CN112967801A - PAI value processing method, PAI value processing device, PAI value processing equipment and storage medium - Google Patents
PAI value processing method, PAI value processing device, PAI value processing equipment and storage medium Download PDFInfo
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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Abstract
The application provides a PAI value processing method, a PAI value processing device, PAI value processing equipment and a storage medium, and relates to the technical field of data processing, wherein the method comprises the following steps: collecting motion data of a user through at least one sensor; determining the current motion state of the user according to the motion data; determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate; and stopping calculating the PAI value at the current moment when the current heart rate is abnormal. Therefore, whether the current heart rate is abnormal or not is judged based on the fact that the user is in the motion state or the non-motion state to determine the reference heart rate, the abnormal heart rate is prevented from being used for PAI value calculation, and accuracy and reliability of PAI value calculation are improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a PAI value processing method, apparatus, device, and storage medium.
Background
Generally, the personal athletic performance index pai (personal Activity intelligence) is a method of converting heart rate data into meaningful health risk indicators, and can perform effective quantitative evaluation on the amount of exercise of an individual, thereby bringing health benefits to users.
In the related art, the PAI value is calculated by directly using the detected heart rate data, and the PAI value is calculated by taking the abnormal heart rate as the normal heart rate, so that the accuracy and the reliability of the PAI value are influenced.
Disclosure of Invention
The present application aims to solve at least to some extent one of the above mentioned technical problems.
Therefore, a first objective of the present application is to provide a PAI value processing method, which solves the technical problems of low accuracy and poor reliability of a PAI value processing method in the prior art, determines whether a current heart rate is abnormal or not by determining a reference heart rate based on whether a user is in a motion state or a non-motion state, avoids that an abnormal heart rate is used for PAI value calculation, and improves the accuracy and reliability of PAI value calculation.
A second object of the present application is to provide a PAI value processing apparatus.
A third object of the present application is to propose a computer device.
A fourth object of the present application is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, a first embodiment of the present application provides a PAI value processing method, including: collecting motion data of a user through at least one sensor; determining the current motion state of the user according to the motion data; determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate; and stopping calculating the personal motor function index PAI value at the current moment when the current heart rate is abnormal.
In one embodiment of the present application, the collecting motion data of the user by at least one sensor includes: acquiring acceleration data through an acceleration sensor; and/or acquiring motion track data through a positioning sensor; and/or acquiring angular velocity data by an angular velocity sensor.
In one embodiment of the present application, determining the current motion state of the user from the motion data comprises: calculating a three-axis resultant acceleration variance according to the acceleration data; and under the condition that the three-axis combined acceleration variance is larger than a preset variance threshold value, determining that the user is in a non-motion state.
In an embodiment of the present application, the PAI value processing method further includes: under the condition that the three-axis combined acceleration variance is smaller than or equal to the preset variance threshold, calculating the current speed of the user according to the motion trail data; and under the condition that the current speed is greater than a preset speed threshold value, determining that the user is in a motion state.
In an embodiment of the present application, the PAI value processing method further includes: under the condition that the current speed is less than or equal to a preset speed threshold value, calculating the step number of the user according to the acceleration data; determining that the user is in a motion state under the condition that the step number is larger than a preset step number threshold value; and under the condition that the step number is less than or equal to the preset step number threshold and is not 0, determining that the user is in a non-motion state.
In an embodiment of the present application, the PAI value processing method further includes: under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity; and inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user.
In an embodiment of the present application, the PAI value processing method further includes: acquiring an acceleration data sample, an angular velocity data sample and an operation state label, and performing time domain and frequency domain calculation on the acceleration data sample and the angular velocity data sample to acquire time domain and frequency domain characteristic samples of acceleration and angular velocity; and training the classification model according to the acceleration and angular speed time domain and frequency domain characteristic samples and the operation state labels.
In an embodiment of the present application, the determining a current exercise state is an exercise state, determining a reference heart rate according to the current exercise state, and determining whether the current heart rate is abnormal according to the reference heart rate includes: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; processing the basic information of the user through a decision tree model to obtain a predicted heart rate; and calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal or not according to the absolute difference value and the reference heart rate.
In an embodiment of the present application, the determining a reference heart rate according to the current exercise state, and determining whether the current heart rate is abnormal according to the reference heart rate includes: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
In an embodiment of the present application, the determining a reference heart rate according to the current exercise state, and determining whether the current heart rate is abnormal according to the reference heart rate includes: acquiring an average heart rate of a target time period, and determining the reference heart rate according to the average heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
To achieve the above object, a second aspect of the present application provides a PAI value processing apparatus, including: the acquisition module is used for acquiring motion data of a user through at least one sensor; the determining module is used for determining the current motion state of the user according to the motion data; the judging module is used for determining a reference heart rate according to the current motion state and judging whether the current heart rate is abnormal or not according to the reference heart rate; and the processing module is used for stopping calculating the PAI value at the current moment under the condition that the current heart rate is abnormal.
In an embodiment of the present application, the acquisition module is specifically configured to: acquiring acceleration data through an acceleration sensor; and/or acquiring motion track data through a positioning sensor; and/or acquiring angular velocity data by an angular velocity sensor.
In an embodiment of the application, the determining module is specifically configured to: calculating a three-axis resultant acceleration variance according to the acceleration data; and under the condition that the three-axis combined acceleration variance is larger than a preset variance threshold value, determining that the user is in a non-motion state.
In an embodiment of the application, the determining module is specifically further configured to: under the condition that the three-axis combined acceleration variance is smaller than or equal to the preset variance threshold, calculating the current speed of the user according to the motion trail data; and under the condition that the current speed is greater than a preset speed threshold value, determining that the user is in a motion state.
In an embodiment of the application, the determining module is specifically further configured to: under the condition that the current speed is less than or equal to a preset speed threshold value, calculating the step number of the user according to the acceleration data; determining that the user is in a motion state under the condition that the step number is larger than a preset step number threshold value; and under the condition that the step number is less than or equal to the preset step number threshold and is not 0, determining that the user is in a non-motion state.
In an embodiment of the application, the determining module is specifically further configured to: under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity; and inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user.
In an embodiment of the present application, the PAI value processing apparatus further includes: the acquisition module is used for acquiring an acceleration data sample, an angular velocity data sample and an operation state label, and performing time domain and frequency domain calculation on the acceleration data sample and the angular velocity data sample to acquire time domain and frequency domain characteristic samples of acceleration and angular velocity; and the training module is used for training the classification model according to the acceleration and angular speed time-domain frequency-domain characteristic samples and the operation state labels.
In an embodiment of the application, the current motion state is a motion state, and the determining module is specifically configured to: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; processing the basic information of the user through a decision tree model to obtain a predicted heart rate; and calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal or not according to the absolute difference value and the reference heart rate.
In an embodiment of the application, the current motion state is a motion state, and the number of steps and the speed are both 0, and the determining module is specifically configured to: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
In an embodiment of the application, the current motion state is a non-motion state, and the determining module is specifically configured to: acquiring an average heart rate of a target time period, and determining the reference heart rate according to the average heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
To achieve the above object, a third aspect of the present application provides a computer device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing the PAI value processing method as described in the embodiments above.
To achieve the above object, a fourth aspect of the present application proposes a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor, enable the processor to implement the PAI value processing method as described in the above embodiments.
The technical scheme provided by the application at least has the following beneficial technical effects:
collecting motion data of a user through at least one sensor; determining the current motion state of the user according to the motion data; determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate; and stopping calculating the PAI value at the current moment when the current heart rate is abnormal. Therefore, whether the current heart rate is abnormal or not is judged based on the fact that the user is in the motion state or the non-motion state to determine the reference heart rate, the abnormal heart rate is prevented from being used for PAI value calculation, and accuracy and reliability of PAI value calculation are improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart illustrating a PAI value processing method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating another PAI value processing method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a PAI value processing apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another PAI value processing apparatus according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The PAI value processing method and apparatus of the embodiments of the present application are described below with reference to the accompanying drawings. The PAI value processing method according to the embodiment of the present application may be executed by any portable terminal device, where the terminal device may be a hardware device with various operating systems, such as a mobile phone, a tablet computer, a personal digital assistant, and a wearable device, and the wearable device may be a smart band, a smart watch, smart glasses, and the like.
Fig. 1 is a schematic flow chart of a PAI value processing method according to an embodiment of the present application. As shown in fig. 1, the PAI value processing method includes:
In this embodiment of the application, one or more sensors may be selected to collect motion data of a user according to application scenario needs, for example, as follows:
in a first example, acceleration data is acquired by an acceleration sensor.
In a second example, the motion trajectory data is acquired by a positioning sensor and the angular velocity data is acquired by an angular velocity sensor.
In this embodiment of the application, different motion data determine that the current state of the user is different, and specifically, the setting is selected according to an application scenario, for example, as follows:
in a first example, a three-axis resultant acceleration variance is calculated according to acceleration data, and in a case that the three-axis resultant acceleration variance is greater than a preset variance threshold, it is determined that a user is in a non-motion state.
In a second example, the three-axis resultant acceleration variance is calculated according to the acceleration data, the current speed of the user is calculated according to the motion trajectory data when the three-axis resultant acceleration variance is less than or equal to a preset variance threshold, and the user is determined to be in a motion state when the current speed is greater than the preset speed threshold.
And 103, determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate.
In the embodiment of the application, a user is in a motion state and a non-motion state, the corresponding reference heart rates are different, for example, when the user is in the motion state, a reasonable heart rate value is predicted by using a decision tree model, and then whether the current heart rate is abnormal or not is judged; when the user is in the non-exercise state, it is determined whether the current heart rate is far higher than the average value of the heart rates at the historical time, which is illustrated as follows:
the first example is that a maximum heart rate is acquired, a reference heart rate is determined according to the maximum heart rate, processing is performed based on basic information of a user through a decision tree model, a predicted heart rate is acquired, an absolute difference value between the predicted heart rate and a current heart rate is calculated, and whether the current heart rate is abnormal or not is determined according to the absolute difference value and the reference heart rate.
In a second example, a maximum heart rate is obtained, a reference heart rate is determined according to the maximum heart rate, and whether the current heart rate is abnormal or not is determined according to a comparison result of the current heart rate and the reference heart rate.
In a third example, an average heart rate of a target time period is obtained, and a reference heart rate is determined according to the average heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
And step 104, stopping calculating the personal motor function index PAI value at the current moment when the current heart rate is abnormal.
In the embodiment of the application, under the condition that the current heart rate is abnormal, the PAI value at the current moment is not calculated, so that the PAI value is prevented from being increased abnormally, and the accuracy and the reliability of PAI value calculation are improved.
The related patent CN107077523A calculates the personal motor function index PAI from the age, sex, resting heart rate and real-time heart rate by equations (1) - (8).
Specifically, the method comprises the following steps:
V＝a2,1,3+2,1,4(1-e-z) (2)
HRth＝RHR+HRR×0.2 (6)
HRR＝MHR-RHR (7)
MHR＝a2,1,6-a2,1,7×age (8)
wherein HR (t) is real-time heart rate; t is the integration time; RHR represents the user's resting heart rate; age represents the age of the user; { a2,1,iI 1,2, …,7 is a set of coefficients that need to be statistically calibrated for different ethnic groups.
In summary, in the PAI value processing method of this embodiment, the motion data of the user is collected by at least one sensor; determining the current motion state of the user according to the motion data; determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate; and under the condition that the current heart rate is abnormal, correcting the heart rate data, and calculating a personal exercise function index (PAI) value according to the corrected heart rate data. Therefore, whether the current heart rate is abnormal or not is judged based on the fact that the user is in the motion state or the non-motion state to determine the reference heart rate, the abnormal heart rate is prevented from being used for PAI value calculation, and accuracy and reliability of PAI value calculation are improved.
In order to make the above process more clear to those skilled in the art, the detailed description is made with reference to fig. 2.
Fig. 2 is a schematic flow chart of another PAI value processing method provided in the embodiments of the present application. As shown in fig. 2, the PAI value processing method includes:
In the embodiment of the application, the heart rate distribution intervals of the person in the exercise and the non-exercise are different. If the current heart rate is obviously inconsistent with the current exercise state, the heart rate is considered to be abnormal, and therefore before judging whether the current heart rate is abnormal or not, whether the user is in the exercise state or the non-exercise state needs to be determined.
In the embodiment of the present application, the PAI value is calculated once per a predetermined period, such as one minute, and the motion data detection frequency is also the same predetermined period, such as one minute, in order to ensure real-time performance.
In the embodiment of the present application, the motion data detection uses an acceleration sensor, an angular velocity sensor such as a gyroscope, and a Positioning sensor such as a GPS (Global Positioning System) sensor.
And 203, under the condition that the three-axis resultant acceleration variance is smaller than or equal to a preset variance threshold, calculating the current speed of the user according to the motion track data, and under the condition that the current speed is larger than the preset speed threshold, determining that the user is in a motion state. In the embodiment of the application, the three-axis resultant acceleration variance a is calculated by the acceleration sensorsumThe calculation formula is as follows, wherein ax is the acceleration of the x axis, ay is the acceleration of the y axis, and az is the acceleration of the z axis. Judgment of asumAnd judging whether the variance is greater than a preset variance threshold or not, if so, performing the next judgment, and if not, judging the motion state.
Wherein the content of the first and second substances,
the preset variance threshold value can be set according to the application scene selection.
In the embodiment of the application, the positioning sensor records the motion track of the user, the motion distance of the user can be calculated through the longitude and latitude of the user track, the current speed is obtained by dividing the distance by the time, therefore, the current speed of the user in one minute is calculated, and if the current speed is greater than a preset speed threshold, the user is judged to be in a motion state, wherein the preset speed threshold can be selectively set according to an application scene.
And 204, calculating the step number of the user according to the acceleration data under the condition that the current speed is less than or equal to the preset speed threshold.
In the embodiment of the application, because a signal of a positioning sensor, such as a GPS signal, is weak at an indoor or high-rise forest stand, effective longitude and latitude data cannot be obtained, and the current speed is defaulted to 0, when the current speed is less than a preset speed threshold or equal to 0, the next judgment is performed.
And step 206, determining that the user is in a non-motion state under the condition that the step number is less than or equal to the preset step number threshold and is not 0.
Specifically, the step number generated in one minute is calculated by utilizing the acquired acceleration data through the existing step counting algorithm of the equipment, and if the step number is smaller than the step number threshold and is not equal to 0, the current state is considered to be in a non-motion state; if the step number is larger than or equal to a preset step number threshold value, the motion state is considered; and if the step number is equal to 0, performing the next judgment.
The preset step number threshold is selected and set according to the application scene.
And step 207, under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of the acceleration and the angular velocity, inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into the trained classification model, and obtaining the current motion state of the user.
Specifically, when the number of steps is 0 and the speed is also 0, the user does not move, and cannot simply determine that the user is in a non-motion state, and may perform some indoor fitness exercises that do not require movement, and it is difficult to recognize the exercise by a simple rule, and therefore, the exercise recognition is performed using acceleration data and angular velocity data.
In the embodiment of the application, an acceleration data sample, an angular velocity data sample and an operation state label are obtained, time domain and frequency domain calculation is performed on the acceleration data sample and the acceleration data sample, a time domain and frequency domain characteristic sample of acceleration and angular velocity is obtained, and a training classification model is performed according to the time domain and frequency domain characteristic sample of acceleration and angular velocity and the operation state label.
Specifically, time domain and frequency domain information of acceleration data and angular velocity data is analyzed, relevant features are extracted, then the motion state labels are classified and trained together to obtain a classification model for motion state identification, time domain and frequency domain calculation which is the same as that in the training process is carried out on the acceleration data and the angular velocity data to obtain time domain and frequency domain features, and classification and identification are carried out by using a classifier model obtained in the training process to obtain an identification result of the motion state category.
And 208, acquiring a maximum heart rate when the current motion state is the motion state, determining a reference heart rate according to the maximum heart rate, processing based on basic information of a user through a decision tree model, acquiring a predicted heart rate, calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal according to the absolute difference value and the reference heart rate.
Specifically, after the exercise state is determined, whether the current heart rate is abnormal or not is judged, for example, when the user is in the exercise state and the speed is not 0, a pre-trained decision tree model is used for predicting a reasonable value of the current heart rate, characteristic values used by the decision tree model include the age, the gender, the height and weight index (BMI) and the speed of the user, an absolute value of a difference value between the predicted heart rate of the decision tree model and the current actual heart rate is calculated, if the following formula is met, the heart rate is considered to be a normal heart rate, otherwise, the heart rate is considered to be an abnormal heart rate, and HR _ max is the maximum heart rate. Wherein HR _ predict-HR _ real | < HR _ max × 10%.
In the embodiment of the present application, if the speed is 0 but there are steps generated, step data in the device is used, a distance is calculated by multiplying the step by the step, the speed is obtained by dividing the distance by the time, and whether the current heart rate is abnormal is determined by using the above decision tree model method.
And step 209, acquiring the maximum heart rate when the current motion state is the motion state and the step number and the speed are both 0, determining the reference heart rate according to the maximum heart rate, and determining whether the current heart rate is abnormal according to the comparison result of the current heart rate and the reference heart rate.
In the embodiment of the present application, if the number of steps and the speed are both 0 and in the exercise state, the heart rate is above 60% of the maximum heart rate, for example, the heart rate is considered to be a normal heart rate, otherwise, the heart rate is an abnormal heart rate.
And step 210, acquiring the average heart rate of the target time period when the current motion state is a non-motion state, determining a reference heart rate according to the average heart rate, and determining whether the current heart rate is abnormal or not according to a comparison result of the current heart rate and the reference heart rate.
Specifically, when the user is in a stationary state, the heart rate is generally low, and a target time period, such as a 10-minute heart rate average value, is calculated, for example, if the current heart rate is greater than 20% of the average heart rate and higher than 65% of the maximum heart rate, the current heart rate is considered to be an abnormal heart rate, and otherwise, the current heart rate is considered to be a normal heart rate.
In step 211, when the current heart rate is abnormal, the calculation of the personal athletic performance index PAI value at the current moment is stopped.
In particular, for abnormal heart rates, the PAI value is not calculated, avoiding the impact of invalid data on PAI accuracy and rationality.
From this, utilize acceleration, angular velocity and GPS sensor etc. to synthesize and judge that the user is in motion or non-motion state, avoid the problem that single sensor motion detection accuracy is low, again according to whether reasonable interval of motion detection result analysis heart rate to judge whether current heart rate takes place unusually, when the heart rate takes place unusually, do not calculate PAI, the PAI unusual circumstances that increases when having avoided non-motion has improved accuracy and reliability that PAI calculated.
To sum up, the PAI value processing method of this embodiment obtains acceleration data through an acceleration sensor, obtains motion trajectory data through a position sensor, and obtains angular velocity data through an angular velocity sensor, calculates a three-axis resultant acceleration variance according to the acceleration data, determines that a user is in a non-motion state when the three-axis resultant acceleration variance is greater than a preset variance threshold, calculates a current velocity of the user according to the motion trajectory data when the three-axis resultant acceleration variance is less than or equal to the preset variance threshold, determines that the user is in a motion state when the current velocity is greater than the preset velocity threshold, calculates a number of steps of the user according to the acceleration data when the current velocity is less than or equal to the preset velocity threshold, determines that the user is in a motion state when the number of steps is greater than or equal to the preset number of steps threshold, and determines that the number of steps is less than or equal to the preset number of steps threshold and is not 0, determining that the user is in a non-motion state, under the condition that the step number is 0, performing time domain and frequency domain calculation on acceleration data and acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity, inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user and obtain the maximum heart rate, determining a reference heart rate according to the maximum heart rate, processing based on the basic information of the user through a decision tree model to obtain a predicted heart rate, calculating an absolute difference value between the predicted heart rate and the current heart rate, determining whether the current heart rate is abnormal according to the absolute difference value and the reference heart rate, acquiring the maximum heart rate, determining the reference heart rate according to the maximum heart rate, determining whether the current heart rate is abnormal according to the comparison result of the current heart rate reference heart rate, acquiring the average heart rate of the target time period, and determining the reference heart rate according to the average heart rate, and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate. Therefore, whether the current heart rate is abnormal or not is judged based on the fact that the user is in the motion state or the non-motion state to determine the reference heart rate, the abnormal heart rate is prevented from being used for PAI value calculation, and accuracy and reliability of PAI value calculation are improved.
In order to implement the above embodiments, the present application also proposes a PAI value processing apparatus.
Fig. 3 is a schematic structural diagram of a PAI value processing apparatus according to an embodiment of the present application.
As shown in fig. 3, the PAI value processing apparatus includes: the device comprises an acquisition module 10, a determination module 20, a judgment module 30 and a processing module 40. Wherein the content of the first and second substances,
the device comprises an acquisition module 10 for acquiring the motion data of the user through at least one sensor.
A determining module 20, configured to determine a current motion state of the user according to the motion data.
And the judging module 30 is configured to determine a reference heart rate according to the current motion state, and judge whether the current heart rate is abnormal according to the reference heart rate.
And the processing module 40 is configured to stop calculating the PAI value at the current moment if the current heart rate is abnormal.
In an embodiment of the present application, the acquisition module 10 is specifically configured to: acquiring acceleration data through an acceleration sensor; and/or acquiring motion track data through a positioning sensor; and/or acquiring angular velocity data by an angular velocity sensor.
In an embodiment of the present application, the determining module 20 is specifically configured to: calculating a three-axis resultant acceleration variance according to the acceleration data; and under the condition that the three-axis combined acceleration variance is larger than a preset variance threshold value, determining that the user is in a non-motion state.
In an embodiment of the present application, the determining module 20 is further specifically configured to: under the condition that the three-axis combined acceleration variance is smaller than or equal to the preset variance threshold, calculating the current speed of the user according to the motion trail data; and under the condition that the current speed is greater than a preset speed threshold value, determining that the user is in a motion state.
In an embodiment of the present application, the determining module 20 is further specifically configured to: under the condition that the current speed is less than or equal to a preset speed threshold value, calculating the step number of the user according to the acceleration data; determining that the user is in a motion state under the condition that the step number is larger than a preset step number threshold value; and under the condition that the step number is less than or equal to the preset step number threshold and is not 0, determining that the user is in a non-motion state.
In an embodiment of the present application, the determining module 20 is further specifically configured to: under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity; and inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user.
In an embodiment of the present application, as shown in fig. 4, on the basis of fig. 3, the PAI value processing apparatus further includes: an acquisition module 50 and a training module 60.
The acquiring module 50 is configured to acquire an acceleration data sample, an angular velocity data sample, and an operation state label, perform time domain and frequency domain calculation on the acceleration data sample and the angular velocity data sample, and acquire time domain and frequency domain characteristic samples of acceleration and angular velocity; and a training module 60, configured to train the classification model according to the acceleration and angular velocity time-domain and frequency-domain feature samples and the operation status labels.
In an embodiment of the present application, the current motion state is a motion state, and the determining module 30 is specifically configured to: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; processing the basic information of the user through a decision tree model to obtain a predicted heart rate; and calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal or not according to the absolute difference value and the reference heart rate.
In an embodiment of the present application, the current motion state is a motion state, and the number of steps and the speed are both 0, and the determining module 30 is specifically configured to: acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
In an embodiment of the present application, the current motion state is a non-motion state, and the determining module 30 is specifically configured to: acquiring an average heart rate of a target time period, and determining the reference heart rate according to the average heart rate; and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
It should be noted that the foregoing explanation of the embodiment of the PAI value processing method is also applicable to the PAI value processing apparatus of this embodiment, and is not repeated here.
In summary, the PAI value processing apparatus of this embodiment collects the motion data of the user through at least one sensor; determining the current motion state of the user according to the motion data; determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate; and stopping calculating the PAI value at the current moment when the current heart rate is abnormal. Therefore, whether the current heart rate is abnormal or not is judged based on the fact that the user is in the motion state or the non-motion state to determine the reference heart rate, the abnormal heart rate is prevented from being used for PAI value calculation, and accuracy and reliability of PAI value calculation are improved.
To implement the foregoing embodiments, the present application further proposes a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the PAI value processing method according to the foregoing embodiments is implemented.
To achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium in which instructions are made to enable execution of the PAI value processing method described in the above embodiments when executed by a processor.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (22)
1. A PAI value processing method, comprising:
collecting motion data of a user through at least one sensor;
determining the current motion state of the user according to the motion data;
determining a reference heart rate according to the current motion state, and judging whether the current heart rate is abnormal or not according to the reference heart rate;
and stopping calculating the personal motor function index PAI value at the current moment when the current heart rate is abnormal.
2. The PAI value processing method of claim 1, wherein the collecting motion data of the user via the at least one sensor comprises:
acquiring acceleration data through an acceleration sensor; and/or the presence of a gas in the gas,
acquiring motion track data through a positioning sensor; and/or the presence of a gas in the gas,
angular velocity data is acquired by an angular velocity sensor.
3. The PAI value processing method of claim 2, wherein determining the current motion state of the user based on the motion data comprises:
calculating a three-axis resultant acceleration variance according to the acceleration data;
and under the condition that the three-axis combined acceleration variance is larger than a preset variance threshold value, determining that the user is in a non-motion state.
4. The PAI value processing method of claim 3, further comprising:
under the condition that the three-axis combined acceleration variance is smaller than or equal to the preset variance threshold, calculating the current speed of the user according to the motion trail data;
and under the condition that the current speed is greater than a preset speed threshold value, determining that the user is in a motion state.
5. The PAI value processing method of claim 4, further comprising:
under the condition that the current speed is less than or equal to a preset speed threshold value, calculating the step number of the user according to the acceleration data;
determining that the user is in a motion state under the condition that the step number is larger than a preset step number threshold value;
and under the condition that the step number is less than or equal to the preset step number threshold and is not 0, determining that the user is in a non-motion state.
6. The PAI value processing method of claim 5, further comprising:
under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity;
and inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user.
7. The PAI value processing method of claim 6, further comprising:
acquiring an acceleration data sample, an angular velocity data sample and an operation state label, and performing time domain and frequency domain calculation on the acceleration data sample and the angular velocity data sample to acquire time domain and frequency domain characteristic samples of acceleration and angular velocity;
and training the classification model according to the acceleration and angular speed time domain and frequency domain characteristic samples and the operation state labels.
8. The PAI value processing method of any one of claims 1 to 7, wherein the current motion state is a motion state, the determining a reference heart rate based on the current motion state, and determining whether the current heart rate is abnormal based on the reference heart rate comprises:
acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate;
processing the basic information of the user through a decision tree model to obtain a predicted heart rate;
and calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal or not according to the absolute difference value and the reference heart rate.
9. The PAI value processing method as claimed in any one of claims 1 to 7, wherein the current motion state is a motion state and the number of steps and the speed are both 0, the determining a reference heart rate based on the current motion state, and the determining whether the current heart rate is abnormal based on the reference heart rate comprises:
acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate;
and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
10. The PAI value processing method of any one of claims 1 to 7, wherein the current motion state is a non-motion state, wherein determining a reference heart rate based on the current motion state and determining whether the current heart rate is abnormal based on the reference heart rate comprises:
acquiring an average heart rate of a target time period, and determining the reference heart rate according to the average heart rate;
and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
11. A PAI value processing apparatus, comprising:
the acquisition module is used for acquiring motion data of a user through at least one sensor;
the determining module is used for determining the current motion state of the user according to the motion data;
the judging module is used for determining a reference heart rate according to the current motion state and judging whether the current heart rate is abnormal or not according to the reference heart rate;
and the processing module is used for stopping calculating the PAI value at the current moment under the condition that the current heart rate is abnormal.
12. The PAI value processing apparatus of claim 11, wherein the acquisition module is further configured to:
acquiring acceleration data through an acceleration sensor; and/or the presence of a gas in the gas,
acquiring motion track data through a positioning sensor; and/or the presence of a gas in the gas,
angular velocity data is acquired by an angular velocity sensor.
13. The PAI value processing apparatus of claim 12, wherein the determining module is specifically configured to:
calculating a three-axis resultant acceleration variance according to the acceleration data;
and under the condition that the three-axis combined acceleration variance is larger than a preset variance threshold value, determining that the user is in a non-motion state.
14. The PAI value processing apparatus as claimed in claim 13, wherein the determining module is further configured to:
under the condition that the three-axis combined acceleration variance is smaller than or equal to the preset variance threshold, calculating the current speed of the user according to the motion trail data;
and under the condition that the current speed is greater than a preset speed threshold value, determining that the user is in a motion state.
15. The PAI value processing apparatus of claim 14, wherein the determining module is further configured to:
under the condition that the current speed is less than or equal to a preset speed threshold value, calculating the step number of the user according to the acceleration data;
determining that the user is in a motion state under the condition that the step number is larger than a preset step number threshold value;
and under the condition that the step number is less than or equal to the preset step number threshold and is not 0, determining that the user is in a non-motion state.
16. The PAI value processing apparatus as claimed in claim 15, wherein the determining module is further configured to:
under the condition that the step number is 0, performing time domain and frequency domain calculation on the acceleration data and the acceleration data to obtain time domain and frequency domain characteristics of acceleration and angular velocity;
and inputting the time domain and frequency domain characteristics of the acceleration and the angular velocity into a trained classification model to obtain the current motion state of the user.
17. The PAI value processing apparatus as in claim 16, further comprising:
the acquisition module is used for acquiring an acceleration data sample, an angular velocity data sample and an operation state label, and performing time domain and frequency domain calculation on the acceleration data sample and the angular velocity data sample to acquire time domain and frequency domain characteristic samples of acceleration and angular velocity;
and the training module is used for training the classification model according to the acceleration and angular speed time-domain frequency-domain characteristic samples and the operation state labels.
18. The PAI value processing apparatus as claimed in any one of claims 11 to 17, wherein the current motion state is a motion state, and the determining module is specifically configured to:
acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate;
processing the basic information of the user through a decision tree model to obtain a predicted heart rate;
and calculating an absolute difference value between the predicted heart rate and the current heart rate, and determining whether the current heart rate is abnormal or not according to the absolute difference value and the reference heart rate.
19. The PAI value processing apparatus as claimed in any one of claims 11 to 17, wherein the current motion state is a motion state and the number of steps and the speed are both 0, the determining module is specifically configured to:
acquiring a maximum heart rate, and determining the reference heart rate according to the maximum heart rate;
and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
20. The PAI value processing apparatus as claimed in any one of claims 11 to 17, wherein the current motion state is a non-motion state, and the determining module is specifically configured to:
acquiring an average heart rate of a target time period, and determining the reference heart rate according to the average heart rate;
and determining whether the current heart rate is abnormal or not according to the comparison result of the current heart rate and the reference heart rate.
21. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the PAI value processing method as claimed in any one of claims 1 to 10 when executing the computer program.
22. A non-transitory computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements a PAI value processing method as claimed in any one of claims 1 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116369876A (en) * | 2023-04-13 | 2023-07-04 | 中国人民解放军空军特色医学中心 | Pilot distress call device |
| WO2023220894A1 (en) * | 2022-05-16 | 2023-11-23 | 北京小米移动软件有限公司 | Statistical method and apparatus for motion physiological data, device, storage medium and chip |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150112158A1 (en) * | 2013-10-23 | 2015-04-23 | Quanttus, Inc. | Health Metrics |
| CN107015646A (en) * | 2017-03-28 | 2017-08-04 | 北京犀牛数字互动科技有限公司 | The recognition methods of motion state and device |
| CN107550475A (en) * | 2017-07-31 | 2018-01-09 | 深圳真时科技有限公司 | A kind of method, apparatus and wearable device for monitoring human heart rate |
| CN108814618A (en) * | 2018-04-27 | 2018-11-16 | 歌尔科技有限公司 | A kind of recognition methods of motion state, device and terminal device |
| CN111214235A (en) * | 2019-12-31 | 2020-06-02 | 河南科技大学 | Human body activity monitoring method and device |
| CN112057066A (en) * | 2020-09-03 | 2020-12-11 | 青岛歌尔智能传感器有限公司 | Heart rate detection method, wearable device and computer storage medium |
-
2021
- 2021-01-28 CN CN202110119176.9A patent/CN112967801A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150112158A1 (en) * | 2013-10-23 | 2015-04-23 | Quanttus, Inc. | Health Metrics |
| CN107015646A (en) * | 2017-03-28 | 2017-08-04 | 北京犀牛数字互动科技有限公司 | The recognition methods of motion state and device |
| CN107550475A (en) * | 2017-07-31 | 2018-01-09 | 深圳真时科技有限公司 | A kind of method, apparatus and wearable device for monitoring human heart rate |
| CN108814618A (en) * | 2018-04-27 | 2018-11-16 | 歌尔科技有限公司 | A kind of recognition methods of motion state, device and terminal device |
| CN111214235A (en) * | 2019-12-31 | 2020-06-02 | 河南科技大学 | Human body activity monitoring method and device |
| CN112057066A (en) * | 2020-09-03 | 2020-12-11 | 青岛歌尔智能传感器有限公司 | Heart rate detection method, wearable device and computer storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023220894A1 (en) * | 2022-05-16 | 2023-11-23 | 北京小米移动软件有限公司 | Statistical method and apparatus for motion physiological data, device, storage medium and chip |
| CN116369876A (en) * | 2023-04-13 | 2023-07-04 | 中国人民解放军空军特色医学中心 | Pilot distress call device |
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