CN114896568A - Swimming data statistical method, device, equipment and medium - Google Patents
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Abstract
The application discloses a swimming data statistical method, a swimming data statistical device, swimming data statistical equipment and a swimming data statistical medium, and relates to the technical field of information fusion. The method is applied to AR swimming goggles, and comprises the following steps: obtaining swimming data; dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times; dividing the turning data according to the length of a sliding window to obtain GM particles; determining the reliability of a turn judgment time window according to the swimming data and the GM particles, wherein the reliability is the reliability representing whether the user turns; when the reliability is judged to be greater than the preset reliability, a turning time node and an estimated covariance are obtained by adopting covariance cross fusion; and outputting the turning time node. According to the method, data which can represent the reliability of a user in the swimming process is marked as turning, data which cannot reach the reliability is not marked as turning, and the accuracy of recording movement data is improved; the user does not need to additionally wear the data recording equipment, and the user experience is improved.
Description
Technical Field
The application relates to the technical field of information fusion, in particular to a swimming data statistical method, a swimming data statistical device, swimming data statistical equipment and a swimming data statistical medium.
Background
With the continuous progress of science and technology, more and more technologies are applied to swimming sports, wherein the estimation of the swimming turn times and time is particularly important, the swimming turn mainly comprises two types of wall-touching turn and rolling turn, the information of people concerned about such as the swimming distance and the swimming calorie can be calculated through the judgment of the turn, more accurate sports data are provided for users, and the user experience is improved.
At present, most swimming data statistical methods in the market are provided by sports bracelets. When a user is swimming, it is often desirable to measure the arm extension of the user, which can help distinguish between minor incidental arm swing movements and true swimming strokes. The sports bracelet obtains swimming data, and when the swimming data are analyzed, parameters generated in the movement process such as the movement time of a user are obtained according to the swimming data. For example: the movement of the arms from the head to the feet of the sports bracelet is regarded as a turn during one swimming, and when the user squats or does an extension exercise in the water, the sports bracelet also records such an action as a turn during one swimming. At this moment, the motion data that the motion bracelet recorded and reachs is inaccurate.
In view of the above problems, it is an endeavor of those skilled in the art to find out how to accurately record the exercise data during swimming.
Disclosure of Invention
The application aims to provide a swimming data statistical method, a swimming data statistical device, equipment and a medium, which are used for accurately recording motion data in a swimming process.
In order to solve the above technical problem, the present application provides a swimming data statistics method, applied to AR swimming goggles, comprising:
obtaining swimming data;
dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times;
dividing the turning data according to the length of a sliding window to obtain GM particles;
determining the reliability of a turn judgment time window according to the swimming data and the GM particles, wherein the reliability is the reliability of representing whether the user turns;
judging whether the reliability is greater than a preset reliability;
if so, acquiring a turning time node and an estimated covariance of the user by adopting covariance cross fusion;
and outputting the turning time node.
Preferably, dividing the swimming data according to the length of the turn judgment time window to obtain turn data comprises:
obtaining the length of swimming data;
dividing the swimming data length by the turning judgment time window length to obtain a first division value;
and rounding the first division value, and dividing the swimming data according to the rounded first division value to obtain turning data.
Preferably, dividing the turn-around data according to the length of the sliding window to obtain the GM particle includes:
dividing the length of the turn-around judgment time window by the length of the sliding window to obtain a second division value;
and rounding the second division value, and dividing the turning data according to the second division value to obtain the GM particles.
Preferably, determining the confidence level characterizing whether the user turns within the turn determination time window according to the swimming data and the GM particle comprises:
initializing GM particles;
determining the probability, the initial reliability and the swimming data amplitude value of whether the user turns according to the GM particles;
updating GM particles according to the probability, the initial reliability and the swimming data amplitude;
and determining the reliability according to the updated GM particles.
Preferably, after outputting the turn-around time node, the method further comprises:
judging whether all the turning times and turning time nodes are output by the first division value turning judgment time windows;
if yes, ending;
if not, returning to the step of obtaining the swimming data.
Preferably, the turn-around judging time window and the sliding window are multiple and do not overlap with each other.
Preferably, after obtaining the swimming data, before dividing the swimming data according to the length of the turn determination time window to obtain the turn data, the method further includes:
and performing Kalman filtering processing on the swimming data.
In order to solve the technical problem, the present application further provides a swimming data statistics apparatus, and the swimming data statistics method applied to the swimming data statistics apparatus includes:
the acquisition module is used for acquiring swimming data;
the first dividing module is used for dividing the swimming data according to the length of the turn-around judging time window to obtain turn-around data, and the turn-around judging time window is used for judging whether a user turns around and counting turn-around times;
the second division module is used for dividing the turning data according to the length of the sliding window to obtain GM particles;
the determining module is used for determining the reliability of whether the user turns according to the swimming data and the GM particles, wherein the reliability represents whether the user turns in the time window of turning judgment;
the judging module is used for judging whether the reliability is greater than the preset reliability;
the obtaining module is used for obtaining a turning time node and an estimated covariance of the user by adopting covariance cross fusion if the reliability obtained by the judging module is greater than the preset reliability;
and the output module is used for outputting the turning time node.
In order to solve the above technical problem, the present application further provides a swimming data statistics apparatus, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the swimming data statistical method when executing the computer program.
In order to solve the technical problem, the present application further provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the swimming data statistical method.
The application provides a swimming data statistical method, is applied to AR swimming goggles, includes: obtaining swimming data; dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times; dividing the turning data according to the length of a sliding window to obtain GM particles; determining the reliability of whether the user turns or not in a turning judgment time window according to the swimming data and the GM particles; judging whether the reliability is greater than a preset reliability; if so, acquiring a turning time node and an estimated covariance of the user by adopting covariance cross fusion; and outputting the turning time node. Because the swimming data are divided, and the corresponding GM particles are obtained, the reliability of the user in the swimming process is further obtained, the data reaching the reliability are marked as one turn, the data which do not reach the reliability are not marked as one turn, and the accuracy of recording the movement data in the swimming process is improved; meanwhile, when the method is applied to AR swimming goggles, a user does not need to additionally wear equipment for recording data, and the user experience is improved.
The application also provides a swimming data statistical device, equipment and medium, and the effect is the same as above.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings needed for 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 that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a flow chart of a statistical method for swimming data according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of another statistical method for swimming data provided by an embodiment of the present application;
FIG. 3 is a block diagram of a swimming data statistics apparatus according to an embodiment of the present application;
fig. 4 is a structural diagram of a swimming data statistics device according to an embodiment of the present application.
Detailed Description
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 a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
The core of the application is to provide a swimming data statistical method, a swimming data statistical device, equipment and a swimming data statistical medium, which can accurately record the motion data in the swimming process.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings.
Fig. 1 is a flowchart of a swimming data statistics method according to an embodiment of the present application. As shown in fig. 1, the swimming data statistical method is applied to AR swimming goggles, in which a 6-channel IMU sensor is disposed, and the sensor is formed by combining two three-axis sensors. The method comprises the following steps:
s10: obtaining swimming data;
obtaining swimming data through a 6-channel IMU sensor and obtaining the length of the swimming data, wherein the sampling frequency of the 6-channel IMU sensor is recorded as
In hertz (Hz). The sampling time for obtaining the swimming data is recorded as S, and the unit is second (S), then the length of the swimming data can be calculated by the following formula:
wherein the swim data acquired for a 6-channel IMU sensor of length L may be represented as
。
Wherein,
an acceleration component representing the x-axis coordinate direction at the k sampling time,
An acceleration component representing the y-axis coordinate direction at the k-sampling time,
An acceleration component representing the z-axis coordinate direction at the k-sampling time;
an angular velocity component representing the x-axis coordinate direction at the k-sampling time,
An angular velocity component representing the y-axis coordinate direction at the k-sampling time,
The angular velocity component representing the z-axis coordinate direction at the time of k-sampling. The acceleration, the angular velocity, the acceleration and the angular velocity may be selected. Considering that some data cannot be well used for judging the swimming turning motion of the user, the optimal implementation mode is to combine the angular speed and the acceleration so as to compensate the data which cannot well judge the swimming turning motion of the user. The swimming data is data acquired in time series. In this embodiment, the three-axis sensor may be one of a three-axis accelerometer, a three-axis magnetometer, a three-axis gyroscope, and a pressure sensor, and the 6-channel IMU sensor may be a combination of one or more of the sensors mentioned above.
S11: dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times;
setting the length of the turn judgment time window as d, and calculating the swimming data according to the number of turn judgment time windows divided by the length of the turn judgment time window by the following formula:
wherein,
and representing a down-rounding function for obtaining a calculation result of the integer, namely taking the maximum integer not greater than x, wherein M is the number of the turn-around judgment time windows and is also a first division value. At this time, for the nth turn determination time window, there is swimming data of length d, which is expressed as:
S12: dividing the turning data according to the length of a sliding window to obtain GM particles;
and setting the length of the sliding window as l, and calculating the turning data according to the number of the sliding windows divided by the length of the sliding window by the following formula:
wherein,
and expressing a down-rounding function for obtaining the calculation result of the integer, namely taking the maximum integer not more than x, wherein N is the number of the sliding windows and the number of the GM particles and is also a second division value. At this time for
A sliding window, where swimming data of length l exist, expressed as:
It should be noted that, in the present embodiment, the turning judgment time window and the sliding window are multiple and do not overlap (mutually exclusive). The sliding window frames a time sequence according to a specified unit length to perform data sampling, thereby calculating data in the frame. The slide block with the designated length slides on the scale, and the data in the slide block can be fed back when the slide block slides one unit. The purpose of setting the sliding window is to segment the time sequence data by using the sliding window with a set length, and sequentially judge that the downward rounding represents that the redundant time sequence is not enough to complete a turn-around action. Wherein a plurality of GM particles is constructed as a random finite set.
S13: determining the reliability of a turn judgment time window according to the swimming data and the GM particles;
the reliability is the reliability of representing whether the user turns around. The swimming data in each sampling sliding window has two conditions of turning and non-turning, a swimming turning variable can be modeled into a random finite set, one sliding window can be regarded as one GM particle, the GM particle is a Gaussian particle, and the condition that a plurality of GM particles exist in one turning judgment time window can be understood. For the first
The swimming data in each sliding window can be expressed as a discrete finite set variable for the swimming turn-around variable at time k, and is recorded as:
. Wherein
Indicating an empty set, i.e. no turn, 1 indicating a turn. Then for the second time
The swimming data in each sliding window was modeled as:
the data described above was modeled as a discrete finite set.
S14: judging whether the reliability is greater than a preset reliability;
if yes, the flow proceeds to step S15: obtaining a turning time node and an estimated covariance of a user by adopting covariance cross fusion;
after the determination, the above-mentioned discrete finite set can be expressed as follows:
it can be seen that, if the data in the kth transition determination time window is represented as 1, it indicates that one transition occurs in the kth transition determination time window. It should be noted that the determination occurs in the gaussian mixture probability hypothesis density estimation model.
S16: and outputting the turning time node.
The turn-around times can be counted and collected and obtained as the turn-around occurrence in which turn-around judgment time window is obtained, and the turn-around time node is determined in the sliding window in the corresponding judgment time window and finally output. The output form can be "turning time node is 14: 29", "turning times is 12 times in total", etc., the above mentioned output form is only one of many embodiments, and the output form is not limited, and the implementation form can be determined according to the specific implementation scenario.
Will be first
Each sliding window is used as a GM particle, and each GM particle contains three data, which are: weight w, distribution mean m, distribution covariance p. Wherein the weight represents the reliability of the turning motion, the distribution mean represents the mean estimation of the turning time point,The distribution covariance represents the degree of dispersion of the turning time point, i.e., can be expressed as follows:
the calculation formula of the weight w, the distribution mean m and the distribution covariance p is as follows:
wherein,
in order to be the a-priori weights,
is at the same time
The amplitude of the swim data at the time of sampling,
for all the time in the sliding window, the nth turn-around judgment time window
Of a sliding window
Is represented as follows:
the GM particle set for the nth turn-around decision time window is represented as follows:
as shown in fig. 2, on the basis of the above embodiment, as a more preferred embodiment, dividing the swimming data according to the length of the turn determination time window to obtain the turn data includes:
obtaining the length of swimming data;
dividing the swimming data length by the turning judgment time window length to obtain a first division value;
and rounding the first division value, and dividing the swimming data according to the rounded first division value to obtain turning data.
Setting the length of the turn judgment time window as d, and calculating the swimming data according to the number of turn judgment time windows divided by the length of the turn judgment time window by the following formula:
wherein,
and representing a down-rounding function for obtaining a calculation result of the integer, namely taking the maximum integer not greater than x, wherein M is the number of the turn-around judgment time windows and is also a first division value. At this time, for the nth turn determination time window, there is swimming data of length d, which is expressed as:
On the basis of the above embodiment, as a more preferred embodiment, dividing the turning data by the length of the sliding window to obtain GM particles includes:
dividing the length of the turn-around judgment time window by the length of the sliding window to obtain a second division value;
and rounding the second division value, and dividing the turning data according to the second division value to obtain the GM particles.
And setting the length of the sliding window as l, and calculating the turning data according to the number of the sliding windows divided by the length of the sliding window by the following formula:
wherein,
and expressing a down-rounding function for obtaining the calculation result of the integer, namely taking the maximum integer not more than x, wherein N is the number of the sliding windows and the number of the GM particles and is also a second division value. At this time for
A sliding window, where swimming data of length l exist, expressed as:
On the basis of the above embodiment, as a more preferable embodiment, the determining the confidence level indicating whether the user turns within the turn judgment time window according to the swimming data and the GM particle includes:
initializing GM particles;
determining the probability, the initial reliability and the swimming data amplitude value of whether the user turns according to the GM particles;
updating GM particles according to the probability, the initial reliability and the swimming data amplitude;
and determining the reliability according to the updated GM particles.
In the nth processing period, inputting swimming data of the nth turn judgment time window.
The swimming data at this time are expressed as:
and the first
The individual sliding windows may be represented as one GM particle, where the GM particle is represented as:
for the first
The GM particles are initialized according to the following formula:
calculating the detection probability of turning according to GM particles
The formula is as follows:
wherein,
is sigmoid function, and H is preset confidence.
For the nth turn-around judgment time window, the reliability of representing whether the user turns around can be represented as:
for GM particle updates:
wherein k represents
When the calculated confidence level is lower than the preset confidence level, the updated posterior GM particles are expressed as:
the updated reliability of the nth turn-around judgment time window is as follows:
in the nth processing period, obtaining the reliability of the updated nth turn-around judgment time window, judging the magnitude relation between the reliability and the preset reliability, and when the reliability is greater than the preset reliability, performing covariance fusion on the posterior GM particles obtained after updating, wherein the formula is as follows:
wherein,
represents the turn-around time point of the nth turn-around judgment time window,
indicating the degree of dispersion of the turn-around time points.
On the basis of the above embodiment, as a more preferred embodiment, after outputting the turn-around time node, the method further includes:
judging whether the first division value turn judgment time window outputs turn times and turn time nodes or not;
if yes, ending;
if not, returning to the step of obtaining swimming data.
In order to make the obtained data more accurate, all the turn-around judgment time windows need to be traversed once, so that the data can be obtained more accurately, and the user experience is improved.
On the basis of the above embodiment, as a more preferred embodiment, after obtaining the swimming data, before dividing the swimming data according to the length of the turn determination time window to obtain the turn data, the method further includes:
and performing Kalman filtering processing on the swimming data. In order to remove clutter. In addition, it should be noted that the noise of the clutter can also be avoided by using a particle filter.
A specific example of obtaining GM particles by dividing swimming data according to the length of the turn determination time window to obtain turn data and dividing the turn data according to the length of the sliding window is given below, and the process is as follows:
the length of the swimming data provided by the IMU sensor of one data 6 channel is 5532, the length of the turn judgment window is 200, after rounding down, the total number of 27 turn judgment windows is obtained, and for one turn judgment windowTurn-around data, corresponding to turn-around and non-turn-around situations, i.e. such data may be described by a random finite set, denoted as
Then, for this whole piece of data there are:
in a turn-around judgment window, the length of the data sample sliding window is 50, and 4 data sample sliding windows are in total. The data in each data sample sliding window may be characterized by a GM particle that contains three dimensions, weight, distribution mean m, and distribution covariance p. In one transition determination window, a plurality of GM particles are included, which constitute a GM particle set. And obtaining the final turning time point according to the formula and the corresponding steps.
Therefore, swimming data are divided, corresponding GM particles are obtained, the reliability of a representation user in the swimming process is obtained, data reaching the reliability are marked as one turn, data not reaching the reliability are not marked as one turn, and the accuracy of recording the movement data in the swimming process is improved; meanwhile, when the method is applied to AR swimming goggles, a user does not need to additionally wear equipment for recording data, and the user experience is improved.
In the above embodiments, the swimming data statistics method is described in detail, and the present application also provides embodiments corresponding to the swimming data statistics apparatus. It should be noted that the present application describes the embodiments of the apparatus portion from two perspectives, one from the perspective of the function module and the other from the perspective of the hardware.
Fig. 3 is a structural diagram of a swimming data statistics device according to an embodiment of the present application. As shown in fig. 3, the present application also provides a swimming data statistics apparatus, comprising:
an acquisition module 30 for acquiring swimming data;
the first dividing module 31 is configured to divide the swimming data according to the length of the turn-around judgment time window to obtain turn-around data, where the turn-around judgment time window is used to judge whether a user turns around and count the turn-around times;
a second dividing module 32, configured to divide the turning data according to the length of the sliding window to obtain GM particles;
the determining module 33 is configured to determine the reliability of the turn determination time window according to the swimming data and the GM particles, where the reliability is a reliability representing whether the user turns;
the judging module 34 is used for judging whether the reliability is greater than the preset reliability;
the obtaining module 35 is configured to obtain a turning time node and an estimated covariance of the user by using covariance cross fusion if the reliability obtained by the judging module is greater than a preset reliability;
and an output module 36, configured to output the turn-around time node.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
Fig. 4 is a block diagram of a swimming data statistics apparatus according to an embodiment of the present application, and as shown in fig. 4, the swimming data statistics apparatus includes:
a memory 40 for storing a computer program;
a processor 41 for implementing the steps of the swimming data statistics method as mentioned in the above embodiments when executing the computer program.
The swimming data statistics device provided by the embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like.
In some embodiments, the swimming data statistics device may further include a display screen, an input/output interface, a communication interface, a power source, and a communication bus.
Those skilled in the art will appreciate that the configuration shown in FIG. 4 does not constitute a limitation of the swim data statistics apparatus and may include more or fewer components than those shown.
The swimming data statistical device provided by the embodiment of the application comprises a memory 40 and a processor 41, wherein the processor 41 can realize a swimming data statistical method when executing a program stored in the memory 40.
Finally, the application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.
It is to be understood that if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application, or all or part of the technical solutions. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (Read-Only Memory), a ROM, a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The swimming data statistical method, the swimming data statistical device, the swimming data statistical equipment and the swimming data statistical medium are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are 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.
Claims (10)
1. A swimming data statistical method is applied to AR swimming goggles and comprises the following steps:
obtaining swimming data;
dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times;
dividing the turning data according to the length of a sliding window to obtain GM particles;
determining the reliability of the turn judgment time window according to the swimming data and the GM particles, wherein the reliability is the reliability representing whether the user turns;
judging whether the reliability is greater than a preset reliability;
if so, acquiring a turning time node and an estimated covariance of the user by adopting covariance cross fusion;
and outputting the turning time node.
2. The statistical method for swimming data according to claim 1, wherein the dividing the swimming data according to the length of the turn judgment time window to obtain turn data comprises:
acquiring the length of the swimming data;
dividing the swimming data length by the turning judgment time window length to obtain a first division value;
and rounding the first division value, and dividing the swimming data according to the rounded first division value to obtain the turning data.
3. The statistical method for swimming data according to claim 1, wherein the dividing the turn-around data according to the length of the sliding window to obtain GM particles comprises:
dividing the length of the turn-around judging time window by the length of the sliding window to obtain a second division value;
and rounding the second division value, and dividing the turning data according to the second division value to obtain the GM particles.
4. The statistical method of swimming data according to claim 1, wherein the determining the confidence level characterizing whether the user turns within the turn determination time window from the swimming data and the GM particles comprises:
initializing the GM particles;
determining the probability, the initial reliability and the swimming data amplitude value which represent whether the user turns according to the GM particles;
updating the GM particles according to the probability, the initial reliability and the swimming data amplitude;
and determining the reliability according to the updated GM particles.
5. The statistical method of swimming data according to claim 2, further comprising, after said outputting the turn time node:
judging whether the turning judgment time windows of the first division values all output the turning times and the turning time nodes;
if yes, ending;
if not, returning to the step of obtaining the swimming data.
6. The statistical method for swimming data according to claim 1, wherein the turn-around judging time window and the sliding window are multiple and do not overlap each other.
7. The statistical method for swimming data according to claim 1, wherein after the obtaining of the swimming data, before the dividing the swimming data according to the length of the turn-around judgment time window to obtain the turn-around data, the statistical method further comprises:
and performing Kalman filtering processing on the swimming data.
8. A swimming data statistics device, characterized in that, the swimming data statistics method applied to any one of claims 1 to 7 comprises:
the acquisition module is used for acquiring swimming data;
the first dividing module is used for dividing the swimming data according to the length of a turn-around judging time window to obtain turn-around data, wherein the turn-around judging time window is used for judging whether a user turns around or not and counting turn-around times;
the second division module is used for dividing the turning data according to the length of a sliding window to obtain GM particles;
the determining module is used for determining the reliability of representing whether the user turns or not in the turning judging time window according to the swimming data and the GM particles;
the judging module is used for judging whether the reliability is greater than the preset reliability;
the obtaining module is used for obtaining a turning time node and an estimated covariance of the user by adopting covariance cross fusion if the reliability obtained by the judging module is greater than a preset reliability;
and the output module is used for outputting the turning time node.
9. A swimming statistics device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the statistical method of swimming data according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the statistical method of swimming data according to any one of claims 1 to 7.
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