CN115191997A

CN115191997A - Motion intensity detection method and wearable device

Info

Publication number: CN115191997A
Application number: CN202110387071.1A
Authority: CN
Inventors: 何奎
Original assignee: Guangdong Genius Technology Co Ltd
Current assignee: Guangdong Genius Technology Co Ltd
Priority date: 2021-04-09
Filing date: 2021-04-09
Publication date: 2022-10-18

Abstract

The embodiment of the invention discloses a motion intensity detection method and wearable equipment, which are applied to the technical field of wearable equipment and can solve the problem of accurately detecting motion intensity in the prior art. The method comprises the following steps: continuously acquiring M acceleration and N heart rate data of a user within a preset time length; calculating to obtain a motion amplitude parameter according to the M accelerated speeds; calculating to obtain a heart rate amplification parameter according to the N heart rate data; if the target parameter is in the target parameter range, determining the motion intensity level of the user in the preset time length as a target intensity level; wherein, the target intensity level corresponds to the target parameter range, and the target parameters include: at least one of a motion amplitude parameter and a heart rate amplification parameter.

Description

Motion intensity detection method and wearable device

Technical Field

The embodiment of the invention relates to the technical field of wearable equipment, in particular to a motion intensity detection method and wearable equipment.

Background

At present, people gradually realize the importance of sports, and the strength of sports is generally regarded as the most scientific, effective and most instructive monitoring index in the process of sports. The exercise intensity is too low, and the body building effect is not obvious; if the exercise intensity is too high, the risk of exercise injury will be increased, and how to accurately detect the exercise intensity becomes a problem that needs to be solved at present.

Disclosure of Invention

The embodiment of the invention provides a motion intensity detection method and wearable equipment, which are used for solving the problem of accurately detecting motion intensity in the prior art. In order to solve the above technical problem, the embodiment of the present invention is implemented as follows:

in a first aspect, a method for detecting exercise intensity is provided, where the method includes: continuously acquiring M acceleration and N heart rate data of a user within a preset time length;

calculating to obtain a motion amplitude parameter according to the M accelerated speeds;

calculating to obtain a heart rate amplification parameter according to the N heart rate data;

if the target parameter is in the target parameter range, determining the motion intensity level of the user in the preset time length as a target intensity level;

wherein the target intensity level corresponds to the target parameter range, and the target parameters include: at least one of the motion amplitude parameter and the heart rate amplification parameter.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, if the target parameter is within the target parameter range, the determining that the exercise intensity level of the user within the preset time period is the target intensity level includes:

if the motion amplitude parameter is larger than a first preset threshold value and/or the heart rate amplification parameter is larger than a second preset threshold value, determining the motion intensity level of the user in the preset time length as a first intensity level;

if the motion amplitude parameter is smaller than or equal to the first preset threshold value and/or the heart rate amplification parameter is smaller than or equal to the second preset threshold value, determining that the motion intensity level of the user in the preset duration is a second intensity level;

wherein the second intensity level is less than the first intensity level.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, the calculating, according to the M accelerations, a motion amplitude parameter includes:

calculating to obtain the motion amplitude parameter according to the M accelerations and a first formula;

wherein the first formula is:

s1 is the motion amplitude parameter, A _i For the ith acceleration among the M accelerations,

is the average of the M accelerations.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, the calculating, according to the N heart rate data, a heart rate amplification parameter includes:

when a user is in a static state, obtaining the resting heart rate of the user;

acquiring a median of the N heart rate data according to the N heart rate data;

calculating to obtain the heart rate amplification parameter according to the median of the N heart rate data, the resting heart rate and a second formula;

wherein the second formula is:

s2 is the heart rate amplification parameter, B _r Is the median of the N heart rate data, B ₁ Is the resting heart rate, B _msx Is the user's maximum heart rate.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, after acquiring the median of the N heart rate data according to the N heart rate data, the method further includes:

acquiring personal information of the user, wherein the personal information at least comprises: the age of the user;

and calculating the maximum heart rate of the user according to the age of the user.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, after determining that the exercise intensity level of the user in the preset time period is the target intensity level if the target parameter is within the target parameter range, the method further includes:

accumulating the preset time length to the total movement time length of the target intensity level;

and if the total movement time length is detected to be greater than a preset time length threshold value, outputting a first prompt message, wherein the first prompt message is used for prompting a user to take a rest.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, after continuously acquiring M accelerations and N heart rate data of the user within the preset time period, the method further includes:

if the M accelerations meet the preset sit-up characteristic, acquiring a target acceleration which is smaller than a first preset acceleration threshold and smaller than an adjacent acceleration in the M accelerations; determining the number of the target acceleration as the sit-up times of the user within the preset time length;

or the like, or a combination thereof,

if the M accelerated speeds meet the preset rope skipping characteristic, acquiring a target accelerated speed which is larger than a second preset accelerated speed threshold value and larger than the adjacent accelerated speed in the M accelerated speeds; determining the number of the target acceleration as the rope skipping times of the user within the preset time length;

wherein the adjacent acceleration values are a previous acceleration and a subsequent acceleration of the target acceleration.

In a second aspect, there is provided a wearable device comprising: the acquisition module is used for continuously acquiring M acceleration and N heart rate data of a user within a preset time length;

the processing module is used for calculating to obtain a motion amplitude parameter according to the M accelerations;

the processing module is further used for calculating a heart rate amplification parameter according to the N heart rate data;

the processing module is further configured to determine the exercise intensity level of the user within the preset duration as a target intensity level if the target parameter is within a target parameter range;

In a third aspect, a wearable device is provided, comprising:

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to execute the exercise intensity detection method in the first aspect of the embodiment of the present invention.

In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program that causes a computer to execute the exercise intensity detection method in the first aspect of the embodiment of the present invention. The computer readable storage medium includes a ROM/RAM, a magnetic or optical disk, or the like.

In a fifth aspect, there is provided a computer program product for causing a computer to perform some or all of the steps of any one of the methods of the first aspect when the computer program product is run on the computer.

A sixth aspect provides an application publishing platform for publishing a computer program product, wherein the computer program product, when run on a computer, causes the computer to perform some or all of the steps of any one of the methods of the first aspect.

Compared with the prior art, the embodiment of the invention has the following beneficial effects:

in the embodiment of the invention, the wearable device can continuously acquire M accelerations and N heart rate data of a user within a preset time length; calculating to obtain a motion amplitude parameter according to the M accelerated speeds; calculating to obtain a heart rate amplification parameter according to the N heart rate data; and the wearable equipment determines the intensity level corresponding to the parameter range according to the motion amplitude parameter and/or the parameter range where the heart rate amplification parameter is located. Through this scheme, wearable equipment can correspond different parameter ranges with every intensity level, and wearable equipment can obtain the motion amplitude parameter of present moment according to real-time acceleration and heart rate data like this, and/or, heart rate amplification parameter just can obtain accurate user's motion intensity level according to the corresponding relation.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a first flowchart illustrating a method for detecting exercise intensity according to an embodiment of the present invention;

fig. 2 is a first schematic data processing diagram of a motion intensity detection method according to an embodiment of the present invention;

fig. 3 is a data processing schematic diagram of a exercise intensity detection method according to an embodiment of the present invention;

fig. 4 is a first schematic structural diagram of a wearable device according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a wearable device according to an embodiment of the present invention;

fig. 6 is a schematic hardware structure diagram of a wearable device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present 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.

The terms "first" and "second," and the like, in the description and in the claims of the present invention are used for distinguishing between different objects and not for describing a particular order of the objects. For example, the first preset threshold, the second preset threshold, and the like are used to distinguish different preset thresholds, rather than describing a specific order of the preset thresholds.

The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.

In the related art, people are gradually aware of the importance of exercise, and the exercise intensity is generally regarded as the most scientific, effective and most instructive monitoring index in the exercise process. The exercise intensity is too low, and the body building effect is not obvious; if the exercise intensity is too high, the risk of exercise injury will be increased, and how to accurately detect the exercise intensity becomes a problem that needs to be solved at present.

In order to solve the above problems, embodiments of the present invention provide a motion intensity detection method and a wearable device, where the wearable device may continuously obtain M accelerations and N heart rate data of a user within a preset time period; calculating to obtain a motion amplitude parameter according to the M accelerated speeds; calculating to obtain a heart rate amplification parameter according to the N heart rate data; and the wearable equipment determines the intensity level corresponding to the parameter range according to the motion amplitude parameter and/or the parameter range where the heart rate amplification parameter is located. Through this scheme, wearable equipment can correspond different parameter ranges with every intensity level, and wearable equipment can obtain the motion amplitude parameter of current moment according to real-time acceleration and heart rate data like this, and/or, heart rate amplification parameter just can obtain accurate user's motion intensity level according to corresponding relation.

The wearable device related to the embodiment of the invention can be an intelligent watch, an intelligent bracelet, a watch phone and the like, and the embodiment of the invention is not limited. The user can wear the wearable device to do exercise, so that the wearable device can detect the exercise intensity of the user according to the exercise intensity detection method provided by the embodiment of the invention.

An execution main body of the exercise intensity detection method provided in the embodiment of the present invention may be the wearable device, or may also be a functional module and/or a functional entity that can implement the exercise intensity detection method in the wearable device, and may be specifically determined according to actual use requirements, which is not limited in the embodiment of the present invention. The following takes a wearable device as an example to exemplarily describe the exercise intensity detection method provided by the embodiment of the present invention.

Example one

As shown in fig. 1, an embodiment of the present invention provides a method for detecting exercise intensity, which may include the following steps:

101. m accelerations and N heart rate data of the user are continuously acquired.

In the embodiment of the invention, the wearable device can continuously acquire M accelerations through the acceleration sensor within a preset time length; and continuously acquiring N heart rate data of the user through a heart rate sensor.

Wherein the M first accelerations correspond to M moments within a first duration; the N heart rate data correspond to N times within the first duration.

It should be noted that M and N are both integers greater than or equal to 1, and M and N may be the same or different.

Optionally, in the three-dimensional rectangular coordinate system, the three-axis acceleration sensor detects accelerations of an X axis, a Y axis, and a Z axis, respectively, to obtain three acceleration components.

Optionally, in this embodiment of the present invention, the M accelerations obtained by the wearable device through the acceleration sensor may be M accelerations including three acceleration components, or M accelerations obtained by calculating a modulus value according to the three acceleration components.

Wherein, the modulus of the acceleration can be calculated by a modulus formula

Wherein, A _x 、A _y And A _z Three acceleration components, respectively.

For example, assuming that the wearable device obtains 5 accelerations through the acceleration sensor, the 5 accelerations may be respectively represented as an acceleration a: (5 m/s) ² ，3m/s ² ，8m/s ² ) The acceleration B: (6 m/s) ² ，2m/s ² ，4m/s ² ) Acceleration C: (3 m/s) ² ，3m/s ² ，3m/s ² ) The acceleration D: (6 m/s) ² ，1m/s ² ，2m/s ² ) The acceleration E: (6 m/s) ² ，6m/s ² ，3m/s ² ) (ii) a The 5 first accelerations may also be respectively expressed as an acceleration a:

acceleration B:

acceleration C:

acceleration D:

acceleration E:

optionally, continuously acquiring M accelerations within a preset time period may specifically include: continuously acquiring K initial accelerations through an acceleration sensor within a preset time length, wherein K is an integer greater than or equal to M; and deleting the initial acceleration of which the difference value with the adjacent acceleration is greater than or equal to a preset difference value threshold value to obtain M processed accelerations.

In the embodiment of the invention, the wearable device can acquire K initial accelerations through the three-axis acceleration sensor within a preset time length, then filter the K initial accelerations, filter out the accelerations with larger errors, and obtain M processed accelerations.

It should be noted that, when filtering the K initial accelerations, the wearable device may obtain a difference between each initial acceleration and an adjacent acceleration; if the difference value between a certain initial acceleration and an adjacent acceleration is larger than or equal to a preset difference value threshold, it indicates that an error occurs when the wearable device measures the initial acceleration, so that the initial acceleration deviates from a normal value, and the wearable device may delete the initial acceleration.

Wherein the adjacent acceleration may be a previous acceleration of the initial acceleration, and/or a subsequent acceleration.

Illustratively, assume a preset difference threshold of 1m/s ² The wearable device acquires 6 initial accelerations, which are respectively an acceleration A:5.1m/s ² The acceleration B:5.2m/s ² Acceleration C:5.5m/s ² And the acceleration D:9.2m/s ² The acceleration E:5.8m/s ² The acceleration F:6.0m/s ² . The wearable device may calculate a difference between each initial acceleration and the adjacent acceleration, and obtain an acceleration D through calculation; 9.2m/s ² Acceleration C adjacent to: 5.5m/s ² And acceleration E:5.8m/s ² Respectively, are 3.7m/s ² And 3.4m/s ² Are all larger than a preset difference threshold value of 1m/s ² Then the wearable device may compare the acceleration D:9.2m/s ² Deleting to obtain the rest five accelerations, namely M accelerations are respectively 5.1M/s ² ，5.2m/s ² ，5.5m/s ² ，5.8m/s ² And 6.0m/s ² 。

Through this optional implementation, wearable device can filter out the initial acceleration that is greater than or equal to preset difference threshold with the difference of adjacent acceleration to obtain accurate acceleration, avoid appearing because the error leads to final motion intensity to calculate the inaccurate condition.

102. And calculating to obtain a motion amplitude parameter according to the M accelerations.

In the embodiment of the present invention, the wearable device may calculate, according to the M accelerations, a motion amplitude parameter, where the motion amplitude parameter may be used to indicate a current amount of motion of the user within a preset time period.

Optionally, the calculating to obtain the motion amplitude parameter according to the M accelerations may specifically include: and calculating to obtain the motion amplitude parameter according to the M accelerations and a first formula.

Optionally, the first formula is:

s1 is a motion amplitude parameter, A _i For the ith acceleration among the M accelerations,

is the average of the M accelerations.

For example, assume that the wearable device acquires 10 accelerations, which are acceleration a:4.2m/s ² And the acceleration B:5.0m/s ² And the acceleration C:5.3m/s ² The acceleration D:6.5m/s ² The acceleration E:8.3m/s ² The acceleration F:9.1m/s ² Acceleration G:10.2m/s ² The acceleration H:9.6m/s ² The acceleration I:8.4m/s ² Acceleration J:7.6m/s ² . The wearable device may average the 10 accelerations first to obtain

The square of the difference between each acceleration and the mean value was then determined to be 10.364 (m/s) in each case ² ) ² 、5.8564(m/s ² ) ² 、4.4944(m/s ² ) ² 、0.8464(m/s ² ) ² 、0.7744(m/s ² ) ² 、2.8224(m/s ² ) ² 、7.7284(m/s ² ) ² 、4.7524(m/s ² ) ² 、0.9604(m/s ² ) ² 、0.0324(m/s ² ) ² (ii) a Then the wearable device sums the 10 values, averages and separates to obtain the motion amplitude S =1.96m/S of the user ² 。

Optionally, the wearable device may also average the M accelerations, with the average representing the motion amplitude of the user.

Optionally, the wearable device may further determine a median of the M accelerations, and use the median to represent the motion amplitude of the user.

103. And calculating to obtain a heart rate amplification parameter according to the N heart rate data.

In the embodiment of the invention, the wearable device can calculate the heart rate amplification parameter according to the N heart rate data, and the heart rate amplification parameter can be used for indicating the heart rate variation degree of the user currently within the preset duration.

Optionally, the calculating the heart rate amplification parameter according to the N heart rate data may specifically include: when the user is in a static state, obtaining the resting heart rate of the user; acquiring a median of the N heart rate data according to the N heart rate data; and calculating to obtain the heart rate amplification parameter according to the median of the N heart rate data, the resting heart rate and a second formula.

Optionally, the second formula is:

s2 is a heart rate amplification parameter, B _r Median of N heart rate data, B ₁ To rest heart rate, B _max The maximum heart rate of the user.

It is noted that the wearable device may detect the user's resting heart rate while the user is in a motionless, stationary state.

Further, the obtaining manner of the maximum heart rate of the user may specifically include: acquiring personal information of a user, wherein the personal information at least comprises: the age of the user; and calculating the maximum heart rate of the user according to the age of the user.

Alternatively, the maximum heart rate may be: b is _max =220-Age; the maximum heart rate may also be: b is _max 206.3-0.711 by age; wherein Age is the Age of the user.

Further, the maximum heart rate may also be related to the age and gender of the user, and when the user is male, the maximum heart rate may be: b is _max =202-0.55 × age; when the user is female, the maximum heart rate may be: b is _max =216-1.09 × age; wherein Age is the Age of the user.

For example, assume that the wearable device acquires 9 heart rate data of the user within a first time period, 91, 93, 102, 88, 110, 79, 96, 100, 83, respectively, a resting heart rate of 60, and an age of 48 years. The wearable device calculates that the median of the 9 heart rate data is 93, and the maximum heart rate can be 220-48=172, so that the heart rate amplification parameter B =29% can be obtained according to the second formula.

104. And determining the motion intensity level of the user in the preset time length as the target intensity level.

In the embodiment of the present invention, if the target parameter is within the target parameter range, the wearable device may determine that the exercise intensity level of the user within the preset duration is the target intensity level.

Wherein, the target parameters include: at least one of a motion amplitude parameter and a heart rate amplification parameter; the target intensity level is an intensity level corresponding to the target parameter range.

Optionally, if the target parameter is within the target parameter range, the wearable device may determine that the exercise intensity level of the user within the preset time period is the target intensity level, and specifically may include: if the motion amplitude parameter is greater than a first preset threshold value and/or the heart rate amplification parameter is greater than a second preset threshold value, determining the motion intensity level of the user within a preset time length as a first intensity level;

if the motion amplitude parameter is smaller than or equal to a first preset threshold value and/or the heart rate amplification parameter is smaller than or equal to a second preset threshold value, determining the motion intensity level of the user in a preset time length as a second intensity level;

wherein the second intensity level is less than the first intensity level.

Optionally, the target parameter is in a target parameter range, which may specifically include the following five cases:

the first condition is as follows: if the target parameter only comprises the motion amplitude parameter, when the motion amplitude parameter is greater than a first preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a first intensity level; when the motion amplitude parameter is less than or equal to the first preset threshold, the wearable device may determine that the motion intensity level of the user within the preset time period is the second intensity level.

It should be noted that, if the target parameter only includes the motion amplitude parameter, the wearable device may set a first preset threshold, where the motion amplitude parameter is greater than the first preset threshold and corresponds to the first intensity level, and the motion amplitude parameter is less than or equal to the first preset threshold and corresponds to the second intensity level.

Illustratively, assume that the first preset threshold is 5m/s ² Then, the wearable device can divide the target parameter range into two intervals according to the first preset threshold, and the two intervals correspond to different intensity levels, and the first intensity level corresponds to the target parameter which is larger than 5m/s ² The second intensity level corresponding to a target parameter less than or equal to 5m/s ² . The wearable device is assumed to acquire a motion amplitude parameter of 3.68m/s ² Then the wearable device can detect 3.68m/s ² Less than 5m/s ² Then the current exercise intensity level of the user may be said to be the second intensity level.

Furthermore, the wearable device can also set a plurality of third preset thresholds, and the target parameter range is divided into a plurality of intervals through the first preset threshold and the third preset thresholds, and each interval corresponds to different intensity levels.

Illustratively, assume that the first preset threshold is 5m/s ² The third preset thresholds are respectively 4m/s ² 、3m/s ² 、2m/s ² 、1m/s ² . The wearable device can target the parametersThe range is divided into a plurality of intervals, each interval is corresponding to different intensity levels, and the target parameter corresponding to the first intensity level is more than 5m/s ² The second intensity level corresponds to a target parameter at 5m/s ² -4m/s ² Within the range, the third intensity level corresponds to a target parameter of 4m/s ² -3m/s ² Within the range, the fourth intensity level corresponds to a target parameter of 3m/s ² -2m/s ² Within the range, the fifth intensity level corresponds to a target parameter of 2m/s ² -1m/s ² Within the range, the sixth intensity level corresponds to a target parameter of less than 1m/s ² . The wearable device is assumed to acquire a motion amplitude parameter of 1.96m/s ² Then the wearable device can detect 1.96m/s ² At 2m/s ² -1m/s ² Within the range, the current exercise intensity level of the user can be indicated as the fifth intensity level.

Case two: if the target parameter only comprises the heart rate amplification parameter, when the heart rate amplification parameter is greater than a second preset threshold, the wearable device can determine the exercise intensity level of the user within a preset time length as a first intensity level; when the heart rate amplification parameter is less than or equal to a second preset threshold, the wearable device may determine that the exercise intensity level of the user within the preset time period is a second intensity level.

It should be noted that, if the target parameter includes only the heart rate amplification parameter, the wearable device may set a second preset threshold, where the heart rate amplification parameter is greater than the second preset threshold and corresponds to the first intensity level, and the motion amplitude parameter is less than or equal to the second preset threshold and corresponds to the second intensity level.

For example, assuming that the second preset threshold is 50%, the wearable device may divide the target parameter range into two intervals according to the second preset threshold, and the two intervals correspond to different intensity levels, where the first intensity level corresponds to the target parameter being greater than 50%, and the second intensity level corresponds to the target parameter being less than or equal to 50%. Assuming that the wearable device obtains a heart rate increase parameter of 29%, the wearable device may detect that 29% is less than 50%, and may indicate that the current exercise intensity level of the user is the second intensity level.

Furthermore, the wearable device can further set a plurality of fourth preset thresholds, and the target parameter range is divided into a plurality of intervals through the second preset thresholds and the fourth preset thresholds, wherein each interval corresponds to different intensity levels.

Illustratively, assuming that the second preset threshold is 50%, the fourth preset thresholds are 40%, 30%, 20%, 10%, respectively. The wearable device may divide the target parameter range into a plurality of intervals, and each interval corresponds to a different intensity level, where the first intensity level corresponds to a target parameter greater than 50%, the second intensity level corresponds to a target parameter within a range of 50% to 40%, the third intensity level corresponds to a target parameter within a range of 40% to 30%, the fourth intensity level corresponds to a target parameter within a range of 30% to 20%, the fifth intensity level corresponds to a target parameter within a range of 20% to 10%, and the sixth intensity level corresponds to a target parameter less than 10%. Assuming that the wearable device obtains a heart rate increase parameter of 29%, the wearable device may detect that 29% is in the range of 30% -20%, and may indicate that the current exercise intensity level of the user is the fourth intensity level.

Case three: if the target parameters include a motion amplitude parameter and a heart rate amplification parameter, when the motion amplitude parameter is greater than a first preset threshold value and/or the heart rate amplification parameter is greater than a second preset threshold value, the wearable device may determine that the motion intensity level of the user within a preset time period is a first intensity level; when the motion amplitude parameter is less than or equal to a first preset threshold and the heart rate amplification parameter is less than or equal to a second preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a second intensity level.

It should be noted that, if the target parameter includes a motion amplitude parameter and a heart rate amplification parameter, the wearable device may set a first preset threshold for the motion amplitude parameter, and set a second preset threshold for the heart rate amplification parameter, where the second intensity level may correspond to a situation where the motion amplitude parameter is less than or equal to the first preset threshold, and the heart rate amplification parameter is less than or equal to the second preset threshold; the first intensity level may correspond to the following three cases:

(1) The motion amplitude parameter is larger than a first preset threshold value, and the heart rate amplification parameter is larger than a second preset threshold value.

(2) The motion amplitude parameter is larger than a first preset threshold value, and the heart rate amplification parameter is smaller than or equal to a second preset threshold value.

(3) The motion amplitude parameter is smaller than or equal to a first preset threshold value, and the heart rate amplification parameter is larger than a second preset threshold value.

Illustratively, assume that the first predetermined threshold is 5m/s ² And the second preset threshold is 50%, then the wearable device can correspond four different conditions and different intensity levels according to the first preset threshold and the second preset threshold, and the corresponding motion amplitude parameter of the first intensity level is greater than 5m/s ² And/or, in the case of a heart rate amplification parameter greater than 50%, the second intensity level corresponds to a movement amplitude parameter less than or equal to 5m/s ² And the heart rate amplification parameter is less than or equal to 50%. The wearable device is assumed to acquire a motion amplitude parameter of 3.68m/s ² With a heart rate amplification parameter of 68%, the wearable device may detect 3.68m/s ² Less than 5m/s ² And 68% is greater than 50%, the current exercise intensity level of the user may be indicated as the first intensity level.

Furthermore, the wearable device can also set a plurality of third preset thresholds and a plurality of fourth preset thresholds, the target parameter range is divided into a plurality of intervals through the first preset threshold, the plurality of third preset thresholds, the second preset threshold and the plurality of fourth preset thresholds, the wearable device can determine the intensity level A according to the interval where the motion amplitude parameter is located, then determine the intensity level B according to the interval where the heart rate amplification parameter is located, and determine the highest level of the intensity level A and the intensity level B as the final intensity level of the user.

Illustratively, assume that the first preset threshold is 5m/s ² The third preset thresholds are respectively 4m/s ² 、3m/s ² 、2m/s ² 、1m/s ² (ii) a A second preset threshold of 50%, a plurality of fourth preset thresholdsThe threshold values were set to 40%, 30%, 20%, and 10%, respectively. The interval for the motion amplitude parameter then comprises: the first intensity level corresponds to a target parameter greater than 5m/s ² The second intensity level corresponds to a target parameter at 5m/s ² -4m/s ² Within the range, the third intensity level corresponds to a target parameter of 4m/s ² -3m/s ² Within the range, the fourth intensity level corresponds to a target parameter of 3m/s ² -2m/s ² Within the range, the fifth intensity level corresponds to a target parameter of 2m/s ² -1m/s ² Within the range, the sixth intensity level corresponds to a target parameter of less than 1m/s ² (ii) a The interval for the motion amplitude parameter includes: the first intensity level corresponds to the target parameter more than 50%, the second intensity level corresponds to the target parameter within the range of 50% -40%, the third intensity level corresponds to the target parameter within the range of 40% -30%, the fourth intensity level corresponds to the target parameter within the range of 30% -20%, the fifth intensity level corresponds to the target parameter within the range of 20% -10%, and the sixth intensity level corresponds to the target parameter less than 10%.

As an example, assume that the wearable device acquires a motion amplitude parameter of 1.96m/s ² With a heart rate amplification parameter of 29%, the wearable device may detect 1.96m/s ² At 2m/s ² -1m/s ² Within the range, it can be stated that, for the motion amplitude parameter, the current motion intensity level of the user is the fifth intensity level; the wearable device can also detect that 29% is in the range of 30% -20%, and then the current exercise intensity level of the user can be indicated as a fourth intensity level; the fourth intensity level is higher than the fifth intensity level, the wearable device may determine that the user's current exercise intensity level is the fourth intensity level.

Case four: if the target parameters include a motion amplitude parameter and a heart rate amplification parameter, when the motion amplitude parameter is greater than a first preset threshold and the heart rate amplification parameter is greater than a second preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a first intensity level; when the motion amplitude parameter is less than or equal to a first preset threshold value, and/or the heart rate amplification parameter is less than or equal to a second preset threshold value, the wearable device may determine that the motion intensity level of the user within a preset time period is a second intensity level.

It should be noted that, if the target parameter includes a motion amplitude parameter and a heart rate amplification parameter, the wearable device may set a first preset threshold for the motion amplitude parameter, and set a second preset threshold for the heart rate amplification parameter, where the first intensity level may correspond to a situation that the motion amplitude parameter is greater than the first preset threshold, and the heart rate amplification parameter is greater than the second preset threshold; the second intensity level may correspond to the following three cases:

(1) The motion amplitude parameter is smaller than or equal to a first preset threshold, and the heart rate amplification parameter is smaller than or equal to a second preset threshold.

Illustratively, assume that the first preset threshold is 5m/s ² If the second preset threshold is 50%, the wearable device may correspond to four different conditions and different intensity levels according to the first preset threshold and the second preset threshold, where the motion amplitude parameter corresponding to the first intensity level is greater than 5m/s ² And when the heart rate amplification parameter is more than 50%, the second intensity level corresponds to the motion amplitude parameter which is less than or equal to 5m/s ² And/or a heart rate amplification parameter of less than or equal to 50%. The wearable device is assumed to acquire a motion amplitude parameter of 3.68m/s ² With a heart rate amplification parameter of 68%, the wearable device may detect 3.68m/s ² Less than 5m/s ² And 68% is greater than 50%, the user's current exercise intensity level may be indicated as the second intensity level.

Case five: if the target parameters include a motion amplitude parameter and a heart rate amplification parameter, when the motion amplitude parameter is greater than a first preset threshold and the heart rate amplification parameter is greater than a second preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a first intensity level; when the motion amplitude parameter is less than or equal to a first preset threshold, or the heart rate amplification parameter is less than or equal to a second preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a third intensity level; when the motion amplitude parameter is less than or equal to a first preset threshold and the heart rate amplification parameter is less than or equal to a second preset threshold, the wearable device may determine that the motion intensity level of the user within a preset time period is a second intensity level.

Wherein the third intensity level is an intensity level between the first intensity level and the second intensity level.

It should be noted that, if the target parameter includes a motion amplitude parameter and a heart rate amplification parameter, the wearable device may set a first preset threshold for the motion amplitude parameter, and set a second preset threshold for the heart rate amplification parameter, where the first intensity level may correspond to a situation where the motion amplitude parameter is greater than the first preset threshold, and the heart rate amplification parameter is greater than the second preset threshold; the second intensity level may correspond to a case where the motion amplitude parameter is less than or equal to a first preset threshold and the heart rate amplification parameter is less than or equal to a second preset threshold; the third intensity level may correspond to the following two cases:

(1) The motion amplitude parameter is larger than a first preset threshold value, and the heart rate amplification parameter is smaller than or equal to a second preset threshold value.

(2) The motion amplitude parameter is smaller than or equal to a first preset threshold value, and the heart rate amplification parameter is larger than a second preset threshold value.

Illustratively, assume that the first preset threshold is 5m/s ² And the second preset threshold is 50%, then the wearable device can correspond four different conditions and different intensity levels according to the first preset threshold and the second preset threshold, and the corresponding motion amplitude parameter of the first intensity level is greater than 5m/s ² And when the heart rate amplitude parameter is greater than 50%, the second intensity level corresponds to the motion amplitude parameter less than or equal to 5m/s ² And the heart rate amplification parameter is less than or equal to 50%, and the third intensity level corresponds to the motion amplitude parameter less than or equal to 5m/s ² Or, a heart rate amplification parameter less than or equal to 50%. The wearable device is assumed to acquire a motion amplitude parameter of 3.68m/s ² With a heart rate amplification parameter of 68%, the wearable device may detect 3.68m/s ² Less than 5m/s ² And 68% is greater than 50%, the current exercise intensity level of the user may be indicated as the third intensity level.

The embodiment of the invention provides a motion intensity detection method.A wearable device can continuously acquire M accelerations and N heart rate data of a user within a preset time length; calculating to obtain a motion amplitude parameter according to the M accelerated speeds; calculating to obtain a heart rate amplification parameter according to the N heart rate data; and the wearable equipment determines the intensity level corresponding to the parameter range according to the motion amplitude parameter and/or the parameter range in which the heart rate amplification parameter is positioned. Through this scheme, wearable equipment can correspond different parameter ranges with every intensity level, and wearable equipment can obtain the motion amplitude parameter of present moment according to real-time acceleration and heart rate data like this, and/or, heart rate amplification parameter just can obtain accurate user's motion intensity level according to the corresponding relation.

As an optional implementation manner, after the wearable device determines that the exercise intensity level of the user in the preset time period is the target intensity level, the method may further include: accumulating the preset time length to the total movement time length of the target intensity level; and if the detected total movement time length is greater than the preset time length threshold value, outputting a first prompt message, wherein the first prompt message is used for prompting the user to take a rest.

It should be noted that, after determining that the exercise intensity level of the user in the preset duration is the target intensity level, the wearable device may add the preset duration to the total exercise duration corresponding to the target intensity level, and if the total exercise duration is greater than a preset duration threshold, output a first prompt message prompting the user to take a rest.

Optionally, when the target intensity level includes a first intensity level and a second intensity level, if the first intensity level is greater than the second intensity level, the wearable device may accumulate the total exercise duration of the first intensity level, and when the total exercise duration of the first intensity level is greater than a preset duration threshold, output a first prompt message prompting the user to take a rest.

Through this optional implementation, wearable device can add up the total length of time of the motion of user target intensity level, if this total length of time of motion is greater than and predetermines the length of time threshold value, then output the first suggestion message that the suggestion user paid attention to the rest, can effectually avoid the user because the long uncomfortable condition of health that leads to of motion time like this.

As an optional implementation manner, after the wearable device continuously acquires M accelerations and N heart rate data of the user, the method may further include: if the M accelerations meet the preset sit-up characteristic, acquiring a target acceleration which is smaller than a first preset acceleration threshold and smaller than an adjacent acceleration from the M accelerations; and determining the number of the target acceleration as the sit-up times of the user within a preset time length.

Wherein the adjacent accelerations are a preceding acceleration and a succeeding acceleration of the target acceleration.

It should be noted that the target acceleration needs to be smaller than the first preset acceleration threshold and smaller than two accelerations adjacent to the target acceleration, so that the target acceleration may be a wave trough, and may be an abdominal crunch number.

Illustratively, assume that the first predetermined acceleration threshold is 2m/s ² The wearable device obtains 10 accelerations, which are acceleration a:2.6m/s ² And the acceleration B:1.8m/s ² Acceleration C:1.9m/s ² And the acceleration D:1.9m/s ² The acceleration E:1.6m/s ² The acceleration F:2.4m/s ² Acceleration G:2.6m/s ² The acceleration H:1.1m/s ² And the acceleration I:0.9m/s ² Acceleration J:1.3m/s ² . The wearable equipment can obtain the acceleration B smaller than 2m/s through calculation ² And is smaller than the adjacent acceleration A and acceleration C, and the acceleration E is smaller than 2m/s ² And is smaller than the adjacent acceleration D and acceleration F, and the acceleration I is smaller than 2m/s ² And is less than the adjacent acceleration H and acceleration J(ii) a That is, the target acceleration includes: acceleration B, acceleration E, and acceleration I, the wearable device may determine the number of target accelerations as the number of sit-ups of the user within the first duration, that is, the number of sit-ups of the user within the first duration is 3.

Optionally, the M first accelerations satisfy the preset sit-up characteristic, and at least two optional implementations may be included:

the implementation method is as follows: determining an acceleration curve according to the M first accelerations; acquiring P acceleration peak values positioned at wave crests and Q acceleration valley values positioned at wave troughs in an acceleration curve; determining a difference between each acceleration peak and an adjacent acceleration valley; and if the difference value is within the preset difference value interval, determining that the M first accelerations meet the preset sit-up characteristic.

In this optional implementation manner, the wearable device may represent the M first accelerations in a rectangular coordinate system according to a time sequence, so as to obtain an acceleration curve, where an abscissa of the rectangular coordinate system is a time, and a ordinate of the rectangular coordinate system is a numerical value of the first acceleration; the wearable device can acquire P acceleration peak values located at wave crests and Q acceleration valley values located at wave troughs from an acceleration curve, wherein both P and Q are integers greater than or equal to 1; determining a difference between each acceleration peak and an acceleration valley adjacent to the acceleration peak; if the difference is within the preset difference interval, it can be said that the M first accelerations satisfy the preset sit-up characteristic.

Alternatively, if the user is performing a sit-up exercise, each jump rope cycle in the acceleration curve may be first decreased to an increased curve, as shown in fig. 2, and within each cycle, there is a trough, and the wearable device may detect the number of the troughs and determine the number of the troughs as the number of sit-ups.

It should be noted that, in the acceleration curve, the peak is the maximum value of the amplitude in a period range of acceleration change; the trough is the minimum of the amplitude over a period of acceleration change.

Exemplary embodiments of the inventionAs shown in fig. 2, the wearable device detects the first acceleration during 0-45ms, and it can be seen from fig. 2 that point a is the peak of the acceleration curve, and the value represented by point a is the acceleration peak during 0-45 ms; it can also be seen that point B is the valley of the acceleration curve, and the value represented by point B is the acceleration valley during 0-45 ms. Assuming that the preset difference interval is 7m/s ² -13m/s ² From FIG. 2, it can be seen that the value represented by the point A is 10.5m/s ² The value represented by point B is 1.5m/s ² Then the difference between point A and point B is 9m/s ² At 7m/s ² -13m/s ² Within the range, it can be stated that the first acceleration during 0-45ms satisfies the preset sit-up characteristic.

The second implementation mode is as follows: determining an acceleration curve according to the M first accelerations; acquiring P acceleration peak values positioned at wave crests and Q acceleration valley values positioned at wave troughs in an acceleration curve; if P acceleration peak values are all located in a first preset interval, and Q acceleration valley values are all located in a second preset interval, determining that M first accelerations meet preset sit-up characteristics.

In this optional implementation manner, the wearable device may represent the M first accelerations in a rectangular coordinate system according to a time sequence, so as to obtain an acceleration curve, where an abscissa of the rectangular coordinate system is a time, and a ordinate of the rectangular coordinate system is a numerical value of the first acceleration; the wearable device can acquire P acceleration peak values located at wave crests and Q acceleration valley values located at wave troughs from an acceleration curve, wherein both P and Q are integers greater than or equal to 1; if the P acceleration peaks are all in the first preset interval and the Q acceleration valleys are all in the second preset interval, it can be said that the M first accelerations satisfy the preset sit-up characteristics.

Alternatively, if the user is performing a sit-up exercise, each jump rope cycle in the acceleration curve may be first decreased to an increased curve, as shown in fig. 2, and within each cycle, there is a trough, and the wearable device may detect the number of troughs and determine the number of troughs as the number of sit-ups.

It should be noted that, in the acceleration curve, the peak is the maximum value of the amplitude within a period range of acceleration change; the trough is the minimum value of the amplitude over a period of acceleration change.

For example, as shown in fig. 2, the wearable device detects a first acceleration during 0-45ms, and it can be seen from fig. 2 that point a is a peak of the acceleration curve, and then the value represented by point a is an acceleration peak during 0-45 ms; it can also be seen that point B is the valley of the acceleration curve, and the value represented by point B is the acceleration valley during 0-45 ms. Assume that the first predetermined interval is 9m/s ² -11m/s ² The second preset interval is 0m/s ² -3m/s ² From FIG. 2, it can be seen that the value represented by the point A is 10.5m/s ² At 9m/s ² -11m/s ² It can also be seen that the value represented by point B is 1.5m/s ² At 0m/s ² -3m/s ² It can be stated that the first acceleration during 0-45ms satisfies the preset sit-up characteristic.

Through this optional implementation, wearable equipment can be detecting under the condition that M acceleration satisfies predetermineeing the sit up characteristic, be less than first predetermineeing acceleration threshold value and be less than the sit up number of times of target acceleration of adjacent acceleration wherein and confirm for the user in predetermineeing the time length, because the sit up is a motion that has periodicity, the user can repeat sit up the motion many times within predetermineeing time length, wearable equipment can detect the periodic law of M acceleration, and select the acceleration that accords with the condition from it, thereby the accurate sit up number of times of confirming according to the acceleration quantity that accords with the condition.

As another optional implementation manner, after the wearable device continuously acquires the M accelerations and the N heart rate data of the user, the method may further include: if the M accelerated speeds meet the preset rope skipping characteristic, acquiring a target accelerated speed which is larger than a second preset accelerated speed threshold value and larger than an adjacent accelerated speed in the M accelerated speeds; and determining the number of the target acceleration as the rope skipping times of the user within a preset time length.

It should be noted that the target acceleration needs to be greater than the second preset acceleration threshold and greater than two accelerations adjacent to the target acceleration, so that the target acceleration is a peak, and the target acceleration is a rope skipping number.

Illustratively, assume that the second predetermined acceleration threshold is 9.5m/s ² The wearable device obtains 10 accelerations, which are acceleration a:9.1m/s ² The acceleration B:10.2m/s ² Acceleration C:9.6m/s ² The acceleration D:9.6m/s ² The acceleration E:9.8m/s ² The acceleration F:10.6m/s ² Acceleration G:10.2m/s ² The acceleration H:10.0m/s ² The acceleration I:10.2m/s ² Acceleration J:9.6m/s ² . The wearable equipment can obtain the acceleration B larger than 9.5m/s through calculation ² And the acceleration F is larger than 9.5m/s ² And is greater than the adjacent acceleration E and acceleration G, and the acceleration I is greater than 9.5m/s ² And is greater than the adjacent acceleration H and acceleration J; that is, the target acceleration includes: acceleration B, acceleration F, and acceleration I, the wearable device may determine the number of target accelerations as the number of rope skips of the user in the first time period, that is, the number of rope skips of the user in the first time period is 3.

Optionally, the M first accelerations satisfy the preset skipping rope characteristic, and at least two optional implementations may be included:

the implementation mode is as follows: determining an acceleration curve according to the M first accelerations; acquiring P acceleration peak values positioned at a wave crest and Q acceleration valley values positioned at a wave trough in an acceleration curve; determining a difference between each acceleration peak value and an adjacent acceleration valley value; and if the difference value is within the preset difference value interval, determining that the M first accelerated speeds meet the preset rope skipping characteristic.

In this optional implementation manner, the wearable device may represent M first accelerations in a rectangular coordinate system according to a time sequence, to obtain an acceleration curve, where an abscissa of the rectangular coordinate system is a time, and a ordinate of the rectangular coordinate system is a value of the first acceleration; the wearable device can acquire P acceleration peak values located at wave crests and Q acceleration valley values located at wave troughs from an acceleration curve, wherein both P and Q are integers greater than or equal to 1; determining a difference between each acceleration peak value and an acceleration valley adjacent to the acceleration peak value; if the difference value is within a preset difference value interval, the M first accelerations can be shown to meet the preset skipping rope characteristic.

Alternatively, if the user is performing a rope skipping movement, each rope skipping cycle in the acceleration curve may be a curve that is increased and decreased first, as shown in fig. 3, and within each cycle, there is one peak, and the wearable device may detect the number of the peaks and determine the number of the peaks as the number of rope skipping.

For example, as shown in fig. 3, the wearable device detects the first acceleration during 0-45ms, and it can be seen from fig. 3 that point a is the peak of the acceleration curve, and then the value represented by point a is the acceleration peak during 0-45 ms; it can also be seen that point B is the valley of the acceleration curve, and the value represented by point B is the acceleration valley during 0-45 ms. Assuming that the preset difference interval is 7m/s ² -13m/s ² From FIG. 3, it can be seen that the value represented by the point A is 10.5m/s ² The value represented by point B is 1.5m/s ² Then the difference between point A and point B is 9m/s ² At 7m/s ² -13m/s ² Within the range, it can be stated that the first acceleration during 0-45ms satisfies the predetermined jump rope characteristic.

The second implementation mode is as follows: determining an acceleration curve according to the M first accelerations; acquiring P acceleration peak values positioned at wave crests and Q acceleration valley values positioned at wave troughs in an acceleration curve; and if the P acceleration peak values are all located in a first preset interval and the Q acceleration valley values are all located in a second preset interval, determining that the M first accelerations meet the preset rope skipping characteristic.

In this optional implementation manner, the wearable device may represent the M first accelerations in a rectangular coordinate system according to a time sequence, so as to obtain an acceleration curve, where an abscissa of the rectangular coordinate system is a time, and a ordinate of the rectangular coordinate system is a numerical value of the first acceleration; the wearable device can obtain P acceleration peak values located at a peak and Q acceleration valley values located at a trough from the acceleration curve, wherein P and Q are integers greater than or equal to 1; if the P acceleration peak values are all in a first preset interval, and the Q acceleration valley values are all in a second preset interval, it can be said that the M first accelerations satisfy the preset rope skipping feature.

For example, as shown in fig. 3, the wearable device detects the first acceleration during 0-45ms, and it can be seen from fig. 3 that point a is the peak of the acceleration curve, and then the value represented by point a is the acceleration peak during 0-45 ms; it can also be seen that point B is the valley of the acceleration curve, and the value represented by point B is the acceleration valley during 0-45 ms. Assume that the first predetermined interval is 9m/s ² -11m/s ² The second preset interval is 0m/s ² -3m/s ² From FIG. 3, it can be seen that the value represented by the point A is 10.5m/s ² At 9m/s ² -11m/s ² It can also be seen that the value represented by point B is 1.5m/s ² At 0m/s ² -3m/s ² Then, it can be said that 0 to 45mThe first acceleration during s satisfies a preset skipping characteristic.

Through the optional implementation mode, the wearable device can determine the number of the target accelerations which are greater than the second preset acceleration threshold and greater than the adjacent acceleration as the rope skipping times of the user in the preset time length when detecting that the M accelerations meet the preset rope skipping characteristics, because the rope skipping is a periodic movement, the user can repeat rope skipping movement for many times in the preset time length, the wearable device can detect the periodic laws of the M accelerations and screen out the accelerations meeting the conditions, and therefore the rope skipping times are accurately determined according to the number of the accelerations meeting the conditions.

Example two

As shown in fig. 4, an embodiment of the present invention provides a wearable device, including:

the acquisition module 401 is configured to continuously acquire M accelerations and N heart rate data of a user within a preset time period;

a processing module 402, configured to calculate a motion amplitude parameter according to the M accelerations;

the processing module 402 is further configured to calculate a heart rate amplification parameter according to the N heart rate data;

the processing module 402 is further configured to determine, if the target parameter is within the target parameter range, the exercise intensity level of the user within the preset duration as a target intensity level;

wherein, the target intensity level corresponds to the target parameter range, and the target parameter includes: at least one of a motion amplitude parameter and a heart rate amplification parameter.

Optionally, the processing module 402 is specifically configured to determine the exercise intensity level of the user within a preset time duration as a first intensity level if the exercise amplitude parameter is greater than a first preset threshold, and/or the heart rate amplification parameter is greater than a second preset threshold;

the processing module 402 is specifically configured to determine the exercise intensity level of the user within a preset duration as a second intensity level if the exercise amplitude parameter is less than or equal to a first preset threshold and/or the heart rate amplification parameter is less than or equal to a second preset threshold;

optionally, calculating to obtain a motion amplitude parameter according to the M accelerations and a first formula;

wherein the first formula is:

the average of the M accelerations.

Optionally, the obtaining module 401 is specifically configured to obtain a resting heart rate of the user when the user is in a stationary state;

the obtaining module 401 is specifically configured to obtain a median of the N heart rate data according to the N heart rate data;

the processing module 402 is specifically configured to calculate a heart rate amplification parameter according to the median of the N heart rate data, the resting heart rate, and the second formula;

wherein the second formula is:

Optionally, the obtaining module 401 is further configured to obtain personal information of the user, where the personal information at least includes: the age of the user;

the processing module 402 is further configured to calculate a maximum heart rate of the user according to the age of the user.

Optionally, the processing module 402 is further configured to add the preset time length to the total movement time length of the target intensity level;

the processing module 402 is further configured to output a first prompt message if it is detected that the total movement duration is greater than the preset duration threshold, where the first prompt message is used to prompt the user to take a rest.

Optionally, the processing module 402 is further configured to, if the M accelerations satisfy the preset sit-up feature, obtain a target acceleration that is smaller than a first preset acceleration threshold and smaller than an adjacent acceleration among the M accelerations; determining the number of the target acceleration as the sit-up times of the user within a preset time length;

or the like, or, alternatively,

the processing module 402 is further configured to, if the M accelerations meet the preset rope skipping feature, obtain a target acceleration, which is greater than a second preset acceleration threshold and greater than an adjacent acceleration, of the M accelerations; determining the number of the target accelerations as the rope skipping times of a user within a preset time length;

wherein the adjacent acceleration values are a preceding acceleration and a following acceleration of the target acceleration.

In the embodiment of the present invention, each module may implement the exercise intensity detection method provided in the above method embodiment, and may achieve the same technical effect, and for avoiding repetition, details are not described here again.

As shown in fig. 5, an embodiment of the present invention further provides a wearable device, where the wearable device may include:

a memory 501 in which executable program code is stored;

a processor 502 coupled to a memory 501;

the processor 502 calls the executable program code stored in the memory 501 to execute the exercise intensity detection method executed by the wearable device in the above embodiments of the methods.

As shown in fig. 6, an embodiment of the present invention further provides a wearable device, which includes but is not limited to: a Radio Frequency (RF) circuit 601, a memory 602, an input unit 603, a display unit 604, a sensor 605, an audio circuit 606, a WiFi (wireless communication) module 607, a processor 608, a power supply 609, and a camera 610. Among other things, the radio frequency circuit 601 includes a receiver 6011 and a transmitter 6012. Those skilled in the art will appreciate that the wearable device structure shown in fig. 6 does not constitute a limitation of the wearable device, and may include more or fewer components than shown, or combine certain components, or a different arrangement of components.

The RF circuit 601 may be used for receiving and transmitting signals during a message transmission or call, and in particular, for receiving downlink messages from a base station and then processing the received downlink messages to the processor 608. The RF circuitry 601 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (GSM), general Packet Radio Service (GPRS), code Division Multiple Access (CDMA), wideband Code Division Multiple Access (WCDMA), long Term Evolution (LTE), email, short Message Service (SMS), etc.

The memory 602 may be used to store software programs and modules, and the processor 608 executes various functional applications and data processing of the wearable device by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phone book, etc.) created according to the use of the wearable device, and the like.

The input unit 603 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the wearable device. Specifically, the input unit 603 may include a touch panel 6031 and other input devices 6032. The touch panel 6031, also referred to as a touch screen, may collect touch operations of a user on or near the touch panel 6031 (e.g., operations of a user on or near the touch panel 6031 using any suitable object or accessory such as a finger, a stylus, etc.) and drive corresponding connection devices according to a preset program.

The display unit 604 may be used to display information input by or provided to the user and various menus of the wearable device. The display unit 604 may include a display panel 6041, and the display panel 6041 may be configured in the form of a Liquid Crystal Display (LCD), an organic light-Emitting diode (OLED), or the like.

The wearable device may also include at least one sensor 605, such as a light sensor, motion sensor, and other sensors. In the embodiment of the present invention, the wearable device may include an acceleration sensor 6051 and a gyroscope sensor 6052, where the acceleration sensor may be used to detect a current acceleration value of the wearable device, and the gyroscope sensor may be used to detect a current rotation angle value of the wearable device.

Audio circuitry 606, speaker 6061, microphone 6062 may provide an audio interface between the user and the wearable device. The audio circuit 606 may transmit the electrical signal converted from the received audio data to the speaker 6061, and convert the electrical signal into a sound signal for output by the speaker 6061; on the other hand, the microphone 6062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 606 and converted into audio data, which is then processed by the audio data output processor 608 and then sent to, for example, another wearable device via the RF circuit 601, or output to the memory 602 for further processing.

WiFi belongs to short-distance wireless transmission technology, and the wearable device can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 607, and provides wireless broadband internet access for the user.

The processor 608 is a control center of the wearable device, and is connected to various parts of the entire wearable device through various interfaces and lines, and performs various functions of the wearable device and processes data by running or executing software programs and/or modules stored in the memory 602 and calling up the data stored in the memory 602, thereby performing overall monitoring of the wearable device.

The wearable device also includes a power supply 609 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 608 via a power management system, such that the power management system may manage charging, discharging, and power consumption. Although not shown, the wearable device may further include a bluetooth module or the like, which is not described herein.

Embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute some or all of the steps of the method as in the above method embodiments.

Embodiments of the present invention also provide a computer program product, wherein the computer program product, when run on a computer, causes the computer to perform some or all of the steps of the method as in the above method embodiments.

Embodiments of the present invention further provide an application publishing platform, where the application publishing platform is configured to publish a computer program product, where the computer program product, when running on a computer, causes the computer to perform some or all of the steps of the method in the above method embodiments.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also appreciate that the embodiments described in this specification are exemplary and alternative embodiments, and that the acts and modules illustrated are not required in order to practice the invention.

In various embodiments of the present invention, it should be understood that the sequence numbers of the above-mentioned processes do not imply an inevitable order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of each embodiment of the present invention.

It will be understood by those skilled in the art that all or part of the steps of the methods of the embodiments described above may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, including Read-Only Memory (ROM), random Access Memory (RAM), programmable Read-Only Memory (PROM), erasable Programmable Read-Only Memory (EPROM), one-time Programmable Read-Only Memory (OTPROM), electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM) or other Memory capable of storing data, a magnetic tape, or any other computer-readable medium capable of storing data.

Claims

1. A motion intensity detection method is applied to a wearable device, and comprises the following steps:

continuously acquiring M acceleration and N heart rate data of a user within a preset time length;

if the target parameter is within the target parameter range, determining the motion intensity level of the user within the preset time length as a target intensity level;

2. The method according to claim 1, wherein the determining the exercise intensity level of the user within the preset time period as the target intensity level if the target parameter is within the target parameter range comprises:

wherein the second intensity level is less than the first intensity level.

3. The method of claim 1, wherein said calculating a motion magnitude parameter from said M accelerations comprises:

wherein, theThe first formula is:

is the average of the M accelerations.

4. The method according to claim 1, wherein calculating a heart rate amplification parameter from the N heart rate data comprises:

when a user is in a static state, obtaining the resting heart rate of the user;

acquiring a median of the N heart rate data according to the N heart rate data;

wherein the second formula is:

s2 is the heart rate amplification parameter, B _r Is the median of the N heart rate data, B ₁ To the resting heart rate, B _msx Is the user's maximum heart rate.

5. The method of claim 4, wherein after obtaining the median of the N heart rate data from the N heart rate data, further comprising:

6. The method according to claim 1, wherein after determining that the exercise intensity level of the user within the preset time period is the target intensity level if the target parameter is within the target parameter range, the method further comprises:

7. The method of claim 1, wherein after continuously acquiring the M accelerations and the N heart rate data of the user for the preset time period, the method further comprises:

if the M accelerations meet the preset sit-up characteristic, acquiring a target acceleration which is smaller than a first preset acceleration threshold and smaller than an adjacent acceleration from the M accelerations; determining the number of the target acceleration as the sit-up times of the user within the preset time length;

or the like, or a combination thereof,

8. A wearable device, comprising:

the acquisition module is used for continuously acquiring M acceleration and N heart rate data of a user within a preset time length;

9. A wearable device, comprising:

a memory storing executable program code;

and a processor coupled to the memory;

the processor calls the executable program code stored in the memory for performing the exercise intensity detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, comprising: the computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the exercise intensity detection method of any one of claims 1 to 7.