Formation speed measuring method and device, wearable equipment and storage medium

Abstract

The invention provides a preparation speed measuring method and device, wearable equipment and a storage medium. The speed setting measurement method provided by the embodiment of the invention comprises the following steps: acquiring measurement data of an IMU (Inertial Measurement Unit) under the condition that a user wears wearable equipment; determining the step number and the step frequency of the user according to the measurement data of the IMU; determining the stride of the user according to the height of the user, a pre-configured mapping relation between strides and stride frequencies under different heights and the stride frequencies; and determining the matching speed of the user according to the stride and the step number. According to the method disclosed by the embodiment of the invention, the speed setting measurement can be efficiently and quickly completed without complex operation and sensors such as a GPS (Global Positioning System), the method can be suitable for various scenes such as indoor, treadmills, mountaineering machines and outdoors, and meanwhile, the real-time requirement of a user on the speed setting measurement can be met.

Description

Technical Field

[0001] The present disclosure relates to a pace measurement method, apparatus, wearable device, and storage medium. Background Art

[0002] Real-time measurement of pace is crucial for users such as professional athletes, sports enthusiasts, and fitness enthusiasts.

[0003] Currently, wearable devices such as smart watches and smart bracelets already have a step counting function. These wearable devices can estimate the user's pace (e.g., x km / h) by recording the user's steps in real time and combining the geographical location changes measured by the Global Positioning System (GPS) or Global Navigation Satellite System (GNSS) technology. However, the pace measurement methods of these wearable devices have poor instantaneity and high latency, and cannot meet the user's instantaneity requirements for pace measurement. Moreover, their use is restricted, and pace measurement cannot be performed indoors or without GPS signals. Summary of the Invention

[0004] In view of this, the present disclosure provides a pace measurement method, apparatus, wearable device, and storage medium.

[0005] According to a first aspect of the present disclosure, there is provided a pace measurement method, which is executed by a wearable device including an inertial measurement unit. The pace measurement method includes: When the user wears the wearable device, obtaining measurement data of the inertial measurement unit; Determining the number of steps and the step frequency of the user according to the measurement data of the inertial measurement unit; Determining the user's step length according to the user's height, the pre-configured mapping relationship between step length and step frequency at different heights, and the step frequency; Determining the user's pace according to the step length and the number of steps.

[0006] In some embodiments of the first aspect of the present disclosure, the measurement data of the inertial measurement unit is acceleration data; or, the measurement data of the inertial measurement unit includes acceleration data, angular velocity data, and magnetic field data.

[0007] In some embodiments of the first aspect of the present disclosure, determining the number of steps and the step frequency of a user based on the measurement data of the inertial measurement unit includes: extracting features from the measurement data of the inertial measurement unit to obtain acceleration amplitude time series data, detecting zero-crossing points of the acceleration amplitude time series data to determine the start of a gait cycle, performing peak detection on the acceleration amplitude time series data to identify the gait cycle, counting the number of gait cycles to obtain the number of steps, and calculating the time interval between two adjacent gait cycles to obtain the step frequency.

[0008] In some embodiments of the first aspect of the present disclosure, determining the number of steps and the step frequency of a user based on the measurement data of the inertial measurement unit includes: extracting gait-related features from the measurement data of the IMU, where the features include angular velocity change features; using the angular velocity change features to determine the start of a gait cycle, using the acceleration peaks to identify the gait cycle, counting the number of gait cycles to obtain the number of steps, and calculating the time interval between two adjacent gait cycles to obtain the step frequency.

[0009] In some embodiments of the first aspect of the present disclosure, the mapping relationship between step length and step frequency at different heights is calibrated in the following manner: collecting historical motion data of people with different heights, where the historical motion data includes the actual number of steps and the actual motion distance at different speeds; using the historical motion data of people with different heights to fit the mapping relationship between step length and step frequency at different heights.

[0010] In some embodiments of the first aspect of the present disclosure, determining the pace of a user based on the step length and the number of steps includes: determining the current pace of the user according to the currently determined step length and the number of steps; and / or calculating the average pace of the user according to the currently determined step length and the number of steps and the previously obtained step length and the number of steps.

[0011] In some embodiments of the first aspect of the present disclosure, the method further includes one or more of the following: Sending the pace to the sports equipment currently being used by the user to display the pace through a display screen on the sports equipment; Announcing the pace by voice.

[0012] According to a second aspect of the present disclosure, there is provided a pace measurement device, where the pace measurement device is applied to a wearable device, and the wearable device includes an inertial measurement unit; The pace measurement device includes: An acquisition unit, configured to acquire the measurement data of the inertial measurement unit when the user wears the wearable device; A gait detection unit, configured to determine the number of steps and the step frequency of the user according to the measurement data of the inertial measurement unit; A stride determination unit, configured to determine the stride of the user according to the user's height, a pre-configured mapping relationship between stride and stride frequency at different heights, and the stride frequency; A pace determination unit, configured to determine the pace of the user according to the stride and the number of steps.

[0013] According to a third aspect of the present disclosure, there is provided a wearable device, including: one or more processors and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to execute the above method.

[0014] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium storing a program, the program including instructions that, when executed by one or more processors, cause the computing device to execute the above method.

[0015] It can be seen from the above technical solutions that in the embodiments of the present disclosure, the number of steps and stride frequency of the user are determined through the measurement data of the IMU, and then the stride of the user is determined based on the user's height and the pre-calibrated mapping relationship between stride and stride frequency at different heights. Finally, the pace of the user is obtained through the stride and the number of steps. The method of the embodiments of the present disclosure can complete the pace measurement efficiently and quickly without complex calculations and without relying on sensors such as GPS, and can be applied to various scenarios such as indoors, treadmills, climbers, outdoors, etc., and can also meet the user's immediate need for pace measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic structural diagram of a system applicable to the embodiments of the present disclosure; Figure 2 It is a flowchart of a pace measurement method provided by the embodiments of the present disclosure; Figure 3 It is a schematic structural diagram of a pace measurement device provided by the embodiments of the present disclosure; Figure 4 It is a schematic structural block diagram of a wearable device provided by the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0019] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and are not intended to limit the present disclosure. The singular forms of "a", "the", and "said" used in the embodiments of the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] Depending on the context, words such as "if" and "when" used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0021] For ease of understanding, an exemplary description of the bionic hand control system provided in the embodiments of the present disclosure will be given below.

[0022] Figure 1 The schematic diagram of the system architecture applicable to the embodiments of the present disclosure is shown. Refer to Figure 1 , the system architecture applicable to the embodiments of the present disclosure may include a wearable device 100 and a sports equipment 200, and the wearable device 100 and the sports equipment 200 can communicate with each other.

[0023] Exemplarily, the wearable device 100 and the sports equipment 200 can communicate through, for example, Bluetooth, local area network, Ethernet or other various wireless or wired communication methods.

[0024] The wearable device 100 may include an Inertial Measurement Unit (IMU). When the user wears the wearable device 100, the IMU can be used to measure data related to the user's movement (collectively referred to as the measurement data of the IMU herein).

[0025] In some embodiments, the IMU may include an accelerometer, a gyroscope, and a magnetometer. The accelerometer can be used to measure the linear acceleration of an object along the three axes of X, Y, and Z, that is, the acceleration change of the object in space. The gyroscope can be used to measure the angular velocity of the object around the three axes of X, Y, and Z, that is, the rotation speed of the object. The magnetometer can be used to measure the direction of the object relative to the Earth's magnetic field, providing magnetic field strength and direction information for determining the heading of the object.

[0026] In some embodiments, the measurement data of the IMU may include but are not limited to: acceleration data, angular velocity data, and magnetic field data. The acceleration data includes the acceleration values along three orthogonal axes (i.e., the X, Y, and Z axes), usually in units of g (acceleration due to gravity). The angular velocity data may include the angular velocity values around the three orthogonal axes (i.e., the X, Y, and Z axes), usually in units of degrees per second (° / s) or radians per second (rad / s). The magnetic field data may include the magnetic field strength values and magnetic field directions along the three orthogonal axes (the X, Y, and Z axes), usually in units of microtesla (μT). Among them, each value in the acceleration data, angular velocity data, and magnetic field data is usually accompanied by a timestamp, which is used to indicate the measurement time of the value.

[0027] In some embodiments, the IMU may only include the aforementioned accelerometer, and the measurement data of the IMU may only include the aforementioned acceleration data.

[0028] In a specific application, for the specific structure and implementation form of the wearable device 100, refer to the following text Figure 4 Regarding the description of the wearable device, it will not be elaborated here. In a specific application, the wearable device 100 may be implemented as but not limited to smart headphones, smart watches, smart bracelets, smart armbands, AR glasses, or other various forms. For the specific implementation form of the wearable device 100, the embodiments of the present disclosure do not make any limitations.

[0029] The sports equipment 200 may include a display screen. After the wearable device 100 measures the user's pace, it is transmitted to the sports equipment 200 for display through the display screen in the sports equipment, so that the user can intuitively and clearly see the pace information while using the sports equipment for exercise.

[0030] In a specific application, the sports equipment 200 may be but not limited to a treadmill, an elliptical machine, or other various sports equipment that requires step counting.

[0031] It should be noted that Figure 1The system shown is only an example. Those skilled in the art should understand that the system architecture applicable to the embodiments of the present disclosure can be flexibly adjusted according to the requirements of actual applications. The present disclosure makes no limitation thereto.

[0032] Figure 2 The flowchart of the pace measurement method provided by the embodiments of the present disclosure is shown. The pace measurement method provided by the embodiments of the present disclosure can be executed by the aforementioned wearable device 100. Refer to Figure 2 , the pace measurement method of the embodiments of the present disclosure may include: Step 201, when the user wears the wearable device, obtain the measurement data of the IMU; Step 202, determine the number of steps and the step frequency of the user according to the measurement data of the IMU; Step 203, determine the step length of the user according to the height of the user, the mapping relationship between the step length and the step frequency at different heights pre-configured, and the step frequency; Wherein, the height of the user can be set by the user in advance, and the mapping relationship between the step length and the step frequency at different heights can be pre-calibrated and configured in the wearable device.

[0033] Step 204, determine the pace of the user according to the step length and the number of steps.

[0034] The following will make a detailed description of the specific implementation manners of each step in the method of the embodiments of the present disclosure.

[0035] The measurement data of the IMU in Step 201 may be the acceleration data collected by the aforementioned accelerometer. Alternatively, the measurement data of the IMU may include the aforementioned acceleration data, angular velocity data, and magnetic field data.

[0036] The IMU measurement data obtained in Step 201 refers to the data collected by the IMU within a certain time period. For example, the measurement data can be sampled according to a pre-set time window. Considering the difference between the walking frequency of a person and the sampling data frequency of the IMU, a time window of 200 ms can be set. For example, the IMU measurement data is taken once every 40 ms, and 5 groups of IMU measurement data are obtained in 200 ms. The subsequent processing is performed using the 5 groups of IMU measurement data in this 200 ms.

[0037] Before Step 202, the measurement data of the IMU can also be pre-processed to reduce noise and smooth the data, thereby improving the accuracy of the step frequency and the number of steps. Specifically, the pre-processing of the IMU measurement data may include, but is not limited to, denoising, filtering, normalization processing, etc. Among them, the filtering may include, but is not limited to, low-pass filtering, high-pass filtering, etc. to remove high-frequency noise and low-frequency drift. In specific applications, a mean filter, a Kalman filter, etc. can be used to implement the filtering of the IMU measurement data.

[0038] In step 202, there can be various specific implementation ways to determine the step frequency and the number of steps based on the IMU measurement data.

[0039] In one implementation way, in step 202, the step frequency and the number of steps can be determined in the following way: Feature extraction is performed on the measurement data of the IMU to obtain the acceleration magnitude time series data, the zero-crossing points of the acceleration magnitude time series data are detected to determine the start of the gait cycle, peak detection is performed on the acceleration magnitude time series data to identify the gait cycle, the number of gait cycles is counted to obtain the number of steps, and the time interval between two adjacent gait cycles is calculated to obtain the step frequency.

[0040] The acceleration magnitude time series data is a time series formed by sorting the acceleration magnitudes according to the timestamps. Among them, the acceleration magnitude can be the acceleration vector magnitude or the Z-axis acceleration value. That is, the acceleration magnitude time series data can be the acceleration vector magnitude time series or the Z-axis acceleration data.

[0041] In some examples, the acceleration vector magnitude can be calculated by the following formula (1).

[0042] Magnitude= (1) where x, y, and z are the acceleration values of the X-axis, the y-axis acceleration value, and the Z-axis acceleration value respectively, and Magnitude represents the acceleration vector magnitude.

[0043] The Z-axis acceleration data is a data set {z1, z2, ……, zn} formed by arranging the Z-axis acceleration values in the acceleration data according to their timestamps. The Z-axis acceleration data reflects the up and down movement of the center of gravity of a person during walking and can well reflect the relevant motion characteristics of the gait.

[0044] The gait cycle is a complete motion cycle from when one foot touches the ground to when the same foot touches the ground again when a person is walking or running. This cycle includes a series of continuous gait phases, and each phase has specific biomechanical characteristics.

[0045] The step frequency is the number of steps per unit time. The step frequency can be obtained by calculating the time interval between two consecutive gait cycles. The step frequency f can be calculated by the following formula (2), T represents the time interval between two adjacent gait cycles, and f represents the step frequency.

[0046] (2) The determination of the start of the gait cycle refers to accurately identifying the starting point of the gait cycle, that is, the beginning of the gait cycle. This is usually achieved by detecting specific gait events, such as Heel Strike or Toe Off. Determining the start of the gait cycle is crucial for gait analysis because it provides a reference point for the analysis of the gait cycle.

[0047] A zero crossing point refers to the point where the acceleration amplitude crosses the zero value point from a positive number to a negative number, or from a negative number to a positive number. In gait analysis, the zero crossing point is associated with specific events in the gait cycle, such as heel strike. When a person is walking or running, the acceleration amplitude changes with the gait cycle. Especially at the moment of heel strike, there is an obvious peak in the acceleration amplitude, which then decreases to zero or a negative value and then increases again. This change point from positive to negative (or from negative to positive) is the zero crossing point, which marks the start of a new gait cycle. The start of the gait cycle can be determined by detecting the zero crossing point.

[0048] By analyzing the zero crossing points of the acceleration data, the start of each gait cycle during walking or running can be identified, which can then be used for step counting and gait analysis. Thus, it is possible to identify the gait without relying on a fixed threshold, but rather on the natural changes in the acceleration data, enabling more accurate detection of the number of steps taken by different users, in various environments, and at different speeds.

[0049] In some examples, peak detection can be achieved in one of the following ways: 1) Detect local maxima in the acceleration amplitude time series. This local maximum is the peak, and the peak corresponds to the moment of foot strike. Each time a peak is detected, it is considered a gait cycle. 2) Use machine learning methods to detect peaks in the acceleration amplitude time series data. Specifically, a shallow neural network can be built to learn the change characteristics of the acceleration amplitude time series data to extract the peaks. The peak corresponds to the moment of foot strike. Each time a peak is detected, it is considered a gait cycle. Detecting peaks using machine learning methods can improve the accuracy of step counting.

[0050] 3) Fit the acceleration amplitude time series data to obtain the sine curve trajectory of the walking motion, and detect the peaks (i.e., local maxima) in the sine curve trajectory. The peak corresponds to the moment of foot strike. Each time a peak is detected, it is considered a gait cycle.

[0051] 4) Find the local maximum in the Z-axis acceleration data. This local maximum is the peak value, which corresponds to the instant when the foot touches the ground. Each detected peak value is considered as a gait cycle. Detecting the peak value through machine learning methods can improve the accuracy of step counting. In specific applications, the findpeaks function in Matlab can be used to find the local maximum in the Z-axis acceleration data.

[0052] In step 202, every time a gait cycle is recognized, the step count can be incremented by one. Thus, the step count can be obtained by counting the number of gait cycles.

[0053] In one implementation, step 202 can determine the step frequency and the step count in the following way: Extract the features related to gait from the measurement data of the IMU. These features include the angular velocity change features; Use the angular velocity change features and the magnetic field change features to determine the start of the gait cycle, perform peak detection on the acceleration data in the IMU measurement data to identify the gait cycle, count the number of gait cycles to obtain the step count, and calculate the time interval between two adjacent gait cycles to obtain the step frequency.

[0054] The start of the gait cycle is usually associated with the action of the foot touching the ground, which causes a sudden change in the angular velocity of the leg. The end of the gait cycle is associated with the action of the foot leaving the ground, which also causes a change in the angular velocity of the leg. The angular velocity change features can be used to identify the swing phase and the stance phase. In the gait cycle, the swing phase is the stage when the leg swings forward. During the swing phase, the angular velocity of the leg will have significant changes, especially at the knee joint and the hip joint, while the stance phase is the stage when the leg touches the ground and supports the body weight. The start and end of the swing phase can be identified through the angular velocity change features, that is, the start of the gait cycle can be determined. In addition, the angular velocity change features can also be used to analyze the stability and balance of the gait. Abnormal angular velocity changes may indicate abnormal gait or unstable walking.

[0055] The above features related to gait can also include magnetic field change features. The magnetic field change features can assist in confirming the start and end of the gait cycle, especially when the data of the accelerometer and the gyroscope are not sufficient to clearly identify the gait cycle. For example, when a person starts walking, the change in the magnetic field direction can be used as an auxiliary signal for the start of the gait cycle.

[0056] During walking, the direction of the human body relative to the Earth's magnetic field changes, especially when turning or changing direction. Therefore, the gait cycle can also be assisted in being identified through the characteristics of magnetic field changes, especially in turning or irregular gaits. Different walking patterns (such as normal walking, running, going up and down stairs) will result in different magnetic field change patterns. By analyzing these patterns, the gait can be classified, thus more accurately identifying the gait cycle.

[0057] The specific implementation process of performing peak detection on the acceleration data in the IMU measurement data to identify the gait cycle is the same as the specific implementation manner of the aforementioned peak detection, and will not be elaborated here. The peak of the acceleration data corresponds to the moment when the foot touches the ground, and each detected peak is considered to be a gait cycle.

[0058] In some embodiments, step 202 may include: implementing a state machine, running the state machine to identify each stage of the gait cycle according to the state judgment conditions of the acceleration data, angular velocity data, and magnetic field data in the IMU measurement data, such as heel strike, flat foot, heel off, toe off, etc. For each identified complete gait cycle, the step counter is incremented by 1. While counting steps, the step frequency can also be calculated in the aforementioned manner.

[0059] Specifically, different states are defined according to the characteristics of the gait cycle to represent each stage of the gait cycle. For example, the following states can be defined: Idle, Swing, Support, HeelStrike, Toe Off, etc. Then, determine the transition conditions between the states, and these conditions are based on the changes in the sensor data. For example, the transition from the idle state to the swing phase may require detecting a specific threshold change in the accelerometer data, while the transition from the swing phase to the support phase may require detecting the angular velocity change in the gyroscope data. Third, implement the logic of the state machine, including the definition of states, the definition of events, and the logic of state transitions. The state machine needs to be able to judge the current state according to the changes in the sensor data and the preset conditions, and perform state transitions when specific conditions are met. Fourth, identify each stage of the gait cycle according to the acceleration data, angular velocity data, and magnetic field data in the IMU measurement data through the operation of the state machine, and increment the step count when a specific state (such as heel strike or toe off) is detected.

[0060] From the above, a gait cycle identification system based on a state machine can be implemented. This system can identify each stage of the gait cycle according to the state judgment conditions of the acceleration data, angular velocity data, and magnetic field data in the IMU measurement data, and perform step counting and step frequency calculation.

[0061] In other implementation manners, in step 202, the step frequency and the number of steps can also be determined in the following manner: First, fuse the acceleration data, angular velocity data, and magnetic field data to obtain the user's attitude data, use the user's attitude data for gait analysis to obtain gait-related feature points (for example, feature points such as heel strike, flat foot, heel off, and toe off), and then identify the gait cycle through these feature points to obtain the number of steps, and calculate the time interval between adjacent gait cycles to obtain the step frequency.

[0062] Specifically, sensor fusion algorithms such as the Extended Kalman Filter (EKF) or complementary filter can be used to combine the acceleration data and angular velocity data to calculate the user's attitude data, and further fuse the magnetic field data with the fused acceleration data and angular velocity data to improve the accuracy of the attitude data. Thus, by fusing the data of the accelerometer, gyroscope, and magnetometer and then counting steps, the accuracy and reliability of step counting can be improved.

[0063] Further, step 202 may further include: performing a debounce operation during the process of identifying the gait cycle. Specifically, after identifying the gait cycle, determine whether the time interval between the current gait cycle and the previous gait cycle is greater than a pre-set time interval threshold. If so, count the current gait cycle into the number of steps; otherwise, ignore the current gait cycle and do not count it into the number of steps. Thus, misstep counting can be avoided.

[0064] In step 202, in order to filter unnecessary incorrect steps, it can be considered that the user is in motion such as walking or running only when the continuous movement is greater than a certain number of steps (for example, 5 steps). After that, step counting and step frequency calculation are performed. Thus, by calculating the number of steps and step frequency after ensuring that the user is in a stable motion state, the accuracy of the number of steps and step frequency can be further improved.

[0065] As can be seen from the above, through step 202, the number of steps and step frequency in motion such as walking or running can be effectively detected through IMU data. In specific applications, different methods can be adopted for step counting and step frequency calculation according to needs. Those skilled in the art should understand that the specific implementation manners of determining the number of steps and step frequency through IMU measurement data are not limited to the above several, and any other method can be applied to the present disclosure.

[0066] Before step 203, the method of the embodiment of the present disclosure may further include: calibrating the mapping relationship between the step length and step frequency for different heights. Specifically, the mapping relationship between the step length and step frequency for different heights can be calibrated in the following manner: Collect historical motion data of people with different heights, where the historical motion data includes the true number of steps and the actual motion distance at different speeds; and use the historical motion data of people with different heights to fit the mapping relationship between the step length and step frequency for different heights.

[0067] Among them, the historical exercise data of people with different heights can be extracted from publicly available datasets or collected after obtaining the consent of relevant personnel.

[0068] For example, multiple height ranges can be set, and for each height range, a first exercise dataset is constructed. The first exercise dataset includes the first historical exercise data of different people with actual heights within this height range. The first historical exercise data of each person can include the actual number of steps taken when running or walking a fixed distance at N predetermined speeds (N is an integer greater than 1). Use the first exercise dataset of each height range to fit the mapping relationship between stride length and stride frequency at this height range. Among them, the stride frequency can be calculated based on the speed, and the stride length can be obtained based on the distance and the number of steps. Thus, the mapping relationship between stride length and stride frequency for each height range can be obtained.

[0069] Another example is that multiple height ranges can be set, and for each height range, a second exercise dataset is constructed. The second exercise dataset includes the second historical exercise data of different people with actual heights within this height range. The second historical exercise data of each person can include the actual number of steps taken when running or walking for a certain duration at S predetermined speeds (S is an integer greater than 1) and the actual exercise distance detected by GPS. Use the second exercise dataset of each height range to fit the mapping relationship between stride length and stride frequency at this height range. Among them, the stride frequency can be calculated based on the speed, and the stride length can be obtained based on the actual exercise distance and the number of steps. Thus, the mapping relationship between stride length and stride frequency for each height range can be obtained.

[0070] The mapping relationship between stride length and stride frequency can be expressed as a functional relationship, and the parameters involved in this functional relationship can be determined by the aforementioned fitting method.

[0071] The mapping relationship between stride length and stride frequency can also be expressed as a mapping data table, which records the stride length values corresponding to different stride frequency values. These stride frequency values and stride length values are obtained by the aforementioned fitting method.

[0072] A person's stride length can be determined by height and stride frequency. By pre-calibrating the mapping relationship between stride length and stride frequency at different heights in the embodiments of the present disclosure, the stride length of the user can be quickly determined based on the user's height and stride frequency without performing complex calculations, which can further reduce the time required for pace measurement and complete the pace measurement quickly and efficiently, thus better meeting the user's immediate need for pace measurement.

[0073] In step 203, first find the mapping relationship between stride length and stride frequency at the user's height, and then use the stride frequency obtained in step 202 and the mapping relationship between stride length and stride frequency at the user's height to determine the user's current stride length.

[0074] In step 204, the pace may include the real-time pace and / or the average pace. Specifically, step 204 may include: determining the current pace of the user according to the currently determined stride and number of steps; and / or, calculating the average pace of the user according to the currently determined stride and number of steps and the previously obtained stride and number of steps. Thus, the pace of the user in the current exercise segment and the average pace of the entire exercise process can be measured in real time during the user's exercise, so that the user can have a more comprehensive understanding of his or her training situation.

[0075] Specifically, the current pace can be calculated by the following formula (3), and the average pace can be calculated by the following formula (4), where Vn represents the pace in the current nth measurement interval (i.e., the current pace), Nn represents the number of steps in the current nth measurement interval, Sn represents the stride in the current nth measurement interval, and Tn represents the duration of the current nth measurement interval. represents the number of steps in the i-th measurement interval, represents the stride of the ith measurement interval, Represents the duration of the i-th measurement interval, i=1,2,…,n.

[0076] (3) (4) Furthermore, after step 204, the method of the embodiment of the present disclosure may also include one or more of the following: 1) sending the pace to the sports equipment currently being used by the user, so as to display the pace on the display screen of the sports equipment; 2) announcing the pace by voice; 3) displaying the pace on the display screen of the wearable device. Thus, the pace reminder can be sent to the user in real time in various ways, so that the user can understand his or her pace in a timely manner.

[0077] The above method of the disclosed embodiment determines the number of steps and the cadence of the user through IMU measurement data, and then determines the user's stride based on the user's height and the mapping relationship between stride and cadence at different heights calibrated in advance, and finally obtains the user's pace through the stride and number of steps. The method of the disclosed embodiment can efficiently and quickly complete the pace measurement without complex calculations or the use of sensors such as GPS, and can be applied to various scenarios such as indoors, on treadmills, climbing machines, and outdoors, and can also meet the user's demand for instant pace measurement.

[0078] Figure 3 1 shows a schematic diagram of the structure of a speed measurement device provided by an embodiment of the present disclosure, wherein the speed measurement device is applied to a wearable device 100, and the wearable device includes an inertial measurement unit. Figure 3 , the pace measurement device 300 may include: An acquisition unit 301, configured to acquire measurement data of the inertial measurement unit when a user wears the wearable device; A gait detection unit 302, configured to determine the number of steps and the step frequency of the user according to the measurement data of the inertial measurement unit; A step length determination unit 303, configured to determine the step length of the user according to the height of the user, a pre-configured mapping relationship between step length and step frequency at different heights, and the step frequency; A pace determination unit 304, configured to determine the pace of the user according to the step length and the number of steps;

[0079] Wherein, the measurement data of the inertial measurement unit is acceleration data; alternatively, the measurement data of the inertial measurement unit includes acceleration data, angular velocity data, and magnetic field data.

[0080] Further, the gait detection unit 302 may specifically be configured to: extract features from the measurement data of the inertial measurement unit to obtain acceleration amplitude time series data, detect zero-crossing points of the acceleration amplitude time series data to determine the start of a gait cycle, perform peak detection on the acceleration amplitude time series data to identify the gait cycle, count the number of gait cycles to obtain the number of steps, and calculate the time interval between two adjacent gait cycles to obtain the step frequency.

[0081] Further, the gait detection unit 302 may specifically be configured to: extract gait-related features from the measurement data of the IMU, where the features include angular velocity change features; use the angular velocity change features to determine the start of a gait cycle, use the acceleration peaks to identify the gait cycle, count the number of gait cycles to obtain the number of steps, and calculate the time interval between two adjacent gait cycles to obtain the step frequency.

[0082] Further, the pace measurement device 300 may further include: a calibration unit 305, and the calibration unit 305 may be configured to calibrate the mapping relationship between step length and step frequency at different heights in the following manner: collect historical motion data of people with different heights, where the historical motion data includes the actual number of steps and the actual motion distance at different speeds; use the historical motion data of people with different heights to fit the mapping relationship between step length and step frequency at different heights.

[0083] Further, the pace determination unit 304 may specifically be configured to: determine the current pace of the user according to the currently determined step length and the number of steps; and / or calculate the average pace of the user according to the currently determined step length and the number of steps and the previously obtained step length and the number of steps.

[0084] Further, the pacing measurement device 300 may further include: a sending unit 306, configured to send the pacing to the sports equipment currently used by the user, so as to display the pacing through a display screen on the sports equipment; and / or, a voice broadcast unit 307, configured to broadcast the pacing by voice.

[0085] For other technical details of the pacing measurement device 300, reference may be made to the foregoing part of the pacing measurement method, which will not be elaborated herein. In specific applications, the pacing measurement device 300 may be implemented by the wearable device 100 in this article, or may be implemented as software in the wearable device 100.

[0086] In addition, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and the program includes instructions, and the instructions implement the steps of the foregoing pacing measurement method when executed by one or more processors.

[0087] Figure 4 The structural schematic diagram of the wearable device provided by the embodiment of the present disclosure is shown. Refer to Figure 4 , the wearable device 100 may include: one or more processors 101, and further includes a memory 102 storing one or more programs, which are executed by the foregoing one or more processors 101 to implement the method flows shown in the foregoing embodiments of the present disclosure and / or the program units corresponding to the respective units in the device.

[0088] Each component is interconnected using different buses and may be installed on a common motherboard or installed in other ways as needed. The processor 101 may process instructions executed within the wearable device, including instructions for storing graphic information of a user interface in the memory or on the memory to be displayed on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses may be used together with multiple memories and multiple memories.

[0089] The processor 101 may include one or more single-core processors or multi-core processors. The processor 101 may include a combination of any general-purpose processor or dedicated processor (such as an image processor, an application processor, a baseband processor, etc.).

[0090] The memory 102 is the computer-readable storage medium provided by the present disclosure, and may be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program instructions / units corresponding to the pacing measurement method shown in Figure 2 The processor 401 executes the non-transitory software programs, instructions, and units stored in the memory 102, thereby executing the programs, instructions, and units corresponding to the pacing measurement method shown in Figure 2 the pacing measurement method shown above.

[0091] The wearable device 100 may further include: an input device 103 and an output device 104. The processor 101, the memory 102, the input device 103, and the output device 104 may be connected through a bus or other means. Figure 4 Taking the connection through the bus as an example.

[0092] The input device 103 can receive input digital or character information and generate signal inputs related to user settings and function controls, such as input devices like touchscreens, keypads, mice, trackpads, touchpads, pointing sticks, one or more mouse buttons, trackballs, joysticks, etc. The output device 104 may include speakers, display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), etc. In some embodiments, the display device may be a touchscreen.

[0093] The wearable device 100 may further include an IMU 105. For the IMU 105, reference can be made to the relevant records above, and details will not be elaborated here.

[0094] The above program (also referred to as software, software application, or code) includes machine instructions for a programmable processor and can implement these computing programs using object-oriented programming languages, assembly, or machine language.

[0095] With the development of time and technology, the meaning of the medium has become more and more extensive. The dissemination path of computer programs is no longer limited to tangible media and can also be directly downloaded from the network, etc. Any combination of one or more computer-readable storage media can be adopted. The computer-readable storage medium can be, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component.

[0096] The above has introduced the technical solutions provided by the present disclosure in detail. Specific examples are used herein to elaborate on the principles and implementation manners of the present disclosure. The description of the above embodiments is only used to help understand the method and its core idea of the present disclosure; at the same time, for those of ordinary skill in the art, according to the idea of the present disclosure, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present disclosure.

[0097] The foregoing is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, etc. made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A pace measurement method, characterized in that: The pace measurement method is performed by a wearable device, wherein the wearable device includes an inertial measurement unit; The pace measurement method comprises: When the user wears the wearable device, obtaining measurement data of the inertial measurement unit; Determine the number of steps and the step frequency of the user according to the measurement data of the inertial measurement unit; Determine the stride of the user according to the height of the user, the preconfigured mapping relationship between stride length and stride frequency at different heights, and the stride frequency; The user's pace is determined based on the stride length and the number of steps.

2. The method according to claim 1, characterized in that The measurement data of the inertial measurement unit is acceleration data; or the measurement data of the inertial measurement unit includes acceleration data, angular velocity data and magnetic field data.

3. The method according to claim 1, characterized in that The method of determining the number of steps and the step frequency of the user based on the measurement data of the inertial measurement unit includes: performing feature extraction on the measurement data of the inertial measurement unit to obtain acceleration amplitude time series data, detecting the zero crossing point of the acceleration amplitude time series data to determine the beginning of the gait cycle, performing peak detection on the acceleration amplitude time series data to identify the gait cycle, counting the number of gait cycles to obtain the number of steps, and calculating the time interval between two adjacent gait cycles to obtain the step frequency.

4. The method according to claim 1, characterized in that: The method of determining the number of steps and the step frequency of the user based on the measurement data of the inertial measurement unit includes: extracting features related to gait from the measurement data of the IMU, wherein the features include angular velocity change features; determining the beginning of a gait cycle using the angular velocity change features, identifying the gait cycle using the acceleration peak value, counting the number of gait cycles to obtain the number of steps, and calculating the time interval between two adjacent gait cycles to obtain the step frequency.

5. The method according to claim 1, characterized in that: The mapping relationship between stride length and stride frequency at different heights is calibrated in the following way: Collect historical sports data of people of different heights, including actual number of steps and actual movement distance at different speeds; The historical sports data of people of different heights are used to fit the mapping relationship between stride length and stride frequency at different heights.

6. The method according to claim 1, characterized in that The determining the user's pace according to the stride and the number of steps includes: Determining the user's current pace based on the currently determined stride length and number of steps; and / or, The user's average pace is calculated based on the currently determined stride length and number of steps and the previously determined stride length and number of steps.

7. The method according to claim 1, characterized in that The method may further include one or more of the following: Sending the pace to a sports equipment currently being used by the user, so that the pace is displayed on a display screen on the sports equipment; The pace is announced by voice.

8. A speed measurement device, characterized in that: The pace measurement device is applied to a wearable device, wherein the wearable device comprises an inertial measurement unit; The speed measurement device comprises: An acquisition unit, configured to acquire measurement data of the inertial measurement unit when a user wears the wearable device; a gait detection unit, used to determine the number of steps and the step frequency of the user according to the measurement data of the inertial measurement unit; A stride determination unit, configured to determine the stride of the user according to the height of the user, a preconfigured mapping relationship between stride and stride frequency at different heights, and the stride frequency; A pace determination unit is used to determine the user's pace according to the stride and the number of steps.

9. A wearable device, characterized in that: include: One or more processors and a memory storing a program, wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, wherein the program comprises instructions, and when the instructions are executed by one or more processors, the instructions cause the computing device to perform the method according to any one of claims 1 to 7.

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