Method and device for determining running speed of wearable device

Abstract

The invention discloses a method and device for determining running matching speed of wearable equipment, and the method comprises the steps: obtaining a resultant acceleration sequence of a three-axis acceleration sensor of the wearable equipment within a first preset time before the current moment, determining the standard deviation of the resultant acceleration sequence according to the acceleration data in the resultant acceleration sequence, and determining the running matching speed of the wearable equipment according to the standard deviation. According to the standard deviation of the resultant acceleration sequence and a speed matching sequence within second preset time before the current moment, the running speed matching of the user at the current moment is determined. The running speed setting is smoothly output by analyzing the acceleration data of the three-axis sensor, so that the problem of inaccurate speed setting data caused by unstable GPS (Global Positioning System) can be avoided, the data fluctuation of the running speed setting is reduced, and the speed setting accuracy is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of wearable devices, and more particularly to a method and apparatus for determining a running pace using 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 user running data, running pace is an important parameter. Currently, running pace is usually obtained by using data output by an acceleration sensor. When the GPS positioning of the wearable device is unstable, it usually causes fluctuations in the pace data, making the running pace inaccurate. Summary of the invention

[0004] The embodiments of the present invention provide a method and apparatus for determining running pace using a wearable device, which can avoid the problem of inaccurate pace data caused by GPS instability, reduce the data fluctuation of running pace, and improve the accuracy of pace.

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

[0006] Obtain a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before the current moment;

[0007] Determining a standard deviation of the combined acceleration sequence based on the acceleration data in the combined acceleration sequence;

[0008] The running pace of the user at the current moment is determined according to the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

[0009] Optionally, determining the running pace of the user at the current moment according to the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment includes:

[0010] Determining whether a standard deviation of the combined acceleration sequence is greater than a threshold;

[0011] If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and averaged to obtain the running pace of the user at the current moment.

[0012] Optionally, taking the pace within a third preset time close to the current moment from the pace sequence within the second preset time, performing average processing, and obtaining the running pace of the user at the current moment includes:

[0013] The running pace of the user at the current moment is determined according to the following formula (1):

[0014]

[0015] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, U is the third preset time, and i is a positive integer.

[0016] Optionally, the method further comprises:

[0017] If the standard deviation of the combined acceleration sequence is not greater than the threshold, the average value of the pace sequence within the second preset time is determined as whether the standard deviation of the combined acceleration sequence is greater than the threshold:

[0018]

[0019] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, and i is a positive integer.

[0020] Optionally, the acquiring a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before the current moment includes:

[0021] Obtaining three-axis acceleration data of the wearable device within a first preset time before the current moment;

[0022] According to the three-axis acceleration data, the combined acceleration sequence is determined:

[0023]

[0024] Among them, acc sum represents the combined acceleration sequence, They respectively represent the x-axis acceleration value sequence, the y-axis acceleration value sequence and the z-axis acceleration value sequence in the three-axis acceleration data within the first preset time before the current moment.

[0025] Optionally, determining the standard deviation of the combined acceleration sequence according to the acceleration data in the combined acceleration sequence includes:

[0026] According to the acceleration data of the combined acceleration sequence, the average value of the combined acceleration sequence is determined:

[0027]

[0028] Among them, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, where j is a positive integer;

[0029] According to the average value of the combined acceleration sequence, the standard deviation of the combined acceleration is determined:

[0030]

[0031] Among them, acc std represents the standard deviation of the combined acceleration sequence, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

[0032] In a second aspect, an embodiment of the present invention provides an apparatus for determining a running pace using a wearable device, comprising:

[0033] An acquisition unit, configured to acquire a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before a current moment;

[0034] The processing unit is used to determine the standard deviation of the combined acceleration sequence based on the acceleration data in the combined acceleration sequence; and determine the running pace of the user at the current moment based on the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

[0035] Optionally, the processing unit is specifically configured to:

[0036] Determining whether a standard deviation of the combined acceleration sequence is greater than a threshold;

[0037] If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and averaged to obtain the running pace of the user at the current moment.

[0038] Optionally, the processing unit is specifically configured to:

[0039] The running pace of the user at the current moment is determined according to the following formula (1):

[0040]

[0041] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, U is the third preset time, and i is a positive integer.

[0042] Optionally, the processing unit is further configured to:

[0043] If the standard deviation of the combined acceleration sequence is not greater than the threshold, the average value of the pace sequence within the second preset time is determined as whether the standard deviation of the combined acceleration sequence is greater than the threshold:

[0044]

[0045] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, and i is a positive integer.

[0046] Optionally, the acquiring unit is specifically used for:

[0047] Obtaining three-axis acceleration data of the wearable device within a first preset time before the current moment;

[0048] According to the three-axis acceleration data, the combined acceleration sequence is determined:

[0049]

[0050] Among them, acc sum represents the combined acceleration sequence, They respectively represent the x-axis acceleration value sequence, the y-axis acceleration value sequence and the z-axis acceleration value sequence in the three-axis acceleration data within the first preset time before the current moment.

[0051] Optionally, the processing unit is specifically configured to:

[0052] According to the acceleration data of the combined acceleration sequence, the average value of the combined acceleration sequence is determined:

[0053]

[0054] Among them, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, where j is a positive integer;

[0055] According to the average value of the combined acceleration sequence, the standard deviation of the combined acceleration is determined:

[0056]

[0057] Among them, acc std represents the standard deviation of the combined acceleration sequence, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

[0058] In a third aspect, an embodiment of the present invention further provides a computing device, including:

[0059] A memory for storing program instructions;

[0060] The processor is used to call the program instructions stored in the memory and execute the method for determining the running pace by the wearable device according to the obtained program.

[0061] 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 a running pace by a wearable device.

[0062] In an embodiment of the present invention, a combined acceleration sequence of a three-axis acceleration sensor of a wearable device within a first preset time before the current moment is obtained, and the standard deviation of the combined acceleration sequence is determined based on the acceleration data in the combined acceleration sequence. The running pace of the user at the current moment is determined based on the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment. By analyzing the acceleration data of the three-axis sensor to smoothly output the running pace, the problem of inaccurate pace data caused by GPS instability can be avoided, the data fluctuation of the running pace can be reduced, and the accuracy of the pace can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] 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.

[0064] Figure 1 A schematic diagram of a system architecture provided for an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of a flow chart of a method for determining a running pace using a wearable device provided in an embodiment of the present invention;

[0066] Figure 3A schematic diagram of the structure of an apparatus for determining running pace using a wearable device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] 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.

[0068] First, Figure 1 The 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.

[0069] 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.

[0070] 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, Low Noise Amplifier), 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 (Global System for Mobile communication, referred to as "GSM"), General Packet Radio Service (General Packet Radio 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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 1In the embodiment, the touch panel 131 and the display panel 141 are used as 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.

[0075] 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.

[0076] 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).

[0077] 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 .

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] It is understandable that the above-mentioned modem processor may not be integrated into the processor 180.

[0084] 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.

[0085] The wearable device 100 also includes a power supply (not shown) for supplying power to various components.

[0086] 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.

[0087] 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.

[0088] Figure 2 A process of determining a running pace by a wearable device provided by an embodiment of the present invention is exemplarily shown. The process can be executed by an apparatus for determining a running pace by a wearable device, and the apparatus can be a wearable device or can be located in the wearable device.

[0089] like Figure 2 As shown in the figure, the process specifically includes:

[0090] Step 201, obtaining a combined acceleration sequence of a three-axis acceleration sensor of a wearable device within a first preset time before a current moment.

[0091] 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.

[0092] The combined acceleration sequence can be determined by the three-axis acceleration sensor. Specifically, the three-axis acceleration data of the three-axis acceleration of the wearable device within the first preset time before the current moment can be obtained. Then, based on the acceleration data of the combined acceleration sequence, the average value of the combined acceleration sequence is determined:

[0093]

[0094] Among them, acc sum represents the combined acceleration sequence, They respectively represent the x-axis acceleration value sequence, the y-axis acceleration value sequence and the z-axis acceleration value sequence in the three-axis acceleration data within the first preset time before the current moment.

[0095] It should be noted that the first preset time can be set based on experience. For example, if the first preset time is 10, it can represent the three-axis acceleration data of the three-axis acceleration sensor within 10 seconds before the current moment. The acceleration data is obtained here in the form of window sliding. The first preset time can be used as the window time length of the window sliding, and then the window is slid once per second according to the window time length to obtain the data corresponding to the window time length.

[0096] Step 202: determining the standard deviation of the combined acceleration sequence according to the acceleration data in the combined acceleration sequence.

[0097] After the combined acceleration sequence is obtained, the average value of the combined acceleration sequence can be determined based on the acceleration data of the combined acceleration sequence:

[0098]

[0099] Among them, acc meanrepresents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

[0100] Then, according to the average value of the combined acceleration sequence, the standard deviation of the combined acceleration is determined:

[0101]

[0102] Among them, acc std represents the standard deviation of the combined acceleration sequence, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

[0103] Step 203, determining the running pace of the user at the current moment according to the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

[0104] After obtaining the standard deviation of the combined acceleration sequence, the running pace of the user at the current moment can be determined. Specifically, it is determined whether the standard deviation of the combined acceleration sequence is greater than a threshold. If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and the average value is processed to obtain the running pace of the user at the current moment.

[0105] The running pace of the user at the current moment can be determined according to the following formula (1):

[0106]

[0107] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, U is the third preset time, and i is a positive integer.

[0108] It should be noted that the second preset time and the third preset time can be set based on experience, and the second preset time is greater than the third preset time.

[0109] For example, the second preset time is 10s and the third preset time is 6s. At this time, the pace sequence within the second preset time before the current moment is a pace sequence within 10s, that is, there are 10 paces in the sequence. At this time, it is necessary to take the pace within the third preset time before the current moment from the 10 paces, that is, take the 6 paces closest to the current moment from the 10 paces, that is, starting from the i=W-U+1=10-6+1=5 pace and ending at the 10th pace, 6 paces are obtained.

[0110] During the specific implementation process, a long window time length can be set to save the running pace per second, that is, the second preset time is the long window time length. When determining the running pace at the current moment, the corresponding running pace can be obtained from the long window time length for calculation.

[0111] If the standard deviation of the combined acceleration sequence is not greater than the threshold, the average value of the pace sequence within the second preset time is determined as whether the standard deviation of the combined acceleration sequence is greater than the threshold:

[0112]

[0113] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, and i is a positive integer.

[0114] The above embodiment shows that the combined acceleration sequence of the three-axis acceleration sensor of the wearable device within the first preset time before the current moment is obtained, and the standard deviation of the combined acceleration sequence is determined based on the acceleration data in the combined acceleration sequence. The running pace of the user at the current moment is determined based on the standard deviation of the combined acceleration sequence and the pace sequence within the second preset time before the current moment. By analyzing the acceleration data of the three-axis sensor to smoothly output the running pace, the problem of inaccurate pace data caused by GPS instability can be avoided, the data fluctuation of the running pace can be reduced, and the accuracy of the pace can be improved.

[0115] Based on the same technical concept, Figure 3 The structure of an apparatus for determining a running pace using a wearable device provided by an embodiment of the present invention is exemplarily shown. The apparatus can execute a process for determining a running pace using a wearable device.

[0116] like Figure 3 As shown, the device may include:

[0117] An acquisition unit 301 is used to acquire a combined acceleration sequence of a three-axis acceleration sensor of a wearable device within a first preset time before a current moment;

[0118] The processing unit 302 is used to determine the standard deviation of the combined acceleration sequence based on the acceleration data in the combined acceleration sequence; and determine the running pace of the user at the current moment based on the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

[0119] Optionally, the processing unit 302 is specifically configured to:

[0120] Determining whether a standard deviation of the combined acceleration sequence is greater than a threshold;

[0121] If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and averaged to obtain the running pace of the user at the current moment.

[0122] Optionally, the processing unit 302 is specifically configured to:

[0123] The running pace of the user at the current moment is determined according to the following formula (1):

[0124]

[0125] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, U is the third preset time, and i is a positive integer.

[0126] Optionally, the processing unit 302 is further configured to:

[0127] If the standard deviation of the combined acceleration sequence is not greater than the threshold, the average value of the pace sequence within the second preset time is determined as whether the standard deviation of the combined acceleration sequence is greater than the threshold:

[0128]

[0129] Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, and i is a positive integer.

[0130] Optionally, the acquiring unit 301 is specifically configured to:

[0131] Obtaining three-axis acceleration data of the wearable device within a first preset time before the current moment;

[0132] According to the three-axis acceleration data, the combined acceleration sequence is determined:

[0133]

[0134] Among them, acc sum represents the combined acceleration sequence, They respectively represent the x-axis acceleration value sequence, the y-axis acceleration value sequence and the z-axis acceleration value sequence in the three-axis acceleration data within the first preset time before the current moment.

[0135] Optionally, the processing unit 302 is specifically configured to:

[0136] According to the acceleration data of the combined acceleration sequence, the average value of the combined acceleration sequence is determined:

[0137]

[0138] Among them, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, where j is a positive integer;

[0139] According to the average value of the combined acceleration sequence, the standard deviation of the combined acceleration is determined:

[0140]

[0141] Among them, acc std represents the standard deviation of the combined acceleration sequence, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

[0142] Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:

[0143] A memory for storing program instructions;

[0144] The processor is used to call the program instructions stored in the memory and execute the method for determining the running pace by the wearable device according to the obtained program.

[0145] 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 a running pace using a wearable device.

[0146] 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.

[0147] 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 comprising 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.

[0148] 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.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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.

[0150] 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 pace using a wearable device, characterized in that: include: Obtain a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before the current moment; Determining a standard deviation of the combined acceleration sequence based on the acceleration data in the combined acceleration sequence; The running pace of the user at the current moment is determined according to the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

2. The method according to claim 1, characterized in that The determining the running pace of the user at the current moment according to the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment includes: Determining whether a standard deviation of the combined acceleration sequence is greater than a threshold; If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and averaged to obtain the running pace of the user at the current moment.

3. The method according to claim 2, characterized in that The taking the pace within the third preset time close to the current moment from the pace sequence within the second preset time, performing average processing, and obtaining the running pace of the user at the current moment, comprises: The running pace of the user at the current moment is determined according to the following formula (1): Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, U is the third preset time, and i is a positive integer.

4. The method according to claim 2, characterized in that The method further comprises: If the standard deviation of the combined acceleration sequence is not greater than the threshold, the average value of the pace sequence within the second preset time is determined as whether the standard deviation of the combined acceleration sequence is greater than the threshold: Among them, pace is the running pace of the user at the current moment, pace(i) is the i-th pace in the pace sequence within the second preset time before the current moment, W is the second preset time, and i is a positive integer.

5. The method according to any one of claims 1 to 4, characterized in that: The step of obtaining a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before the current moment includes: Obtaining three-axis acceleration data of the wearable device within a first preset time before the current moment; According to the three-axis acceleration data, the combined acceleration sequence is determined: Among them, acc sum represents the combined acceleration sequence, They respectively represent the x-axis acceleration value sequence, the y-axis acceleration value sequence and the z-axis acceleration value sequence in the three-axis acceleration data within the first preset time before the current moment.

6. The method according to claim 5, characterized in that The step of determining the standard deviation of the combined acceleration sequence according to the acceleration data in the combined acceleration sequence includes: According to the acceleration data of the combined acceleration sequence, the average value of the combined acceleration sequence is determined: Among them, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, where j is a positive integer; According to the average value of the combined acceleration sequence, the standard deviation of the combined acceleration is determined: Among them, acc std represents the standard deviation of the combined acceleration sequence, acc mean represents the average value of the combined acceleration sequence, n is the number of acceleration data in the combined acceleration sequence, acc sum (j) is the jth acceleration data in the combined acceleration sequence, and j is a positive integer.

7. A device for determining running pace using a wearable device, characterized in that: include: An acquisition unit, configured to acquire a combined acceleration sequence of a three-axis acceleration sensor of the wearable device within a first preset time before a current moment; The processing unit is used to determine the standard deviation of the combined acceleration sequence based on the acceleration data in the combined acceleration sequence; and determine the running pace of the user at the current moment based on the standard deviation of the combined acceleration sequence and the pace sequence within a second preset time before the current moment.

8. The device according to claim 7, characterized in that The processing unit is specifically used for: Determining whether a standard deviation of the combined acceleration sequence is greater than a threshold; If so, the pace within the third preset time close to the current moment is taken from the pace sequence within the second preset time, and averaged to obtain the running pace of the user at the current moment.

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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