the user from saturday to friday has obvious change, the ratio of lying is increased by much, it is explained that the user sleeps or has a rest in the usual time, and at this time.
Fig. 3 is another exercise amount statistics diagram provided in this embodiment of the present application, except that the exercise amount of the user in one day time is counted using one day time as a boundary, and the ratio of the exercise amount of each exercise state to the total exercise amount in one day time is calculated, as shown in fig. 3, the exercise amount of the user may also be counted using the exercise distance of each exercise state in one day of the user, the ratio of the exercise distance of each exercise state to the total exercise distance is calculated, a threshold value of the exercise amount of the user per day is set according to a health level, whether each exercise state reaches a threshold level is detected, and if the exercise state does not reach the threshold level, the user is recorded and reminded.
In addition, statistics can be performed by taking a certain time period as a statistical term, or the percentage data in fig. 2 and 3 can be replaced by specific time and distance values.
The statistical graphs shown in fig. 2 and 3 are not statistical graphs presented to the user, and after data statistics is performed through fig. 2 and 3, the movement distance of each movement state of the user and the duration time period of the movement state are presented to the user, and movement suggestions are provided for the user according to the movement amount information of the user, if the user moves excessively, the user is suggested to reduce the movement amount, and if the duration time of a certain movement state of the user is too long, the user is prompted to balance each movement state.
The motion amount of the user is subjected to all-dimensional statistics, and the statistical result is presented to the user, so that the user can clearly know the motion condition of the user, follow-up motion planning and improvement are achieved, and the user experience is improved.
Fig. 4 is a schematic view of a motion amount measuring apparatus according to an embodiment of the present application, as shown in fig. 4, the apparatus includes:
an environment information obtaining module 41, configured to obtain environment information of an area where a target user is located through a radar signal, where the radar signal is a signal returned after a millimeter wave radar sends a detection signal to the target user;
the data processing module 42 is configured to determine motion amount information of the target user according to environment information of an area where the target user is located, where the motion amount information includes a duration corresponding to a motion state of the target user and a motion distance corresponding to the motion state;
and the data display module 43 is used for displaying the exercise amount information to the target user through the terminal equipment.
Optionally, the device further includes a storage module, which is composed of a storage medium and a related circuit, and is configured to store the collected signals of the millimeter wave radar and the statistical motion amount data.
Optionally, the device further comprises a communication module, which is a chip module with a wireless transmission function and is used for transmitting the collected millimeter wave radar signals and sending the target user motion amount data to a user terminal (a mobile phone or a computer) and a cloud platform server in real time.
Optionally, the data processing module 42 includes an identity recognition unit, configured to determine identity information and location change information of the target user according to environment information of an area where the target user is located, where the location change information is a displacement and a consumed time for the target user to move from a first location to a second location; the device also comprises a motion state acquisition unit, a motion state detection unit and a motion state detection unit, wherein the motion state acquisition unit is used for acquiring target Doppler wave data generated by a target user in different motion states and determining the motion state of the target user according to the target Doppler wave data; and the motion amount calculation unit is used for determining the motion amount information of the target user according to the position change information and the motion state of the target user.
Optionally, the identity recognizing unit is further configured to: determining objects and positions of the objects in the environmental information according to a K-means clustering algorithm; acquiring Doppler wave data and position change information generated by a moving object in the environment information; and obtaining Doppler characteristic data in the Doppler wave data, and inputting the Doppler characteristic data corresponding to each moving object into an identity discrimination classifier to obtain identity information and position change information of the target user.
Optionally, the identity recognizing unit is further configured to: training by taking Doppler characteristic data of different users with known identities as identity discrimination samples to obtain an identity discrimination classifier; and respectively inputting the acquired Doppler characteristic data into an identity discrimination classifier to obtain a user identity corresponding to a sample with the highest correlation in the identity discrimination classifier, and determining identity information and position change information of a target user.
Optionally, the motion state acquisition unit is further configured to input doppler feature data of the target user into the motion state classifier model, so as to obtain the motion states of the target user in different time periods.
Optionally, the motion state acquisition unit is further configured to input the target doppler wave data as a motion state sample into a support vector machine classifier for training, so as to obtain a motion state classifier model.
Optionally, the motion amount calculation unit is further configured to: acquiring position change information of a target user at different times; and determining the movement time and the corresponding movement distance of the target user in each movement state according to the position change information and the movement states of the target user at different times, wherein the movement time is the accumulated time length of the target user in the corresponding movement state.
Fig. 5 is a schematic view of an internal structure of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the electronic device stores an operating system and may also store a program, which when executed by the processor, causes the processor to implement the motion quantity measuring method. The internal memory may also have stored therein a program that, when executed by the processor, causes the processor to perform the motion amount measurement method. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic device, an external keyboard, a touch pad or a mouse, and the like.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device), or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
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| CN201911358623.5A CN111208508B (en) | 2019-12-25 | 2019-12-25 | Exercise quantity measuring method, device and electronic equipment |
| PCT/CN2020/112930 WO2021128923A1 (en) | 2019-12-25 | 2020-09-02 | Exercise amount measurement method and apparatus, and electronic device |
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| CN111208508B (en) | 2024-11-19 |
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