Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting and correcting exercise training effect based on wearing equipment, which can be used for predicting and correcting the exercise training effect and recovery time of user exercise through the wearing equipment.
In a first aspect, a method for predicting and correcting a training effect based on a wearable device according to an embodiment of the present invention includes:
Acquiring heart rate data and exercise data in the exercise process of a user;
Determining the current exercise intensity of the user according to the heart rate data;
determining the EPOC of the current moment of the user according to the current motion intensity of the user and the motion data, and predicting the EPOC of the next moment;
And correcting the predicted EPOC at the next moment according to the current motion intensity of the user and the motion data, and obtaining the actual training load and training effect.
Optionally, the determining the current exercise intensity of the user according to the heart rate data includes:
determining the current motion intensity of the user according to formula (1);
The formula (1) is:
Exs_Intensity=(a1*pHR^2-a2*pHR+a3)*100...............(1)
wherein Exs _density is the current exercise Intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.
Optionally, the determining the EPOC of the current moment of the user according to the current exercise intensity of the user and the exercise data includes:
Determining the EPOC of the current moment of the user according to the current motion intensity, the speed matching of the user, the EPOC of the previous moment and the formula (2);
The formula (2) is:
EPOC(t)=f(HR,Exs_Intensity,V,EPOC(t-1))............(2)
Wherein EPOC (t) is the EPOC at the current time, HR is the heart rate, exs _density is the current exercise Intensity, V is the pace, and EPOC (t-1) is the EPOC at the previous time.
Optionally, the predicting the EPOC at the next time includes:
Predicting EPOC for the next time according to equation (3);
the formula (3) is:
EPOC(t+1)=αEPOC(t)+(1-α)EPOC(t-1)............(3)
wherein EPOC (t+1) is the next EPOC, EPOC (t) is the current EPOC, and EPOC (t-1) is the previous EPOC.
Optionally, the method further comprises:
determining a predicted training load and training effect according to the predicted EPOC at the next moment;
And comparing the predicted training load and the predicted training effect with the actual training load and the actual training effect to obtain reference data of the next training.
In a second aspect, an embodiment of the present invention provides a device for predicting and correcting a training effect based on a wearable device, including:
The acquisition unit is used for acquiring heart rate data and exercise data in the exercise process of the user;
The processing unit is used for determining the current exercise intensity of the user according to the heart rate data, determining the EPOC of the user at the current moment according to the current exercise intensity of the user and the exercise data, predicting the EPOC at the next moment, and correcting the predicted EPOC at the next moment according to the current exercise intensity of the user and the exercise data, so as to obtain the actual training load and training effect.
Optionally, the processing unit is specifically configured to:
determining the current motion intensity of the user according to formula (1);
The formula (1) is:
Exs_Intensity=(a1*pHR^2-a2*pHR+a3)*100...............(1)
wherein Exs _density is the current exercise Intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.
Optionally, the processing unit is specifically configured to:
Determining the EPOC of the current moment of the user according to the current motion intensity, the speed matching of the user, the EPOC of the previous moment and the formula (2);
The formula (2) is:
EPOC(t)=f(HR,Exs_Intensity,V,EPOC(t-1))............(2)
Wherein EPOC (t) is the EPOC at the current time, HR is the heart rate, exs _density is the current exercise Intensity, V is the pace, and EPOC (t-1) is the EPOC at the previous time.
Optionally, the processing unit is specifically configured to:
Predicting EPOC for the next time according to equation (3);
the formula (3) is:
EPOC(t+1)=αEPOC(t)+(1-α)EPOC(t-1)............(3)
wherein EPOC (t+1) is the next EPOC, EPOC (t) is the current EPOC, and EPOC (t-1) is the previous EPOC.
Optionally, the processing unit is further configured to:
determining a predicted training load and training effect according to the predicted EPOC at the next moment;
And comparing the predicted training load and the predicted training effect with the actual training load and the actual training effect to obtain reference data of the next training.
In a third aspect, embodiments of the present invention also provide a computing device, including:
A memory for storing program instructions;
And the processor is used for calling the program instructions stored in the memory and executing the exercise training effect prediction correction method based on the wearable equipment according to the obtained program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable nonvolatile storage medium, including computer-readable instructions, which when read and executed by a computer, cause the computer to execute the above-mentioned exercise training effect prediction correction method based on a wearable device.
According to the embodiment of the invention, heart rate data and motion data in the motion process of a user are obtained, the current motion intensity of the user is determined according to the heart rate data, the EPOC at the current moment of the user is determined according to the current motion intensity of the user and the motion data, the EPOC at the next moment is predicted, the predicted EPOC at the next moment is corrected according to the current motion intensity of the user and the motion data, and the actual training load and training effect are obtained. The heart rate of the user exercise process is collected through the wearing equipment, the actual training load and training effect of the user are determined according to the heart rate, and the purposes of predicting and correcting the training effect and recovering the time length of the user exercise through the wearing equipment can be achieved.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
First, a wearable device to which the embodiment of the present invention is applied will be described by taking the structure shown in fig. 1 as an example. In an embodiment of the present invention, the wearable device 100 may include, but is not limited to, a Radio Frequency (RF) circuit 110, a memory 120, an input unit 130, a WiFi module 170, a display unit 140, a sensor 150, an audio circuit 160, a processor 180, and a motor 190.
Wherein it will be appreciated by those skilled in the art that the configuration of the wearable device 100 shown in fig. 1 is merely exemplary and not limiting, the wearable device 100 may also include more or fewer components than illustrated, or may combine certain components, or may be a different arrangement of components.
The RF circuit 110 may be used for receiving and transmitting signals during the process of receiving and transmitting information or communication, specifically, receiving downlink information of the base station, and then processing the downlink information by the processor 180, and in addition, transmitting uplink data of the wearable device 100 to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low noise amplifiers (LNAs, low Noise Amplifier), diplexers, and the like. In addition, RF circuit 110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System for Mobile communication, abbreviated as "GSM"), general packet Radio Service (GENERAL PACKET Radio Service, abbreviated as "GPRS"), code division multiple access (Code Division Multiple Access, abbreviated as "CDMA"), wideband code division multiple access (Wideband Code Division Multiple Access, abbreviated as "WCDMA"), long term evolution (Long Term Evolution, abbreviated as "LTE"), email, short message Service (Short MESSAGING SERVICE, abbreviated as "SMS"), and the like.
The memory 120 may be used to store software programs and modules, and the processor 180 executes various functional applications and data processing of the wearable device 100 by executing the software programs and modules stored in the memory 120. The memory 120 may mainly include a storage program area that may store an operating system, an application program required for at least one function (e.g., a sound playing function, an image playing function, etc.), etc., and a storage data area that may store data (e.g., audio data, a phonebook, etc.) created according to the use of the wearable device 100, etc. In addition, memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The input unit 130 may be used to receive input numeric or character information and generate key signals related to user settings and function control of the wearable device 100. Specifically, the input unit 130 may include a touch panel 131, an image pickup device 132, and other input devices 133. The image capturing device 132 may take a picture of an image to be acquired, and then transmit the image to the processor 180 for processing, and finally present the image to the user through the display panel 141. The touch panel 131, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 131 or thereabout by using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 131 may include two parts of a touch detection device and a touch controller. The touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch panel 131 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 130 may include other input devices 133 in addition to the touch panel 131 and the image pickup device 132. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, joystick, etc.
Among them, the display unit 140 may be used to display information input by a user or information provided to the user and various menus of the wearable device 100. The display unit 140 may include a display panel 141, and alternatively, the display panel 141 may be configured in the form of a Liquid Crystal Display (LCD) unit, an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 131 may cover the display panel 141, and when the touch panel 131 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event.
The visual output external display panel 141 that can be recognized by the human eye may be used as a display device in the embodiment of the present invention to display text information or image information. Although in fig. 1, the touch panel 131 and the display panel 141 implement the input and output functions of the wearable device 100 as two independent components, in some embodiments, the touch panel 131 and the display panel 141 may be integrated to implement the input and output functions of the wearable device 100.
In addition, the wearable device 100 may also include at least one sensor 150, such as a gesture sensor, a distance sensor, a light sensor, and other sensors.
Specifically, the attitude sensor may also be referred to as a motion sensor, and as one of the motion sensor, an angular velocity sensor (also referred to as a gyroscope) for measuring the rotational angular velocity of the wearing device 100 in a state of motion when being deflected, tilted when being arranged in the wearing device 100 is cited, so that the gyroscope can accurately analyze and judge the actual motion of the user using the wearing device 100, and further, perform a corresponding operation on the wearing device 100. For example, feel, shake-shake (shake the wearable device 100 to perform some functions), inertial navigation according to the object motion state when the global positioning system (Global Positioning System, GPS) is not signaled (e.g., in a tunnel).
The sensor may be a photosensor, which is mainly used to collect information such as wavelength and intensity of various light rays of light, and to adjust the backlight intensity of the display panel 141.
In addition, in the embodiment of the present invention, as the sensor 150, other sensors such as a barometer, a hygrometer, a thermometer, an infrared sensor, etc. may be configured, and will not be described herein.
The light sensor may also include a proximity sensor that may turn off the display panel 141 and/or backlight when the wearable device 100 is moved to the ear.
Audio circuitry 160, speaker 161, microphone 162 may provide an audio interface between the user and the wearable device 100. The audio circuit 160 may transmit the received electrical signal converted from audio data to the speaker 161 for conversion into sound signals for output by the speaker 161, while the microphone 162 may convert the collected sound signals into electrical signals for reception by the audio circuit 160 for conversion into audio data for processing by the audio data output processor 180 for transmission to, for example, another wearable device 100 via the RF circuit 110 or for outputting to the memory 120 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and the wearable device 100 can help a user to send and receive e-mails, browse web pages, access streaming media and the like through the WiFi module 170, so that wireless broadband Internet access is provided for the user. Although fig. 1 shows a WiFi module 170, it is understood that it does not belong to the necessary constitution of the wearable device 100, and may be omitted entirely as needed within a range that does not change the essence of the invention.
The processor 180 is a control center of the wearable device 100, connects various parts of the entire wearable device 100 using various interfaces and lines, and performs various functions of the wearable device 100 and processes data by running or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the wearable device 100. Alternatively, the processor 180 may include one or more processing units, and preferably the processor 180 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications.
It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The wearable device 100 may further include at least one motor 190, and since the wearable device 100 is a power consumption device, the motor 190 may be a small-sized motor, and at the same time, a plurality of motors may be configured for the wearable device 100 according to the amount of power that the motor can provide.
The wearable device 100 further comprises a power supply (not shown in the figures) for powering the various components.
Preferably, the power supply may be logically connected to the processor 180 through a power management system, so that functions of managing charge, discharge, and power consumption are performed through the power management system. Although not shown, the wearable device 100 may further include a bluetooth module or the like, which is not described herein.
It should be noted that the structure shown in fig. 1 is merely an example, and the embodiment of the present invention is not limited thereto.
Fig. 2 illustrates an exemplary process of predicting and correcting a training effect based on a wearable device according to an embodiment of the present invention, where the process may be performed by a training effect predicting and correcting apparatus based on a wearable device.
As shown in fig. 2, the process specifically includes:
Step 201, heart rate data and exercise data in the process of user exercise are acquired.
In the embodiment of the invention, the wearing equipment adopts a triaxial acceleration sensor, acceleration data of a user can be acquired through the triaxial acceleration sensor, and the triaxial acceleration sensor can also be called a behavior data sensor and also can comprise a gyroscope, a geomagnetic sensor and the like. In addition, various other types of sensors can be arranged in the wearable device, for example, the environment sensor can collect environment information, for example, the environment sensor generally comprises a temperature sensor, a humidity sensor, a light sensor and the like, and can collect environment information of temperature, humidity, light and the like. The physiological signal sensor may collect physiological data of the user, for example, may include a heart rate sensor, a blood oxygen sensor, a blood pressure sensor, etc., and collect physiological data of the user such as heart rate data, blood oxygen data, blood pressure data, etc.
Heart rate data are acquired through a heart rate sensor, and motion data are acquired through an acceleration sensor, a gyroscope and a geomagnetic sensor.
Step 202, determining the current exercise intensity of the user according to the heart rate data.
After obtaining the heart rate data, the heart rate data can be used to determine the current exercise intensity of the user, specifically, the heart rate data can be determined by the formula (1), wherein the formula (1) is:
Exs_Intensity=(a1*pHR^2-a2*pHR+a3)*100...............(1)
wherein Exs _intensity is the current exercise Intensity, pHR is the ratio of the user's average heart rate to the maximum heart rate, that is, phr=hr_avg/hr_max, where hr_avg is the user's average heart rate, hr_max is the user's maximum heart rate, and a1, a2, and a3 are constants.
Step 203, determining EPOC (process Post-Exercise Oxygen Consumption) of the current moment of the user according to the current motion intensity of the user and the motion data, and predicting EPOC of the next moment.
Specifically, the EPOC at the current time of the user can be determined according to the current motion intensity, the matching speed, the EPOC at the previous time of the user and the formula (2).
The formula (2) is:
EPOC(t)=f(HR,Exs_Intensity,V,EPOC(t-1))............(2)
Wherein EPOC (t) is the EPOC at the current time, HR is heart rate, exs _intensity is the current exercise Intensity, V is the pace, EPOC (t-1) is the EPOC at the previous time, and f () is a nonlinear function relationship fitted according to running training data.
Predicting EPOC for the next time according to equation (3);
the formula (3) is:
EPOC(t+1)=αEPOC(t)+(1-α)EPOC(t-1)............(3)
wherein EPOC (t+1) is the next EPOC, EPOC (t) is the current EPOC, EPOC (t-1) is the previous EPOC, and α is a parameter, i.e., a coefficient, fitted by the data of the current EPOC and the previous EPOC.
And step 204, correcting the predicted EPOC at the next moment according to the current motion intensity of the user and the motion data, and obtaining the actual training load and training effect.
After obtaining the predicted EPOC at the next time, the predicted EPOC at the next time may be corrected based on the current motion strength, the speed, the maximum oxygen uptake, and other motion information. And then obtaining the aerobic training load and the anaerobic training load through the EPOC at the next time after correction. Thereby obtaining an aerobic training effect ate and an anaerobic training effect ante. In correcting EPOC at the next time of the prediction, correction can be performed according to the following equation (4):
EPOC’(t+1)=f(EPOC(t+1),Exs_Intensity,VO2,V)............(4)
Wherein EPOC' (t+1) is the EPOC at the next time after correction, EPOC (t+1) is the EPOC at the predicted next time, exs _density is the current exercise Intensity, VO2 is the maximum oxygen uptake, and V is the matching speed.
The process of obtaining aerobic training load and anaerobic training load and aerobic training effect and anaerobic training effect by EPOC is prior art and the embodiments of the present invention are not described in detail.
In addition, the predicted training load and training effect can be determined according to the predicted EPOC at the next moment, and the predicted training load and training effect are compared with the actual training load and training effect to obtain the reference data of the next training. The reference data of the next training is how much training effect and load can be achieved with the same training intensity and time, so that the exercise intensity and exercise time are modified. By comparing the predicted training load and training effect with the actual training load and training effect, a user can learn the difference in the self training process, and can learn whether the aerobic training is insufficient or excessive or the anaerobic training is insufficient or excessive, so that the user can correspondingly change the motion strength and the motion time in the next training.
The embodiment shows that heart rate data and motion data in the motion process of a user are obtained, the current motion intensity of the user is determined according to the heart rate data, the EPOC at the current moment of the user is determined according to the current motion intensity of the user and the motion data, the EPOC at the next moment is predicted, the predicted EPOC at the next moment is corrected according to the current motion intensity of the user and the motion data, and the actual training load and the actual training effect are obtained. The heart rate of the user exercise process is collected through the wearing equipment, the actual training load and training effect of the user are determined according to the heart rate, and the purposes of predicting and correcting the training effect and recovering the time length of the user exercise through the wearing equipment can be achieved.
Based on the same technical concept, fig. 3 exemplarily shows a structure of a training effect prediction and correction device based on a wearable device, which is provided by an embodiment of the present invention, and the device may execute a training effect prediction and correction flow based on the wearable device.
As shown in fig. 3, the apparatus may include:
an acquiring unit 301, configured to acquire heart rate data and exercise data during exercise of a user;
The processing unit 302 is configured to determine the current exercise intensity of the user according to the heart rate data, determine the EPOC at the current time of the user according to the current exercise intensity of the user and the exercise data, predict the EPOC at the next time, and correct the predicted EPOC at the next time according to the current exercise intensity of the user and the exercise data, thereby obtaining an actual training load and training effect.
Optionally, the processing unit 302 is specifically configured to:
determining the current motion intensity of the user according to formula (1);
The formula (1) is:
Exs_Intensity=(a1*pHR^2-a2*pHR+a3)*100...............(1)
wherein Exs _density is the current exercise Intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.
Optionally, the processing unit 302 is specifically configured to:
Determining the EPOC of the current moment of the user according to the current motion intensity, the speed matching of the user, the EPOC of the previous moment and the formula (2);
The formula (2) is:
EPOC(t)=f(HR,Exs_Intensity,V,EPOC(t-1))............(2)
Wherein EPOC (t) is the EPOC at the current time, HR is the heart rate, exs _density is the current exercise Intensity, V is the pace, and EPOC (t-1) is the EPOC at the previous time.
Optionally, the processing unit 302 is specifically configured to:
Predicting EPOC for the next time according to equation (3);
the formula (3) is:
EPOC(t+1)=αEPOC(t)+(1-α)EPOC(t-1)............(3)
wherein EPOC (t+1) is the next EPOC, EPOC (t) is the current EPOC, and EPOC (t-1) is the previous EPOC.
Optionally, the processing unit 302 is further configured to:
determining a predicted training load and training effect according to the predicted EPOC at the next moment;
And comparing the predicted training load and the predicted training effect with the actual training load and the actual training effect to obtain reference data of the next training.
Based on the same technical concept, the embodiment of the invention further provides a computing device, which comprises:
A memory for storing program instructions;
And the processor is used for calling the program instructions stored in the memory and executing the exercise training effect prediction correction method based on the wearable equipment according to the obtained program.
Based on the same technical concept, the embodiment of the invention also provides a computer-readable nonvolatile storage medium, which comprises computer-readable instructions, wherein when the computer reads and executes the computer-readable instructions, the computer is caused to execute the motion training effect prediction correction method based on the wearable device.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.