CN115060285A - Step counting statistical method and device based on acceleration sensor - Google Patents
Step counting statistical method and device based on acceleration sensor Download PDFInfo
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Abstract
The invention discloses a step counting statistical method and a step counting statistical device based on an acceleration sensor, wherein the method comprises the following steps: the wearable device obtains data of the acceleration sensor, detects the data of the acceleration sensor according to a preset sliding window, continuously extracts walking frequency, and updates estimation frequency in real time. Determining whether the wearable device is in a forced walking state or not according to the walking frequency and the estimation frequency, if so, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated. The walking process is divided into a strong walking running state and a weak walking running state through asynchronous walking frequency in different time periods, statistics of walking counting is refined, loss of step numbers is reduced, and counting accuracy is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent equipment, in particular to a step counting statistical method and device based on an acceleration sensor.
Background
In recent years, wearable devices have become popular, i.e. wearable devices that are worn directly on the body or are a portable device integrated into the clothing or accessories of the user. Wearable equipment is not only a hardware equipment, realizes powerful function through software support and data interaction, high in the clouds interaction more, and wearable equipment will bring very big transition to our life, perception.
At present, the wearable device can realize statistics of walking counting, but most of the wearable device collects signal frequency of the acceleration sensor, the mode is single, the obtained walking data is inaccurate, and partial data can be omitted. Therefore, a counting scheme that can improve the accuracy of the walking data is needed.
Disclosure of Invention
The embodiment of the invention provides a step counting statistical method and device based on an acceleration sensor, which can automatically count the walking steps of a user in the walking process and improve the counting accuracy.
In a first aspect, an embodiment of the present invention provides a step counting statistical method based on an acceleration sensor, including:
the wearable equipment acquires data of an acceleration sensor;
the wearable device detects data of the acceleration sensor according to a preset sliding window, continuously extracts walking frequency, and updates estimation frequency in real time;
the wearable device determines whether the wearable device is in a strong running state according to the walking frequency and the estimated frequency;
if so, estimating the step number by the wearable device through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated.
Optionally, the wearable device detects the walking frequency continuously according to the data of the acceleration sensor through a preset sliding window, and updates the estimation frequency in real time, including:
when the wearable device detects that the data of the acceleration sensor in the preset sliding window has periodic signals, counting the continuous wave crest intervals in the preset sliding window, and carrying out mean value processing to obtain the walking frequency in the preset sliding window;
and the wearable device performs mean processing on the walking frequency in the preset sliding window at this time and the walking frequency in the preset sliding window at the last time to obtain the estimation frequency, and updates the estimation frequency in real time.
Optionally, the determining, by the wearable device, whether the wearable device is in the forced-walking state according to the walking frequency and the estimated frequency includes:
when the difference value between the walking frequency and the estimation frequency is continuously smaller than a preset threshold value, the wearable device is determined to be in a strong walking state; otherwise, determining that the wearable device is in a non-forced running state.
Optionally, when determining that the wearable device is in the weak walking state according to the walking frequency and the estimated frequency, the wearable device does not update the total number of steps, including:
when the difference value between the walking frequency and the estimation frequency is larger than a preset threshold value, the wearable device is determined to be in the weak walking state, the number of steps in the weak walking state is continuously counted, and the total number of steps is not updated.
Optionally, after the wearable device determines that the wearable device is in the weak walking state, the method further includes:
when the wearable device determines to reenter the running state of the strong walking, the step number counted in the running state of the weak walking before reentering the running state of the strong walking is accumulated to the total step number, the step number after reentering the running state of the strong walking is continuously accumulated, and the total step number is updated.
Optionally, after the wearable device determines that the wearable device is in the weak walking state, the method further includes:
and when the wearable device determines that the duration time of the weak walking state exceeds a preset time threshold, determining that the wearable device is in a non-walking state, and not updating the total steps.
In a second aspect, an embodiment of the present invention provides an acceleration sensor-based step counting statistical apparatus, including:
an acquisition unit for acquiring data of the acceleration sensor;
the processing unit is used for detecting the data of the acceleration sensor according to a preset sliding window, continuously extracting the walking frequency and updating the estimation frequency in real time; determining whether the wearable device is in a strong running state according to the walking frequency and the estimated frequency; if yes, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated.
Optionally, the processing unit is specifically configured to:
when the data of the acceleration sensor in the preset sliding window are detected to have periodic signals, counting the continuous wave crest intervals in the preset sliding window, and carrying out mean value processing to obtain the walking frequency in the preset sliding window;
and carrying out average processing on the walking frequency in the preset sliding window and the walking frequency in the preset sliding window at the last time to obtain the estimation frequency, and updating in real time.
Optionally, the processing unit is specifically configured to:
when the difference value between the walking frequency and the estimation frequency is continuously smaller than a preset threshold value, determining that the wearable device is in a strong walking state; otherwise, determining that the wearable device is in a non-forced running state.
Optionally, the processing unit is specifically configured to:
when the difference value between the walking frequency and the estimation frequency is larger than a preset threshold value, the wearable device is determined to be in the weak walking state, the number of steps in the weak walking state is continuously counted, and the total number of steps is not updated.
Optionally, the processing unit is further configured to:
after the weak walking state is determined, when the strong walking state is determined to be re-entered, the step number counted under the weak walking state before the strong walking state is re-entered is accumulated to the total step number, the step number after the strong walking state is re-entered is continuously accumulated, and the total step number is updated.
Optionally, the processing unit is further configured to:
after the wearing equipment is determined to be in the weak walking state, when the duration time of the weak walking state is determined to exceed a preset time threshold, the wearing equipment is determined to be in the non-walking state, and the total step number is not updated.
In a third aspect, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instruction stored in the memory and executing the step counting statistical method based on the acceleration sensor according to the obtained program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer reads and executes the computer-readable instructions, the computer is caused to execute the above step counting statistical method based on an acceleration sensor.
In the embodiment of the invention, the wearable device acquires data of the acceleration sensor, detects the data of the acceleration sensor according to the preset sliding window, continuously extracts the walking frequency, and updates the estimation frequency in real time. Determining whether the wearable device is in a forced walking state or not according to the walking frequency and the estimation frequency, if so, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated. The walking process is divided into a strong walking running state and a weak walking running state through asynchronous walking frequency in different time periods, statistics of walking counting is refined, loss of step numbers is reduced, and counting accuracy is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a step counting statistical method based on an acceleration sensor according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a step counting statistical method based on an acceleration sensor according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a step counting device based on an acceleration sensor according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
First, a wearable device to which an embodiment of the present invention is applied will be described with reference to a structure shown in fig. 1. 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.
Wherein those skilled in the art will appreciate that the wearable device 100 configuration shown in fig. 1 is merely exemplary and not limiting, the wearable device 100 may also include more or fewer components than shown, or combine certain components, or arrange different components.
The RF circuit 110 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, for processing downlink information of a base station after receiving the downlink information; in addition, the uplink data of the wearable device 100 is sent to the base station. Typically, the RF circuitry includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communication ("GSM"), General Packet Radio Service ("GPRS"), Code Division Multiple Access ("CDMA"), Wideband Code Division Multiple Access ("WCDMA"), Long Term Evolution ("LTE"), email, Short message Service ("SMS"), and the like.
The memory 120 may 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 operating 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 program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the wearable device 100, and the like. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The input unit 130 may be used to receive input numeric or character information and 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, an image pickup device 132, and other input devices 133. The image capturing device 132 can photograph the image to be captured, so as to transmit the image to the processor 150 for processing, and finally, present the image to the user through the display panel 141. The touch panel 131, also referred to as a touch screen, may collect touch operations of a user on or near the touch panel 131 (e.g., operations of the user on or near the touch panel 131 using any suitable object or accessory such as a finger or a stylus pen), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 131 may include two parts, i.e., a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch panel 131 may be implemented by various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 130 may include other input devices 132 in addition to the touch panel 131 and the image pickup device 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a joystick, and the like.
Among them, the display unit 140 may 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, and optionally, the Display panel 141 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 131 can cover the display panel 141, and when the touch panel 131 detects a touch operation on or near the touch panel 131, the touch operation is transmitted to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event.
The visual output external display panel 141 that can be recognized by human eyes can be used as a display device in the embodiment of the present invention to display text information or image information. Although in fig. 1, the touch panel 131 and the display panel 141 are two separate components to implement the input and output functions of the wearable device 100, in some embodiments, the touch panel 131 and the display panel 141 may be integrated to implement the input and output functions of the wearable device 100.
In addition, the wearable device 100 may also include at least one sensor 150, such as a posture sensor, a distance sensor, a light sensor, and other sensors.
Specifically, the attitude sensor may also be referred to as a motion sensor, and as one of the motion sensors, an angular velocity sensor (also referred to as a gyroscope) may be cited, which is configured to measure a rotational angular velocity of the wearable device 100 in a state of motion when the wearable device 100 is deflected or tilted, so that the gyroscope can accurately analyze and determine an actual motion of a user using the wearable device 100, and perform a corresponding operation on the wearable device 100. For example: the motion sensing and the shake (the shake of the wearable device 100 achieves some functions) and the inertial navigation can be achieved according to the motion state of the object when no signal is available in a Global Positioning System (GPS for short), such as in a tunnel.
The sensor may be an optical sensor, which is mainly used to collect information such as wavelength and intensity of various light beams of light and adjust backlight intensity of the display panel 141.
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 be further configured, which are not described herein again.
The light sensor may also include a proximity sensor that may turn off the display panel 141 and/or backlight when the wearable device 100 is moved to the ear.
WiFi belongs to short distance wireless transmission technology, and the wearable device 100 can help the user send and receive e-mail, browse web pages, access streaming media, etc. through the WiFi module 170, which provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 170, it is understood that it does not belong to the essential constitution of the wearable device 100, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 180 is a control center of the wearable device 100, connects various parts of the whole wearable device 100 by using various interfaces and lines, and performs various functions of the wearable device 100 and processes data by running or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the wearable device 100. Alternatively, processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications.
It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The wearable device 100 may further include at least one motor 190, and since the wearable device 100 is a power consuming device, the motor 190 may be a small motor, and at the same time, a plurality of motors may be configured for the wearable device 100 according to the amount of power that the motors can provide.
The wearable device 100 also includes a power source (not shown) for powering the various components.
Preferably, the power source may be logically connected to the processor 180 through a power management system, so as to implement functions of managing charging, discharging, and power consumption management through the power management system. Although not shown, the wearable device 100 may further include a bluetooth module or the like, which is not described in detail herein.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited thereto.
Fig. 2 exemplarily shows an acceleration sensor-based step counting statistical process provided by an embodiment of the present invention, which may be performed by an acceleration sensor-based step counting statistical apparatus, which may be a wearable device or may be located in a wearable device.
As shown in fig. 2, the process specifically includes:
In the embodiment of the invention, the wearable device can acquire the data of the acceleration sensor in real time, namely the three-axis acceleration, in the walking process of the user to form an acceleration curve.
Specifically, when the wearable device detects that data of the acceleration sensor in the preset sliding window has a periodic signal, the wearable device counts continuous wave crest intervals in the preset sliding window, and performs mean processing to obtain the walking frequency in the preset sliding window. And then the wearable device performs mean processing on the walking frequency in the preset sliding window and the walking frequency in the preset sliding window at the last time to obtain an estimated frequency, and updates the estimated frequency in real time. The preset sliding window may be set empirically, which is a time window.
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 may be acquired, and at this time, the 6 groups of peak intervals may be subjected to mean processing, so that the walking frequency in the preset sliding window may be obtained, and the walking frequency in the preset sliding window 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.
Specifically, when the difference value between the walking frequency and the estimated frequency is continuously smaller than a preset threshold value, the wearable device is determined to be in a strong walking state; otherwise, determining that the wearable device is in a non-forced running state. The preset threshold may be set empirically. The strong-walking state can be understood as a state in which the walking frequency is high.
The non-strong running state may also be referred to as a weak running state, and the weak running state may be determined as long as a difference between the walking frequency and the estimated frequency is greater than a preset threshold.
Specifically, the wearable device calculates the interval time (i.e., time difference) between each step according to the walking frequency, and then accumulates the number of steps according to the time to count the total number of steps. When the difference value between the walking frequency and the estimation frequency is larger than the preset threshold value, the wearable device is determined to be in the weak walking state, the step number in the weak walking state is continuously counted, and the total step number is not updated.
It should be noted that the number of steps in the duration of the weak walking state may be stored by another variable in the weak walking state, but is not accumulated in the total number of steps, i.e., the total number of steps is not updated.
After the weak walking state is determined, if the strong walking state is determined to be re-entered, the step number counted in the weak walking state before the strong walking state is re-entered is accumulated to the total step number, the step number after the strong walking state is re-entered is continuously accumulated, and the total step number is updated. And when the condition is met, the step number in the weak walking running state can be accumulated into the total step number, so that the total step number is updated, and the total step number after the strong walking running state is re-entered can be continuously updated.
And if the duration time of the weak walking state exceeds the preset time threshold, determining that the wearable device is in the non-walking state, and at the moment, not updating the total steps. The preset time threshold may be set empirically.
In order to better explain the embodiment of the present invention, the following describes the step counting statistical process based on the acceleration sensor in a specific implementation scenario, as shown in fig. 3, including:
Wearing equipment can be at user's walking in-process, real-time acquisition acceleration sensor's data.
Whether the wearing equipment has periodic signals or not is detected by data of the acceleration sensor in the preset sliding window.
When the data of the acceleration sensor in the preset sliding window is detected to have periodic signals, the continuous wave crest intervals in the preset sliding window are counted, and the average value is processed to obtain the walking frequency in the preset sliding window.
In step 304, the difference value from the original frequency is lower than the threshold value, if yes, the step 305 is executed, otherwise, the step 311 is executed.
And when the difference value between the walking frequency and the estimated frequency is continuously smaller than the preset threshold value, determining that the wearable device is in a forced walking state.
When the accumulated frequency stabilization duration is continuously smaller than the preset threshold, it may be determined that the running state is in a strong running state.
When the duration is greater than the time threshold, that is, when the difference between the step frequency and the estimation frequency is continuously smaller than the preset threshold.
In step 309, the difference value between the original frequency and the original frequency is greater than the threshold, if yes, the step 310 is executed, otherwise, the step 308 is executed.
And determining whether the difference value of the walking frequency and the estimated frequency is greater than a preset threshold value, and if so, entering a weak walking state.
In step 310, the accumulated time is greater than the experience threshold, if yes, step 311 is performed, otherwise step 309 is performed.
If the accumulated time for entering the weak running state is greater than the experience threshold, the strong running state can be jumped out, and the non-running state is entered.
And step 311, jumping out of the strong running state.
1. Detecting a periodic signal through acceleration data, and estimating a potential walking frequency;
2. continuously extracting frequency through a sliding window, and updating the estimated frequency in real time;
3. when the difference value between the new extracted frequency and the estimated frequency is continuously lower than the experience threshold value, the running state is in a forced running state, and the step number is estimated through the time difference;
4. if the difference is larger than the experience threshold value in the strong running state, counting as the weak running state, and not updating the step number;
5. if the strong walking state is entered again, the step number of the previous weak walking state is accumulated and merged into the total step number;
6. if the experience time length is exceeded in the weak walking and running state, the non-walking and running state is entered, and the step number is kept unchanged.
The embodiment shows that the wearable device acquires data of the acceleration sensor, continuously extracts walking frequency according to the data of the acceleration sensor detected by the preset sliding window, and updates the estimation frequency in real time. Determining whether the wearable equipment is in a forced walking state or not according to the walking frequency and the estimation frequency, if so, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated. The walking process is divided into a strong walking running state and a weak walking running state through asynchronous walking frequency in different time periods, statistics of walking counting is refined, loss of step numbers is reduced, and counting accuracy is improved.
Based on the same technical concept, fig. 4 exemplarily shows the structure of an acceleration sensor-based step counting statistical apparatus provided by an embodiment of the present invention, which can execute an acceleration sensor-based step counting statistical process.
As shown in fig. 4, the apparatus may include:
an acquisition unit 401 configured to acquire data of an acceleration sensor;
a processing unit 402, configured to continuously extract a walking frequency according to data of the acceleration sensor detected by a preset sliding window, and update an estimated frequency in real time; determining whether the wearable device is in a strong running state according to the walking frequency and the estimated frequency; if yes, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated.
Optionally, the processing unit 402 is specifically configured to:
when the data of the acceleration sensor in the preset sliding window are detected to have periodic signals, counting the continuous wave crest intervals in the preset sliding window, and carrying out mean value processing to obtain the walking frequency in the preset sliding window;
and carrying out average processing on the walking frequency in the preset sliding window and the walking frequency in the preset sliding window at the last time to obtain the estimation frequency, and updating in real time.
Optionally, the processing unit 402 is specifically configured to:
when the difference value between the walking frequency and the estimation frequency is continuously smaller than a preset threshold value, determining that the wearable device is in a strong walking state; otherwise, determining that the wearable device is in a non-forced running state.
Optionally, the processing unit 402 is specifically configured to:
when the difference value between the walking frequency and the estimation frequency is larger than a preset threshold value, the wearable device is determined to be in the weak walking state, the number of steps in the weak walking state is continuously counted, and the total number of steps is not updated.
Optionally, the processing unit 402 is further configured to:
after the weak walking state is determined, when the strong walking state is determined to be re-entered, the step number counted under the weak walking state before the strong walking state is re-entered is accumulated to the total step number, the step number after the strong walking state is re-entered is continuously accumulated, and the total step number is updated.
Optionally, the processing unit 402 is further configured to:
after the wearing equipment is determined to be in the weak walking state, when the duration time of the weak walking state is determined to exceed a preset time threshold, the wearing equipment is determined to be in the non-walking state, and the total step number is not updated.
Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instruction stored in the memory and executing the step counting statistical method based on the acceleration sensor according to the obtained program.
Based on the same technical concept, the embodiment of the invention also provides a computer-readable non-volatile storage medium, which comprises computer-readable instructions, and when the computer reads and executes the computer-readable instructions, the computer is enabled to execute the step counting statistical method based on the acceleration sensor.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such 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 such modifications and variations.
Claims (10)
1. A step counting statistical method based on an acceleration sensor is characterized by comprising the following steps:
the wearable equipment acquires data of an acceleration sensor;
the wearable device detects data of the acceleration sensor according to a preset sliding window, continuously extracts walking frequency, and updates estimation frequency in real time;
the wearable device determines whether the wearable device is in a strong running state according to the walking frequency and the estimated frequency;
if yes, the wearable device estimates the step number through the time difference and counts the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated.
2. The method of claim 1, wherein the wearable device detects the data of the acceleration sensor according to a preset sliding window to continuously extract the walking frequency and updates the estimated frequency in real time, comprising:
when the wearable device detects that the data of the acceleration sensor in the preset sliding window has periodic signals, counting the continuous wave crest intervals in the preset sliding window, and carrying out mean value processing to obtain the walking frequency in the preset sliding window;
and the wearable device performs mean processing on the walking frequency in the preset sliding window and the walking frequency in the preset sliding window at the last time to obtain the estimation frequency, and updates the estimation frequency in real time.
3. The method of claim 1, wherein the wearable device determining whether the wearable device is in a hard-walk state as a function of the walking frequency and the estimated frequency comprises:
when the difference value between the walking frequency and the estimation frequency is continuously smaller than a preset threshold value, the wearable device is determined to be in a strong walking state; otherwise, determining that the wearable device is in a non-forced running state.
4. The method of claim 1, wherein the wearable device not updating the total number of steps when determining that the wearable device is in the weak walking state based on the walking frequency and the estimated frequency comprises:
when the difference value between the walking frequency and the estimation frequency is larger than a preset threshold value, the wearable device is determined to be in a weak walking state, the number of steps in the weak walking state is continuously counted, and the total number of steps is not updated.
5. The method of claim 4, wherein after the wearable device determines that the wearable device is in the weak walking state, further comprising:
the wearable device is in the definite reentry when walking the running state by force, will be in the reentry before walking the running state by force the step number of statistics under the running state by force is accumulated to total step number to continue the accumulative reentry step number after walking the running state by force is updated total step number.
6. The method of any of claims 1 to 5, wherein after the wearable device determines that the walking-weak state is present, further comprising:
and when the wearable device determines that the duration time of the weak walking state exceeds a preset time threshold, determining that the wearable device is in a non-walking state, and not updating the total steps.
7. An acceleration sensor-based step counting statistical device, comprising:
an acquisition unit for acquiring data of the acceleration sensor;
the processing unit is used for detecting the data of the acceleration sensor according to a preset sliding window, continuously extracting the walking frequency and updating the estimation frequency in real time; determining whether the wearable device is in a strong running state according to the walking frequency and the estimated frequency; if yes, estimating the step number through the time difference, and counting the total step number; and when the wearable device is determined to be in the weak walking state according to the walking frequency and the estimation frequency, the total step number is not updated.
8. The apparatus as claimed in claim 7, wherein said processing unit is specifically configured to:
when the data of the acceleration sensor in the preset sliding window is detected to have periodic signals, counting the continuous wave crest intervals in the preset sliding window, and carrying out average value processing to obtain the walking frequency in the preset sliding window;
and carrying out average processing on the walking frequency in the preset sliding window and the walking frequency in the preset sliding window at the last time to obtain the estimation frequency, and updating in real time.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 6 in accordance with the obtained program.
10. A computer-readable non-transitory storage medium including computer-readable instructions which, when read and executed by a computer, cause the computer to perform the method of any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801046A (en) * | 2023-02-08 | 2023-03-14 | 深圳市思创捷物联科技有限公司 | Flight state automatic identification method, system, equipment and computer storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130080255A1 (en) * | 2011-09-22 | 2013-03-28 | Microsoft Corporation | Step detection and step length estimation |
| CN103344249A (en) * | 2013-06-17 | 2013-10-09 | 中国人民解放军总参谋部第六十一研究所 | Step counting method based on short-time spectral analysis of inertial sensor |
| CN106441350A (en) * | 2016-10-09 | 2017-02-22 | 深圳市金立通信设备有限公司 | Step counting method and terminal |
| CN108592941A (en) * | 2018-06-15 | 2018-09-28 | 成都云卫康医疗科技有限公司 | A kind of step-recording method based on 3-axis acceleration |
| US10360510B1 (en) * | 2017-04-25 | 2019-07-23 | Verily Life Sciences Llc | Systems and methods for gating a step count |
-
2022
- 2022-08-01 CN CN202210912497.9A patent/CN115060285A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130080255A1 (en) * | 2011-09-22 | 2013-03-28 | Microsoft Corporation | Step detection and step length estimation |
| CN103344249A (en) * | 2013-06-17 | 2013-10-09 | 中国人民解放军总参谋部第六十一研究所 | Step counting method based on short-time spectral analysis of inertial sensor |
| CN106441350A (en) * | 2016-10-09 | 2017-02-22 | 深圳市金立通信设备有限公司 | Step counting method and terminal |
| US10360510B1 (en) * | 2017-04-25 | 2019-07-23 | Verily Life Sciences Llc | Systems and methods for gating a step count |
| CN108592941A (en) * | 2018-06-15 | 2018-09-28 | 成都云卫康医疗科技有限公司 | A kind of step-recording method based on 3-axis acceleration |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801046A (en) * | 2023-02-08 | 2023-03-14 | 深圳市思创捷物联科技有限公司 | Flight state automatic identification method, system, equipment and computer storage medium |
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