Method and device for determining running dynamic parameters of wearable equipment

Abstract

The invention discloses a method and device for determining running dynamic parameters of a wearable device, and the method comprises the steps: obtaining the three-axis acceleration data of a user in each stepping period, and obtaining the running dynamic parameters of the wearable device according to the three-axis acceleration data; determining a theoretical stepping distance generated by resultant acceleration in each stepping period and a time interval sequence of three-axis acceleration data in each stepping period, and determining a vertical amplitude value of running in each stepping period according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping period; according to the time interval sequence of the three-axis acceleration data and the three-axis grounding time judgment threshold value, the grounding time of running in each stepping period is determined. The running vertical amplitude value and the grounding time are determined by analyzing the data of the three-axis sensor, complex technologies such as machine learning, neural network training and image recognition do not need to be adopted, the complexity of running parameter calculation can be correspondingly reduced, energy consumption is reduced, and the accuracy is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of wearable devices, and in particular, to a method and device for determining running dynamic parameters of a wearable device. Background Art

[0002] In recent years, wearable devices have become very popular. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Wearable devices will bring great changes to our lives and perceptions.

[0003] At present, when wearable devices detect the parameters of a user's running posture, they usually use machine learning and neural network training to perform corresponding calculations. Some also use complex technologies such as image recognition and computer vision recognition to achieve this. These technologies involve complex calculations, which will cause a lot of energy consumption for wearable devices and have low accuracy. Summary of the invention

[0004] The embodiments of the present invention provide a method and apparatus for determining running dynamic parameters of a wearable device, which can reduce the complexity of running parameter calculation, save energy consumption, and improve accuracy.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining running dynamic parameters of a wearable device, comprising:

[0006] Obtain the three-axis acceleration data of the three-axis acceleration sensor in each step cycle of the user;

[0007] Determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle;

[0008] The vertical amplitude value of running in each stepping cycle is determined according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and the ground contact time of running in each stepping cycle is determined according to the time interval sequence of the three-axis acceleration data and the three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

[0009] Optionally, the acquiring of three-axis acceleration data of a three-axis acceleration sensor in each stepping cycle of the user includes:

[0010] Get the user's running cadence value and determine the duration of each step cycle during the running process:

[0011] Tstep =1 / fre step ………………(1)

[0012] Among them, T step Indicates the time taken for each step cycle, fre step Indicates the step frequency value;

[0013] The three-axis acceleration data of the three-axis acceleration sensor is collected within the time consumption in each step cycle.

[0014] Optionally, determining the theoretical stepping distance generated by the combined acceleration in each stepping cycle based on the three-axis acceleration data includes:

[0015] According to the three-axis acceleration data, the combined acceleration sequence of the three-axis acceleration in each step cycle is determined:

[0016]

[0017] in, represents the total acceleration sequence within the step cycle, They represent the x-axis acceleration value sequence, y-axis acceleration value sequence and z-axis acceleration value sequence in the stepping cycle respectively;

[0018] According to the combined acceleration sequence of the three-axis acceleration in each stepping cycle, the average value of the combined acceleration sequence in each stepping cycle is determined:

[0019]

[0020] Among them, acc mean It represents the average value of the combined acceleration sequence in each step cycle, and n is the number of acceleration points in the combined acceleration sequence;

[0021] According to the average value of the combined acceleration sequence in each step cycle and the time consumed in each step cycle, the theoretical step distance generated by the combined acceleration in each step cycle is determined:

[0022] Distance acc =1 / 2*acc mean *T 2 step ………………(4)

[0023] Among them, distance acc Indicates the theoretical step distance generated by the total acceleration in each step cycle, acc mean Represents the average value of the combined acceleration sequence in each step cycle, T 2 step Indicates the time taken for each step cycle.

[0024] Optionally, determining the vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle includes:

[0025] Get the stepping distance output by the positioning module in each stepping cycle;

[0026] According to the theoretical step distance, the step distance output by the positioning module in each step cycle and the trigonometric function relationship, the vertical amplitude value of running in each step cycle is approximately estimated:

[0027]

[0028] Among them, VO represents the vertical amplitude value of running in each step cycle, distance acc Indicates the theoretical step distance generated by the combined acceleration in each step cycle, distance gps Indicates the step distance output by the positioning module in each step cycle.

[0029] Optionally, analyzing the sampled data during the running test to determine the three-axis ground contact time judgment threshold includes:

[0030] Acquire the data of the three-axis acceleration sensor of the wearable device worn by the user during the running test and the data of the pressure sensor of the pedal pressure sensor test platform;

[0031] Determining a calibrated ground contact time period of a pressure sensor data curve according to data of a pressure sensor of the pedal pressure sensor test platform;

[0032] Determining a triaxial acceleration data sequence of the triaxial acceleration sensor corresponding to the calibrated contact time period of the pressure sensor data curve and the data of the triaxial acceleration sensor;

[0033] According to the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated touchdown time period, the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence is determined:

[0034]

[0035] Among them, Δt x It represents the time interval between each acceleration point in the x-axis of the three-axis acceleration sensor during the calibration contact time period, Δt y It represents the time interval between each acceleration point in the y-axis of the three-axis acceleration sensor during the calibration contact time period, Δt zrepresents the time interval between each acceleration point in the z-axis of the three-axis acceleration sensor during the calibration contact period, t x (i) represents the time of the i-th acceleration point in the x-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t y (i) represents the time of the i-th acceleration point in the y-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t z (i) represents the time of the i-th acceleration point in the z-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration touchdown period; i is an integer;

[0036] The time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence is averaged:

[0037]

[0038] Among them, Δtx average represents the average value of the time interval between each acceleration point on the x-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δty average The average value of the time interval between each acceleration point on the y-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δtz average represents the average value of the time interval between each acceleration point on the z-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence;

[0039] Similarly, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the non-contact time period in each step cycle is processed to obtain the average time interval between each acceleration point of the three-axis acceleration sequence in the non-contact time period in each step cycle;

[0040] According to the average value of the time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence and the average value of the time interval between each acceleration point in the three-axis acceleration sequence in the non-contact time period, statistical analysis is performed on the sampling data of the three-axis acceleration sensor of the user to determine the characteristic value of the time interval of the three-axis acceleration points in the calibrated contact time period and the characteristic value of the time interval of the three-axis acceleration points in the non-contact time period;

[0041] A touchdown time determination threshold is determined according to the time interval characteristic value of the three-axis acceleration points in the calibrated touchdown time period and the time interval characteristic value of the three-axis acceleration points in the non-touchdown time period.

[0042] Optionally, determining the ground contact time of running in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis ground contact time judgment threshold includes:

[0043] Determine the time interval sequence of each axis in the touchdown time period in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold;

[0044] According to the time interval sequence of each axis in the ground contact time period in each step cycle, the total ground contact time of each axis in each step cycle is determined:

[0045]

[0046] Among them, real_x contact Indicates the total ground contact time of the x-axis in each step cycle, real_y contact Indicates the total ground contact time of the y-axis in each step cycle, real_z contact represents the total contact time of the z-axis in each step cycle, real_time_x(k1) represents the k1th time interval in the time interval sequence of the x-axis of the contact time period in each step cycle, real_time_y(k2) represents the k2th time interval in the time interval sequence of the y-axis of the contact time period in each step cycle, and real_time_x(k3) represents the k3th time interval in the time interval sequence of the z-axis of the contact time period in each step cycle;

[0047] Based on the total ground contact time of each axis in each step cycle, the ground contact time of running in each step cycle is determined:

[0048]

[0049] step contact Indicates the ground contact time of running during each stride cycle.

[0050] In a second aspect, an embodiment of the present invention provides a device for determining running dynamic parameters of a wearable device, including:

[0051] An acquisition unit, used for acquiring three-axis acceleration data of a three-axis acceleration sensor in each step cycle of a user;

[0052] The processing unit is used to determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle; determine a vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and determine a ground contact time of running in each stepping cycle according to the time interval sequence of the three-axis acceleration data and a three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

[0053] Optionally, the acquiring unit is specifically used for:

[0054] Get the user's running cadence value and determine the duration of each step cycle during the running process:

[0055] T step =1 / fre step ………………(1)

[0056] Among them, T step Indicates the time taken for each step cycle, fre step Indicates the step frequency value;

[0057] The three-axis acceleration data of the three-axis acceleration sensor is collected within the time consumption in each step cycle.

[0058] Optionally, the processing unit is specifically configured to:

[0059] According to the three-axis acceleration data, the combined acceleration sequence of the three-axis acceleration in each step cycle is determined:

[0060]

[0061] in, represents the total acceleration sequence within the step cycle, They represent the x-axis acceleration value sequence, y-axis acceleration value sequence and z-axis acceleration value sequence in the stepping cycle respectively;

[0062] According to the combined acceleration sequence of the three-axis acceleration in each stepping cycle, the average value of the combined acceleration sequence in each stepping cycle is determined:

[0063]

[0064] Among them, acc mean It represents the average value of the combined acceleration sequence in each step cycle, and n is the number of acceleration points in the combined acceleration sequence;

[0065] According to the average value of the combined acceleration sequence in each step cycle and the time consumed in each step cycle, the theoretical step distance generated by the combined acceleration in each step cycle is determined:

[0066] Distance acc =1 / 2*acc mean *T 2 step ………………(4)

[0067] Among them, distance accIndicates the theoretical step distance generated by the total acceleration in each step cycle, acc mean Represents the average value of the combined acceleration sequence in each step cycle, T 2 step Indicates the time taken for each step cycle.

[0068] Optionally, the processing unit is specifically configured to:

[0069] Get the stepping distance output by the positioning module in each stepping cycle;

[0070] According to the theoretical step distance, the step distance output by the positioning module in each step cycle and the trigonometric function relationship, the vertical amplitude value of running in each step cycle is approximately estimated:

[0071]

[0072] Among them, VO represents the vertical amplitude value of running in each step cycle, distance acc Indicates the theoretical step distance generated by the combined acceleration in each step cycle, distance gps Indicates the step distance output by the positioning module in each step cycle.

[0073] Optionally, the processing unit is specifically configured to:

[0074] Acquire the data of the three-axis acceleration sensor of the wearable device worn by the user during the running test and the data of the pressure sensor of the pedal pressure sensor test platform;

[0075] Determining a calibrated ground contact time period of a pressure sensor data curve according to data of a pressure sensor of the pedal pressure sensor test platform;

[0076] Determining a triaxial acceleration data sequence of the triaxial acceleration sensor corresponding to the calibrated contact time period of the pressure sensor data curve and the data of the triaxial acceleration sensor;

[0077] According to the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated touchdown time period, the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence is determined:

[0078]

[0079] Among them, Δt x It represents the time interval between each acceleration point in the x-axis of the three-axis acceleration sensor during the calibration contact time period, Δt yIt represents the time interval between each acceleration point in the y-axis of the three-axis acceleration sensor during the calibration contact time period, Δt z represents the time interval between each acceleration point in the z-axis of the three-axis acceleration sensor during the calibration contact period, t x (i) represents the time of the i-th acceleration point in the x-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t y (i) represents the time of the i-th acceleration point in the y-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t z (i) represents the time of the i-th acceleration point in the z-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration touchdown period; i is an integer;

[0080] The time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence is averaged:

[0081]

[0082] Among them, Δtx average represents the average value of the time interval between each acceleration point on the x-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δty average The average value of the time interval between each acceleration point on the y-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δtz average represents the average value of the time interval between each acceleration point on the z-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence;

[0083] Similarly, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the non-contact time period in each step cycle is processed to obtain the average time interval between each acceleration point of the three-axis acceleration sequence in the non-contact time period in each step cycle;

[0084] According to the average value of the time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence and the average value of the time interval between each acceleration point in the three-axis acceleration sequence in the non-contact time period, statistical analysis is performed on the sampling data of the three-axis acceleration sensor of the user to determine the characteristic value of the time interval of the three-axis acceleration points in the calibrated contact time period and the characteristic value of the time interval of the three-axis acceleration points in the non-contact time period;

[0085] A touchdown time determination threshold is determined according to the time interval characteristic value of the three-axis acceleration points in the calibrated touchdown time period and the time interval characteristic value of the three-axis acceleration points in the non-touchdown time period.

[0086] Optionally, the processing unit is specifically configured to:

[0087] Determine the time interval sequence of each axis in the touchdown time period in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold;

[0088] According to the time interval sequence of each axis in the ground contact time period in each step cycle, the total ground contact time of each axis in each step cycle is determined:

[0089]

[0090] Among them, real_x contact Indicates the total ground contact time of the x-axis in each step cycle, real_y contact Indicates the total ground contact time of the y-axis in each step cycle, real_z contact represents the total contact time of the z-axis in each step cycle, real_time_x(k1) represents the k1th time interval in the time interval sequence of the x-axis of the contact time period in each step cycle, real_time_y(k2) represents the k2th time interval in the time interval sequence of the y-axis of the contact time period in each step cycle, and real_time_x(k3) represents the k3th time interval in the time interval sequence of the z-axis of the contact time period in each step cycle;

[0091] Based on the total ground contact time of each axis in each step cycle, the ground contact time of running in each step cycle is determined:

[0092]

[0093] step contact Indicates the ground contact time of running during each stride cycle.

[0094] In a third aspect, an embodiment of the present invention further provides a computing device, including:

[0095] A memory for storing program instructions;

[0096] The processor is used to call the program instructions stored in the memory and execute the above method for determining the running dynamic parameters of the wearable device according to the obtained program.

[0097] In a fourth aspect, an embodiment of the present invention further provides a computer-readable non-volatile storage medium, comprising computer-readable instructions. When a computer reads and executes the computer-readable instructions, the computer executes the above-mentioned method for determining the running dynamic parameters of a wearable device.

[0098] In the embodiment of the present invention, the three-axis acceleration data of the three-axis acceleration sensor in each step cycle of the user is obtained, and the theoretical step distance generated by the combined acceleration in each step cycle and the time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each step cycle are determined based on the three-axis acceleration data. The vertical amplitude value of running in each step cycle is determined based on the theoretical step distance and the step distance output by the positioning module in each step cycle, and the touchdown time of running in each step cycle is determined based on the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold. The three-axis touchdown time judgment threshold is determined by analyzing the sampled data during the running test. By analyzing the data of the three-axis sensor to determine the vertical amplitude value and touchdown time of running, there is no need to use complex technologies such as machine learning, neural network training, and image recognition, which can reduce the complexity of running parameter calculation accordingly, save energy consumption, and improve accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of 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.

[0100] Figure 1 A schematic diagram of a system architecture provided for an embodiment of the present invention;

[0101] Figure 2 A schematic diagram of a flow chart of a method for determining running dynamic parameters of a wearable device provided by an embodiment of the present invention;

[0102] Figure 3 A schematic diagram of vertical amplitude during running provided by an embodiment of the present invention;

[0103] Figure 4 A schematic diagram of the structure of an apparatus for determining running dynamic parameters of a wearable device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all 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.

[0105] First, Figure 1The structure shown is used as an example to introduce the wearable device applicable to the embodiment of the present invention. In the embodiment of the present invention, the wearable device 100 may include, but is not limited to, a radio frequency (RF) circuit 110, a memory 120, an input unit 130, a WiFi module 170, a display unit 140, a sensor 150, an audio circuit 160, a processor 180, and a motor 190.

[0106] Among them, those skilled in the art can understand that Figure 1 The structure of the wearable device 100 shown in the figure is only an example and not a limitation. The wearable device 100 may also include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0107] The RF circuit 110 can be used to receive and send 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 processed by the processor 180; in addition, the uplink data of the wearable device 100 is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA, LowNoiseAmplifier), a duplexer, etc. In addition, the RF circuit 110 can also communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the Global System for Mobile communication (GlobalSystem for Mobile communication, referred to as "GSM"), General Packet Radio Service (General PacketRadio Service, referred to as "GPRS"), Code Division Multiple Access (Code Division Multiple Access, referred to as "CDMA"), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, referred to as "WCDMA"), Long Term Evolution (Long Term Evolution, referred to as "LTE"), email, Short Messaging Service (Short Messaging Service, referred to as "SMS"), etc.

[0108] The memory 120 can be used to store software programs and modules, and the processor 180 executes various functional applications and data processing of the wearable device 100 by running the software programs and modules stored in the memory 120. The memory 120 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 100 (such as audio data, a phone book, etc.), etc. In addition, the memory 120 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.

[0109] The input unit 130 can be used to receive input digital or character information, and to generate key signals related to user settings and function control of the wearable device 100. Specifically, the input unit 130 may include a touch panel 131, a camera 132, and other input devices 133. The camera 132 can take pictures of the images to be acquired, thereby transmitting the images to the processor 180 for processing, and finally presenting the graphics to the user through the display panel 141. The touch panel 131, also known as a touch screen, can collect the user's touch operations on or near it (such as the user's operation on the touch panel 131 or near the touch panel 131 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 131 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, 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 the touch point coordinates, and then sends it to the processor 180, and can receive the command sent by the processor 180 and execute it. In addition, the touch panel 131 can be implemented in various types such as resistive, capacitive, infrared and surface acoustic wave. In addition to the touch panel 131 and the camera 132, the input unit 130 can also include other input devices 133. Specifically, other input devices 132 can 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 joystick, etc.

[0110] Among them, the display unit 140 can be used to display information input by the user or information provided to the user and various menus of the wearable device 100. The display unit 140 may include a display panel 141. Optionally, the display panel 141 may be configured in the form of a liquid crystal display unit (LCD, Liquid Crystal Display), an organic light-emitting diode (OLED, Organic Light-Emitting Diode), etc. Further, the touch panel 131 may cover the display panel 141. When the touch panel 131 detects a touch operation on or near it, it is transmitted to the processor 180 to determine the type of touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of touch event.

[0111] Among them, the visual output external display panel 141 that can be recognized by the human eye can be used as a display device in the embodiment of the present invention to display text information or image information. Figure 1 In the embodiment, the touch panel 131 and the display panel 141 are two independent components to implement the input and output functions of the wearable device 100, but in some embodiments, the touch panel 131 and the display panel 141 can be integrated to implement the input and output functions of the wearable device 100.

[0112] In addition, the wearable device 100 may further include at least one sensor 150, such as a posture sensor, a distance sensor, a light sensor, and other sensors.

[0113] Specifically, the attitude sensor can also be called a motion sensor, and as one of the motion sensors, an angular velocity sensor (also called a gyroscope) can be listed. When it is configured in the wearable device 100, it is used to measure the rotational angular velocity of the wearable device 100 in motion when it deflects or tilts. Thus, the gyroscope can accurately analyze and determine the actual action of the user using the wearable device 100, and then perform corresponding operations on the wearable device 100. For example: somatosensory, shake (shaking the wearable device 100 to achieve some functions), and inertial navigation based on the motion state of the object when there is no signal from the Global Positioning System (GPS) (such as in a tunnel).

[0114] The sensor may also be a light sensor, which is mainly used to collect information such as the wavelength and intensity of various light rays to adjust the backlight intensity of the display panel 141 .

[0115] In addition, in the embodiment of the present invention, as the sensor 150, other sensors such as a barometer, a hygrometer, a thermometer and an infrared sensor may also be configured, which will not be described in detail here.

[0116] The light sensor may further include a proximity sensor, which may turn off the display panel 141 and / or the backlight when the wearable device 100 is moved to the ear.

[0117] The audio circuit 160, the speaker 161, and the microphone 162 can provide an audio interface between the user and the wearable device 100. The audio circuit 160 can transmit the electrical signal converted from the received audio data to the speaker 161, which is converted into a sound signal for output; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, which is received by the audio circuit 160 and converted into audio data, and then the audio data is output to the processor 180 for processing, and then sent to, for example, another wearable device 100 through the RF circuit 110, or the audio data is output to the memory 120 for further processing.

[0118] WiFi is a short-range wireless transmission technology. The wearable device 100 can help users send and receive emails, browse web pages, and access streaming media through the WiFi module 170. It provides users with wireless broadband Internet access. Figure 1 A WiFi module 170 is shown, but it is understandable that it is not a necessary component of the wearable device 100 and can be omitted as needed without changing the essence of the invention.

[0119] The processor 180 is the control center of the wearable device 100. It uses various interfaces and lines to connect various parts of the entire wearable device 100, and executes various functions and processes data of the wearable device 100 by running or executing software programs and / or modules stored in the memory 120, and calling data stored in the memory 120, so as to monitor the wearable device 100 as a whole. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 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.

[0120] It is understandable that the above-mentioned modem processor may not be integrated into the processor 180.

[0121] The wearable device 100 may further include at least one motor 190. Since the wearable device 100 is a power-consuming device, the motor 190 may be a small motor. Meanwhile, a plurality of motors may be configured for the wearable device 100 according to the power that the motor can provide.

[0122] The wearable device 100 also includes a power supply (not shown) for supplying power to various components.

[0123] Preferably, the power supply can be logically connected to the processor 180 through a power management system, so that the power management system can manage charging, discharging, power consumption, etc. Although not shown, the wearable device 100 can also include a Bluetooth module, etc., which will not be repeated here.

[0124] It should be noted that the above Figure 1 The structure shown is only an example and is not limited to this embodiment of the present invention.

[0125] Figure 2 A process for determining running dynamic parameters of a wearable device provided by an embodiment of the present invention is exemplarily shown. The process can be executed by an apparatus for determining running dynamic parameters of a wearable device, and the apparatus can be a server or can be located in a server.

[0126] like Figure 2 As shown in the figure, the process specifically includes:

[0127] Step 201, obtaining three-axis acceleration data of a three-axis acceleration sensor in each step cycle of the user.

[0128] In the embodiment of the present invention, the wearable device adopts a three-axis acceleration sensor, through which the acceleration data of the user can be collected. This three-axis acceleration sensor can also be called a behavior data sensor, and can also include a gyroscope, a geomagnetic sensor, etc. In addition, a variety of other types of sensors can be set in the wearable device, such as an environmental sensor that can collect environmental information, such as generally including a temperature sensor, a humidity sensor, a light sensor, etc., which can collect environmental information such as temperature, humidity, light, etc. The physiological signal sensor can collect the physiological data of the user, for example, it can include a heart rate sensor, a blood oxygen sensor, a blood pressure sensor, etc., to collect the user's heart rate data, blood oxygen data, blood pressure data and other physiological data.

[0129] The three-axis acceleration sensor can be used to obtain the user's running cadence value and determine the duration of each step cycle during the running process:

[0130] T step =1 / fre step ………………(1)

[0131] Among them, T step Indicates the time taken for each step cycle, fre step Indicates the step frequency value;

[0132] Then, the three-axis acceleration data of the three-axis acceleration sensor within the time consumption of each step cycle can be collected.

[0133] Step 202: Determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle.

[0134] After obtaining the three-axis acceleration data, the theoretical step distance generated by the combined acceleration and the time interval sequence of the three-axis acceleration data can be determined. Specifically, based on the three-axis acceleration data, the combined acceleration sequence of the three-axis acceleration in each step cycle is determined:

[0135]

[0136] in, represents the total acceleration sequence within the step cycle, They represent the x-axis acceleration value sequence, y-axis acceleration value sequence and z-axis acceleration value sequence in the stepping cycle respectively;

[0137] According to the combined acceleration sequence of the three-axis acceleration in each stepping cycle, the average value of the combined acceleration sequence in each stepping cycle is determined:

[0138]

[0139] Among them, acc mean It represents the average value of the combined acceleration sequence in each step cycle, and n is the number of acceleration points in the combined acceleration sequence;

[0140] According to the average value of the combined acceleration sequence in each step cycle and the time consumed in each step cycle, the theoretical step distance generated by the combined acceleration in each step cycle is determined:

[0141] Distance acc =1 / 2*acc mean *T 2 step ………………(4)

[0142] Among them, distance acc Indicates the theoretical step distance generated by the total acceleration in each step cycle, acc mean Represents the average value of the combined acceleration sequence in each step cycle, T 2 step Indicates the time taken for each step cycle.

[0143] In addition, by extracting the time intervals between adjacent acceleration points in the three-axis acceleration data, the time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each step cycle can be obtained, wherein the acceleration point refers to the time point corresponding to each acceleration data in the three-axis acceleration data.

[0144] Step 203: Determine the vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and determine the ground contact time of running in each stepping cycle according to the time interval sequence of the three-axis acceleration data and the three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

[0145] After obtaining the theoretical step distance, the vertical amplitude value of the user's running can be determined. Specifically, the step distance output by the positioning module in each step cycle is obtained. According to the theoretical step distance, the step distance output by the positioning module in each step cycle and the trigonometric function relationship, the vertical amplitude value of running in each step cycle is approximately estimated:

[0146]

[0147] Among them, VO represents the vertical amplitude value of running in each step cycle, distance acc Indicates the theoretical step distance generated by the combined acceleration in each step cycle, distance gps Indicates the step distance output by the positioning module in each step cycle.

[0148] In the embodiment of the present invention, the vertical amplitude value refers to the distance that the user's body center of gravity moves up and down during the running process. Figure 3 shown.

[0149] It should be noted that the above three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test, and can be determined specifically according to the following steps:

[0150] First, the data of the three-axis acceleration sensor of the wearable device worn by the user during the running test and the data of the pressure sensor of the pedal pressure sensor test platform can be obtained. According to the data of the pressure sensor of the pedal pressure sensor test platform, the calibrated contact time period of the pressure sensor data curve is determined. According to the calibrated contact time period of the pressure sensor data curve and the data of the three-axis acceleration sensor, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated contact time period is determined. Among them, the pedal pressure sensor test platform is used to test the pressure of the user's pedal when running.

[0151] According to the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated touchdown time period, the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence is determined:

[0152]

[0153] Among them, Δt x It represents the time interval between each acceleration point in the x-axis of the three-axis acceleration sensor during the calibration contact time period, Δt y It represents the time interval between each acceleration point in the y-axis of the three-axis acceleration sensor during the calibration contact time period, Δt z represents the time interval between each acceleration point in the z-axis of the three-axis acceleration sensor during the calibration contact period, t x (i) represents the time of the i-th acceleration point in the x-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t y (i) represents the time of the i-th acceleration point in the y-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t z (i) represents the time of the i-th acceleration point in the z-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period; i is an integer.

[0154] Then, the time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence is averaged:

[0155]

[0156] Among them, Δtx average represents the average value of the time interval between each acceleration point on the x-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δty average The average value of the time interval between each acceleration point on the y-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δtz average It represents the average value of the time interval between each acceleration point on the z-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence.

[0157] Similarly, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the non-contact time period in each step cycle is processed to obtain the average time interval between each acceleration point of the three-axis acceleration sequence in the non-contact time period in each step cycle.

[0158] Finally, according to the average value of the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence and the average value of the time interval between each acceleration point in the three-axis acceleration sequence in the non-touchdown time period, a statistical analysis is performed on the sampling data of the user's three-axis acceleration sensor to determine the characteristic value of the time interval of the three-axis acceleration points in the calibrated touchdown time period and the characteristic value of the time interval of the three-axis acceleration points in the non-touchdown time period; according to the characteristic value of the time interval of the three-axis acceleration points in the calibrated touchdown time period and the characteristic value of the time interval of the three-axis acceleration points in the non-touchdown time period, a touchdown time judgment threshold is determined.

[0159] It should be noted that the determination of the above-mentioned characteristic values ​​requires the analysis of the data measured by the pressure sensor; for example, when the foot touches the ground, the acceleration points are relatively dense and the time interval is small; when the foot leaves the ground, the acceleration points are relatively sparse and the time interval is large. Therefore, after collecting a batch of data samples, the time interval threshold of the ground contact time and the time interval threshold of the non-ground contact time can be statistically obtained.

[0160] In the specific implementation, a large number of data samples can be collected, and the characteristic value feature_x of the time interval of the three-axis acceleration points in the touchdown period can be determined by statistical analysis method. contact 、feature_y contact 、feature_z contact The time interval feature value feature_x of the three-axis acceleration point during the non-touchdown period air 、feature_y air 、feature_z air According to the above two sets of characteristic values, the touchdown time judgment threshold thresholdx_ conta 、threshold_y contact 、threshold_z contact .

[0161] After obtaining the ground contact time judgment threshold according to the above steps, the real-time ground contact time calculation can be performed. Specifically:

[0162] According to the time interval sequence of the three-axis acceleration data and the three-axis contact time judgment threshold, the time interval sequence of each axis in the contact time period within each step cycle is determined.

[0163] That is to say, after the touchdown time judgment threshold of the three-axis acceleration data is obtained by statistics of a large number of data samples, the time interval sequence of each axis of the three-axis acceleration data that is less than threshold_x contact 、threshold_y contact 、threshold_z contactThe time interval of the three-axis acceleration data is determined as the moment when the foot touches the ground, that is, the ground contact time.

[0164] According to the time interval sequence of each axis in the ground contact time period in each step cycle, the total ground contact time of each axis in each step cycle is determined:

[0165]

[0166] Among them, real_x contact Indicates the total ground contact time of the x-axis in each step cycle, real_y contact Indicates the total ground contact time of the y-axis in each step cycle, real_z contact represents the total contact time of the z-axis in each stepping cycle, real_time_x(k1) represents the k1th time interval in the time interval sequence of the x-axis of the contact time period in each stepping cycle, real_time_y(k2) represents the k2th time interval in the time interval sequence of the y-axis of the contact time period in each stepping cycle, and real_time_x(k3) represents the k3th time interval in the time interval sequence of the z-axis of the contact time period in each stepping cycle.

[0167] That is, after obtaining the time interval sequence of each axis in the ground contact time period within each step cycle, the time intervals in the time interval sequence of each axis determined as the ground contact time are accumulated, referring to formula (8).

[0168] Based on the total ground contact time of each axis in each step cycle, the ground contact time of running in each step cycle is determined:

[0169]

[0170] Among them, step contact Indicates the ground contact time of running during each stride cycle.

[0171] Specifically, in a single step cycle, when the ground contact time real_x of each axis is obtained contact 、real_y contact 、real_z contact Afterwards, the estimated ground contact time of the three axes is averaged to approximate the ground contact time of a single step cycle.

[0172] The above embodiment shows that the three-axis acceleration data of the three-axis acceleration sensor in each step cycle of the user is obtained, and based on the three-axis acceleration data, the theoretical step distance generated by the combined acceleration in each step cycle and the time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each step cycle are determined, and the vertical amplitude value of the running in each step cycle is determined according to the theoretical step distance and the step distance output by the positioning module in each step cycle, and the touchdown time of the running in each step cycle is determined according to the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold, and the three-axis touchdown time judgment threshold is determined by analyzing the sampled data during the running test. By analyzing the data of the three-axis sensor to determine the vertical amplitude value and touchdown time of the running, there is no need to use complex technologies such as machine learning, neural network training, and image recognition, which can reduce the complexity of running parameter calculation accordingly, save energy consumption, and improve accuracy.

[0173] Based on the same technical concept, Figure 4 The structure of a device for determining running dynamic parameters of a wearable device provided by an embodiment of the present invention is exemplarily shown. The device can execute the process of determining the running dynamic parameters of the wearable device.

[0174] like Figure 4 As shown, the device may include:

[0175] The acquisition unit 401 is used to acquire the three-axis acceleration data of the three-axis acceleration sensor in each step cycle of the user;

[0176] The processing unit 402 is used to determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle; determine a vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and determine a ground contact time of running in each stepping cycle according to the time interval sequence of the three-axis acceleration data and a three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

[0177] Optionally, the acquiring unit 401 is specifically configured to:

[0178] Get the user's running cadence value and determine the duration of each step cycle during the running process:

[0179] T step =1 / fre step ………………(1)

[0180] Among them, T stepIndicates the time taken for each step cycle, fre step Indicates the step frequency value;

[0181] The three-axis acceleration data of the three-axis acceleration sensor is collected within the time consumption in each step cycle.

[0182] Optionally, the processing unit 402 is specifically configured to:

[0183] According to the three-axis acceleration data, the combined acceleration sequence of the three-axis acceleration in each step cycle is determined:

[0184]

[0185] in, represents the total acceleration sequence within the step cycle, They represent the x-axis acceleration value sequence, y-axis acceleration value sequence and z-axis acceleration value sequence in the stepping cycle respectively;

[0186] According to the combined acceleration sequence of the three-axis acceleration in each stepping cycle, the average value of the combined acceleration sequence in each stepping cycle is determined:

[0187]

[0188] Among them, acc mean It represents the average value of the combined acceleration sequence in each step cycle, and n is the number of acceleration points in the combined acceleration sequence;

[0189] According to the average value of the combined acceleration sequence in each step cycle and the time consumed in each step cycle, the theoretical step distance generated by the combined acceleration in each step cycle is determined:

[0190] Distance acc =1 / 2*acc mean *T 2 step ………………(4)

[0191] Among them, distance acc Indicates the theoretical step distance generated by the total acceleration in each step cycle, acc mean Represents the average value of the combined acceleration sequence in each step cycle, T 2 step Indicates the time taken for each step cycle.

[0192] Optionally, the processing unit 402 is specifically configured to:

[0193] Get the stepping distance output by the positioning module in each stepping cycle;

[0194] According to the theoretical step distance, the step distance output by the positioning module in each step cycle and the trigonometric function relationship, the vertical amplitude value of running in each step cycle is approximately estimated:

[0195]

[0196] Among them, VO represents the vertical amplitude value of running in each step cycle, distance acc Indicates the theoretical step distance generated by the combined acceleration in each step cycle, distance gps Indicates the step distance output by the positioning module in each step cycle.

[0197] Optionally, the processing unit 402 is specifically configured to:

[0198] Acquire the data of the three-axis acceleration sensor of the wearable device worn by the user during the running test and the data of the pressure sensor of the pedal pressure sensor test platform;

[0199] Determining a calibrated ground contact time period of a pressure sensor data curve according to data of a pressure sensor of the pedal pressure sensor test platform;

[0200] Determining a triaxial acceleration data sequence of the triaxial acceleration sensor corresponding to the calibrated contact time period of the pressure sensor data curve and the data of the triaxial acceleration sensor;

[0201] According to the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated touchdown time period, the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence is determined:

[0202]

[0203] Among them, Δt x It represents the time interval between each acceleration point in the x-axis of the three-axis acceleration sensor during the calibration contact time period, Δt y It represents the time interval between each acceleration point in the y-axis of the three-axis acceleration sensor during the calibration contact time period, Δt z represents the time interval between each acceleration point in the z-axis of the three-axis acceleration sensor during the calibration contact period, t x (i) represents the time of the i-th acceleration point in the x-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t y (i) represents the time of the i-th acceleration point in the y-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t z(i) represents the time of the i-th acceleration point in the z-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration touchdown period; i is an integer;

[0204] The time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence is averaged:

[0205]

[0206] Among them, Δtx average represents the average value of the time interval between each acceleration point on the x-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δty average The average value of the time interval between each acceleration point on the y-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δtz average represents the average value of the time interval between each acceleration point on the z-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence;

[0207] Similarly, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the non-contact time period in each step cycle is processed to obtain the average time interval between each acceleration point of the three-axis acceleration sequence in the non-contact time period in each step cycle;

[0208] According to the average value of the time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence and the average value of the time interval between each acceleration point in the three-axis acceleration sequence in the non-contact time period, statistical analysis is performed on the sampling data of the three-axis acceleration sensor of the user to determine the characteristic value of the time interval of the three-axis acceleration points in the calibrated contact time period and the characteristic value of the time interval of the three-axis acceleration points in the non-contact time period;

[0209] A touchdown time determination threshold is determined according to the time interval characteristic value of the three-axis acceleration points in the calibrated touchdown time period and the time interval characteristic value of the three-axis acceleration points in the non-touchdown time period.

[0210] Optionally, the processing unit 402 is specifically configured to:

[0211] Determine the time interval sequence of each axis in the touchdown time period in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold;

[0212] According to the time interval sequence of each axis in the ground contact time period in each step cycle, the total ground contact time of each axis in each step cycle is determined:

[0213]

[0214] Among them, real_x contact Indicates the total ground contact time of the x-axis in each step cycle, real_y contact Indicates the total ground contact time of the y-axis in each step cycle, real_z contact represents the total contact time of the z-axis in each step cycle, real_time_x(k1) represents the k1th time interval in the time interval sequence of the x-axis of the contact time period in each step cycle, real_time_y(k2) represents the k2th time interval in the time interval sequence of the y-axis of the contact time period in each step cycle, and real_time_x(k3) represents the k3th time interval in the time interval sequence of the z-axis of the contact time period in each step cycle;

[0215] Based on the total ground contact time of each axis in each step cycle, the ground contact time of running in each step cycle is determined:

[0216]

[0217] step contact Indicates the ground contact time of running during each stride cycle.

[0218] Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:

[0219] A memory for storing program instructions;

[0220] The processor is used to call the program instructions stored in the memory and execute the above method for determining the running dynamic parameters of the wearable device according to the obtained program.

[0221] Based on the same technical concept, an embodiment of the present invention also provides a computer-readable non-volatile storage medium, including computer-readable instructions. When a computer reads and executes the computer-readable instructions, the computer executes the above-mentioned method for determining the running dynamic parameters of a wearable device.

[0222] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0223] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0224] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0225] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0226] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining running dynamic parameters of a wearable device, characterized in that: include: Obtain the three-axis acceleration data of the three-axis acceleration sensor in each step cycle of the user; Determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle; The vertical amplitude value of running in each stepping cycle is determined according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and the ground contact time of running in each stepping cycle is determined according to the time interval sequence of the three-axis acceleration data and the three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

2. The method according to claim 1, characterized in that The obtaining of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle of the user includes: Get the user's running cadence value and determine the duration of each step cycle during the running process: T step =1 / fre step ………………(1) Among them, T step Indicates the time taken for each step cycle, fre step Indicates the step frequency value; The three-axis acceleration data of the three-axis acceleration sensor is collected within the time consumption in each step cycle.

3. The method according to claim 1, characterized in that Determining the theoretical stepping distance generated by the combined acceleration in each stepping cycle based on the three-axis acceleration data includes: According to the three-axis acceleration data, the combined acceleration sequence of the three-axis acceleration in each step cycle is determined: in, represents the total acceleration sequence within the step cycle, They represent the x-axis acceleration value sequence, y-axis acceleration value sequence and z-axis acceleration value sequence in the stepping cycle respectively; According to the combined acceleration sequence of the three-axis acceleration in each stepping cycle, the average value of the combined acceleration sequence in each stepping cycle is determined: Among them, acc mean It represents the average value of the combined acceleration sequence in each step cycle, and n is the number of acceleration points in the combined acceleration sequence; According to the average value of the combined acceleration sequence in each step cycle and the time consumed in each step cycle, the theoretical step distance generated by the combined acceleration in each step cycle is determined: distance acc =1 / 2*acc mean *T 2 step ………………(4) Among them, distance acc Indicates the theoretical step distance generated by the total acceleration in each step cycle, acc mean Represents the average value of the combined acceleration sequence in each step cycle, T 2 step Indicates the time taken for each step cycle.

4. The method according to claim 3, characterized in that Determining the vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle includes: Get the stepping distance output by the positioning module in each stepping cycle; According to the theoretical step distance, the step distance output by the positioning module in each step cycle and the trigonometric function relationship, the vertical amplitude value of running in each step cycle is approximately estimated: Among them, VO represents the vertical amplitude value of running in each step cycle, and distance acc Indicates the theoretical step distance generated by the combined acceleration in each step cycle, distance gps Indicates the step distance output by the positioning module in each step cycle.

5. The method according to any one of claims 1 to 4, characterized in that: The analyzing the sampled data during the running test to determine the three-axis ground contact time judgment threshold comprises: Acquire the data of the three-axis acceleration sensor of the wearable device worn by the user during the running test and the data of the pressure sensor of the pedal pressure sensor test platform; Determining a calibrated ground contact time period of a pressure sensor data curve according to data of a pressure sensor of the pedal pressure sensor test platform; Determining a triaxial acceleration data sequence of the triaxial acceleration sensor corresponding to the calibrated contact time period of the pressure sensor data curve and the data of the triaxial acceleration sensor; According to the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the calibrated touchdown time period, the time interval between each acceleration point corresponding to the calibrated touchdown time period in the three-axis acceleration data sequence is determined: Among them, Δt x It represents the time interval between each acceleration point in the x-axis of the three-axis acceleration sensor during the calibration contact time period, Δt y It represents the time interval between each acceleration point in the y-axis of the three-axis acceleration sensor during the calibration contact time period, Δt z represents the time interval between each acceleration point in the z-axis of the three-axis acceleration sensor during the calibration contact period, t x (i) represents the time of the i-th acceleration point in the x-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t y (i) represents the time of the i-th acceleration point in the y-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration contact time period, t z (i) represents the time of the i-th acceleration point in the z-axis acceleration data sequence of the three-axis acceleration data sequence within the calibration touchdown period; i is an integer; The time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence is averaged: Among them, Δtx average represents the average value of the time interval between each acceleration point on the x-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δty average The average value of the time interval between each acceleration point on the y-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence, Δtz average represents the average value of the time interval between each acceleration point on the z-axis corresponding to the calibrated contact time period in the three-axis acceleration data sequence; Similarly, the three-axis acceleration data sequence of the three-axis acceleration sensor corresponding to the non-contact time period in each step cycle is processed to obtain the average time interval between each acceleration point of the three-axis acceleration sequence in the non-contact time period in each step cycle; According to the average value of the time interval between each acceleration point corresponding to the calibrated contact time period in the three-axis acceleration data sequence and the average value of the time interval between each acceleration point in the three-axis acceleration sequence in the non-contact time period, statistical analysis is performed on the sampling data of the three-axis acceleration sensor of the user to determine the characteristic value of the time interval of the three-axis acceleration points in the calibrated contact time period and the characteristic value of the time interval of the three-axis acceleration points in the non-contact time period; A touchdown time determination threshold is determined according to the time interval characteristic value of the three-axis acceleration points in the calibrated touchdown time period and the time interval characteristic value of the three-axis acceleration points in the non-touchdown time period.

6. The method according to claim 5, characterized in that Determining the ground contact time of running in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis ground contact time judgment threshold includes: Determine the time interval sequence of each axis in the touchdown time period in each step cycle according to the time interval sequence of the three-axis acceleration data and the three-axis touchdown time judgment threshold; According to the time interval sequence of each axis in the ground contact time period in each step cycle, the total ground contact time of each axis in each step cycle is determined: Among them, real_x contact Indicates the total ground contact time of the x-axis in each step cycle, real_y contact Indicates the total ground contact time of the y-axis in each step cycle, real_z contact represents the total contact time of the z-axis in each step cycle, real_time_x(k1) represents the k1th time interval in the time interval sequence of the x-axis of the contact time period in each step cycle, real_time_y(k2) represents the k2th time interval in the time interval sequence of the y-axis of the contact time period in each step cycle, and real_time_x(k3) represents the k3th time interval in the time interval sequence of the z-axis of the contact time period in each step cycle; Based on the total ground contact time of each axis in each step cycle, the ground contact time of running in each step cycle is determined: step contact Indicates the ground contact time of running during each stride cycle.

7. A device for determining running dynamic parameters of a wearable device, characterized in that: include: An acquisition unit, used for acquiring three-axis acceleration data of a three-axis acceleration sensor in each step cycle of a user; The processing unit is used to determine, based on the three-axis acceleration data, a theoretical stepping distance generated by the combined acceleration in each stepping cycle and a time interval sequence of the three-axis acceleration data of the three-axis acceleration sensor in each stepping cycle; determine a vertical amplitude value of running in each stepping cycle according to the theoretical stepping distance and the stepping distance output by the positioning module in each stepping cycle; and determine a ground contact time of running in each stepping cycle according to the time interval sequence of the three-axis acceleration data and a three-axis ground contact time judgment threshold, wherein the three-axis ground contact time judgment threshold is determined by analyzing the sampled data during the running test.

8. The device according to claim 1, characterized in that The acquisition unit is specifically used for: Get the user's running cadence value and determine the duration of each step cycle during the running process: T step =1 / fre step ………………(1) Among them, T step Indicates the time taken for each step cycle, fre step Indicates the step frequency value; The three-axis acceleration data of the three-axis acceleration sensor is collected within the time consumption in each step cycle.

9. A computing device, characterized in that include: A memory for storing program instructions; A processor, configured to call the program instructions stored in the memory, and execute the method according to any one of claims 1 to 6 according to the obtained program.

10. A computer-readable non-volatile storage medium, characterized in that: The method comprises computer-readable instructions, and when a computer reads and executes the computer-readable instructions, the computer is caused to execute the method according to any one of claims 1 to 6.

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