A step counting method and device based on acceleration sensor

Abstract

The present invention discloses a step counting method and device based on an acceleration sensor, the method comprising: a wearable device acquires data from an acceleration sensor, detects the data from the acceleration sensor according to a preset sliding window, continuously extracts the walking frequency, and updates the estimated frequency in real time. Determine whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency, if so, estimate the number of steps by time difference, and include it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state according to the walking frequency and the estimated frequency, do not update the total number of steps. The walking process is divided into a strong walking or running state and a weak walking or running state by different walking frequencies in different time periods, so that the statistics of walking counts are further refined, the loss of steps is reduced, and the accuracy of counting is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of smart devices, and in particular to a step counting method and device based on an acceleration sensor. 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] Currently, wearable devices can count walking steps, but most of them collect the signal frequency of the acceleration sensor. This method is relatively simple, and the walking data obtained is inaccurate, and some data may be missed. Therefore, a counting solution that can improve the accuracy of walking data is urgently needed. Summary of the invention

[0004] The embodiment of the present invention provides a step counting method and device based on an acceleration sensor, which can automatically count the number of steps of a user during walking and improve the counting accuracy.

[0005] In a first aspect, an embodiment of the present invention provides a step counting method based on an acceleration sensor, comprising:

[0006] The wearable device obtains data from the acceleration sensor;

[0007] The wearable device continuously extracts walking frequency based on data of the acceleration sensor detected by a preset sliding window, and updates the estimated frequency in real time;

[0008] The wearable device determines whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency;

[0009] If so, the wearable device estimates the number of steps through the time difference and includes it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state based on the walking frequency and the estimated frequency, the total number of steps is not updated.

[0010] Optionally, the wearable device continuously extracts the walking frequency based on the data of the acceleration sensor detected by a preset sliding window, and updates the estimated frequency in real time, including:

[0011] When the wearable device detects that the data of the acceleration sensor in the preset sliding window has a periodic signal, it counts the continuous peak intervals in the preset sliding window and performs mean processing to obtain the walking frequency in the preset sliding window;

[0012] The wearable device performs mean processing on the walking frequency in the preset sliding window this time and the walking frequency in the preset sliding window last time to obtain the estimated frequency, and updates it in real time.

[0013] Optionally, the wearable device determines whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency, including:

[0014] When the wearable device determines that the difference between the walking frequency and the estimated frequency is continuously less than a preset threshold, it determines that the wearable device is in a strong walking and running state; otherwise, it determines that the wearable device is in a non-strong walking and running state.

[0015] Optionally, when the wearable device determines that the wearable device is in a weak walking or running state according to the walking frequency and the estimated frequency, the total number of steps is not updated, including:

[0016] When the wearable device determines that the difference between the walking frequency and the estimated frequency is greater than a preset threshold, it determines that the wearable device is in a weak walking and running state, continuously counts the number of steps in the weak walking and running state, and does not update the total number of steps.

[0017] Optionally, after the wearable device determines that it is in a weak walking or running state, the process further includes:

[0018] When the wearable device determines to re-enter the strong walking and running state, the number of steps counted in the weak walking and running state before re-entering the strong walking and running state is accumulated to the total number of steps, and the number of steps after re-entering the strong walking and running state is continued to be accumulated to update the total number of steps.

[0019] Optionally, after the wearable device determines that it is in a weak walking or running state, the process further includes:

[0020] When the wearable device determines that the duration of the weak walking and running state exceeds a preset time threshold, it is determined that the wearable device is in a non-walking and running state, and the total number of steps is not updated.

[0021] In a second aspect, an embodiment of the present invention provides a step counting device based on an acceleration sensor, comprising:

[0022] An acquisition unit, used for acquiring data of an acceleration sensor;

[0023] A processing unit is used to continuously extract the walking frequency based on the data of the acceleration sensor detected by a preset sliding window, and update the estimated frequency in real time; determine whether the wearable device is in a strong walking or running state based on the walking frequency and the estimated frequency; if so, estimate the number of steps through the time difference and include it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state based on the walking frequency and the estimated frequency, the total number of steps is not updated.

[0024] Optionally, the processing unit is specifically used for:

[0025] When detecting that the data of the acceleration sensor in the preset sliding window has a periodic signal, counting the continuous peak intervals in the preset sliding window, and performing mean processing to obtain the walking frequency in the preset sliding window;

[0026] The walking frequency in the preset sliding window this time and the walking frequency in the preset sliding window last time are averaged to obtain the estimated frequency, and the estimated frequency is updated in real time.

[0027] Optionally, the processing unit is specifically used for:

[0028] When it is determined that the difference between the walking frequency and the estimated frequency is continuously less than a preset threshold, it is determined that the wearable device is in a strong walking and running state; otherwise, it is determined that the wearable device is in a non-strong walking and running state.

[0029] Optionally, the processing unit is specifically used for:

[0030] When it is determined that the difference between the walking frequency and the estimated frequency is greater than a preset threshold, it is determined that the wearable device is in a weak walking and running state, the number of steps in the weak walking and running state is continuously counted, and the total number of steps is not updated.

[0031] Optionally, the processing unit is further used for:

[0032] After determining that the user is in the weak walking and running state, when determining to re-enter the strong walking and running state, the number of steps counted in the weak walking and running state before re-entering the strong walking and running state is accumulated to the total number of steps, and the number of steps after re-entering the strong walking and running state is continued to be accumulated to update the total number of steps.

[0033] Optionally, the processing unit is further used for:

[0034] After determining that the wearable device is in a weak walking and running state, when it is determined that the duration of the weak walking and running state exceeds a preset time threshold, it is determined that the wearable device is in a non-walking and running state, and the total number of steps is not updated.

[0035] In a third aspect, an embodiment of the present invention further provides a computing device, including:

[0036] A memory for storing program instructions;

[0037] The processor is used to call the program instructions stored in the memory and execute the above-mentioned step counting method based on the acceleration sensor according to the obtained program.

[0038] 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 step counting method based on an acceleration sensor.

[0039] In an embodiment of the present invention, the wearable device obtains data from an acceleration sensor, detects the data from the acceleration sensor based on a preset sliding window, continuously extracts the walking frequency, and updates the estimated frequency in real time. Determine whether the wearable device is in a strong walking or running state based on the walking frequency and the estimated frequency. If so, estimate the number of steps based on the time difference and include it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state based on the walking frequency and the estimated frequency, the total number of steps is not updated. The walking process is divided into a strong walking or running state and a weak walking or running state by different walking frequencies in different time periods, which further refines the statistics of walking counts, reduces the loss of steps, and improves the accuracy of counting. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 A schematic diagram of a system architecture provided for an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a flow chart of a step counting method based on an acceleration sensor provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a flow chart of a step counting method based on an acceleration sensor provided in an embodiment of the present invention;

[0044] Figure 4 A schematic structural diagram of a step counting device based on an acceleration sensor provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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 150 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 132. 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.

[0051] 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.

[0052] 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 used as two independent components to implement the input and output functions of the wearable device 100. However, 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.

[0053] 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.

[0054] 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, shaking (shaking the wearable device 100 to achieve some functions), and inertial navigation based on the object's motion state when there is no signal from the Global Positioning System (GPS) (such as in a tunnel).

[0055] 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 .

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] It is understandable that the above-mentioned modem processor may not be integrated into the processor 180.

[0062] 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.

[0063] The wearable device 100 also includes a power supply (not shown) for supplying power to various components.

[0064] 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.

[0065] 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.

[0066] Figure 2 A step counting process based on an acceleration sensor provided in an embodiment of the present invention is exemplarily shown. The process can be executed by a step counting device based on an acceleration sensor, and the device can be a wearable device or can be located in a wearable device.

[0067] like Figure 2 As shown in the figure, the process specifically includes:

[0068] Step 201: The wearable device obtains data from an acceleration sensor.

[0069] In an embodiment of the present invention, the wearable device can collect data from the acceleration sensor, that is, three-axis acceleration, in real time while the user is walking, to form an acceleration curve.

[0070] In step 202, the wearable device continuously extracts walking frequency based on data of the acceleration sensor detected by a preset sliding window, and updates the estimated frequency in real time.

[0071] Specifically, when the wearable device detects that the data of the acceleration sensor in the preset sliding window has a periodic signal, it counts the continuous peak intervals in the preset sliding window and performs mean processing to obtain the walking frequency in the preset sliding window. Then the wearable device averages the walking frequency in this preset sliding window with the walking frequency in the previous preset sliding window to obtain an estimated frequency, and updates it in real time. The preset sliding window can be set based on experience, which is a time window.

[0072] For example, when it is detected that the data of the acceleration sensor in the preset sliding window has a periodic signal, 6 consecutive groups of peak intervals can be collected. At this time, the 6 groups of peak intervals can be averaged to obtain the walking frequency in the preset sliding window, which is an estimated value. Then, the walking frequency extracted this time and the walking frequency extracted last time are averaged to obtain an estimated frequency.

[0073] Step 203: The wearable device determines whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency.

[0074] Specifically, when the wearable device determines that the difference between the walking frequency and the estimated frequency is continuously less than a preset threshold, it determines that the wearable device is in a strong walking and running state; otherwise, it determines that the wearable device is in a non-strong walking and running state. The preset threshold can be set based on experience. The strong walking and running state can be understood as a state with a high walking frequency.

[0075] The non-strong walking and running state may also be referred to as a weak walking and running state. As long as the difference between the walking frequency and the estimated frequency is greater than a preset threshold, it can be determined to be a weak walking and running state.

[0076] Step 204: If yes, the wearable device estimates the number of steps by the time difference and includes it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state based on the walking frequency and the estimated frequency, the total number of steps is not updated.

[0077] Specifically, the wearable device calculates the interval between each step (i.e., the time difference) based on the walking frequency, and then accumulates the steps based on the time and counts them into the total number of steps. When the wearable device determines that the difference between the walking frequency and the estimated frequency is greater than the preset threshold, it determines that the wearable device is in a weak walking and running state, and continues to count the steps in the weak walking and running state, without updating the total number of steps.

[0078] It should be noted that in the weak walking and running state, the number of steps during the duration of the weak walking and running state can be stored through another variable, but it will not be accumulated into the total number of steps, that is, the total number of steps will not be updated.

[0079] After the weak walking and running state is determined, if it is determined to re-enter the strong walking and running state, the steps counted in the weak walking and running state before re-entering the strong walking and running state are accumulated to the total step count, and the steps after re-entering the strong walking and running state are continued to be accumulated to update the total step count. At this time, if the conditions are met, the steps counted during the weak walking and running state can be accumulated to the total step count to achieve the update of the total step count, and the total step count after re-entering the strong walking and running state will continue to be updated.

[0080] If it is determined that the duration of the weak walking and running state exceeds the preset time threshold, it is determined that the wearable device is in the non-walking and running state, and the total number of steps is not updated. The preset time threshold can be set based on experience.

[0081] In order to better explain the embodiment of the present invention, the above-mentioned step counting process based on the acceleration sensor will be described in a specific implementation scenario below. Figure 3 As shown, including:

[0082] Step 301, acquiring data from an acceleration sensor.

[0083] Wearable devices can collect data from acceleration sensors in real time while the user is walking.

[0084] Step 302, whether a periodic signal is detected, if so, proceed to step 303, otherwise proceed to step 301.

[0085] The wearable device detects whether there is a periodic signal in the data of the acceleration sensor within the preset sliding window.

[0086] Step 303, determining the walking frequency.

[0087] When it is detected that the data of the acceleration sensor in the preset sliding window has a periodic signal, the continuous peak intervals in the preset sliding window are counted and averaged to obtain the walking frequency in the preset sliding window.

[0088] Step 304: The difference between the current frequency and the original frequency is lower than the threshold. If so, proceed to step 305; otherwise, proceed to step 311.

[0089] When the difference between the walking frequency and the estimated frequency is continuously smaller than the preset threshold, it is determined that the wearable device is in a strong walking or running state.

[0090] Step 305, accumulating the frequency stabilization time.

[0091] When the cumulative frequency stability time is continuously less than a preset threshold, it can be determined that the state is a strong walking or running state.

[0092] Step 306 , whether the duration is greater than the time threshold, if so, proceed to step 307 , otherwise proceed to step 305 .

[0093] When the duration is greater than the time threshold, that is, when the difference between the walking frequency and the estimated frequency is continuously less than the preset threshold.

[0094] Step 307, enter the strong walking and running state.

[0095] Step 308, update the number of steps in real time.

[0096] Step 309 , the difference between the current frequency and the original frequency is greater than the threshold. If so, proceed to step 310 , otherwise proceed to step 308 .

[0097] Determine whether the difference between the walking frequency and the estimated frequency is greater than a preset threshold, and if so, enter a weak walking and running state.

[0098] Step 310 , the accumulated time is greater than the experience threshold. If so, proceed to step 311 , otherwise proceed to step 309 .

[0099] If the accumulated time in the weak walking and running state is greater than the experience threshold, you can jump out of the strong walking and running state and enter the non-walking and running state.

[0100] Step 311, exit the strong walking and running state.

[0101] 1. Detect periodic signals through acceleration data and estimate potential walking frequency;

[0102] 2. Continuously extract frequency through sliding window and update the estimated frequency in real time;

[0103] 3. When the difference between the newly extracted frequency and the estimated frequency is continuously lower than the empirical threshold, it is in a strong walking and running state, and the number of steps is estimated by the time difference;

[0104] 4. If the difference is greater than the empirical threshold in the strong walking and running state, it is counted as a weak walking and running state and the step count is not updated;

[0105] 5. If you re-enter the strong walking and running state, the steps in the previous weak walking and running state will be accumulated and incorporated into the total steps;

[0106] 6. If the experience time is exceeded in the weak walking and running state, the state will be entered into the non-walking and running state, and the number of steps will remain unchanged.

[0107] The above embodiment shows that the wearable device obtains data from the acceleration sensor, continuously extracts the walking frequency based on the data of the acceleration sensor detected by the preset sliding window, and updates the estimated frequency in real time. Determine whether the wearable device is in a strong walking and running state based on the walking frequency and the estimated frequency. If so, estimate the number of steps by time difference and include it in the total number of steps; and when it is determined that the wearable device is in a weak walking and running state based on the walking frequency and the estimated frequency, the total number of steps is not updated. The walking process is divided into a strong walking and running state and a weak walking and running state by different walking frequencies in different time periods, which further refines the statistics of walking counts, reduces the loss of steps, and improves the accuracy of counting.

[0108] Based on the same technical concept, Figure 4 The structure of a step counting device based on an acceleration sensor provided in an embodiment of the present invention is exemplarily shown. The device can execute a step counting process based on an acceleration sensor.

[0109] like Figure 4 As shown, the device may include:

[0110] An acquisition unit 401 is used to acquire data from an acceleration sensor;

[0111] The processing unit 402 is used to continuously extract the walking frequency based on the data of the acceleration sensor detected by a preset sliding window, and update the estimated frequency in real time; determine whether the wearable device is in a strong walking or running state based on the walking frequency and the estimated frequency; if so, estimate the number of steps through the time difference and include it in the total number of steps; and when it is determined that the wearable device is in a weak walking or running state based on the walking frequency and the estimated frequency, the total number of steps is not updated.

[0112] Optionally, the processing unit 402 is specifically configured to:

[0113] When detecting that the data of the acceleration sensor in the preset sliding window has a periodic signal, counting the continuous peak intervals in the preset sliding window, and performing mean processing to obtain the walking frequency in the preset sliding window;

[0114] The walking frequency in the preset sliding window this time and the walking frequency in the preset sliding window last time are averaged to obtain the estimated frequency, and the estimated frequency is updated in real time.

[0115] Optionally, the processing unit 402 is specifically configured to:

[0116] When it is determined that the difference between the walking frequency and the estimated frequency is continuously less than a preset threshold, it is determined that the wearable device is in a strong walking and running state; otherwise, it is determined that the wearable device is in a non-strong walking and running state.

[0117] Optionally, the processing unit 402 is specifically configured to:

[0118] When it is determined that the difference between the walking frequency and the estimated frequency is greater than a preset threshold, it is determined that the wearable device is in a weak walking and running state, the number of steps in the weak walking and running state is continuously counted, and the total number of steps is not updated.

[0119] Optionally, the processing unit 402 is further configured to:

[0120] After determining that the user is in the weak walking and running state, when determining to re-enter the strong walking and running state, the number of steps counted in the weak walking and running state before re-entering the strong walking and running state is accumulated to the total number of steps, and the number of steps after re-entering the strong walking and running state is continued to be accumulated to update the total number of steps.

[0121] Optionally, the processing unit 402 is further configured to:

[0122] After determining that the wearable device is in a weak walking and running state, when it is determined that the duration of the weak walking and running state exceeds a preset time threshold, it is determined that the wearable device is in a non-walking and running state, and the total number of steps is not updated.

[0123] Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:

[0124] A memory for storing program instructions;

[0125] The processor is used to call the program instructions stored in the memory and execute the above-mentioned step counting method based on the acceleration sensor according to the obtained program.

[0126] Based on the same technical concept, an embodiment of the present invention further 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 step counting method based on the acceleration sensor.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 step counting method based on an acceleration sensor, characterized in that: include: The wearable device obtains data from the acceleration sensor; The wearable device continuously extracts walking frequency based on data of the acceleration sensor detected by a preset sliding window, and updates the estimated frequency in real time; The wearable device determines whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency; If yes, the wearable device estimates the number of steps by the time difference and includes it in the total number of steps; When it is determined that the wearable device is in a weak walking or running state according to the walking frequency and the estimated frequency, the total number of steps is not updated; The wearable device continuously extracts walking frequency based on data of the acceleration sensor detected by a preset sliding window, and updates the estimated frequency in real time, including: When the wearable device detects that the data of the acceleration sensor in the preset sliding window has a periodic signal, it counts the continuous peak intervals in the preset sliding window and performs mean processing to obtain the walking frequency in the preset sliding window; The wearable device performs mean processing on the walking frequency in the preset sliding window this time and the walking frequency in the preset sliding window last time to obtain the estimated frequency, and updates it in real time.

2. The method according to claim 1, characterized in that The wearable device determines whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency, including: When the wearable device determines that the difference between the walking frequency and the estimated frequency is continuously less than a preset threshold, it determines that the wearable device is in a strong walking and running state; otherwise, it determines that the wearable device is in a non-strong walking and running state.

3. The method according to claim 1, characterized in that When the wearable device determines that the wearable device is in a weak walking or running state according to the walking frequency and the estimated frequency, not updating the total number of steps includes: When the wearable device determines that the difference between the walking frequency and the estimated frequency is greater than a preset threshold, it determines that the wearable device is in a weak walking and running state, continuously counts the number of steps in the weak walking and running state, and does not update the total number of steps.

4. The method according to claim 3, characterized in that After the wearable device determines that the wearable device is in a weak walking or running state, the method further includes: When the wearable device determines to re-enter the strong walking and running state, the number of steps counted in the weak walking and running state before re-entering the strong walking and running state is accumulated to the total number of steps, and the number of steps after re-entering the strong walking and running state is continued to be accumulated to update the total number of steps.

5. The method according to any one of claims 1 to 4, characterized in that: After the wearable device determines that the wearable device is in a weak walking or running state, the method further includes: When the wearable device determines that the duration of the weak walking and running state exceeds a preset time threshold, it is determined that the wearable device is in a non-walking and running state, and the total number of steps is not updated.

6. A step counting device based on an acceleration sensor, characterized in that: include: An acquisition unit, used for acquiring data of an acceleration sensor; A processing unit, configured to continuously extract walking frequency according to data of the acceleration sensor detected by a preset sliding window, and update the estimated frequency in real time; and determine whether the wearable device is in a strong walking or running state according to the walking frequency and the estimated frequency; If yes, the number of steps is estimated by the time difference and counted into the total number of steps; and when it is determined that the wearable device is in a weak walking or running state according to the walking frequency and the estimated frequency, the total number of steps is not updated; The processing unit is specifically used for: When detecting that the data of the acceleration sensor in the preset sliding window has a periodic signal, counting the continuous peak intervals in the preset sliding window, and performing mean processing to obtain the walking frequency in the preset sliding window; The walking frequency in the preset sliding window this time and the walking frequency in the preset sliding window last time are averaged to obtain the estimated frequency, and the estimated frequency is updated in real time.

7. 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 5 according to the obtained program.

8. 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 5.

Images