A motion distance measurement method and wearable device

Abstract

The embodiment of the present invention discloses a motion ranging method and a wearable device, which can solve the problem of how to achieve accurate motion ranging, which is an urgent problem to be solved. The method includes: continuously acquiring a three-axis acceleration signal within a first time length; filtering the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtracting the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculating the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length; calculating the first step length of the user according to the acceleration component signal and the height of the user; calculating the movement distance of the user within the first time length according to the first step length and the number of steps within the first time length.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of intelligent wearable devices, and in particular to a motion ranging method and a wearable device. Background Art

[0002] When running, a person's posture changes at a high speed, and it is difficult to obtain high-precision posture through posture calculation algorithms. In addition, for users wearing smart watches, the movement of their arms contains a lot of noise and signals unrelated to motion during running. Robust signal processing methods are required to achieve accurate motion ranging. Therefore, how to achieve accurate motion ranging is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the embodiments of the present invention provide a motion distance measurement method and a wearable device to achieve accurate motion distance measurement. The embodiments of the present invention are implemented as follows:

[0004] In a first aspect, a motion ranging method is provided, which is applied to a wearable device and includes:

[0005] Continuously acquiring three-axis acceleration signals within a first time period;

[0006] Filtering the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal;

[0007] Subtracting the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal;

[0008] Calculating the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length;

[0009] Calculating the step length of the user according to the acceleration component signal and the height of the user;

[0010] The movement distance of the user within the first time period is calculated according to the first step length and the number of steps within the first time period.

[0011] Optionally, before calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes:

[0012] Performing peak detection on the acceleration component signal within the first time length;

[0013] The number of peak values ​​of the acceleration component signal is determined, and the number of steps in the first time period is determined according to the number of peak values ​​of the acceleration component signal.

[0014] Optionally, calculating the first step length of the user according to the acceleration component signal and the height of the user includes:

[0015] Calculate the first step length of the user according to the acceleration component signal, the height of the user, and the following step length calculation formula;

[0016] The step length calculation formula is:

[0017] k1 and K2 are hyperparameters, a i is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to the a i , h is the sampling time, and h is the height of the user.

[0018] Optionally, after calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes:

[0019] Calculating a span distance of a Global Positioning System (GPS) signal within the first time period, wherein the span distance is a moving distance indicated by the GPS signal within the first time period;

[0020] If it is determined that the GPS signal within the first time period is a credible signal, calculating a second step length according to the span distance and the number of steps within the first time period;

[0021] Compare the second step length with the first step length to obtain a comparison result;

[0022] According to the comparison result, k1 and / or K2 in the step length calculation formula are adjusted.

[0023] Optionally, the determining that the GPS signal within the first duration is a credible signal includes:

[0024] Fitting the GPS signal within the first time period by the least square method to calculate the straight line fitting error; if the difference between the span distance and the first preset distance is less than a preset value, and / or the straight line fitting error is less than a preset error, then determining that the GPS signal within the first time period is a credible signal;

[0025] or,

[0026] The signal strength of the GPS signal within the first duration is detected. If the signal strength is greater than a preset strength and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first duration is a credible signal.

[0027] Optionally, after calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes:

[0028] Detecting received signal strengths (RSSIs) of multiple wireless network (Wireless Fidelity, WIFI) signals received within the first time period;

[0029] If the change in the received signal strength RSSI of the multiple WIFI signals is greater than a preset change, determining that the movement of the user within the first time period is a valid movement;

[0030] If the change amount of the RSSI of the plurality of WIFI signals is less than a preset change amount, it is determined that the movement of the user within the first time period is invalid movement.

[0031] Optionally, after calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes:

[0032] Detecting RSSIs of multiple WIFI signals received within the first time period;

[0033] Calculating a span distance of the GPS signal within the first duration, wherein the span distance is a moving distance indicated by the GPS signal within the first duration;

[0034] If the span distance is greater than or equal to a second preset distance, and the change in the RSSI of the plurality of WIFI signals is greater than a preset change, it is determined that the movement of the user within the first time period is a valid movement;

[0035] If the span distance is less than a second preset distance, and the change in the RSSI of the plurality of WIFI signals is less than a preset change, it is determined that the movement of the user within the first time period is invalid movement.

[0036] In a second aspect, a wearable device is provided, the wearable device comprising:

[0037] An acquisition module, used for continuously acquiring a three-axis acceleration signal within a first time period;

[0038] A processing module is used to filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculate the component of the motion acceleration signal in the gravity direction to obtain the acceleration component signal within the first time length; calculate the user's step length according to the acceleration component signal and the user's height; calculate the user's movement distance within the first time length according to the first step length and the number of steps within the first time length.

[0039] In a third aspect, a wearable device is provided, comprising:

[0040] A memory storing executable program code;

[0041] a processor coupled to the memory;

[0042] The processor calls the executable program code stored in the memory to execute all or part of the steps in any one of the methods disclosed in the first aspect of the embodiment of the present invention.

[0043] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute all or part of the steps in any one of the methods disclosed in the first aspect of the embodiment of the present invention. The computer-readable storage medium includes a ROM / RAM, a magnetic disk or an optical disk, etc.

[0044] According to a fifth aspect, a computer program product is provided. When the computer program product is run on a computer, the computer is caused to execute part or all of the steps of any one of the methods according to the first aspect.

[0045] According to a sixth aspect, an application publishing platform is provided, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps of any one method of the first aspect.

[0046] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0047] A motion ranging method provided in an embodiment of the present invention is applied to a wearable device, and continuously obtains a three-axis acceleration signal within a first time length; filters the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtracts the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculates the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length; calculates the user's step length based on the acceleration component signal and the user's height; and calculates the user's motion distance within the first time length based on the first step length and the number of steps within the first time length. Through this scheme, in this scheme, the three-axis acceleration is preprocessed (the three-axis acceleration signal is filtered by a low-pass filter to obtain the corresponding gravity acceleration signal; the corresponding gravity acceleration signal is subtracted from the three-axis acceleration signal to obtain the motion acceleration signal, and the component of the motion acceleration signal in the gravity direction is calculated), and peak detection is performed based on the acceleration component signal obtained after preprocessing. In this way, during the exercise process, the interference of some signals can be eliminated, and the accurate number of steps can be calculated. Combined with the user's own height parameters, the calculated exercise distance can be made more in line with the actual situation, thereby achieving accurate motion ranging. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The following is a flow chart of a motion ranging method provided by an embodiment of the present invention. Figure 1 ;

[0050] Figure 2 is a schematic diagram of a lower limb movement provided by an embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of an arm movement provided by an embodiment of the present invention;

[0052] Figure 4 The following is a flow chart of a motion ranging method provided by an embodiment of the present invention. Figure 2 ;

[0053] Figure 5 It is a specific flow chart of an optimization step length calculation formula provided by an embodiment of the present invention;

[0054] Figure 6 It is a specific flow chart of judging the validity of a motion process provided by an embodiment of the present invention;

[0055] Figure 7 is a schematic diagram of the structure of a wearable device provided by an embodiment of the present invention;

[0056] Figure 8 Schematic diagram of the hardware structure of a wearable device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The terms "first" and "second" in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order of objects. For example, a first resolution and a second resolution are used to distinguish different resolutions rather than to describe a specific order of resolutions.

[0059] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatus.

[0060] It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0061] In the related technology, during running, a person's posture is in a high-speed changing process, and it is difficult to obtain a high-precision posture through a posture solution algorithm. In addition, for users wearing smart watches, the movement of their arms during running contains a lot of noise and signals unrelated to the movement. A robust signal processing method is required to achieve accurate motion ranging. Therefore, how to achieve accurate motion ranging is an urgent problem to be solved.

[0062] The embodiment of the present invention provides a motion ranging method and a wearable device. In the scheme, the acquired three-axis acceleration signal is preprocessed (the three-axis acceleration signal is filtered by a low-pass filter to obtain a corresponding gravity acceleration signal; the corresponding gravity acceleration signal is subtracted from the three-axis acceleration signal to obtain a motion acceleration signal, and the component of the motion acceleration signal in the gravity direction is calculated), and peak detection is performed according to the acceleration component signal obtained after preprocessing. In this way, during the exercise process, the interference of some signals can be eliminated, and the accurate number of steps can be calculated. In combination with the user's own height parameters, the calculated motion distance can be made more in line with the actual situation, thereby realizing accurate motion ranging.

[0063] The wearable device involved in the embodiment of the present invention may be a smart watch, a smart bracelet, a phone watch, a smart anklet, a smart earring, a smart necklace, a smart headset, etc., and the embodiment of the present invention is not limited thereto.

[0064] Optionally, the wearable device involved in the embodiment of the present invention is a wrist wearable device, such as a phone watch or a smart bracelet.

[0065] The execution subject of the motion ranging method provided by the embodiment of the present invention can be the above-mentioned wearable device, or it can be a functional module and / or functional entity in the wearable device that can implement the motion ranging method. The specific can be determined according to actual use requirements, and the embodiment of the present invention is not limited. The following takes a wearable device as an example to exemplarily illustrate the motion ranging method provided by the embodiment of the present invention.

[0066] The motion distance measurement method provided by the embodiment of the present invention can be applied to the scenario of detecting the motion distance of a wearable device during motion.

[0067] Embodiment 1

[0068] like Figure 1 As shown, an embodiment of the present invention provides a motion ranging method, which may include the following steps:

[0069] 101. Continuously acquire three-axis acceleration signals within a first time period.

[0070] Optionally, the gyroscope signal may be continuously acquired within the first time period, and the energy value of the gyroscope signal may be calculated. If the energy value is greater than a preset energy value, it is determined that the user of the wearable device is in motion.

[0071] The preset energy value may be calculated based on a gyroscope signal when the user of the wearable device is in motion.

[0072] Generally, when the user of the wearable device is in motion, the energy value of the gyroscope signal is relatively large. In the embodiment of the present invention, when it is detected that the energy value of the gyroscope signal is relatively large, it can be determined that the user of the wearable device is in motion.

[0073] Optionally, in an embodiment of the present invention, when the wearable device determines that the user is in motion, the wearable device may be triggered to execute the following 102.

[0074] Optionally, the three-axis acceleration signal and the gyroscope signal may be measured by a six-axis sensor, wherein the six-axis sensor may integrate the functions of a three-axis accelerometer and a three-axis gyroscope.

[0075] Optionally, the above-mentioned three-axis acceleration signal can be measured by a three-axis acceleration sensor, and the above-mentioned gyroscope signal can be measured by a three-axis gyroscope sensor. The three-axis acceleration sensor and the three-axis acceleration sensor can be independent different sensors in the wearable device.

[0076] 102. Filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal.

[0077] When people are in motion (for example, running or walking), it is difficult to separate the horizontal motion due to the different acceleration and arm swinging postures. Each step of the user will be accompanied by slight up and down vibrations of the arms (vertical direction). Although everyone's running posture is different, the gravity acceleration always exists and is constant. We believe that during running, the acceleration is a constant value and a signal with a frequency of 0, so the gravity acceleration signal can be obtained from the three-axis acceleration signal through a low-pass filter.

[0078] Exemplarily, a signal with a frequency of 0 may be set to pass through a low-pass recorder, so that the three-axis acceleration signal may be filtered through a low-pass filter to obtain a corresponding gravity acceleration signal.

[0079] 103. Subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal.

[0080] When a person is in motion (for example, running or walking), the three-axis acceleration signal includes a motion acceleration signal + a gravity acceleration signal, so the motion acceleration signal can be obtained by subtracting the corresponding gravity acceleration signal from the three-axis acceleration signal.

[0081] 104. Calculate the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within a first time length.

[0082] In order to obtain the projection of the user's motion acceleration signal in the vertical direction, the gravity acceleration signal is dot-producted with the user acceleration signal to obtain the component of the motion acceleration signal in the gravity direction, that is, the acceleration component signal within the first time length.

[0083] 105. Calculate the length of the user's first step based on the acceleration component signal and the user's height.

[0084] Optionally, the user's first step length may be calculated based on the acceleration component signal, the user's height, and the following step length calculation formula;

[0085] The above step length calculation formula is:

[0086] Among them, k1 and K2 are hyperparameters, a i is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to a i The sampling time is h, and h is the height of the user.

[0087] Optionally, the above K1 and K2 may be a constant, and the K1 and K2 may be optimized. The optimization method for the K1 and K2 will be described in detail in the following embodiment 2 and will not be described in detail here.

[0088] Optionally, for the case where the wearable device is a wrist wearable device, the derivation process of the above step length formula is as follows:

[0089] Since the wrist wearable device measures the movement of the arm, the motion odometry is to derive the movement of the feet. Here, the formula from the lower limb movement to the upper limb movement is derived. The specific derivation process is as follows:

[0090] (1) Combination Figure 2 The figure shows a schematic diagram of a lower limb movement. Usually, when a user takes a step, the leg will pass through Figure 2 The leg state of (1) is Figure 2 The leg state changes in (2), and from Figure 2 The leg state of (2) changes to Figure 2 The leg state shown in (3) during this change process is as follows: Figure 2 In the figure, S is the step size and b is the vertical displacement.

[0091] The step length formula corresponding to the lower limb movement is shown in the following formula 1:

[0092] Among them, S is the step length, l is the leg length, b is the vertical displacement, and k is a hyperparameter.

[0093] (2) Usually, every time something like Figure 2The leg movements shown are accompanied by Figure 3 An arm movement is shown, wherein an arm movement process will go through the process from (i) to (ii), and from (ii) to (iii). During this change process, Figure 3 As shown in , d is the horizontal displacement of the wrist, m is the length of the arm, r1 is the vertical displacement of the arm from the vertical direction to the back, and r2 is the vertical displacement of the wrist from the vertical direction to the front.

[0094] For wrist movements, the relationship between the various parameters is shown in the following formula 2:

[0095] Formula 2:

[0096] Among them, d is the horizontal displacement of the wrist, m is the length of the arm, r1 is the vertical displacement of the arm from the vertical direction to the back, and r2 is the vertical displacement of the wrist from the vertical direction to the front.

[0097] Among them, r1 and r2 in formula 2 can be calculated by the following formula:

[0098]

[0099] Among them, a i is the acceleration value corresponding to the acceleration component signal, and t is the acceleration value corresponding to a i The sampling time is r1 or r2.

[0100] According to the above formula 1 and formula 2, the above compensation calculation formula can be derived.

[0101] 106. Calculate the movement distance of the user in the first time period according to the first step length and the number of steps in the first time period.

[0102] Optionally, peak detection may be performed on the acceleration component signal within the first time length, and then the number of peak values ​​of the acceleration component signal may be determined, and the number of steps within the first time length may be determined based on the number of peak values ​​of the acceleration component signal.

[0103] Furthermore, the movement distance of the user within the first time period can be calculated based on the calculated number of steps within the first time period and the first step length calculated according to the above step length calculation formula.

[0104] A motion ranging method provided in an embodiment of the present invention is applied to a wearable device, and continuously obtains a three-axis acceleration signal within a first time length; filters the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtracts the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculates the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length; calculates the user's step length based on the acceleration component signal and the user's height; and calculates the user's motion distance within the first time length based on the first step length and the number of steps within the first time length. Through this solution, the three-axis acceleration, that is, the obtained three-axis acceleration signal, is preprocessed (the three-axis acceleration signal is filtered by a low-pass filter to obtain the corresponding gravity acceleration signal; the corresponding gravity acceleration signal is subtracted from the three-axis acceleration signal to obtain the motion acceleration signal, and the component of the motion acceleration signal in the gravity direction is calculated), and peak detection is performed based on the acceleration component signal obtained after preprocessing. In this way, during the exercise process, the interference of some signals can be eliminated, and the accurate number of steps can be calculated. Combined with the user's own height parameters, the calculated exercise distance can be made more in line with the actual situation, thereby achieving accurate exercise ranging.

[0105] Embodiment 2

[0106] like Figure 4 As shown, an embodiment of the present invention provides a motion ranging method, which may include the following steps:

[0107] 401. Continuously obtain a three-axis acceleration signal within a first time period.

[0108] 402. Filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal.

[0109] 403. Subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal.

[0110] 404. Calculate the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within a first time length.

[0111] 405. Calculate the user's first step length according to the acceleration component signal, the user's height, and a step length calculation formula.

[0112] 406. Calculate the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period.

[0113] For the above 401 to 406, reference may be made to the relevant descriptions for 101 to 106 in the first embodiment, which will not be repeated here.

[0114] In this scheme, the three-axis acceleration, that is, the acquired gravity acceleration information, is preprocessed (the three-axis acceleration signal is filtered by a low-pass filter to obtain the corresponding gravity acceleration signal; the corresponding gravity acceleration signal is subtracted from the three-axis acceleration signal to obtain the motion acceleration signal, and the component of the motion acceleration signal in the gravity direction is calculated). Peak detection is performed based on the acceleration component signal obtained after preprocessing. In this way, during the exercise process, the interference of some signals can be eliminated, and the accurate number of steps can be calculated. Combined with the user's own height parameters, the calculated exercise distance can be made more in line with the actual situation, thereby achieving accurate motion ranging.

[0115] 407. Calculate the span distance of the GPS signal within the first time period.

[0116] The span distance is the moving distance indicated by the GPS signal within the first time period.

[0117] Optionally, after the original GPS signal is acquired, the original GPS signal may be preprocessed. Usually, due to the influence of the environment and hardware equipment, there may be some GPS position points in the original GPS signal that are outliers, and certain means are required to process the original GPS signal. In the embodiment of the present invention, the outlier detection of the original GPS signal is realized through the LOF outlier detection algorithm, mainly by detecting the density of the GPS position points in a certain area. When the density of the GPS position points in a partial area is lower than a certain density value, the GPS position points in the partial area are considered to be outliers, and these outliers can be removed from the original GPS signal.

[0118] The above-mentioned preprocessing process of the GPS signal eliminates the information of some abnormal position points in the GPS signal. The processed GPS signal can provide a more reliable and effective data basis for the subsequent calculation of the span distance and the optimization of the step length calculation formula, thereby further improving the accuracy of the motion ranging in this application.

[0119] Optionally, determining that the GPS signal within the first duration is a credible signal includes the following implementations:

[0120] Implementation method 1: Fit the GPS signal within the first time period through the least squares method to calculate the straight line fitting error; if the difference between the span distance and the first preset distance is less than the preset value, and / or the above-mentioned straight line fitting error is less than the preset error, it is determined that the GPS signal within the first time period is a credible signal.

[0121] In the above implementation method 1, the accuracy of the GPS signal is judged by judging the condition: if the difference between the span distance and the first preset distance is less than the preset value, and / or the straight line fitting error is less than the preset error, when it is determined that the GPS accuracy is high, that is, the GPS signal is a credible signal, subsequent processing is performed based on these GPS signals, which provides a more reliable and effective data basis for subsequent processing, and further improves the accuracy of motion ranging in this application.

[0122] Implementation method 2: Detect the signal strength of the GPS signal within the first time period. If the signal strength is greater than a preset strength and / or the straight line fitting error is less than a preset error, determine that the GPS signal within the first time period is a credible signal.

[0123] In the above-mentioned implementation method 2, the accuracy of the GPS signal is judged by judging the conditions: the signal strength is greater than the preset strength, and / or the straight line fitting error is less than the preset error. When it is determined that the GPS accuracy is high, that is, the GPS signal is a credible signal, subsequent processing is performed based on these GPS signals, which provides a more reliable and effective data basis for subsequent processing, and further improves the accuracy of motion ranging in this application.

[0124] 408. If it is determined that the GPS signal within the first time period is a credible signal, calculate a second step length according to the span distance and the number of steps within the first time period.

[0125] The quotient of the span distance divided by the number of steps in the first time length may be calculated, and the quotient value may be used as the second step length.

[0126] 409. Compare the second step length with the first step length to obtain a comparison result.

[0127] Optionally, the step length difference between the second step length and the first step length may be calculated, and the hyperparameters in the step length calculation formula may be adjusted based on the step length difference.

[0128] 410. According to the comparison results, adjust the hyperparameters in the step length calculation formula to optimize the step length calculation formula.

[0129] Among them, the step length calculation formula can be adjusted according to the comparison results: The hyperparameters k1 and / or k2 in .

[0130] For example, Figure 5 The following is a schematic diagram of the specific process of an optimization step length calculation formula: Figure 5It can be seen that the process specifically includes the following steps: 51. Acquire GPS signal; 52. Eliminate LOF outliers for the acquired GPS signal; 53. Calculate span distance according to the eliminated GPS signal (hereinafter referred to as effective GPS signal); 54. Calculate straight line fitting error according to the effective GPS signal (calculate the maximum distance by traversing the effective GPS signal, and fit the GPS by the least squares method); 55. Evaluate the GPS signal according to the straight line fitting error. If the effective GPS signal is a credible signal, further execute 56. Use the GPS signal to optimize the step length calculation formula.

[0131] In the embodiment of the present invention, the second step length calculated by the GPS signal can be used to optimize the step length calculation formula in the embodiment of the present invention, so that the step length calculation formula can be continuously optimized at the back end to further improve the accuracy of motion ranging.

[0132] Optionally, after the above 406, in the motion ranging method provided by the embodiment of the present invention, other signals may be further used to detect whether the motion within the first duration is a valid motion. The specific implementation method may include:

[0133] Implementation method (1): Detect the received signal strength RSSI of multiple WIFI signals received within a first time period; if the change in the RSSI of the multiple WIFI signals is greater than a preset change, determine that the user's movement within the first time period is valid movement; if the change in the RSSI of the multiple WIFI signals is less than the preset change, determine that the user's movement within the first time period is invalid movement.

[0134] Since usually, when the moving distance is small or there is no movement, the change in the RSSI of the WIFI signal is small (that is, the similarity of the RSSI of the same WIFI signal detected within the first time period is high), in order to avoid misdetecting the user's local movement detected by the wearable device as a motion process, it is possible to judge whether the user's movement within the first time period is valid movement based on the change in the RSSI of the WIFI signal to avoid recording invalid movement distance.

[0135] Implementation method (2): Detect RSSI of multiple WIFI signals received within a first time length; calculate the span distance of the GPS signal within the first time length, the span distance being the moving distance indicated by the GPS signal within the first time length; if the span distance is greater than or equal to a second preset distance, and the change in the RSSI of the multiple WIFI signals is greater than the preset change, then determine that the user's movement within the first time length is valid movement; if the span distance is less than the second preset distance, and the change in the RSSI of the multiple WIFI signals is less than the preset change, then determine that the user's movement within the first time length is invalid movement.

[0136] Furthermore, in addition to judging whether the user's movement within the first time period is effective movement based on the change in the RSSI of the WIFI signal, the GPS signal can also be used to calculate the span distance within the first time period, and the span distance can be used to judge whether the user's movement within the first time period is effective movement, which can further increase the accuracy of the judgment.

[0137] Optionally, in addition to the above two implementations, the span distance within the first time period may be calculated solely through the GPS signal to determine whether the user's movement within the first time period is a valid movement.

[0138] Optionally, the GPS signal and the WIFI RSSI may be combined to calculate the span distance within the first time period, and the span distances calculated by the two may be combined to determine whether the user's movement within the first time period is a valid movement.

[0139] Optionally, the GPS signal and WIFI RSSI can be combined to calculate the span distance within the first time length, and the straight line fitting error calculated by combining the GPS signal and WIFI RSSI, and the span distance calculated by the two, as well as the straight line fitting error, to determine whether the user's movement within the first time length is effective movement.

[0140] For example, Figure 6 As shown in the figure, it is a specific process diagram for judging the effectiveness of the exercise process: Figure 6 As can be seen from the figure, the process specifically includes the following steps:

[0141] 61. Get GPS signal;

[0142] 62. Eliminate LOF outliers for the acquired GPS signals;

[0143] 63. Calculate the span distance based on the GPS signal after elimination;

[0144] The proposed GPS signal is referred to as the effective GPS signal below;

[0145] 64. Calculate the straight line fitting error based on the effective GPS signal;

[0146] Optionally, traverse the valid GPS signals to calculate the maximum distance, and fit the GPS by the least square method;

[0147] 65. Get the receivable WIFI RSSI;

[0148] 66. Determine the similarity of the WIFI RSSI received within the first time period;

[0149] 67. Calculate the span distance according to the change value of WIFI RSSI;

[0150] Furthermore, the validity of the motion process may be judged based on the span distance calculated in step 63 , the span distance calculated in step 67 , and the straight line fitting error calculated in step 64 , that is, step 68 is executed.

[0151] 68. Judgment of the effectiveness of the exercise process.

[0152] Optionally, the validity of the motion process within the first time period can be determined when the span distance calculated at 63 and the span distance calculated at 67 are both greater than a preset distance and the straight line fitting error calculated at 64 is less than a preset error.

[0153] In the above implementation scheme, the GPS signal and / or WIFI RSSI can be combined to calculate the span distance within the first time period, and the span distance calculated by the two can be combined to determine whether the user's movement within the first time period is valid movement, thereby avoiding recording invalid movement data.

[0154] Embodiment 3

[0155] like Figure 7 As shown, an embodiment of the present invention provides a wearable device, the wearable device comprising:

[0156] An acquisition module 701 is used to continuously acquire a three-axis acceleration signal within a first time period;

[0157] The processing module 702 is used to filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculate the component of the motion acceleration signal in the gravity direction to obtain the acceleration component signal within a first time length; calculate the user's step length according to the acceleration component signal and the user's height; calculate the user's movement distance within the first time length according to the first step length and the number of steps within the first time length.

[0158] Optionally, the processing module 702 is further used to perform peak detection on the acceleration component signal within the first time length before calculating the user's movement distance within the first time length based on the first step length and the number of steps within the first time length; determine the number of peak values ​​of the acceleration component signal, and determine the number of steps within the first time length based on the number of peak values ​​of the acceleration component signal.

[0159] Optionally, the processing module 702 is specifically configured to calculate the first step length of the user according to the acceleration component signal, the height of the user, and the following step length calculation formula;

[0160] The step length calculation formula is:

[0161] k1 and K2 are hyperparameters, a i is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to a i The sampling time is h, and h is the height of the user.

[0162] Optionally, the processing module 702 is further configured to calculate the movement distance of the user in the first time period according to the first step length and the number of steps in the first time period, and then calculate the span distance of the GPS signal in the first time period, where the span distance is the movement distance indicated by the GPS signal in the first time period;

[0163] If it is determined that the GPS signal within the first time period is a credible signal, the second step length is calculated according to the span distance and the number of steps within the first time period;

[0164] Compare the second step length with the first step length to obtain a comparison result;

[0165] According to the comparison result, adjust k1 and / or K2 in the step length calculation formula.

[0166] Optionally, the processing module 702 is specifically used to fit the GPS signal within the first time length by the least square method to calculate the straight line fitting error; if the difference between the span distance and the first preset distance is less than a preset value, and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first time length is a credible signal;

[0167] Optionally, the processing module 702 is specifically used to detect the signal strength of the GPS signal within the first time period. If the signal strength is greater than a preset strength and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first time period is a credible signal.

[0168] Optionally, the processing module 702 is further used to calculate the user's movement distance in the first time length according to the first step and the number of steps in the first time length, and then detect the received signal strength RSSI of multiple WIFI signals received in the first time length; if the change in the received signal strength RSSI of the multiple WIFI signals is greater than a preset change, it is determined that the user's movement in the first time length is a valid movement; if the change in the RSSI of the multiple WIFI signals is less than a preset change, it is determined that the user's movement in the first time length is an invalid movement.

[0169] Optionally, the processing module 702 is further used to calculate the user's movement distance in the first time length according to the first step and the number of steps in the first time length, and then detect the RSSI of multiple WIFI signals received in the first time length; calculate the span distance of the GPS signal in the first time length, and the span distance is the moving distance indicated by the GPS signal in the first time length; if the span distance is greater than or equal to a second preset distance, and the change in the RSSI of the multiple WIFI signals is greater than the preset change, it is determined that the user's movement in the first time length is valid movement; if the span distance is less than the second preset distance, and the change in the RSSI of the multiple WIFI signals is less than the preset change, it is determined that the user's movement in the first time length is invalid movement.

[0170] Optionally, an embodiment of the present invention provides a wearable device, the wearable device comprising: a memory storing executable program code;

[0171] a processor coupled to the memory;

[0172] The processor calls the executable program code stored in the memory to execute the motion ranging method in the embodiment of the present invention.

[0173] Figure 8 A hardware structure diagram of a wearable device for implementing various embodiments of the present invention is shown in FIG. Figure 8 As shown, the wearable device includes but is not limited to: radio frequency (RF) circuit 801, memory 802, input unit 803, display unit 804, sensor 805, audio circuit 806, wireless communication (WiFi) module 807, processor 808, power supply 809, and camera 810. Among them, the RF circuit 801 includes a receiver 8011 and a transmitter 8012. Those skilled in the art can understand that Figure 8 The wearable device structure shown in the figure does not constitute a limitation on the wearable device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0174] The RF circuit 801 can be used for receiving and sending signals during the process of sending and receiving information or making calls. In particular, after receiving the downlink information of the base station, it is sent to the processor 808 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit 801 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF circuit 801 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to the global system of mobile communication (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), long term evolution (LTE), email, short messaging service (SMS), etc.

[0175] The memory 802 can be used to store software programs and modules. The processor 808 executes various functional applications and data processing of the wearable device by running the software programs and modules stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the wearable device (such as audio data, a phone book, etc.), etc. In addition, the memory 802 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0176] The input unit 803 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the wearable device. Specifically, the input unit 803 may include a touch panel 8031 ​​and other input devices 8032. The touch panel 8031, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 8031 ​​or near the touch panel 8031 ​​using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 8031 ​​may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 808, and can receive and execute commands sent by the processor 808. In addition, the touch panel 8031 ​​can be implemented in a variety of ways such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 8031, the input unit 803 may also include other input devices 8032. Specifically, the other input devices 8032 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, etc.

[0177] The display unit 804 can be used to display information input by the user or information provided to the user and various menus of the wearable device. The display unit 804 may include a display panel 8041. Optionally, the display panel 8041 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 8031 ​​may cover the display panel 8041. When the touch panel 8031 ​​detects a touch operation on or near it, it is transmitted to the processor 808 to determine the touch event. Subsequently, the processor 808 provides a corresponding visual output on the display panel 8041 according to the touch event. Although in Figure 8 In the embodiment, the touch panel 8031 ​​and the display panel 8041 are used as two independent components to implement the input and output functions of the wearable device, but in some embodiments, the touch panel 8031 ​​and the display panel 8041 can be integrated to implement the input and output functions of the wearable device.

[0178] The wearable device may also include a sensor 805, which may be a motion sensor. Optionally, it may also include a light sensor, and other sensors. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the wearable device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the wearable device may also be configured with a motion sensor including a gyroscope sensor. The above-mentioned light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 8041 according to the brightness of the ambient light, and the proximity sensor may exit the display panel 8041 and / or the backlight when the wearable device is moved to the ear.

[0179] The audio circuit 806, the speaker 8061, and the microphone 8062 can provide an audio interface between the user and the wearable device. The audio circuit 806 can transmit the electrical signal converted from the received audio data to the speaker 8061, which is converted into a sound signal for output; on the other hand, the microphone 8062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 806 and converted into audio data, and then the audio data is output to the processor 808 for processing, and then sent to another wearable device through the RF circuit 801, or the audio data is output to the memory 802 for further processing.

[0180] WiFi is a short-range wireless transmission technology. The wearable device can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 807. It provides users with wireless broadband Internet access. Figure 8 A WiFi module 807 is shown, but it is understandable that it is not a necessary component of the wearable device and can be omitted as needed without changing the essence of the invention.

[0181] The processor 808 is the control center of the wearable device. It uses various interfaces and lines to connect the various parts of the entire wearable device. By running or executing the software programs and / or modules stored in the memory 802, and calling the data stored in the memory 802, it performs various functions of the wearable device and processes data, thereby monitoring the wearable device as a whole. Optionally, the processor 808 may include one or more processing units; preferably, the processor 808 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 808.

[0182] The wearable device also includes a power supply 809 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 808 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. Although not shown, the wearable device can also include a Bluetooth module, etc., which will not be repeated here.

[0183] In the embodiment of the present invention, the processor 808 is used to control the sensor 805 to continuously acquire the three-axis acceleration signal within a first time period;

[0184] Processor 808 is used to filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculate the component of the motion acceleration signal in the gravity direction to obtain the acceleration component signal within a first time length; calculate the user's step length according to the acceleration component signal and the user's height; calculate the user's movement distance within the first time length according to the first step length and the number of steps within the first time length.

[0185] Optionally, processor 808 is further used to perform peak detection on the acceleration component signal within the first time length before calculating the user's movement distance within the first time length based on the first step length and the number of steps within the first time length; determine the number of peak values ​​of the acceleration component signal, and determine the number of steps within the first time length based on the number of peak values ​​of the acceleration component signal.

[0186] Optionally, the processor 808 is specifically configured to calculate the first step length of the user according to the acceleration component signal, the height of the user, and the following step length calculation formula;

[0187] The step length calculation formula is:

[0188] k1 and K2 are hyperparameters, a i is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to a i The sampling time is h, and h is the height of the user.

[0189] Optionally, the processor 808 is further configured to calculate the movement distance of the user in the first time period according to the first step length and the number of steps in the first time period, and then calculate the span distance of the GPS signal in the first time period, where the span distance is the movement distance indicated by the GPS signal in the first time period;

[0190] If it is determined that the GPS signal within the first time period is a credible signal, the second step length is calculated according to the span distance and the number of steps within the first time period;

[0191] Compare the second step length with the first step length to obtain a comparison result;

[0192] According to the comparison result, adjust k1 and / or K2 in the step length calculation formula.

[0193] Optionally, the processor 808 is specifically configured to fit the GPS signal within the first time period by the least square method to calculate a straight line fitting error; if the difference between the span distance and the first preset distance is less than a preset value, and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first time period is a credible signal;

[0194] Optionally, the processor 808 is specifically used to detect the signal strength of the GPS signal within the first time period. If the signal strength is greater than a preset strength and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first time period is a credible signal.

[0195] Optionally, the processor 808 is further used to calculate the user's movement distance in the first time length based on the first step and the number of steps in the first time length, and then detect the received signal strength RSSI of multiple WIFI signals received in the first time length; if the change in the received signal strength RSSI of the multiple WIFI signals is greater than a preset change, it is determined that the user's movement in the first time length is valid movement; if the change in the RSSI of the multiple WIFI signals is less than the preset change, it is determined that the user's movement in the first time length is invalid movement.

[0196] Optionally, the processor 808 is further used to calculate the user's movement distance in the first time length based on the first step and the number of steps in the first time length, and then detect the RSSI of multiple WIFI signals received in the first time length; calculate the span distance of the GPS signal in the first time length, and the span distance is the moving distance indicated by the GPS signal in the first time length; if the span distance is greater than or equal to a second preset distance, and the change in the RSSI of the multiple WIFI signals is greater than the preset change, it is determined that the user's movement in the first time length is valid movement; if the span distance is less than the second preset distance, and the change in the RSSI of the multiple WIFI signals is less than the preset change, it is determined that the user's movement in the first time length is invalid movement.

[0197] An embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the motion ranging method provided by the above method embodiment is implemented.

[0198] An embodiment of the present invention further provides a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps of the methods in the above method embodiments.

[0199] An embodiment of the present invention further provides an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps of the method in the above method embodiments.

[0200] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0201] The wearable device provided in the embodiment of the present invention can implement each process shown in the above method embodiment, and will not be described again here to avoid repetition.

[0202] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the above-mentioned processes does not mean the necessary order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0203] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0204] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0205] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the above methods of various embodiments of the present invention.

[0206] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

Claims

1. A motion ranging method, characterized in that: Applied to a wearable device, the method comprises: Continuously acquiring three-axis acceleration signals within a first time period; Filtering the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; Subtracting the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; Calculating the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length; Calculating the first step length of the user according to the acceleration component signal and the height of the user; Calculating a movement distance of the user within the first time period according to the first step length and the number of steps within the first time period; The step of calculating the first step length of the user according to the acceleration component signal and the height of the user comprises: Calculate the first step length of the user according to the acceleration component signal, the height of the user, and a step length calculation formula; The step length calculation formula is: + ; Among them, k1 and k2 are hyperparameters, is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to the The sampling time is, h is the height of the user; By formula It is calculated that, is the vertical displacement of the arm from the vertical direction to the back, It is the vertical displacement of the wrist from vertical to the front of the body.

2. The method according to claim 1, characterized in that Before calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes: Performing peak detection on the acceleration component signal within the first time length; The number of peak values ​​of the acceleration component signal is determined, and the number of steps in the first time period is determined according to the number of peak values ​​of the acceleration component signal.

3. The method according to claim 1, characterized in that: After calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes: Calculating a span distance of the GPS signal within the first duration, wherein the span distance is a moving distance indicated by the GPS signal within the first duration; If it is determined that the GPS signal within the first time period is a credible signal, calculating a second step length according to the span distance and the number of steps within the first time period; Compare the second step length with the first step length to obtain a comparison result; According to the comparison result, k1 and / or k2 in the step length calculation formula are adjusted.

4. The method according to claim 3, characterized in that The determining that the GPS signal within the first duration is a credible signal includes: Fitting the GPS signal within the first time period by the least square method to calculate a straight line fitting error; if the difference between the span distance and the first preset distance is less than a preset value, and / or the straight line fitting error is less than a preset error, determining that the GPS signal within the first time period is a credible signal; or, The signal strength of the GPS signal within the first duration is detected. If the signal strength is greater than a preset strength and / or the straight line fitting error is less than a preset error, it is determined that the GPS signal within the first duration is a credible signal.

5. The method according to claim 1, characterized in that After calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes: Detecting received signal strength RSSI of multiple WIFI signals received within the first time period; If the change in the received signal strength RSSI of the multiple WIFI signals is greater than a preset change, determining that the movement of the user within the first time period is a valid movement; If the change amount of the RSSI of the plurality of WIFI signals is less than a preset change amount, it is determined that the movement of the user within the first time period is invalid movement.

6. The method according to claim 5, characterized in that After calculating the movement distance of the user within the first time period according to the first step length and the number of steps within the first time period, the method further includes: Detecting RSSIs of multiple WIFI signals received within the first time period; Calculating a span distance of the GPS signal within the first duration, wherein the span distance is a moving distance indicated by the GPS signal within the first duration; If the span distance is greater than or equal to a second preset distance, and the change in the RSSI of the plurality of WIFI signals is greater than a preset change, it is determined that the movement of the user within the first time period is a valid movement; If the span distance is less than a second preset distance, and the change in the RSSI of the plurality of WIFI signals is less than a preset change, it is determined that the movement of the user within the first time period is invalid movement.

7. A wearable device, characterized in that: include: An acquisition module, used for continuously acquiring a three-axis acceleration signal within a first time period; A processing module, configured to filter the three-axis acceleration signal through a low-pass filter to obtain a corresponding gravity acceleration signal; subtract the corresponding gravity acceleration signal from the three-axis acceleration signal to obtain a motion acceleration signal; calculate the component of the motion acceleration signal in the gravity direction to obtain an acceleration component signal within the first time length; calculate the first step length of the user according to the acceleration component signal and the height of the user; Calculating a movement distance of the user within the first time period according to the first step length and the number of steps within the first time period; The processing module is specifically used to calculate the first step length of the user according to the acceleration component signal, the height of the user, and a step length calculation formula; The step length calculation formula is: + ; Among them, k1 and k2 are hyperparameters, is the acceleration value corresponding to the acceleration component signal, t is the acceleration value corresponding to the The sampling time is, h is the height of the user; By formula It is calculated that, is the vertical displacement of the arm from the vertical direction to the back, It is the vertical displacement of the wrist from vertical to the front of the body.

8. A wearable device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the motion ranging method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: include: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the motion ranging method according to any one of claims 1 to 6 is implemented.

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