CN110960222B - Motion type detection method and device - Google Patents
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Abstract
The invention discloses a method for detecting a motion type, which comprises the following steps: firstly, determining user characteristic parameters; then, according to the user characteristic parameters, determining a target motion state corresponding to the user characteristic parameters; and then, determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state. In the embodiment, the motion state and the motion type can be obtained according to the user characteristic parameters, wherein the motion state can be used for reflecting the motion intensity corresponding to the user characteristic parameters, so that the motion intensity and the motion type of the user can be determined according to the user characteristic parameters; therefore, compared with the prior art, the method and the device have the advantages that the fineness of the detected result according to the characteristic parameters of the user is higher, and the user experience is improved.
Description
Technical Field
The invention relates to the field of health detection, in particular to a method and a device for detecting motion types.
Background
With the development and progress of science and technology, applications on intelligent mobile terminals (such as smart phones) are continuously emerging and increasingly abundant. Most applications on the smart mobile terminal are related to the work and life of the user, for example, a motion state detection application that can be used to monitor the motion state of the user.
Currently, the detection method applied to the motion state detection can only be used for detecting whether the user moves, but cannot further determine the intensity of the user's motion or the type of the motion. With the increasing demand for quality of life, the level of fineness of exercise status results detected by exercise status detection applications is also required to be higher, however, the level of fineness of exercise status results detected by the existing exercise status detection methods cannot meet the demand of people. Therefore, a motion state detection method capable of detecting a motion state result with a higher degree of fineness is required.
Disclosure of Invention
The invention provides a motion type detection method and device, which can improve the fineness of a result detected according to a user characteristic parameter, thereby improving user experience.
In a first aspect, the present invention provides a method for detecting a motion type, including:
determining a user characteristic parameter;
determining a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter, wherein the target motion state is used for reflecting the motion intensity degree corresponding to the user characteristic parameter;
and determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state.
In a second aspect, the present invention provides a motion type detecting apparatus, comprising:
a parameter determining unit for determining a user characteristic parameter;
the state determining unit is used for determining a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter, wherein the motion state is used for reflecting the motion intensity degree corresponding to the user characteristic parameter;
and the type determining unit is used for determining the target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
According to the technical scheme, the user characteristic parameters are determined firstly; then, according to the user characteristic parameters, determining a target motion state corresponding to the user characteristic parameters; and then, determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state. In the embodiment, the motion state and the motion type can be obtained according to the user characteristic parameters, wherein the motion state can be used for reflecting the motion intensity corresponding to the user characteristic parameters, so that the motion intensity and the motion type of the user can be determined according to the user characteristic parameters; therefore, compared with the prior art, the method and the device have the advantages that the fineness of the detected result according to the characteristic parameters of the user is higher, and the user experience is improved.
Further effects of the above-described unconventional preferred modes will be described below in conjunction with the detailed description.
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In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a block diagram of an exemplary application scenario provided in an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for detecting a motion type according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a motion type detection apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device 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 technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
In the prior art, the detection method of the current motion state detection application can only be used for detecting whether the user moves, but cannot further determine the intensity of the user motion or the type of the motion. With the increasing demand for quality of life, people also demand more and more for the fineness of exercise status results detected by exercise status detection applications, however, the fineness of exercise status results detected by the existing exercise status detection methods cannot meet the demand of people. Therefore, a motion state detection method capable of detecting a motion state result with a higher degree of fineness is required.
To solve the above problems. The invention provides a motion type detection method, which comprises the steps of firstly determining characteristic parameters of a user; then, according to the user characteristic parameters, determining a target motion state corresponding to the user characteristic parameters; and then, determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state. In the embodiment, the motion state and the motion type can be obtained according to the user characteristic parameters, wherein the motion state can be used for reflecting the motion intensity corresponding to the user characteristic parameters, so that the motion intensity and the motion type of the user can be determined according to the user characteristic parameters; therefore, compared with the prior art, the method and the device can detect whether the user moves or not, and can also detect the intensity degree and the movement type of the movement of the user, so that the precision degree of the result detected according to the characteristic parameters of the user is higher, and the user experience is improved.
For example, embodiments of the present invention may be applied to the scenario shown in FIG. 1. In this scenario, a server 101 with a data processing function and a wearable device 102 (which may be a smart band, for example) for data acquisition are included, where the server 101 and the wearable device 102 are communicatively connected. Specifically, the wearing device 102 is worn on the body of the user, and the wearing device 102 collects the user characteristic parameters and sends the user characteristic parameters to the server 101; after the server 101 receives the user characteristic parameter, the server 101 may first determine a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter, and then, the server 101 may determine a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state. Therefore, the intensity and the motion type of the motion of the user can be determined according to the characteristic parameters of the user, and compared with the prior art, the method and the device have the advantages that the precision of the result detected according to the characteristic parameters of the user is higher, and the user experience is improved.
It is to be understood that in the application scenarios described above, while the actions of the embodiments of the present invention are described as being performed by the server 101, these actions may also be performed by the wearable device 102. The invention is not limited in its implementation to the details of execution, provided that the acts disclosed in the embodiments of the invention are performed.
It should be noted that the above application scenarios are only shown for the convenience of understanding the present application, and the embodiments of the present application are not limited in any way in this respect. Rather, embodiments of the present application may be applied to any scenario where applicable.
Various non-limiting embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 2, a motion type detection method in an embodiment of the present invention is shown. In this embodiment, the method may include, for example, the steps of:
s201: a user characteristic parameter is determined.
In this embodiment, the user characteristic parameter may be understood as a parameter capable of reflecting physiological changes generated by the user during a life activity, that is, a parameter capable of reflecting physiological changes caused by various activities and jobs of the user, for example, the user characteristic parameter may be a heart rate, or acceleration components of the arm in an x axis, a y axis and a z axis, or an angular velocity corresponding to the arm. For example, the user characteristic parameters may include parameters reflecting the load magnitude of the person during activity or work, such as exercise, heart rate during walking, acceleration components of the arm on the x-axis, y-axis and z-axis during walking and exercise, or angular velocity corresponding to the arm, and the like, and the user characteristic parameters may also include parameters reflecting the load magnitude of the person during static state, such as heart rate during sleep, acceleration components of the arm on the x-axis, y-axis and z-axis during sleep, or angular velocity corresponding to the arm, and the like.
As an example, the user characteristic parameters may be acquired by a relevant sensor; for example, when the characteristic parameter of the user is a heart rate, pulse wave signals (such as red capacitance volume pulse wave signals and infrared capacitance volume pulse wave signals) may be acquired by a pulse wave sensor worn on the user, and the heart rate is determined according to the pulse wave signals, and for example, when the characteristic parameter of the user is acceleration components of the user on an x axis, a y axis and a z axis, acceleration components of the user on the x axis, the y axis and the z axis may be obtained by measuring with a three-axis accelerometer worn on the user, and for example, when the characteristic parameter of the user is an angular velocity corresponding to the user, an angular velocity corresponding to the user may be obtained by measuring with a gyroscope or an angular velocity sensor worn on the user.
As another example, the user characteristic parameters may be obtained by receiving user characteristic parameters sent by other devices, for example, when the method is applied to a server, and a smart bracelet worn on a hand of a user measures user characteristic parameters such as a heart rate, acceleration components of an arm on an x axis, a y axis, and a z axis, and an angular velocity corresponding to the arm, the server may obtain the user characteristic parameters to be processed by receiving the user characteristic parameters sent by the smart bracelet.
S202: and determining a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter.
In order to determine whether the user moves or not, and further determine the intensity of the user movement, so as to improve the fineness of determining the user movement condition, in this embodiment, after the user characteristic parameter is obtained, the movement state corresponding to the user characteristic parameter may be determined according to the obtained user characteristic parameter. It should be noted that the exercise state may be understood as a condition capable of reflecting the exercise intensity of the user, that is, the exercise state may be used to reflect the exercise intensity corresponding to the characteristic parameter of the user, for example, the exercise state may be a rest state, a slight exercise state or an intense exercise state, where the exercise intensity of the rest state is lower than that of the slight exercise state, and the slight exercise state is lower than that of the intense exercise state.
In one implementation of this embodiment, the determined user characteristic parameter may include a plurality of different user characteristic parameters. Therefore, after a plurality of different user characteristic parameters are determined, the motion state corresponding to each user characteristic parameter can be determined according to each user characteristic parameter, for example, the user characteristic parameter a, the user characteristic parameter B and the user characteristic parameter C are acquired, and the user characteristic parameter a, the user characteristic parameter B and the user characteristic parameter C are different from each other, so that the motion state corresponding to the user characteristic parameter a, the motion state corresponding to the user characteristic parameter B and the motion state corresponding to the user characteristic parameter C can be determined respectively.
Then, an exercise state may be determined according to the exercise state corresponding to each user characteristic parameter, where for convenience of description, the exercise state determined according to the exercise state corresponding to each user characteristic parameter may be referred to as a target exercise state, that is, the target exercise state is a final exercise state determined by integrating the plurality of user characteristic parameters. It should be noted that, in a possible implementation manner, the target state may be determined according to the motion state corresponding to each user characteristic parameter based on a weighting manner, for example, assuming that the motion state corresponding to the user characteristic parameter a is a resting state, the motion state corresponding to the user characteristic parameter B is a slight motion state, the motion state corresponding to the user characteristic parameter C is a resting state, the weight corresponding to the motion state corresponding to the user characteristic parameter A is 0.5, the weight corresponding to the motion state corresponding to the user characteristic parameter B is 0.3, the weight corresponding to the motion state corresponding to the user characteristic parameter C is 0.2, the weighted sum corresponding to each motion state can be determined, and the motion state with the maximum weighted sum is taken as the target motion state, and determining the motion state of the target as the rest state because the sum of the weights corresponding to the rest state is the maximum.
S203: and determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state.
After the target motion state corresponding to the determined user characteristic parameter is obtained, a specific motion type of the user motion can be determined according to the user characteristic parameter and the target motion state, so that the fineness of the detection result of the user motion can be improved. The exercise type may be understood as a category corresponding to an exercise performed by the user, for example, the exercise type may include deep sleep, shallow sleep, walking, running, badminton, and the like, and for convenience of description, the exercise type corresponding to the determined user characteristic parameter may be referred to as a target exercise type.
It should be noted that, the motion states corresponding to different motion types may be different, for example, the sleep state belongs to a motion state (i.e., a resting state) in which the user is relatively still, and when the user runs, the user is in the relative motion state but not in the relative resting motion state, i.e., the running does not correspond to the resting state; the user characteristic parameters corresponding to different motion types may also be different, for example, the acceleration component and the angular velocity of the arm in each direction when playing badminton are different from the acceleration component and the angular velocity of the arm in each direction when running. Therefore, the motion type in a certain range may be determined according to the target motion state, and then the target motion type may be determined from the motion types in the range according to the determined user characteristic parameter, for example, after the target motion state is determined to be a resting state, the range of the motion types possibly corresponding to the resting state may be determined to include shallow sleep and deep sleep, and then, the target motion type may be further determined to be the shallow sleep or the deep sleep from the range according to the determined user characteristic parameter.
According to the technical scheme, the user characteristic parameters are determined firstly; then, according to the user characteristic parameters, determining a target motion state corresponding to the user characteristic parameters; and then, determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state. In the embodiment, the motion state and the motion type can be obtained according to the user characteristic parameters, wherein the motion state can be used for reflecting the motion intensity corresponding to the user characteristic parameters, so that the motion intensity and the motion type of the user can be determined according to the user characteristic parameters; therefore, compared with the prior art, the method and the device have the advantages that the fineness of the detected result according to the characteristic parameters of the user is higher, and the user experience is improved.
Fig. 2 shows only a basic embodiment of the method for detecting a type of motion according to the present invention, and based on this, certain optimization and expansion are performed, and other preferred embodiments of the method can also be obtained.
Next, another specific embodiment of a method for detecting a type of exercise according to the present invention will be described, which will be described mainly in the implementation of the method for detecting a type of exercise in the case where the determined characteristic parameters of the user include a heart rate, acceleration components in the x-axis, y-axis, and z-axis, respectively, and an angular velocity. Specifically, in this embodiment, the method specifically includes the following steps:
s301: a user characteristic parameter is determined.
In this embodiment, the heart rate, the acceleration components on the x-axis, the y-axis and the z-axis, and the user characteristic parameters such as the angular velocity may be determined first, and it should be noted that the determined user characteristic parameters may be the user characteristic parameters within a preset time, for example, the user characteristic parameters within five minutes. For example, a pulse wave signal may be acquired by a pulse wave sensor worn on the user, and a heart rate may be determined from the pulse wave signal; the acceleration components in the x-axis, y-axis, and z-axis may be measured by a three-axis accelerometer worn on the user, and the angular velocity may be measured by a gyroscope or an angular velocity sensor worn on the user.
S302: and determining the motion state corresponding to the heart rate according to the heart rate and a first threshold condition.
After the heart rate of the user is determined, the corresponding motion state can be determined according to the determined heart rate and the first threshold condition; it should be noted that, if the determined heart rate is within a period of time, the motion state corresponding to the heart rate may be determined according to the average value of the heart rates within the period of time and the first threshold condition.
The first threshold condition is a condition for determining a motion state corresponding to the heart rate, for example, the first threshold condition is: if the heart rate is less than 80 times/minute, determining that the exercise state corresponding to the heart rate is a resting state; if the heart rate is more than 80 times/minute and less than 140 times/minute, determining that the exercise state corresponding to the heart rate is a slight exercise state; and if the heart rate is more than 140 times/minute, determining that the exercise state corresponding to the heart rate is a violent exercise state. It should be noted that the specific threshold corresponding to the first threshold condition may be adjusted according to the actual situation, for example, may be changed along with the collection of the user personalized data to adapt to the actual situation of each user.
S303: and determining an acceleration comprehensive value according to the acceleration components on the x axis, the y axis and the z axis, and determining the motion states corresponding to the acceleration components on the x axis, the y axis and the z axis according to the acceleration comprehensive value and a second threshold condition.
After determining the acceleration components on the x-axis, the y-axis, and the z-axis, for example, the acceleration components of the arm on the x-axis, the y-axis, and the z-axis, a corresponding acceleration integrated value may be determined according to the acceleration components on the x-axis, the y-axis, and the z-axis, for example, an acceleration integrated value may be determined according to formula (1):
AMP1＝x 2 +y 2 +z 2 (1)，
wherein x represents acceleration in the lateral direction; y represents a longitudinal acceleration perpendicular to x; z represents a vertical acceleration perpendicular to x and y; AMP1 represents the integrated acceleration value.
Then, motion states corresponding to the acceleration components on the x axis, the y axis and the z axis can be determined according to the acceleration comprehensive value and a second threshold condition; it should be noted that, if the acceleration components on the x-axis, the y-axis, and the z-axis in a period of time are determined, a plurality of integrated acceleration values may be determined according to the acceleration components on the x-axis, the y-axis, and the z-axis in the period of time, and then the motion states corresponding to the acceleration components on the x-axis, the y-axis, and the z-axis may be determined according to an average value of the plurality of integrated acceleration values and a second threshold condition.
Wherein the second threshold condition is a condition for determining a motion state corresponding to the acceleration components in the x-axis, the y-axis, and the z-axis. For example, the second threshold condition is: if the integrated acceleration value is less than m, determining that the motion state is a rest state; if the comprehensive acceleration value is larger than m and smaller than n, determining that the motion state is a slight motion state; if the comprehensive value of the acceleration is larger than n, determining that the motion state is a violent motion state, wherein m is smaller than n, and both m and n are larger than 0; the specific threshold (i.e., m, n) corresponding to the second threshold condition may be adjusted according to actual conditions.
S304: and determining the acceleration components on the X axis, the Y axis and the Z axis in a standard coordinate system according to the acceleration components on the X axis, the Y axis and the Z axis and the angular velocity.
Because the acceleration that some bracelet motion monitoring facilities gathered is instantaneous acceleration only, does not consider current angular velocity, consequently, the acceleration that gathers does not belong to the acceleration on the standard coordinate system, if only confirm the motion state with instantaneous acceleration, the motion state of confirming can have the possibility of certain error. Therefore, in order to improve the accuracy of the determined motion state, after the acceleration components of the user in the x, y, and z axes may be determined, the acceleration components in the x, y, and z axes may be normalized.
Specifically, the acceleration components on the X-axis, the Y-axis and the Z-axis in the corresponding standard coordinate system may be determined according to the acceleration components on the X-axis, the Y-axis and the Z-axis and the angular velocity, wherein the angular velocity may include a direction angle, an inclination angle and a rotation angle, for example, the acceleration components on the X-axis, the Y-axis and the Z-axis in the standard coordinate system may be determined according to equations (2) - (4),
X＝x*(cos gama*cos alpha-sin gama*sin beta*sin alpha)+y*cos beta*sin alpha–z*(sin gama*cos alpha+cos gama*sin beta*sin alpha) (2)，
Y＝-x(cos gama*sin alpha+sin gama*sin beta*cos alpha)+y*cos beta*cos alpha+z*(sin gama*sin alpha-cos gama*sin beta*cos alpha) (3)，
Z＝z*cos gama*cos beta+x*sin gama*cos beta+y*sin beta (4)，
wherein x represents acceleration in the lateral direction; y represents a longitudinal acceleration perpendicular to x; z represents a vertical acceleration perpendicular to x and y; alpha represents the direction angle; beta represents the tilt angle; gama represents the rotation angle; x represents the transverse acceleration in a standard coordinate system; y represents the longitudinal acceleration perpendicular to X in the standard coordinate system; z represents the vertical acceleration perpendicular to X and Y in a standard coordinate system.
S305: and determining a standard acceleration comprehensive value according to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, and determining the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system according to the standard acceleration comprehensive value and a third threshold condition.
After determining the acceleration components of the user in the X-axis, the Y-axis, and the Z-axis in the standard coordinate system, for example, the acceleration components of the arm in the X-axis, the Y-axis, and the Z-axis in the standard coordinate system, a corresponding standard acceleration integrated value may be determined according to the acceleration components in the X-axis, the Y-axis, and the Z-axis in the standard coordinate system, for example, the acceleration integrated value may be determined according to formula (5):
AMP2＝X 2 +Y 2 +Z 2 (5)，
wherein X represents the transverse acceleration in a standard coordinate system; y represents the longitudinal acceleration perpendicular to X in the standard coordinate system; z represents a vertical acceleration perpendicular to X and Y in a standard coordinate system; AMP2 represents the standard acceleration integrated value.
Then, determining motion states corresponding to acceleration components on an X axis, a Y axis and a Z axis in a standard coordinate system according to the standard acceleration comprehensive value and a third threshold condition; it should be noted that, if the acceleration components on the X axis, the Y axis, and the Z axis in the standard coordinate system within a period of time are determined, a plurality of standard acceleration integrated values may be determined according to the acceleration components on the X axis, the Y axis, and the Z axis in the standard coordinate system within the period of time, and then the motion states corresponding to the acceleration components on the X axis, the Y axis, and the Z axis in the standard coordinate system may be determined according to an average value of the plurality of standard acceleration integrated values and a third threshold condition.
The third threshold condition is a condition for determining a motion state corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis in the standard coordinate system. For example, the third threshold condition is: if the standard acceleration comprehensive value is less than m, determining that the motion state is a rest state; if the comprehensive acceleration value is larger than m and smaller than n, determining that the motion state is a slight motion state; if the standard acceleration comprehensive value is larger than n, determining that the motion state is a violent motion state, wherein m is smaller than n, and both m and n are larger than 0; the specific threshold (i.e., m, n) corresponding to the third threshold condition may be adjusted according to actual conditions.
S306: and determining the target motion state according to the motion state corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system and preset weights corresponding to the characteristic parameters of each user.
When the target motion state is determined, a dynamic weight distribution algorithm may be adopted for calculation based on the motion state corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis, and the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system.
Specifically, in this embodiment, corresponding preset weights may be set for the motion states corresponding to the heart rate, the motion states corresponding to the acceleration components on the X-axis, the Y-axis, and the Z-axis, and the motion states corresponding to the acceleration components on the X-axis, the Y-axis, and the Z-axis in the standard coordinate system, respectively. The motion state corresponding to each user characteristic parameter and the preset weight corresponding to each user characteristic parameter can be determined, the weight sum corresponding to each motion state is determined, and the motion state with the maximum weight sum is used as the target motion state. Assuming that the motion state corresponding to the heart rate is a rest state, the motion states corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis are slight motion states, the motion states corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis in the standard coordinate system are rest states, the preset weight corresponding to the motion state corresponding to the heart rate is 0.5, the preset weight corresponding to the motion states corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis is 0.3, and the preset weight corresponding to the motion states corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis in the standard coordinate system is 0.2, it can be determined that the weight sum corresponding to the rest state is 0.7 and the preset weight sum corresponding to the slight motion state is 0.3, and since the preset weight sum corresponding to the rest state is the largest, it is determined that the target motion state is the rest state. It is emphasized that if the preset weighted sum of a plurality of motion states is equal and is the maximum preset weighted sum, and the plurality of motion states includes a motion state corresponding to the heart rate, the motion state corresponding to the heart rate may be taken as the target motion state.
It should be noted that the preset weight corresponding to the heart rate and the preset weights corresponding to the acceleration components on the x axis, the y axis and the z axis are determined according to the historical acquisition times of the user characteristic parameters; and the preset weights corresponding to the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system are fixed and unchanged.
In an implementation manner, when the historical collection times of the user characteristic parameter are less than the preset collection times, the preset weight corresponding to the heart rate and the preset weights corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis may also be fixed values, for example, the preset weight corresponding to the motion state corresponding to the heart rate, the preset weight corresponding to the motion state corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis, and the preset weights corresponding to the motion states corresponding to the acceleration components on the X-axis, the Y-axis and the Z-axis in the standard coordinate system may be respectively fixed to 0.5, 0.3, 0.2.
When the historical collection times of the user characteristic parameters are equal to or greater than the preset collection times, the preset weight corresponding to the motion state corresponding to the heart rate may be: a + C (x/D), and the preset weights corresponding to the motion states corresponding to the acceleration components on the x axis, the y axis and the z axis are as follows: B-C (x/D); when the historical acquisition times of the characteristic parameters of the user represented by A are less than the preset acquisition times, the preset weight corresponding to the motion state corresponding to the heart rate is obtained; b represents a preset weight corresponding to the motion state corresponding to the acceleration components on the x axis, the y axis and the z axis when the historical acquisition times of the user characteristic parameters are smaller than the preset acquisition times; c is a preset weight corresponding to the motion state corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system; d is a preset collection frequency; x the number of historical acquisitions of the characteristic parameter of the user.
S307: and if the target motion state is a resting state, determining the target motion type according to the standard acceleration comprehensive value and the angular velocity.
When the target motion state is determined to be a resting state, the range of the target motion type may be determined to be a sleep rest type motion type, for example, deep sleep may be performed, shallow sleep may be performed, and the target motion type may be determined according to the standard acceleration integrated value and the angular velocity. Next, how to determine the types of target motion as deep sleep, shallow sleep, respectively, will be described:
first, a way of determining the type of target motion as deep sleep is introduced: and if the variance of the integrated value of the standard acceleration is smaller than or equal to a first preset threshold value and the average value of the integrated value of the standard acceleration is the earth acceleration within a preset time, and the variance mean value of the angular velocity is smaller than or equal to a second preset threshold value and the average value of the angular velocity is 0, determining that the target motion type is deep sleep. That is, after determining that the moving state of the target is the resting state, the average value of the integrated values of the standard acceleration within the preset time and the average value of the angular velocity may be calculated; if the average value of the angular velocity is 0 and the average value of the integrated value of the standard acceleration is the earth acceleration, which indicates that the user does not perform the movement such as turning over, then calculating the variance of the integrated value of the standard acceleration and the variance of the angular velocity within the preset time; if the variance of the integrated value of the standard acceleration is smaller than or equal to a first preset threshold value, and the mean value of the variance of the angular velocity is smaller than or equal to a second preset threshold value, which indicates that the sleep state of the user is stable, it may be determined that the target motion type is deep sleep.
Next, a way of determining the type of target movement as light sleep is introduced: and if the variance of the comprehensive value of the standard acceleration is greater than the first preset threshold value within the preset time, the average value of the comprehensive value of the standard acceleration is not the earth acceleration, and the variance mean value of the angular velocity is greater than the second preset threshold value, and the average value of the angular velocity is not 0, determining that the target motion type is shallow sleep. That is, after the moving state of the target is determined to be the resting state, the average value of the integrated values of the standard acceleration within the preset time and the average value of the angular velocity may be calculated; if the average value of the angular velocity is not 0 and the average value of the integrated value of the standard acceleration is not the earth acceleration, which indicates that the user is in a relatively static state but occasionally performs a movement such as turning over, then calculating the variance of the integrated value of the standard acceleration and the variance of the angular velocity within the preset time; if the variance of the integrated value of the standard acceleration is greater than a first preset threshold value and the mean variance of the angular velocity is greater than a second preset threshold value, which indicates that the sleep state of the user is unstable, it may be determined that the target motion type is light sleep.
Since the angular velocity may include the direction angle, the inclination angle, and the rotation angle, the variances corresponding to the direction angle, the inclination angle, and the rotation angle may be calculated, and then the average of the variances corresponding to the direction angle, the inclination angle, and the rotation angle may be used as the variance mean of the angular velocity.
S308: and if the target motion state is a slight motion state or a severe motion state, determining the target motion type according to the motion tracks corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system.
After the target motion state is determined to be not a rest state but a slight motion state or a severe motion state, the motion trajectory corresponding to the user may be determined according to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, for example, the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system may be integrated to obtain the velocities in the three directions of the X axis, the Y axis and the Z axis, respectively, and then the velocities in the three directions may be integrated to obtain the motion distances in the three directions of the X axis, the Y axis and the Z axis, and the motion distances in the three directions of the X axis, the Y axis and the Z axis may be combined to obtain the spatial motion trajectory.
Then, according to the motion trail, the target motion type corresponding to the motion trail can be determined. It should be noted that, in an implementation manner of this embodiment, motion trajectories of different motion types may be stored in advance, and since the motion trajectories of different motion types are different in a normal case, for example, the motion trajectories corresponding to the swing arm during walking and running have different amplitudes and frequencies, the motion trajectories corresponding to the acceleration components on the X axis, the Y axis, and the Z axis in the standard coordinate system may be respectively matched with each motion trajectory stored in advance, and a motion type corresponding to a motion trajectory with the highest matching degree in the motion trajectories stored in advance may be used as the target motion type.
Therefore, in the embodiment, a weight distribution algorithm is adopted, a calculation algorithm is customized according to the accumulation of the user historical user characteristic parameters and the difference of each user, the motion states corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis, and the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system are combined, the target motion state is determined through comprehensive analysis, and the accuracy and the fineness of the determined target motion state are improved; in addition, the embodiment can also determine whether the motion type of the user is shallow sleep or deep sleep according to the standard acceleration comprehensive value and the angular velocity, and determine the specific motion type item of the user motion according to the motion tracks corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, so that the accuracy and the fineness of the determined target motion type are improved.
Therefore, the embodiment realizes the motion type detection method process by combining with a specific application scene. It should be understood that the above scenarios are only exemplary scenarios and are not intended to limit the method provided by the present invention. The method provided by the invention can be applied to the detection process of other motion types with the same principle in an extensive way.
Fig. 3 shows a specific embodiment of the motion type detecting device according to the present invention. The apparatus of this embodiment is a physical apparatus for executing the method of the above embodiment. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in this embodiment includes:
a parameter determining unit 301, configured to determine a user characteristic parameter;
a state determining unit 302, configured to determine, according to the user characteristic parameter, a target motion state corresponding to the user characteristic parameter, where the motion state is used to reflect a motion intensity degree corresponding to the user characteristic parameter;
a type determining unit 303, configured to determine, according to the user characteristic parameter and the target motion state, a target motion type corresponding to the user characteristic parameter.
Optionally, the state determining unit 302 is specifically configured to:
determining the motion state corresponding to each user characteristic parameter according to each user characteristic parameter;
and determining the target motion state according to the motion state corresponding to each user characteristic parameter.
Optionally, the user characteristic parameters include heart rate, acceleration components on an x-axis, a y-axis and a z-axis, respectively, and angular velocity; the state determining unit 302 is further specifically configured to:
determining a motion state corresponding to the heart rate according to the heart rate and a first threshold condition;
determining an acceleration comprehensive value according to the acceleration components on the x axis, the y axis and the z axis, and determining motion states corresponding to the acceleration components on the x axis, the y axis and the z axis according to the acceleration comprehensive value and a second threshold condition;
determining acceleration components on an X axis, a Y axis and a Z axis in a standard coordinate system according to the acceleration components on the X axis, the Y axis and the Z axis and the angular velocity;
determining a standard acceleration comprehensive value according to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, and determining the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system according to the standard acceleration comprehensive value and a third threshold condition;
correspondingly, the state determining unit 302 is further specifically configured to:
and determining the target motion state according to the motion state corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system and preset weights corresponding to the characteristic parameters of each user.
Optionally, the preset weight corresponding to the heart rate and the preset weights corresponding to the acceleration components on the x axis, the y axis and the z axis are determined according to the historical collection times of the user characteristic parameters.
Optionally, the category determining unit 303 is specifically configured to:
if the target motion state is a resting state, determining the target motion type according to the standard acceleration comprehensive value and the angular velocity;
and if the target motion state is a slight motion state or a severe motion state, determining the target motion type according to the motion tracks corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system.
Optionally, the category determining unit 303 is specifically configured to:
if the variance of the integrated value of the standard acceleration is smaller than or equal to a first preset threshold value and the average value of the integrated value of the standard acceleration is the earth acceleration within a preset time, and the variance mean value of the angular velocity is smaller than or equal to a second preset threshold value and the average value of the angular velocity is 0, determining that the target motion type is deep sleep;
and if the variance of the integrated value of the standard acceleration is greater than the first preset threshold value and the average value of the integrated value of the standard acceleration is not the earth acceleration within the preset time, and the variance mean of the angular velocity is greater than the second preset threshold value and the average value of the angular velocity is not 0, determining that the target motion type is shallow sleep.
Optionally, the category determining unit 303 is further specifically configured to:
determining a motion track corresponding to a user according to acceleration components on an X axis, a Y axis and a Z axis in the standard coordinate system;
and determining the target motion type corresponding to the motion track according to the motion track.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the non-volatile memory into the memory and then runs the execution instruction, and the corresponding execution instruction can also be obtained from other equipment, so as to form the motion type detection device on a logic level. The processor executes the execution instructions stored in the memory to implement the method for detecting a motion type provided in any embodiment of the invention through the executed execution instructions.
The method performed by the motion type detection apparatus according to the embodiment of the invention shown in fig. 2 can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The embodiment of the present invention further provides a readable medium, where the readable medium stores an execution instruction, and the stored execution instruction, when executed by a processor of an electronic device, enables the electronic device to perform the method for detecting a motion type provided in any embodiment of the present invention, and is specifically configured to perform the method for detecting a motion type.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. A method for detecting a type of motion, comprising:
determining user characteristic parameters, wherein the user characteristic parameters comprise heart rate, acceleration components on an x axis, a y axis and a z axis respectively, and angular velocity;
determining a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter, wherein the target motion state is used for reflecting the motion intensity degree corresponding to the user characteristic parameter;
determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state;
the determining the target motion state corresponding to the user characteristic parameter according to the user characteristic parameter includes:
determining a motion state corresponding to the heart rate according to the heart rate and a first threshold condition;
determining an acceleration comprehensive value according to the acceleration components on the x axis, the y axis and the z axis, and determining motion states corresponding to the acceleration components on the x axis, the y axis and the z axis according to the acceleration comprehensive value and a second threshold condition;
determining acceleration components on an X axis, a Y axis and a Z axis in a standard coordinate system according to the acceleration components on the X axis, the Y axis and the Z axis and the angular velocity;
determining a standard acceleration comprehensive value according to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, and determining the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system according to the standard acceleration comprehensive value and a third threshold condition;
and determining the target motion state according to the motion state corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system and the preset weight corresponding to the characteristic parameter of the user.
2. The method according to claim 1, wherein the preset weight corresponding to the heart rate and the preset weights corresponding to the acceleration components in the x-axis, the y-axis and the z-axis are determined according to historical collection times of the user characteristic parameters.
3. The method according to claim 1 or 2, wherein the determining the target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state comprises:
if the target motion state is a resting state, determining the target motion type according to the standard acceleration comprehensive value and the angular velocity;
and if the target motion state is a slight motion state or a severe motion state, determining the target motion type according to the motion tracks corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system.
4. The method of claim 3, wherein the determining the type of the target motion according to the integrated value of the standard acceleration and the corresponding angular velocity of the user comprises:
if the variance of the integrated value of the standard acceleration is smaller than or equal to a first preset threshold value and the average value of the integrated value of the standard acceleration is the earth acceleration within a preset time, and the variance mean value of the angular velocity is smaller than or equal to a second preset threshold value and the average value of the angular velocity is 0, determining that the target motion type is deep sleep;
and if the variance of the integrated value of the standard acceleration is greater than the first preset threshold value and the average value of the integrated value of the standard acceleration is not the earth acceleration within the preset time, and the variance mean of the angular velocity is greater than the second preset threshold value and the average value of the angular velocity is not 0, determining that the target motion type is light sleep.
5. The method according to claim 4, wherein the determining the target motion type according to the motion trajectories corresponding to the acceleration components in the X-axis, the Y-axis and the Z-axis in the standard coordinate system comprises:
determining a motion track corresponding to a user according to acceleration components on an X axis, a Y axis and a Z axis in the standard coordinate system;
and determining the target motion type corresponding to the motion track according to the motion track.
6. A motion type detection apparatus, comprising:
a parameter determination unit for determining user characteristic parameters including a heart rate, acceleration components on an x-axis, a y-axis and a z-axis, respectively, and an angular velocity;
the state determining unit is used for determining a target motion state corresponding to the user characteristic parameter according to the user characteristic parameter, wherein the motion state is used for reflecting the motion intensity degree corresponding to the user characteristic parameter;
the type determining unit is used for determining a target motion type corresponding to the user characteristic parameter according to the user characteristic parameter and the target motion state;
a state determination unit, specifically configured to: determining a motion state corresponding to the heart rate according to the heart rate and a first threshold condition; determining an acceleration comprehensive value according to the acceleration components on the x axis, the y axis and the z axis, and determining motion states corresponding to the acceleration components on the x axis, the y axis and the z axis according to the acceleration comprehensive value and a second threshold condition; determining acceleration components on an X axis, a Y axis and a Z axis in a standard coordinate system according to the acceleration components on the X axis, the Y axis and the Z axis and the angular velocity; determining a standard acceleration comprehensive value according to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system, and determining the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system according to the standard acceleration comprehensive value and a third threshold condition; and determining the target motion state according to the motion state corresponding to the heart rate, the motion states corresponding to the acceleration components on the X axis, the Y axis and the Z axis in the standard coordinate system and the preset weight corresponding to the characteristic parameter of the user.
7. A readable medium comprising executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1-5.
8. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-5 when the processor executes the execution instructions stored by the memory.
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