CN112539763A - Motion state classification method, step counting device and readable storage medium - Google Patents
Motion state classification method, step counting device and readable storage medium Download PDFInfo
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- CN112539763A CN112539763A CN202011425972.7A CN202011425972A CN112539763A CN 112539763 A CN112539763 A CN 112539763A CN 202011425972 A CN202011425972 A CN 202011425972A CN 112539763 A CN112539763 A CN 112539763A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
- G01C22/006—Pedometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
Abstract
The invention discloses a classification method of motion states, a step counting device and a readable storage medium, wherein the classification method of the motion states comprises the following steps: acquiring an acceleration fluctuation value in a preset time interval; determining the current motion state according to the acceleration fluctuation value and a preset threshold value; when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state, and improving the classification speed of the motion state.
Description
Technical Field
The invention relates to the technical field of wearable device motion detection, in particular to a motion state classification method, a step counting device and a readable storage medium.
Background
At present, intelligent wearable equipment relates to the technical function of motion detection, and the existing classification method of motion states is to obtain motion frequency through Fourier transform and judge the motion state according to the motion frequency; or acquiring the acceleration variance first and then judging the motion state according to the acceleration variance. Because the calculation amount of Fourier transformation and the calculation amount of obtaining the acceleration variance are large, the classification speed of the existing motion state classification method is low, and the accuracy is not high.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a classification method of motion states, a step counting device and a readable storage medium, aiming at improving the classification speed and accuracy of the motion states.
In order to achieve the above object, the present invention provides a method for classifying motion states, including the steps of:
acquiring an acceleration fluctuation value in a preset time interval;
determining the current motion state according to the acceleration fluctuation value and a preset threshold value;
when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state.
Optionally, the step of acquiring the acceleration fluctuation value within the preset time interval includes:
acquiring a range value between the maximum value and the minimum value of the acceleration signal corresponding to each coordinate axis;
and determining the acceleration fluctuation value according to the range value.
Optionally, the step of determining the acceleration fluctuation value according to the difference value includes:
acquiring a maximum polar difference value corresponding to each coordinate axis;
and taking the maximum deviation value as the acceleration fluctuation value.
Optionally, after the step of determining the current motion state according to the acceleration fluctuation value and a preset threshold, the method further includes:
acquiring step counting parameters corresponding to the motion state;
and sending the step counting parameters to a step counting module so that the step counting module updates initial step counting parameters according to the step counting parameters.
Optionally, after the step of sending the step counting parameter to the step counting module, the step of sending the step counting parameter to the step counting module further includes:
controlling the step counting module to carry out step counting operation;
and acquiring and outputting the step counting information of the step counting module at fixed time.
Optionally, before the step of obtaining the acceleration fluctuation value within the preset time interval, the method further includes:
acquiring user information of a current user, wherein the user information comprises at least one of a user identifier, a user type and a user age;
and acquiring the preset threshold corresponding to the user information.
Optionally, before the step of obtaining the acceleration fluctuation value within the preset time interval, the method further includes:
acquiring wearing data detected by a wearing detection module;
determining whether the step counting device is in a wearing state according to the wearing data;
and when the step counting device is in a wearing state, executing the step of acquiring the acceleration fluctuation value within a preset time interval.
In addition, in order to achieve the above object, the present invention further provides a step counting device, including: an acceleration sensor memory, a processor and a classification program of motion states stored on the memory and executable on the processor, which classification program of motion states, when executed by the processor, implements the steps of the method of classification of motion states as described above.
In addition, to achieve the above object, the present invention further proposes a readable storage medium, on which a classification program of a motion state is stored, which, when being executed by a processor, implements the steps of the method for classifying a motion state as described above.
The classification method, the step counting device and the readable storage medium of the motion state provided by the embodiment of the invention are used for acquiring an acceleration fluctuation value in a preset time interval; determining the current motion state according to the acceleration fluctuation value and a preset threshold value; when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is the walking state, thereby improving the classification speed and the accuracy of the motion state.
Drawings
FIG. 1 is a schematic diagram of a hardware architecture of a step-counting device according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for classifying motion states according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for classifying motion states according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a method for classifying motion states according to a third embodiment of the present invention;
FIG. 5 is a flowchart illustrating a method for classifying motion states according to a fourth embodiment of the present invention;
fig. 6 is a flowchart illustrating a fifth embodiment of the method for classifying motion states according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
At present, intelligent wearable equipment relates to the technical function of motion detection, and the existing classification method of motion states is to obtain motion frequency through Fourier transform and judge the motion state according to the motion frequency; or acquiring the acceleration variance first and then judging the motion state according to the acceleration variance. Because the calculation amount of Fourier transformation and the calculation amount of obtaining the acceleration variance are large, the classification speed of the existing motion state classification method is low, and the accuracy is not high.
In order to solve the above-mentioned drawbacks, an embodiment of the present invention provides a method for classifying motion states, a step counter, and a readable storage medium, wherein the method for classifying motion states mainly includes the following steps:
acquiring an acceleration fluctuation value in a preset time interval;
determining the current motion state according to the acceleration fluctuation value and a preset threshold value;
when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state.
The current motion state is determined through the acceleration fluctuation value and the preset threshold, and the acceleration fluctuation value is a very poor value between the maximum value and the minimum value in the acceleration signal, namely when the acceleration fluctuation value is obtained, only simple subtraction calculation is needed, and a large amount of complex operation is not needed, so that the classification speed and the accuracy of the motion state are improved.
As shown in fig. 1, fig. 1 is a schematic diagram of a hardware architecture of a step counting device according to an embodiment of the present invention.
The step counting device of the embodiment of the invention can be equipment such as an intelligent bracelet, an intelligent watch, an intelligent mobile phone and the like.
As shown in fig. 1, the step counting device may include: a processor 1001, such as a CPU, a user interface 1003, an acceleration sensor 1004, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include an output unit such as a display screen (DiS0play), an input unit such as keys, etc., and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory step-counting device separate from the processor 1001.
It will be appreciated by those skilled in the art that the hardware architecture of the step counter shown in fig. 1 does not constitute a limitation of the step counter and may comprise more or less components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include an operating system, a user interface module, and a classification program of motion states therein.
In the step counting device shown in fig. 1, the processor 1001 may be configured to call a classification program of motion states stored in the memory 1005, and perform the following operations:
acquiring an acceleration fluctuation value in a preset time interval;
determining the current motion state according to the acceleration fluctuation value and a preset threshold value;
when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
acquiring a range value between the maximum value and the minimum value of the acceleration signal corresponding to each coordinate axis;
and determining the acceleration fluctuation value according to the range value.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
acquiring a maximum polar difference value corresponding to each coordinate axis;
and taking the maximum deviation value as the acceleration fluctuation value.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
acquiring step counting parameters corresponding to the motion state;
and sending the step counting parameters to a step counting module so that the step counting module updates initial step counting parameters according to the step counting parameters.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
controlling the step counting module to carry out step counting operation;
and acquiring and outputting the step counting information of the step counting module at fixed time.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
acquiring user information of a current user, wherein the user information comprises at least one of a user identifier, a user type and a user age;
and acquiring the preset threshold corresponding to the user information.
Further, the processor 1001 may be configured to invoke a classification procedure for the motion state stored in the memory 1005, and further perform the following operations:
acquiring wearing data detected by a wearing detection module;
determining whether the step counting device is in a wearing state according to the wearing data;
and when the step counting device is in a wearing state, executing the step of acquiring the acceleration fluctuation value within a preset time interval.
As shown in fig. 2, in the first embodiment of the method for classifying a motion state of the present invention, the method for classifying a motion state includes the steps of:
step S010, acquiring an acceleration fluctuation value in a preset time interval;
step S020, determining the current motion state according to the acceleration fluctuation value and a preset threshold value;
when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state.
In the present embodiment, the acceleration fluctuation value is a very different value between a maximum value and a minimum value of the acceleration signal within a preset time interval.
The acceleration sensor sends an acceleration signal to the processor at regular time, namely after a preset time interval, the acceleration sensor sends the acceleration signal in the preset time interval to the processor; when receiving an acceleration signal sent by an acceleration sensor, a processor determines the maximum value and the minimum value of acceleration in the acceleration signal within a preset time interval, then obtains a range value between the maximum value and the minimum value, takes the range value as an acceleration fluctuation value, then compares the acceleration fluctuation value with a preset threshold value, and judges that the current motion state is a running state when the acceleration fluctuation value is greater than the preset threshold value; and when the acceleration fluctuation value is less than or equal to a preset threshold value, judging that the current motion state is a walking state.
In the technical scheme disclosed in this embodiment, the current motion state is determined by the acceleration fluctuation value and the preset threshold, and since the acceleration fluctuation value is the extreme difference between the maximum value and the minimum value in the acceleration signal, that is, when the acceleration fluctuation value is obtained, only simple subtraction calculation is needed, and a large amount of complex operations are not needed, so that the classification speed of the motion state is improved.
Optionally, as shown in fig. 3, in a second embodiment of the method for classifying a motion state according to the present invention based on the first embodiment, the step S010 further includes:
step S011, acquiring a range difference value between the maximum value and the minimum value of the acceleration signal corresponding to each coordinate axis;
and step S012, determining the acceleration fluctuation value according to the difference value.
In this embodiment, the acceleration sensor may be a three-axis acceleration sensor, and the acceleration signal sent by the acceleration sensor to the processor includes acceleration signals of three coordinate axes.
After receiving acceleration signals of each coordinate axis within a preset time interval, a processor determines a first maximum value and a first minimum value of a first acceleration signal corresponding to a first coordinate axis, acquires a first polar difference value between the first maximum value and the first minimum value, and acquires a second polar difference value corresponding to a second coordinate axis and a third polar difference value corresponding to a third coordinate axis by the same process; and finally, acquiring a range difference value meeting a preset condition as an acceleration fluctuation value.
Exemplarily, the step S012 further includes:
step S0121, obtaining the maximum polar difference value corresponding to each coordinate axis;
and step S0122, using the maximum difference value as the acceleration fluctuation value.
In this embodiment, the preset condition may be that the current range value is greater than or equal to a remaining range value, where the remaining range value is a range value other than the current range value, that is, a maximum value is selected from the obtained range values.
The processor compares the first pole difference value, the second pole difference value and the third pole difference value to obtain the maximum value of the three pole difference values, namely the maximum pole difference value, and the maximum pole difference value is used as the acceleration fluctuation value.
In the technical scheme disclosed in this embodiment, since the acceleration is a vector value with a direction, acceleration signals of three coordinate axes are obtained by using the three-axis acceleration sensor, and then an acceleration fluctuation value is obtained from the three acceleration signals, thereby avoiding the situation that accurate measurement data cannot be obtained due to different acceleration directions, and improving the accuracy of classification.
Optionally, as shown in fig. 4, in a third embodiment of the method for classifying a motion state according to the present invention based on the first embodiment, after step S020, the method further includes:
step S030, obtaining step counting parameters corresponding to the motion state;
and step S040, sending the step counting parameter to a step counting module so that the step counting module updates an initial step counting parameter according to the step counting parameter.
In this embodiment, the step-counting parameter is a replacement value for replacing the initial step-counting parameter in the step-counting module.
After determining the current motion state, the processor acquires step counting parameters corresponding to the current motion state and sends the step counting parameters to the step counting module; and after receiving the step counting parameters, the step counting module replaces the initial step counting parameters with the received step counting parameters.
In the technical scheme disclosed in this embodiment, the step counting module is sent the step counting parameter corresponding to the current motion state, so that the initial step counting parameter is updated according to the step counting parameter, and the step counting accuracy of the step counting module is improved.
Optionally, after step S040, the method further includes:
step S050, controlling the step counting module to carry out step counting operation;
and step S060, acquiring and outputting the step counting information of the step counting module at regular time.
In this embodiment, after the step counting module updates the step counting parameter, the processor controls the step counting module to start counting steps, and then obtains the step counting information of the step counting module at regular time, which may be obtaining the step counting information every one minute, and then outputting the step counting information on the display screen.
In the technical scheme disclosed in this embodiment, the step counting information of the step counting module is output at regular time, so that the user can obtain the current latest step counting data.
Optionally, as shown in fig. 5, in a fourth embodiment of the method for classifying a motion state according to the present invention based on the first embodiment, before the step S010, the method further includes:
step S070, obtaining user information of a current user, where the user information includes at least one of a user identifier, a user type, and a user age;
and S080, acquiring the preset threshold corresponding to the user information.
In this embodiment, a processor first obtains user information of a current user, where the user information includes at least one of a user identifier, a user type, and a user age; and then acquiring a preset threshold corresponding to the user information.
Specifically, a mapping relationship between the user identifier and a preset threshold, a mapping relationship between the user type and the preset threshold, and a mapping relationship between the user age and the preset threshold are stored in the memory; the priority level exists in the user information, namely the user identification is higher than the user type and then higher than the user age. The processor acquires a corresponding preset threshold according to the priority of the user information after acquiring the user information, and illustratively acquires the preset threshold corresponding to the user identifier when the user information comprises the user identifier, the user type and the user age; and when the user information comprises the user type and the user age, acquiring a preset threshold corresponding to the user type.
In the technical scheme disclosed in this embodiment, by setting the mapping relationship between the user information and the preset threshold and determining the preset threshold according to the user information, the judgment deviation caused by different motion habits of different users is reduced, and the accuracy of the classification of the motion states is improved.
Optionally, as shown in fig. 6, in a fifth embodiment of the method for classifying a motion state according to the present invention, based on the first embodiment, before the step S010, the method further includes:
s090, obtaining wearing data detected by a wearing detection module;
step S100, determining whether the step counting device is in a wearing state according to the wearing data;
and step S110, when the step counting device is in a wearing state, executing the step of acquiring the acceleration fluctuation value in a preset time interval.
In this embodiment, the wearing data is a trigger signal sent by the wearing detection module after the step counting device is detected to be worn, specifically, the wearing detection module is an infrared sensor or a photoelectric sensor, and when the wearing detection module detects human skin, the wearing detection module sends the trigger signal, so that the step counting device can determine the wearing state of the step counting device according to the trigger signal.
After the step counting device is started, the processor acquires wearing data detected by the wearing detection module, determines whether the step counting device is in a wearing state according to the wearing data, and executes the process of acquiring the motion state when the step counting device is in the wearing state; specifically, when the trigger signal is not present in the wearing data, the step-counting device is not in the wearing state.
In the technical scheme disclosed in this embodiment, when the step counting device is an intelligent watch or an intelligent bracelet, whether the step counting device is normally worn is detected first, and then subsequent classification process and step counting process are performed, so that the number of wrong step counting steps is reduced, and the step counting accuracy is improved.
In addition, an embodiment of the present invention further provides a step counting device, where the step counting device includes an acceleration sensor, a memory, a processor, and a classification program of a motion state stored in the memory and executable on the processor, and when the classification program of the motion state is executed by the processor, the steps of the classification method of the motion state according to the above embodiments are implemented.
In addition, an embodiment of the present invention further provides a readable storage medium, where a classification program of a motion state is stored, and when the classification program of a motion state is executed by a processor, the steps of the classification method of a motion state as described in the above embodiments are implemented.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a step-counting device (e.g. smart band, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A method for classifying a motion state, comprising the steps of:
acquiring an acceleration fluctuation value in a preset time interval;
determining the current motion state according to the acceleration fluctuation value and a preset threshold value;
when the acceleration fluctuation value is larger than a preset threshold value, judging that the current motion state is a running state; and when the acceleration fluctuation value is less than or equal to the preset threshold value, judging that the current motion state is a walking state.
2. The method for classifying motion states according to claim 1, wherein the step of obtaining the acceleration fluctuation value within a preset time interval comprises:
acquiring a range value between the maximum value and the minimum value of the acceleration signal corresponding to each coordinate axis;
and determining the acceleration fluctuation value according to the range value.
3. The method for classifying motion states according to claim 2, wherein the step of determining the acceleration fluctuation value based on the range value comprises:
acquiring a maximum polar difference value corresponding to each coordinate axis;
and taking the maximum deviation value as the acceleration fluctuation value.
4. The method for classifying motion states according to claim 1, wherein the step of determining the current motion state according to the acceleration fluctuation value and a preset threshold value further comprises:
acquiring step counting parameters corresponding to the motion state;
and sending the step counting parameters to a step counting module so that the step counting module updates initial step counting parameters according to the step counting parameters.
5. The method for classifying an exercise state of claim 4, wherein the step of sending the step-counting parameter to a step-counting module is followed by further comprising:
controlling the step counting module to carry out step counting operation;
and acquiring and outputting the step counting information of the step counting module at fixed time.
6. The method for classifying motion states according to claim 1, wherein the step of obtaining the acceleration fluctuation value within the preset time interval is preceded by the step of:
acquiring user information of a current user, wherein the user information comprises at least one of a user identifier, a user type and a user age;
and acquiring the preset threshold corresponding to the user information.
7. The method for classifying motion states according to claim 1, wherein the step of obtaining the acceleration fluctuation value within the preset time interval is preceded by the step of:
acquiring wearing data detected by a wearing detection module;
determining whether the step counting device is in a wearing state according to the wearing data;
and when the step counting device is in a wearing state, executing the step of acquiring the acceleration fluctuation value within a preset time interval.
8. A step counting device, characterized in that the step counting device comprises: acceleration sensor, memory, processor and a classification program of a state of motion stored on the memory and executable on the processor, which classification program of a state of motion, when executed by the processor, implements the steps of the method of classification of a state of motion according to any one of claims 1 to 7.
9. A readable storage medium, characterized in that the readable storage medium has stored thereon a classification program of a motion state, which when executed by a processor implements the steps of the method of classification of a motion state according to any one of claims 1 to 7.
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