Exercise training effect prediction and correction method and device based on wearable device

Abstract

The invention discloses an exercise training effect prediction and correction method and device based on wearable equipment. The method comprises the steps that heart rate data and exercise data in the exercise process of a user are acquired; 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 and the exercise data of the user, and predicting the EPOC of the user 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, and obtaining an actual training load and a training effect. The heart rate of the user in the exercise process is collected through the wearable device, the actual training load and training effect of the user are determined according to the heart rate, and the purpose of predicting and correcting the training effect and recovery time of user exercise through the wearable device can be achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of wearable devices, and particularly to a method and device for predicting and correcting the effect of exercise training based on wearable devices. Background Art

[0002] In recent years, wearable devices have become very popular. A wearable device is a portable device that can be directly worn on the body or integrated into the user's clothes or accessories. A wearable device is not just a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. Wearable devices will bring great changes to our lives and perceptions.

[0003] Currently, with the increase in the number of wearable device users and running enthusiasts, users have become dependent on monitoring exercises through wearable devices. Due to the portability of wearable devices, users are accustomed to using wearable devices to collect exercise data and conduct exercise training. However, during exercise, if one wants to achieve an ideal exercise goal, it is necessary to evaluate one's own physical functions in order to detect whether the current exercise is reasonable and at the same time provide a basis for the next exercise plan. Therefore, there is an urgent need for a solution that can monitor the exercise effect after each exercise. Summary of the Invention

[0004] The embodiments of the present invention provide a method and device for predicting and correcting the effect of exercise training based on wearable devices, which can predict and correct the training effect and recovery duration of the user's exercise through wearable devices.

[0005] In a first aspect, a method for predicting and correcting the effect of exercise training based on wearable devices provided by the embodiments of the present invention includes:

[0006] Obtain the heart rate data and exercise data of the user during exercise;

[0007] Determine the current exercise intensity of the user according to the heart rate data;

[0008] Determine the EPOC of the user at the current moment according to the current exercise intensity of the user and the exercise data, and predict the EPOC of the next moment;

[0009] Correct the predicted EPOC of the next moment according to the current exercise intensity of the user and the exercise data, and obtain the actual training load and training effect.

[0010] Optionally, the determining the current exercise intensity of the user according to the heart rate data includes:

[0011] Determine the current exercise intensity of the user according to formula (1);

[0012] The formula (1) is:

[0013] Exs_Intensity = (a1 * pHR^2 - a2 * pHR + a3) * 100 ……………(1)

[0014] Wherein, Exs_Intensity is the current exercise intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.

[0015] Optionally, determining the EPOC at the current moment of the user according to the current exercise intensity of the user and the exercise data includes:[[]]

[0016] Determining the EPOC at the current moment of the user according to the current exercise intensity, pace, EPOC at the previous moment of the user and formula (2);

[0017] The formula (2) is:[[]]

[0018] EPOC(t) = f(HR, Exs_Intensity, V, EPOC(t - 1)) …………(2)

[0019] Wherein, EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, and EPOC(t - 1) is the EPOC at the previous moment.

[0020] Optionally, predicting the EPOC at the next moment includes:[[]]

[0021] Predicting the EPOC at the next moment according to formula (3);

[0022] The formula (3) is:[[]]

[0023] EPOC(t + 1) = αEPOC(t) + (1 - α)EPOC(t - 1) …………(3)

[0024] Wherein, EPOC(t + 1) is the EPOC at the next moment, EPOC(t) is the EPOC at the current moment, and EPOC(t - 1) is the EPOC at the previous moment.

[0025] Optionally, the method further includes:[[]]

[0026] Determining the predicted training load and training effect according to the predicted EPOC at the next moment;

[0027] Comparing the predicted training load and training effect with the actual training load and training effect to obtain reference data for the next training.

[0028] Second aspect, an embodiment of the present invention provides a device for predicting and correcting the effect of sports training based on a wearable device, including:

[0029] An acquisition unit, configured to acquire heart rate data and exercise data of a user during exercise;

[0030] A processing unit, configured to determine the current exercise intensity of the user according to the heart rate data; determine the EPOC at the current moment of the user according to the current exercise intensity of the user and the exercise data, and predict the EPOC at the next moment; correct the predicted EPOC at the next moment according to the current exercise intensity of the user and the exercise data, and obtain the actual training load and training effect.

[0031] Optionally, the processing unit is specifically configured to:

[0032] Determine the current exercise intensity of the user according to formula (1);

[0033] The formula (1) is:

[0034] Exs_Intensity = (a1 * pHR^2 - a2 * pHR + a3) * 100 …………… (1)

[0035] Wherein, Exs_Intensity is the current exercise intensity, and pHR is the ratio of the average heart rate and the maximum heart rate of the user.

[0036] Optionally, the processing unit is specifically configured to:

[0037] Determine the EPOC at the current moment of the user according to the current exercise intensity of the user, the pace, the EPOC at the previous moment, and formula (2);

[0038] The formula (2) is:

[0039] EPOC(t) = f(HR, Exs_Intensity, V, EPOC(t - 1)) ………… (2)

[0040] Wherein, EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, and EPOC(t - 1) is the EPOC at the previous moment.

[0041] Optionally, the processing unit is specifically configured to:

[0042] Predict the EPOC at the next moment according to formula (3);

[0043] The formula (3) is:

[0044] EPOC(t + 1) = αEPOC(t) + (1 - α)EPOC(t - 1) ………… (3)

[0045] Wherein, EPOC(t + 1) is the EPOC at the next moment, EPOC(t) is the EPOC at the current moment, and EPOC(t - 1) is the EPOC at the previous moment.

[0046] Optionally, the processing unit is further configured to:

[0047] Determine the predicted training load and training effect according to the predicted EPOC at the next moment;

[0048] Compare the predicted training load and training effect with the actual training load and training effect to obtain reference data for the next training.

[0049] In a third aspect, an embodiment of the present invention further provides a computing device, including:

[0050] A memory for storing program instructions;

[0051] A processor for calling the program instructions stored in the memory and executing the above-mentioned method for predicting and correcting the exercise training effect based on a wearable device according to the obtained program.

[0052] In a fourth aspect, an embodiment of the present invention further provides a computer-readable non-volatile storage medium, including computer-readable instructions, which when read and executed by a computer, cause the computer to execute the above-mentioned method for predicting and correcting the exercise training effect based on a wearable device.

[0053] In the embodiment of the present invention, heart rate data and exercise data during the user's exercise are acquired; according to the heart rate data, the current exercise intensity of the user is determined; according to the current exercise intensity of the user and the exercise data, the EPOC at the current moment of the user is determined and the EPOC at the next moment is predicted; the predicted EPOC at the next moment is corrected according to the current exercise intensity of the user and the exercise data, and the actual training load and training effect are obtained. By collecting the heart rate during the user's exercise through a wearable device and determining the actual training load and training effect of the user based on the heart rate, the purpose of predicting and correcting the exercise training effect and recovery duration of the user through the wearable device can be achieved. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 A schematic diagram of a system architecture provided by an embodiment of the present invention;

[0056] Figure 2 A schematic flowchart of a method for predicting and correcting the effect of sports training based on a wearable device provided by an embodiment of the present invention;

[0057] Figure 3 A schematic structural diagram of a device for predicting and correcting the effect of sports training based on a wearable device provided by an embodiment of the present invention. Detailed implementation manners

[0058] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] First, take Figure 1 The wearable device applicable to the embodiment of the present invention is introduced by taking the shown structure as an example. In the embodiment of the present invention, the wearable device 100 may include, but is not limited to, components such as 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.

[0060] Among them, those skilled in the art can understand that Figure 1 The structure of the wearable device 100 shown is only an example and not a limitation. The wearable device 100 may further include more or fewer components than shown, or combine some components, or have different component arrangements.

[0061] The RF circuit 110 can be used for receiving and transmitting signals during information reception, transmission, or call processes. Specifically, after receiving the downlink information from the base station, it is processed by the processor 180. Additionally, the data uplinked by the wearable device 100 is transmitted to the base station. Generally, the RF circuit includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Moreover, the RF circuit 110 can also communicate with the network and other devices via wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the Global System for Mobile communication (abbreviated as "GSM"), General Packet Radio Service (abbreviated as "GPRS"), Code Division Multiple Access (abbreviated as "CDMA"), Wideband Code Division Multiple Access (abbreviated as "WCDMA"), Long Term Evolution (abbreviated as "LTE"), email, Short Messaging Service (abbreviated as "SMS"), etc.

[0062] Among them, the memory 120 can be used to store software programs and modules. The processor 180 executes various functional applications and data processing of the wearable device 100 by running the software programs and modules stored in the memory 120. The memory 120 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the wearable device 100 (such as audio data, a phone book, etc.). In addition, the memory 120 can include high-speed random access memory and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0063] The input unit 130 can be used to receive input numerical 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, a camera device 132, and other input devices 133. The camera device 132 can take pictures of the images to be acquired, transmit the images to the processor 180 for processing, and finally present the graphics to the user through the display panel 141. The touch panel 131, also known as a touch screen, can collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel 131), and drive the corresponding connection device according to a preset program. Optionally, the touch panel 131 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user and the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 180, and can receive and execute the commands sent by the processor 180. In addition, the touch panel 131 can be implemented in multiple types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 131 and the camera device 132, the input unit 130 may further include other input devices 133. Specifically, the other input device 132 may include, but is not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a joystick, etc.

[0064] Among them, the display unit 140 can be used to display information input by the 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. Optionally, the display panel 141 can be configured in forms such as a liquid crystal display unit (LCD, Liquid Crystal Display), an organic light-emitting diode (OLED, Organic Light-Emitting Diode), etc. Further, the touch panel 131 can cover the display panel 141. After the touch panel 131 detects a touch operation on or near it, it transmits the operation to the processor 180 to determine the type of the touch event. Subsequently, the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event.

[0065] Among them, the visual output outer display panel 141 that can be recognized by the human eye can be used as the display device in the embodiments of the present invention to display text information or image information. Although Figure 1In this case, the touch panel 131 and the display panel 141 are implemented as two independent components to achieve the input and output functions of the wearable device 100. However, in some embodiments, the touch panel 131 and the display panel 141 can be integrated to achieve the input and output functions of the wearable device 100.

[0066] In addition, the wearable device 100 may further include at least one sensor 150, such as an attitude sensor, a distance sensor, a light sensor, and other sensors.

[0067] Specifically, the attitude sensor can also be called a motion sensor. And, as one type of this motion sensor, an angular velocity sensor (also called a gyroscope) can be cited. When it is configured in the wearable device 100, it is used to measure the rotational angular velocity of the wearable device 100 in a deflected or tilted state during motion. Thus, the gyroscope can accurately analyze and determine the actual actions of the user using the wearable device 100, and then perform corresponding operations on the wearable device 100. For example: body sensing, shake (shaking the wearable device 100 to achieve some functions), and inertial navigation according to the motion state of the object when the Global Positioning System (GPS) has no signal (such as in a tunnel).

[0068] The sensor can also include a light sensor, which is mainly used to collect information such as the wavelength and intensity of various light rays, and to achieve functions such as adjusting the backlight intensity of the display panel 141.

[0069] In addition, in the embodiments of the present invention, as the sensor 150, other sensors such as a barometer, a hygrometer, a thermometer, and an infrared sensor can also be configured, which will not be elaborated here.

[0070] The light sensor may further include a proximity sensor, which can turn off the display panel 141 and / or the backlight when the wearable device 100 is moved to the ear.

[0071] The audio circuit 160, the speaker 161, and the microphone 162 can provide an audio interface between the user and the wearable device 100. The audio circuit 160 can transmit the electrical signal converted from the received audio data to the speaker 161, and the speaker 161 converts it into a sound signal for output; on the other hand, the microphone 162 converts the collected sound signal into an electrical signal, which is received by the audio circuit 160 and then converted into audio data. After the audio data is output and processed by the processor 180, it is sent via the RF circuit 110 to, for example, another wearable device 100, or the audio data is output to the memory 120 for further processing.

[0072] WiFi belongs to short - range wireless transmission technology. The wearable device 100 can help users send and receive emails, browse the web, and access streaming media through the WiFi module 170. It provides users with wireless broadband Internet access. Although Figure 1 the WiFi module 170 is shown, it can be understood that it does not belong to the essential components of the wearable device 100 and can be omitted entirely within the scope of not changing the essence of the invention as needed.

[0073] The processor 180 is the control center of the wearable device 100, connecting various parts of the entire wearable device 100 through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 120, and calling data stored in the memory 120, it executes various functions of the wearable device 100 and processes data, thereby monitoring the wearable device 100 as a whole. Optionally, the processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication.

[0074] It can be understood that the above - mentioned modem processor may not be integrated into the processor 180 either.

[0075] The wearable device 100 may also include at least one motor 190. Since the wearable device 100 is a power - consuming device, the motor 190 can be a small - sized electric motor. At the same time, multiple motors can be configured for the wearable device 100 according to the power that the motor can provide.

[0076] The wearable device 100 also includes a power supply (not shown in the figure) for powering each component.

[0077] Preferably, the power supply can be logically connected to the processor 180 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. Although not shown, the wearable device 100 may also include a Bluetooth module, etc., which will not be elaborated here.

[0078] It should be noted that the above Figure 1 shown structure is only an example, and the embodiments of the present invention do not limit this.

[0079] Figure 2 An exemplary flowchart of predicting and correcting the effect of sports training based on a wearable device provided by an embodiment of the present invention is shown. This process can be executed by a device for predicting and correcting the effect of sports training based on a wearable device.

[0080] As Figure 2 shown, this process specifically includes:

[0081] Step 201: Obtain the heart rate data and exercise data of the user during exercise.

[0082] In the embodiment of the present invention, the wearable device uses a three-axis acceleration sensor. Through the three-axis acceleration sensor, the acceleration data of the user can be collected. This three-axis acceleration sensor can also be called a behavior data sensor, and can also include a gyroscope, a geomagnetic sensor, etc. In addition, various other types of sensors can be provided in the wearable device. For example, the environmental sensor can collect environmental information, which generally includes a temperature sensor, a humidity sensor, a light sensor, etc., and can collect environmental information such as temperature, humidity, light, etc. The physiological signal sensor can collect the physiological data of the user, such as a heart rate sensor, a blood oxygen sensor, a blood pressure sensor, etc., and collect physiological data such as the heart rate data, blood oxygen data, and blood pressure data of the user.

[0083] The heart rate data is collected by the heart rate sensor, and the exercise data is collected by the acceleration sensor, the gyroscope, and the geomagnetic sensor.

[0084] Step 202: Determine the current exercise intensity of the user according to the heart rate data.

[0085] After obtaining the heart rate data, it can be used to determine the current exercise intensity of the user. Specifically, it can be determined by formula (1), and the formula (1) is:

[0086] Exs_Intensity=(a1*pHR^2 - a2*pHR + a3)*100……………(1)

[0087] Where, Exs_Intensity is the current exercise intensity, pHR is the ratio of the user's average heart rate and maximum heart rate, that is, pHR = HR_avg / HR_max, where HR_avg is the user's average heart rate and HR_max is the user's maximum heart rate, and a1, a2, and a3 are constants.

[0088] Step 203: Determine the EPOC (Excess Post-Exercise Oxygen Consumption) of the user at the current moment according to the current exercise intensity of the user and the exercise data, and predict the EPOC of the next moment.

[0089] Specifically, the EPOC of the user at the current moment can be determined according to the current exercise intensity, pace, EPOC of the previous moment of the user and formula (2).

[0090] The formula (2) is:

[0091] EPOC(t) = f(HR, Exs_Intensity, V, EPOC(t - 1)) …………(2)

[0092] Wherein, EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, EPOC(t - 1) is the EPOC at the previous moment, and f() is a non-linear function relationship fitted based on running training data.

[0093] Predict the EPOC at the next moment according to formula (3);

[0094] The said formula (3) is:[[]]

[0095] EPOC(t + 1) = αEPOC(t) + (1 - α)EPOC(t - 1) …………(3)

[0096] Wherein, EPOC(t + 1) is the EPOC at the next moment, EPOC(t) is the EPOC at the current moment, EPOC(t - 1) is the EPOC at the previous moment, and α is a parameter, i.e., a coefficient, fitted through the data of EPOC at the current moment and EPOC at the previous moment.

[0097] Step 204, correct the predicted EPOC at the next moment according to the user's current exercise intensity and the said exercise data, and obtain the actual training load and training effect.

[0098] After obtaining the predicted EPOC at the next moment, the predicted EPOC at the next moment can be corrected according to exercise information such as the current exercise intensity, pace, and maximum oxygen uptake. Then, the aerobic training load and anaerobic training load are obtained through the corrected EPOC at the next moment. Thus, the aerobic training effect ate and anaerobic training effect ante are obtained. When correcting the predicted EPOC at the next moment, the correction can be carried out according to the following formula (4):

[0099] EPOC’(t + 1) = f(EPOC(t + 1), Exs_Intensity, VO2, V) …………(4)

[0100] Wherein, EPOC’(t + 1) is the corrected EPOC at the next moment, EPOC(t + 1) is the predicted EPOC at the next moment, Exs_Intensity is the current exercise intensity, VO2 is the maximum oxygen uptake, and V is the pace.

[0101] The process of obtaining the aerobic training load, anaerobic training load, aerobic training effect, and anaerobic training effect through EPOC is the prior art, and the embodiments of the present invention do not describe it in detail.

[0102] In addition, based on the predicted EPOC at the next moment, the predicted training load and training effect can be determined; by comparing the predicted training load and training effect with the actual training load and training effect, reference data for the next training can be obtained. The reference data for the next training indicates how much training effect and load can be achieved with the same training intensity and time, so as to modify the exercise intensity and exercise time. By comparing the predicted training load and training effect with the actual training load and training effect, the user can know the gap in their training process, and can know whether there is insufficient or excessive aerobic training or anaerobic training, so that the user can correspondingly change the exercise intensity and exercise time during the next training.

[0103] The above embodiments show that the heart rate data and exercise data of the user during exercise are obtained; according to the heart rate data, the current exercise intensity of the user is determined; according to the current exercise intensity of the user and the exercise data, the EPOC at the current moment of the user is determined, and the EPOC at the next moment is predicted; according to the current exercise intensity of the user and the exercise data, the predicted EPOC at the next moment is corrected, and the actual training load and training effect are obtained. By collecting the heart rate of the user during exercise through a wearable device and determining the actual training load and training effect of the user based on the heart rate, the purpose of predicting and correcting the training effect and recovery duration of the user's exercise through the wearable device can be achieved.

[0104] Based on the same technical concept, Figure 3 Exemplarily, the structure of a motion training effect prediction and correction device provided by an embodiment of the present invention is shown. This device can execute the motion training effect prediction and correction process based on a wearable device.

[0105] As Figure 3 shown, the device may include:

[0106] An acquisition unit 301, configured to acquire heart rate data and exercise data of the user during exercise;

[0107] A processing unit 302, configured to determine the current exercise intensity of the user according to the heart rate data; determine the EPOC at the current moment of the user and predict the EPOC at the next moment according to the current exercise intensity of the user and the exercise data; correct the predicted EPOC at the next moment according to the current exercise intensity of the user and the exercise data, and obtain the actual training load and training effect.

[0108] Optionally, the processing unit 302 is specifically configured to:

[0109] Determine the current exercise intensity of the user according to formula (1);

[0110] The formula (1) is as follows:

[0111] Exs_Intensity=(a1*pHR^2 - a2*pHR + a3)*100……………(1)

[0112] Wherein, Exs_Intensity is the current exercise intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.

[0113] Optionally, the processing unit 302 is specifically configured to:

[0114] Determine the EPOC at the current moment of the user according to the current exercise intensity, pace, EPOC at the previous moment, and formula (2) of the user;

[0115] The formula (2) is as follows:

[0116] EPOC(t)=f(HR, Exs_Intensity, V, EPOC(t - 1))…………(2)

[0117] Wherein, EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, and EPOC(t - 1) is the EPOC at the previous moment.

[0118] Optionally, the processing unit 302 is specifically configured to:

[0119] Predict the EPOC at the next moment according to formula (3);

[0120] The formula (3) is as follows:

[0121] EPOC(t + 1)=αEPOC(t)+(1 - α)EPOC(t - 1)…………(3)

[0122] Wherein, EPOC(t + 1) is the EPOC at the next moment, EPOC(t) is the EPOC at the current moment, and EPOC(t - 1) is the EPOC at the previous moment.

[0123] Optionally, the processing unit 302 is further configured to:

[0124] Determine the predicted training load and training effect according to the predicted EPOC at the next moment;

[0125] Compare the predicted training load and training effect with the actual training load and training effect to obtain reference data for the next training.

[0126] Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:

[0127] A memory for storing program instructions;

[0128] A processor for calling the program instructions stored in the memory and executing the above-mentioned method for predicting and correcting the exercise training effect based on the wearable device according to the obtained program.

[0129] Based on the same technical concept, an embodiment of the present invention further provides a computer-readable non-volatile storage medium, including computer-readable instructions, which, when read and executed by a computer, cause the computer to execute the above-mentioned method for predicting and correcting the exercise training effect based on the wearable device.

[0130] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0131] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0133] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0134] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting and correcting the effect of sports training based on a wearable device, characterized in that including: Obtain the heart rate data and exercise data during the user's exercise; Determine the current exercise intensity of the user according to the heart rate data; Determine the EPOC at the current moment of the user according to the current exercise intensity of the user and the exercise data, and predict the EPOC at the next moment; Correct the predicted EPOC at the next moment according to the current exercise intensity of the user and the exercise data, and obtain the actual training load and training effect.

2. The method according to claim 1, wherein The determining the current exercise intensity of the user according to the heart rate data includes: Determine the current exercise intensity of the user according to formula (1); The formula (1) is: Exs_Intensity=(a1*pHR^2 - a2*pHR + a3)*100……………(1) where Exs_Intensity is the current exercise intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.

3. The method according to claim 1, characterized in that, The determining the EPOC at the current moment of the user according to the current exercise intensity of the user and the exercise data includes: Determine the EPOC at the current moment of the user according to the current exercise intensity of the user, the pace, the EPOC at the previous moment, and formula (2); The formula (2) is: EPOC(t)=f(HR,Exs_Intensity,V,EPOC(t - 1))…………(2) where EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, and EPOC(t - 1) is the EPOC at the previous moment.

4. The method according to claim 3, wherein The predicting the EPOC at the next moment includes: Predict the EPOC at the next moment according to formula (3); The formula (3) is: EPOC(t + 1)=αEPOC(t)+(1 - α)EPOC(t - 1)…………(3) where EPOC(t + 1) is the EPOC at the next moment, EPOC(t) is the EPOC at the current moment, and EPOC(t - 1) is the EPOC at the previous moment.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Determine the predicted training load and training effect according to the predicted EPOC at the next moment; Compare the predicted training load and training effect with the actual training load and training effect to obtain the reference data for the next training.

6. A motion training effect prediction and correction device based on a wearable device, characterized in that, including: An acquisition unit for acquiring the heart rate data and exercise data during the user's exercise; A processing unit for determining the current exercise intensity of the user according to the heart rate data; determining the EPOC at the current moment of the user according to the current exercise intensity of the user and the exercise data, and predicting the EPOC at the next moment; Correct the predicted EPOC at the next moment according to the current exercise intensity of the user and the exercise data, and obtain the actual training load and training effect.

7. The device according to claim 6, characterized in that, The processing unit is specifically used for: Determine the current exercise intensity of the user according to formula (1); The formula (1) is: Exs_Intensity=(a1*pHR^2 - a2*pHR + a3)*100……………(1) Among them, Exs_Intensity is the current exercise intensity, and pHR is the ratio of the average heart rate to the maximum heart rate of the user.

8. The device according to claim 6, wherein, The processing unit is specifically configured to: Determine the EPOC at the current moment of the user according to the current exercise intensity, pace, EPOC at the previous moment of the user, and formula (2); The formula (2) is: EPOC(t) = f(HR, Exs_Intensity, V, EPOC(t - 1))…………(2) Among them, EPOC(t) is the EPOC at the current moment, HR is the heart rate, Exs_Intensity is the current exercise intensity, V is the pace, and EPOC(t - 1) is the EPOC at the previous moment.

9. A computing device, characterized in that, It includes: A memory for storing program instructions; A processor for calling the program instructions stored in the memory and executing the method according to any one of claims 1 to 5 according to the obtained program.

10. A computer-readable non-volatile storage medium, characterized in that, It includes computer-readable instructions, and when a computer reads and executes the computer-readable instructions, it causes the computer to execute the method according to any one of claims 1 to 5.

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