CN110006445B - Running distance calculation method and device - Google Patents
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
Abstract
The invention relates to the technical field of wearable equipment, in particular to a running distance calculation method and device, which are applied to the wearable equipment, and an accelerometer sensor is arranged in the wearable equipment. Specifically, user motion data acquired by an accelerometer sensor is received, a first user motion characteristic and a second user motion characteristic are obtained based on user motion data analysis, wherein the first user motion characteristic is the installation position of the wearable device, the second user motion characteristic is the running state of the user, and then the first user motion characteristic and the second user motion characteristic can be calculated according to a preset regression model to obtain the running distance of the user. The scheme is based on the sensor to detect the motion data, is calculated according to the algorithm, does not depend on communication equipment, is suitable for various scenes, and has strong practicability.
Description
Technical Field
The invention relates to the technical field of wearable equipment, in particular to a running distance calculation method and device.
Background
With the continuous progress of science and technology, people can monitor their own exercise data by using many electronic devices and analyze their own exercise state by using the data, wherein the running distance is a relatively important data. The running distance is calculated mainly by a GPS (global positioning system) based method, and the distance is calculated by using the change of GPS coordinates before and after the runner moves, but the method has high requirement on environment, and results are inaccurate once signals are interfered.
However, the running distance calculation method based on the GPS is widely applied to devices such as mobile phones and sports watches, and requires that the devices have a GPS communication module to communicate with a satellite in real time to acquire the geographic coordinates of the current position and calculate the running distance through a corresponding algorithm. In terms of experience, the GPS-based computing method has high requirements on signal quality, and the use scene is limited.
Disclosure of Invention
The invention aims to provide a running distance calculation method which is independent of communication equipment, low in cost and capable of being used in any scene.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a running distance calculation method, which is applied to a wearable device, where an accelerometer sensor is disposed in the wearable device, and the method includes: receiving user motion data collected by the accelerometer sensor; analyzing to obtain a first user motion characteristic and a second user motion characteristic based on the user motion data, wherein the first user motion characteristic is an installation position of the wearable device, and the second user motion characteristic is a running state of the user; and calculating the first user motion characteristic and the second user motion characteristic according to a preset regression model to obtain the running distance of the user.
In a second aspect, an embodiment of the present invention further provides a running distance calculating device, which is applied to a wearable device, where an accelerometer sensor is disposed in the wearable device, and the device includes: the receiving and transmitting module is used for receiving the user motion data acquired by the accelerometer sensor; the processing module is used for analyzing and obtaining a first user motion characteristic and a second user motion characteristic based on the user motion data, wherein the first user motion characteristic is the installation position of the wearable device, and the second user motion characteristic is the running state of the user; the processing module is further used for calculating the first user motion characteristic and the second user motion characteristic according to a preset regression model to obtain a user running distance.
The running distance calculation method and device provided by the embodiment of the invention are applied to wearing equipment, and an accelerometer sensor is arranged in the wearing equipment. Specifically, user motion data acquired by an accelerometer sensor is received, a first user motion characteristic and a second user motion characteristic are obtained based on user motion data analysis, wherein the first user motion characteristic is the installation position of the wearable device, the second user motion characteristic is the running state of the user, and then the first user motion characteristic and the second user motion characteristic can be calculated according to a preset regression model to obtain the running distance of the user. The scheme is based on the sensor to detect the motion data, is calculated according to the algorithm, does not depend on communication equipment, is suitable for various scenes, and has strong practicability.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart illustrating a running distance calculating method according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of a waveform provided by an embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating functional modules of a running distance calculating device according to an embodiment of the present invention.
The figure is as follows: 200-running distance calculating means; 210-a transceiver module; 220-processing module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
In this scheme, the user need tie up a wearing equipment on the vamp when running, including processing chip and accelerometer sensor in this wearing equipment, the accelerometer sensor will gather the acceleration data transmission that the user ran the in-process in real time and to handle the chip to acceleration data and handle the distance of running that obtains the user to can transmit and show on the electronic equipment such as bracelet or the cell-phone of being connected with this wearing equipment and look over in the user. According to the scheme, the user motion data are detected based on the accelerometer sensor, the characteristic extraction and the distance calculation are performed on the user motion data based on the built-in algorithm, the communication module is not relied on, the cost is lower, the method can be generally applied to more scenes, and the practicability is improved.
Fig. 1 is a schematic flow chart of a running distance calculating method according to an embodiment of the present invention, including:
and S110, receiving user motion data collected by the accelerometer sensor.
Specifically, the accelerometer sensor collects user motion data during running of the user, where the user motion data includes forward motion acceleration data and upward motion acceleration data during running of the user, and for convenience of description, the forward motion acceleration data is represented by x, and the upward motion acceleration data is represented by z.
Further, the accelerometer sensor sends the detected user motion data to a processing chip in the wearable device for data processing.
The processing chip performs smooth filtering on the received forward motion acceleration data and the received upward motion acceleration data to eliminate noise in the user motion data. The specific way of smoothing and filtering the data is as follows:
the value of N is 8, that is, the smooth filtered forward motion acceleration is obtained by averaging every 8 forward motion acceleration data, and the smooth filtered upward motion acceleration data is obtained by averaging every 8 upward motion acceleration data. It is easy to understand that the value of N can be set according to actual needs.
And S120, analyzing and obtaining a first user motion characteristic and a second user motion characteristic based on the user motion data, wherein the first user motion characteristic is the installation position of the wearable device, and the second user motion characteristic is the running state of the user.
First, a first user motion characteristic is obtained based on user motion data analysis.
Specifically, the first user motion characteristic is an installation position of the wearable device, and the user installing the wearable device on the shoe upper at different inclination angles will affect the acquired upward motion acceleration data and forward motion acceleration data, so that it is necessary to determine the installation position of the wearable device as the first user motion characteristic. Because the installation position of wearing equipment is the discrete variable, this scheme further characterizes the installation angle of wearing equipment through the dummy variable, represents through two dummy variables x1 and x2 respectively. The processing chip is preset with an initial value range of the upward motion acceleration data and corresponding dummy variables, such as an x1 value of 1 and an x2 value of 0 when the upward motion acceleration data is [0,300 ]; when the upward movement acceleration data is (300,600], the value of x1 is 0 and the value of x2 is 1, and when the upward movement acceleration data is (600, 1000), the value of x1 is 0 and the value of x2 is 0.
Further, a specific numerical value of the upward movement acceleration data in the initial predetermined number of steps is calculated, for example, a specific numerical value of the upward movement acceleration data of the user in the initial two steps (that is, data collected in the two steps is taken as a basis) is calculated, and an initial value range in which the specific data is located is determined, so that a dummy variable numerical value corresponding to the initial value range in which the specific numerical value is located is determined as the first user movement characteristic. If the specific value of the upward movement acceleration data is 200, the upward movement acceleration data falls within an initial value range [0,300], and a dummy variable x1 corresponding to the initial value range is selected to be 1, and x2 is 0 to serve as the first user movement characteristic, wherein the dummy variable can represent the installation angle of the wearing equipment currently worn by the user.
And then, analyzing the user motion data to obtain a second user motion characteristic.
Specifically, the second user motion characteristic is a user running state, and the user running state specifically includes a user running amplitude and a user running speed. The second user motion characteristic calculation mode is as follows:
first, forward motion acceleration data and upward motion acceleration data within a predetermined number of steps are fused to obtain two fusion data.
And carrying out fusion processing on the forward motion acceleration data and the upward motion acceleration data which are subjected to smooth filtering in pairs, so that the finally obtained fusion data can give consideration to the characteristics of the forward motion acceleration data and the upward motion acceleration data. The fused data is passed through miThe fusion mode is as follows:
mi＝zi-xi
secondly, performing waveform detection on the plurality of fusion data to obtain a plurality of peak data and trough data.
Fig. 2 is a waveform diagram representing the result of waveform detection according to a plurality of fusion data, the waveform diagram representing the result of waveform detection according to two steps of motion data of the user, the abscissa representing time, and the ordinate representing acceleration, wherein p1, p2, and p3 are selected peak data and valley data.
And finally, calculating the data of the plurality of wave crests and wave troughs according to a preset rule to obtain a second user motion characteristic.
The second user motion characteristic includes two aspects, namely the user running amplitude and the user running speed.
The running amplitude of the user is calculated in the following mode:
x3＝(|m[p3]-m[p2]|+|m[p1]-m[p2]|)/23000
the ordinate of p1, p2 and p3 is substituted in the formula to obtain x3, and x3 represents the running amplitude of the user.
In addition, the running speed of the user is calculated by the following steps:
x4＝(p3-p1)/60
the formula brings the abscissa of p1 and p3 into calculation to obtain x4, wherein x4 represents the running speed of the user.
Therefore, the processing chip finally obtains four user motion characteristics of x1, x2, x3 and x4 based on the analysis of the user motion data, wherein x1 and x2 are first user motion characteristics representing the installation state of the wearable device, and x3 and x4 are second user motion characteristics representing the motion state of the user.
And S130, calculating the first user motion characteristic and the second user motion characteristic according to a preset regression model to obtain the running distance of the user.
The predetermined regression model is obtained by training, and the training method comprises the following steps:
the method comprises the steps of collecting a large number of user motion data samples in the running process of a user, wherein each user motion data sample comprises upward motion acceleration data and forward motion acceleration data, performing smooth filtering, data fusion, waveform detection and feature calculation on the upward motion acceleration data and the forward motion acceleration data according to the same method to determine a first user motion feature and a second user motion feature, substituting the first user motion feature and the second user motion feature into an initial regression model for calculation, and outputting an initial user running distance. Although a large amount of samples are trained, the initial running distance of the user is still different from the actual running distance, and in order to ensure the prediction accuracy of the regression model, the scheme also improves the initial regression model, namely, the calculation error of the regression model is made up. The loss function of the linear regression is:
wherein h isθ(x)＝θTX is a representation of the regression model. For the minimization problem (error minimization) of J (θ), which needs to be solved using a gradient descent method, the iterative formula of θ:
after the optimal model parameter theta is obtained through iteration, the running distance can be obtained by multiplying the parameter with the first user motion characteristic and the second user motion characteristic, and the initial regression model can be corrected in such a way that the running distance can be accurately calculated through the preset regression model obtained through training.
Furthermore, in the running process of the user, the running distance is calculated for the first user motion characteristic and the second user motion characteristic by using a preset regression model in the following mode: and directly inputting the first user motion characteristic and the second user motion characteristic into the trained preset regression model to obtain the running distance of the user. It should be noted that, in the scheme, since each two steps are used as a unit for processing the user exercise data in the early stage, the running distance of the user calculated by the model is also the running distance of the two steps of the user, which is easy to understand, and for convenience of calculation, the analysis and distance measurement can be directly performed by using one step as a unit or using multiple steps as a unit. And finally, accumulating and summing the running distances of the user obtained in each stage to obtain the total running distance of the user in the whole running process.
In addition, the accelerometer sensor detects data in the user movement process, noise or other unstable factors may occur, and then the user running distance obtained through final calculation is abnormal, and in order to prevent the abnormal running distance, a preset stride comparison table is stored in the processing chip. And then after the processing chip calculates the running distance of the user, comparing the running distance of the user with a preset stride comparison table, if the difference value between the running distance of the user and the corresponding stride in the preset stride comparison table is larger than a threshold value, determining that the calculation is wrong, and replacing the running distance of the user with the stride in the preset stride comparison table.
The preset stride comparison table is determined according to the height and the speed of the user and is prestored in the processing chip, the stride length in the preset stride comparison table is consistent with the step number of the user motion data analysis in the calculation method, and if the user motion data in the calculation method is based on every two steps, the stride in the preset stride comparison table corresponds to the two-step length of the user.
In practical application, a user only needs to configure wearing equipment on a vamp before running, an accelerometer sensor in the wearing equipment measures user motion data in the running process of the user and sends the user motion data to a processing chip in the wearing equipment in real time, the processing chip processes the received user motion data according to the method flow to obtain the total running distance of the user, and then the total running distance of the user is directly displayed on the wearing equipment or sent to other electronic equipment (such as a bracelet and a mobile phone) connected with the wearing equipment for displaying. The scheme does not need an additional communication module, can be realized based on a built-in algorithm, ensures the precision and can be widely applied to different scenes.
Referring to fig. 3, a functional module of a running distance calculating device 200 according to an embodiment of the present invention is shown, and the device includes a transceiver module 210 and a processing module 220.
And the transceiver module 210 is configured to receive the user movement data acquired by the accelerometer sensor.
In the embodiment of the present invention, S110 may be performed by the transceiver module 210.
The processing module 220 is configured to obtain a first user motion characteristic and a second user motion characteristic based on the user motion data analysis, where the first user motion characteristic is an installation location of the wearable device, and the second user motion characteristic is a running state of the user.
In an embodiment of the present invention, S120 may be performed by the processing module 220.
The processing module 220 is further configured to calculate the running distance of the user according to the predetermined regression model for the first user motion characteristic and the second user motion characteristic.
In an embodiment of the present invention, S130 may be performed by the processing module 220.
Since the running distance calculating method has been described in detail in the running distance calculating method section, it will not be described in detail here.
In summary, the running distance calculating method and device provided by the embodiments of the present invention are applied to a wearable device, and an accelerometer sensor is disposed in the wearable device. Specifically, user motion data acquired by an accelerometer sensor is received, a first user motion characteristic and a second user motion characteristic are obtained based on user motion data analysis, wherein the first user motion characteristic is the installation position of the wearable device, the second user motion characteristic is the running state of the user, and then the first user motion characteristic and the second user motion characteristic can be calculated according to a preset regression model to obtain the running distance of the user. The scheme is based on the sensor to detect the motion data, is calculated according to the algorithm, does not depend on communication equipment, is suitable for various scenes, and has strong practicability.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. It is noted that, herein, 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 above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A running distance calculation method is applied to wearable equipment, the wearable equipment is installed on an upper, an accelerometer sensor is arranged in the wearable equipment, and the method is characterized by comprising the following steps:
receiving user motion data collected by the accelerometer sensor; the user motion data comprises forward motion acceleration data and upward motion acceleration data during running of the user;
selecting a dummy variable value corresponding to a preset range in which the upward movement acceleration data is located in an initial preset step number as a first user movement characteristic, wherein the preset range corresponds to the dummy variable value one by one, the first user movement characteristic is an installation position of the wearable device, and the installation position represents an installation angle of the wearable device;
fusing the forward motion acceleration data and the upward motion acceleration data within a preset step number to obtain a plurality of fused data;
carrying out waveform detection on the plurality of fusion data to obtain a plurality of peak and trough data;
calculating the plurality of peak and valley data according to a preset rule to obtain a second user motion characteristic, wherein the second user motion characteristic is a user running state, and the user running state represents a user running amplitude and a user running speed;
and calculating the first user motion characteristic and the second user motion characteristic according to a preset regression model to obtain the running distance of the user.
2. The method of claim 1, wherein the method further comprises:
and comparing the running distance of the user with a preset stride comparison table, and if the difference value between the running distance of the user and the stride in the preset stride comparison table is greater than a threshold value, selecting the stride in the preset stride comparison table to replace the running distance of the user, wherein the preset stride comparison table is determined according to the height and the speed of the user.
3. The method of claim 1, wherein the method further comprises:
and accumulating and summing the running distances of the user obtained in each stage to obtain the total running distance in the running process of the user.
4. The method of claim 1, further comprising, after receiving the user motion data collected by the accelerometer sensor, the steps of:
and performing smooth filtering on the user motion data.
5. The utility model provides a distance of running calculation device is applied to wearing equipment, wearing equipment installs in the vamp, be provided with accelerometer sensor in the wearing equipment, its characterized in that, the device includes:
the receiving and transmitting module is used for receiving the user motion data acquired by the accelerometer sensor; the user motion data comprises forward motion acceleration data and upward motion acceleration data during running of the user;
the processing module is used for selecting a dummy variable number value corresponding to a preset range in which the upward movement acceleration data is located in an initial preset step number as a first user movement characteristic, the preset range corresponds to the dummy variable number value one by one, the first user movement characteristic is an installation position of the wearable device, and the installation position represents an installation angle of the wearable device;
the processing module is further used for fusing the forward motion acceleration data and the upward motion acceleration data within a preset step number to obtain a plurality of fused data;
the processing module is further used for carrying out waveform detection on the plurality of fusion data to obtain a plurality of wave crest and wave trough data;
the processing module is further used for calculating the plurality of peak and valley data according to a preset rule to obtain a second user motion characteristic, wherein the second user motion characteristic is a user running state, and the user running state represents a user running amplitude and a user running speed;
the processing module is further used for calculating the first user motion characteristic and the second user motion characteristic according to a preset regression model to obtain a user running distance.
6. The apparatus of claim 5, wherein the processing module is further to:
and comparing the running distance of the user with a preset stride comparison table, and if the difference value between the running distance of the user and the stride in the preset stride comparison table is greater than a threshold value, selecting the stride in the preset stride comparison table to replace the running distance of the user, wherein the preset stride comparison table is determined according to the height and the speed of the user.
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| CN112308171A (en) * | 2020-11-23 | 2021-02-02 | 浙江天行健智能科技有限公司 | Vehicle position prediction modeling method based on simulated driver |
| CN113810845B (en) * | 2021-09-17 | 2022-10-14 | 广州悦跑信息科技有限公司 | Effective running distance statistical method and system based on multi-angle monitoring |
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