Methods, apparatuses, media, and electronic devices for guiding action frequency

Abstract

The present disclosure provides a method, device, medium and electronic device for guiding action frequency. The method comprises: acquiring activity data detected in a movement process of a user, the activity data comprising physiological measurement data; determining variation information of at least one physiological parameter of the user in a first time period according to the physiological measurement data; and determining a first guidance frequency of the user according to the variation information of the at least one physiological parameter of the user in the first time period, the first guidance frequency comprising at least one of a guidance action frequency and a guidance beat frequency. The guidance frequency can be improved in pertinence to the user, and the movement training effect and safety of the user can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of motion guidance technology, and more particularly to a method, apparatus, medium, and electronic device for guiding the frequency of movements. Background Technology

[0002] In a range of sports activities such as cycling, walking, and running, movement frequency is a core element determining the effectiveness of training. Properly controlling movement frequency plays a crucial role in optimizing training results and improving an athlete's physical fitness and endurance. Conversely, an inappropriate movement frequency not only hinders the achievement of training objectives but may also lead to excessive fatigue and significantly increase the risk of sports injuries.

[0003] In related technologies, athletes are usually guided to move at a fixed recommended frequency during exercise training. This makes it difficult to meet the individual differences in their needs, resulting in training effects that are hard to achieve as expected. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, apparatus, medium, and electronic device for guiding movement frequency, which can improve the training effect of sports training.

[0005] According to a first aspect of the present disclosure, a method for guiding motion frequency is provided, the method comprising:

[0006] Acquire activity data detected during the user's exercise process, including physiological measurement data;

[0007] Based on the physiological measurement data, determine the change information of at least one physiological parameter of the user within a first time period;

[0008] Based on the changes in at least one physiological parameter of the user during a first time period, a first guidance frequency for the user is determined, wherein the first guidance frequency includes at least one of guidance action frequency and guidance beat frequency.

[0009] In some embodiments, the change information includes at least one of change trend, change amount, and change rate.

[0010] In some embodiments, determining the first guidance frequency of the user based on the change information of at least one physiological parameter of the user within a first time period includes: determining the first guidance frequency based on at least one of the change trend and change amount of the at least one physiological parameter within the first time period.

[0011] In some embodiments, a user’s reference action frequency can be determined, and a first guidance frequency can be determined based on the change information of at least one physiological parameter in a first time period and the user’s reference action frequency.

[0012] In some embodiments, the activity data further includes motion data.

[0013] In some embodiments, determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes:

[0014] Based on the user's movement data during the first time period and the change information of the at least one physiological parameter during the first time period, the user's first guidance frequency is determined.

[0015] In some embodiments, the user's current movement frequency can be determined based on the user's movement data within a first time period, and the user's first guidance frequency can be determined based on the user's current movement frequency and the change information of at least one physiological parameter within the first time period.

[0016] In some embodiments, the method further includes:

[0017] Based on the motion data included in the activity data, information on the change in the user's real-time action frequency within the first time period is obtained.

[0018] The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes:

[0019] The first guidance frequency of the user is determined based on the changes in the user's real-time action frequency during the first time period and the changes in at least one physiological parameter during the first time period.

[0020] In some embodiments, determining the user's first guidance frequency based on the change information of the action frequency during the first time period and the change information of the at least one physiological parameter during the first time period includes:

[0021] The user's first guidance frequency is determined by comparing the changes in the user's real-time action frequency and the changes in at least one physiological parameter during the first time period.

[0022] In some embodiments, the method further includes:

[0023] Obtain the initial guidance frequency of the user in the first time period;

[0024] The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes:

[0025] The first guidance frequency for the user is determined based on the initial guidance frequency and the changes in at least one physiological parameter during the first time period.

[0026] In some examples, a frequency adjustment strategy can be determined based on the changes in the at least one physiological parameter within a first time period, and the initial guidance frequency can be adjusted based on the frequency adjustment strategy to obtain a first guidance frequency.

[0027] In some examples, the frequency adjustment strategy may include increasing, keeping it unchanged, or decreasing.

[0028] Optionally, the adjustment step size is preset. Optionally, the adjustment step size is determined based on at least one of the following: user attribute information, the user group to which the user belongs, and the user's historical movement or activity data.

[0029] In some examples, the frequency adjustment strategy further includes adjusting the step size.

[0030] In some embodiments, the method further includes:

[0031] Obtain the user group to which the user belongs, including experienced users or novice users;

[0032] The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes:

[0033] Based on the user's user group and the change information of the at least one physiological parameter during the first time period, the first guidance frequency of the user is determined.

[0034] In some embodiments, the user's historical movement or activity data can be acquired, and a first guidance frequency can be determined based on the user's historical movement or activity data and the change information of the at least one physiological parameter within a first time period.

[0035] In some embodiments, user attribute data can be acquired, and a first guidance frequency can be determined based on the user attribute data and the change information of the at least one physiological parameter within a first time period.

[0036] In some embodiments, a first guidance frequency can be determined based on the user group to which the user belongs, the user's motion data during a first time period, and the change information of at least one physiological parameter during the first time period.

[0037] In some embodiments, a first guidance frequency can be determined based on the user's historical movement or activity data, the user's movement data within a first time period, and the change information of the at least one physiological parameter within the first time period.

[0038] In some embodiments, the method further includes:

[0039] Based on the first guidance frequency, action frequency guidance information is provided to the user.

[0040] In some embodiments, action frequency guidance information can be generated based on the first guidance frequency.

[0041] In some embodiments, providing the user with motion frequency guidance information based on the first guidance frequency includes:

[0042] According to the first guiding frequency, a beat tone is output through an audio output device; or

[0043] Modify the action frequency guidance information displayed on the display device according to the first guidance frequency; or

[0044] Based on the first guidance frequency, control at least one operating parameter of the flashlight of the third output device.

[0045] In some embodiments, a beat tone can be output through an audio output device based on the first guidance frequency, and the action frequency guidance frequency displayed on the display device can be modified.

[0046] In some embodiments, the output audio output device may output a beat tone according to the first guidance frequency, and control at least one operating parameter of the flashlight of the third output device.

[0047] In some embodiments, providing the user with motion frequency guidance information based on the first guidance frequency includes:

[0048] Obtain the difference between the user's current action frequency and the first guidance frequency;

[0049] Based on the differences, the user is provided with guidance information on the frequency of actions.

[0050] Optionally, if the difference is less than or equal to a preset difference threshold, the action frequency guidance information is used to instruct the user to adjust the action frequency to a first guidance frequency.

[0051] Optionally, if the difference is greater than a preset difference threshold, the action frequency guidance information is used to instruct the user to adjust the action frequency in the direction of the first guidance frequency.

[0052] Optionally, if the difference is greater than a preset difference threshold, the action frequency guidance information is used to instruct the user to adjust the action frequency to the guidance frequency corresponding to the preset difference threshold.

[0053] Optionally, if the difference is less than or equal to a preset difference threshold, the action frequency guidance information is used to instruct the user to adjust the action frequency to the first guidance frequency in the next first time period.

[0054] Optionally, if the difference is greater than a preset difference threshold, the action frequency guidance frequency is used to instruct the user to gradually adjust the action frequency to the first guidance frequency over the next few time periods.

[0055] In some embodiments, the method further includes:

[0056] Obtain information on the changes of at least one physiological parameter of the user during a second time period, which is after the first time period;

[0057] The second guidance frequency for the user is determined based on the changes in the at least one physiological parameter during the second time period and the first guidance frequency.

[0058] According to a second aspect of the present disclosure, another method for guiding motion frequency is provided, the method comprising:

[0059] Obtain the initial guidance frequency from users;

[0060] Acquire the user's real-time activity data, which includes real-time physiological measurement data;

[0061] Based on the user's real-time physiological measurement data, the initial guidance frequency is adjusted to obtain an updated guidance frequency.

[0062] In some embodiments, obtaining the user's initial guidance frequency includes:

[0063] The system acquires at least one of the user's historical movement or activity data, the user's attribute data, and the user group to which the user belongs, wherein the user group includes novice users or experienced users.

[0064] The initial guidance frequency is determined based on at least one of the historical movement or activity data, the user's attribute data, and the user group to which the user belongs.

[0065] In some embodiments, the real-time activity data may further include real-time motion data.

[0066] In some embodiments, adjusting the initial guidance frequency based on the user's real-time physiological measurement data to obtain an updated guidance frequency includes:

[0067] Obtain at least one of the user group to which the user belongs and the real-time motion data included in the real-time activity data;

[0068] The initial guidance frequency is adjusted based on the user's user group, at least one of the real-time motion data, and the real-time physiological measurement data to obtain an updated guidance frequency.

[0069] In some embodiments, the initial guidance frequency can be adjusted in any possible implementation of the first aspect to obtain an updated guidance frequency.

[0070] In some embodiments, the initial guidance frequency is the initial guidance frequency in any possible implementation of the first aspect, and the updated guidance frequency is the first guidance frequency in any possible implementation of the first aspect.

[0071] In some embodiments, the initial guidance frequency is a first guidance frequency in any possible implementation of the first aspect, and the updated guidance frequency is a second guidance frequency in any possible implementation of the first aspect.

[0072] According to a third aspect of the present disclosure, another method for guiding motion frequency is provided, the method comprising:

[0073] Acquire activity data detected during user exercise, wherein the activity data includes at least one of physiological measurement data and exercise data;

[0074] Based on the activity data, determine the frequency of the user's guided actions;

[0075] Based on the frequency of the guided actions, determine at least one operating parameter of the flashlight.

[0076] In some embodiments, the flashlight may include at least one light-emitting element, and at least one of the operating parameters of the flashlight may include at least one of the following: light emission wavelength, light emission intensity, light emission mode, and selectively illuminated light-emitting elements.

[0077] In some embodiments, the method further includes: controlling the flashlight to emit a light signal based on at least one operating parameter of the flashlight.

[0078] According to a fourth aspect of the present disclosure, an apparatus for guiding the frequency of movements is provided, the apparatus comprising:

[0079] At least one module or unit is used to perform the method provided by any of the above aspects for guiding the frequency of actions.

[0080] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for guiding the frequency of actions provided in any of the above aspects.

[0081] According to a sixth aspect of the present disclosure, an electronic device is provided, the electronic device comprising:

[0082] Processor; memory for storing instructions executable by the processor;

[0083] The processor is configured to execute the method provided by any of the above aspects for guiding the frequency of actions.

[0084] The technical solutions provided in this disclosure may have the following beneficial effects:

[0085] By detecting the user's activity data during exercise, the changes in the user's physiological parameters can be determined based on the physiological measurement data in the activity data. Then, based on the changes in physiological parameters, the user's first guidance frequency can be determined, and the user's movement frequency can be guided according to the first guidance frequency. This can ensure that the user's guidance frequency is consistent with the individual's condition, improve the scientific nature of the exercise training, and thus improve the exercise effect and safety of the exerciser. Attached Figure Description

[0086] Figure 1 This is a schematic flowchart illustrating an example of a method for guiding the frequency of actions provided in some embodiments of this disclosure;

[0087] Figure 2 This is an example of a user interface provided in some embodiments of this disclosure;

[0088] Figure 3 This is an example of a user interface provided in some embodiments of this disclosure;

[0089] Figure 4 This is an example of a user interface provided in some embodiments of this disclosure;

[0090] Figure 5 This is a schematic flowchart illustrating an example of a method for determining a first guidance frequency provided in some embodiments of this disclosure;

[0091] Figure 6 This is a schematic flowchart illustrating an example of a method for determining a first guidance frequency provided in some embodiments of this disclosure;

[0092] Figure 7 This is a schematic flowchart illustrating an example of a method for determining a first guidance frequency provided in some embodiments of this disclosure;

[0093] Figure 8This is a schematic flowchart illustrating an example of a method for determining a first guidance rate provided in some embodiments of this disclosure;

[0094] Figure 9 This is a schematic flowchart illustrating an example of a method for determining a first guidance rate provided in some embodiments of this disclosure;

[0095] Figure 10 This is a schematic flowchart illustrating an example of a method for determining a first guidance frequency provided in some embodiments of this disclosure;

[0096] Figure 11 This is a schematic diagram illustrating user interface examples provided in some embodiments of this disclosure;

[0097] Figure 12 This is a schematic flowchart illustrating the provision of motion frequency guidance information to a user, provided in some embodiments of this disclosure;

[0098] Figure 13 This is a schematic diagram of the playback volume of motion audio provided in some embodiments of this disclosure;

[0099] Figure 14 This is a block diagram of a device for guiding the frequency of actions provided in some embodiments of this disclosure;

[0100] Figure 15 This is a schematic flowchart illustrating an example of a method for guiding the frequency of actions provided in some embodiments of this disclosure;

[0101] Figure 16 This is a block diagram of a device for guiding the frequency of actions provided in some embodiments of this disclosure;

[0102] Figure 17 This is a schematic diagram of the structure of an electronic device provided in some embodiments of this disclosure. Detailed Implementation

[0103] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0104] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0105] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0106] This disclosure provides an action frequency guidance system, which may include one or more wearable devices, a server, and an intermediate device.

[0107] A wearable device is a wearable computing device configured to be worn by a human user during operation. Wearable devices can be implemented as watches, wristbands, bracelets, rings, armbands, legbands, anklebands, headphones, smart glasses, head-mounted devices, or other types of forms. Wearable devices include a set of sensors, which may include one or more sensors for detecting data from the user wearing the wearable device. In some examples, the sensor set may include one or more motion sensors that can be used to measure the user's motion data. For example, the sensor set may include one or more of an accelerometer, gyroscope, magnetic field sensor, velocity sensor, displacement sensor, etc., or any combination thereof. Motion data may include measurable motion parameters related to the movement of the user wearing the wearable device. For example, motion data may include one or more of acceleration, velocity, displacement, Euler angles, etc., or any combination thereof. Motion sensors may continuously or otherwise intermittently or periodically collect the user's motion data. In other examples, the sensor set may include one or more physiological sensors for measuring one or more physiological parameters of the user, such as heart rate, body temperature, mood, psychological stress, blood oxygen, blood pressure, blood glucose, and other blood analyte indicators. In still other examples, the sensor set may include one or more environmental sensors for measuring environmental data of the user's environment. For example, location, indoor or outdoor status, ambient temperature, ambient humidity, air quality, air pressure, altitude, etc., or any combination thereof. The sensor set may also include other types of sensors, which are not listed here.

[0108] In some examples, wearable devices also include a processor and memory. The memory is used to store applications or other executable instructions; in some cases, it may also store data collected by the sensor array. The processor is used to run the applications or other executable instructions to process the data collected by the sensor array.

[0109] In some examples, wearable devices may also include input and / or output components, such as one or more of a display, an audio output component, a haptic output component, or other types of output components. The display may include a touch sensor, i.e., the display is a touch display, but this is not a limitation.

[0110] A server may include a hardware server, a software server (e.g., a web server), and / or a virtual server. A database and server programs may be configured on the server. The server programs can access the database to perform at least some of their functions. The database may be used to store, manage, or otherwise provide data for delivering the functionality of the server programs. The database may be a relational database management system, an object database, an XML database, a configuration management database, a management information base, one or more flat files, other suitable non-transitory storage mechanisms, or any combination thereof.

[0111] Intermediate devices can be used to facilitate communication between wearable devices and servers. Intermediate devices can be computing devices, such as mobile terminals (e.g., smartphones, tablets, laptops, or other mobile devices) or fixed terminals (e.g., desktop computers or other non-mobile computers). Alternatively, intermediate devices can be network hardware, such as routers, switches, load balancers, etc., or any combination thereof. Alternatively, intermediate devices can be chargers for wearable devices.

[0112] Depending on the specific implementation of the intermediate device, it can run applications. These applications configure the intermediate device to send or receive data from the wearable device, and / or send or receive data from a server. Additionally, the application can receive commands from the intermediate device in response to user actions. For example, if the intermediate device is a computing device with a touchscreen display, the user can receive commands through portions of the touchscreen display corresponding to user interface elements.

[0113] In some implementations, the intermediate device can be a client device with access to the server program. The intermediate device can run a client application to communicate with the server program. For example, the client application could be a mobile application capable of accessing some or all of the server program's functionality and / or data.

[0114] In some implementations, intermediate devices can communicate with the server via a network. This network could be a local area network (LAN), a wide area network (WAN), a machine-to-machine network, a virtual private network (VPN), or another public or private network. The network can use remote communication protocols. For example, remote communication protocols could be Ethernet, TCP, IP, power line communication, Wi-Fi, GPRS, GSM, CDMA, other protocols, or combinations thereof.

[0115] In some implementations, the intermediate device can communicate with the wearable device using either a wired connection or a wireless communication protocol. For example, the intermediate device can use... Short-range communication protocols such as low-energy, infrared, Z-wave, or ZigBee are used to exchange data with wearable devices.

[0116] In some implementations, one or more devices may be omitted. In some examples, intermediate devices may be omitted, and the wearable device may be configured to communicate directly with the server via a network. In other examples, the wearable device may be omitted, and the intermediate device may include a set of sensors. This application does not limit the specific implementation of the motion frequency guidance system.

[0117] In embodiments of this disclosure, the method for guiding the action frequency can be executed by the action frequency guidance system. For example, the method for guiding the action frequency can be executed by any one of a wearable device, an intermediate device, or a server. Alternatively, the method for guiding the action frequency can be executed collaboratively by at least two of the wearable device, intermediate device, and server.

[0118] For example, a wearable device acquires activity data (e.g., physiological measurement data and motion data) detected by a set of sensors during a user's movement and sends this activity data to an intermediate device. The intermediate device analyzes and processes the activity data to obtain an intermediate result, which is then sent to a server. Based on this intermediate result, the server determines the user's guidance frequency and then sends this guidance frequency to at least one of the intermediate device and the wearable device, enabling the intermediate device and / or the wearable device to provide frequency guidance to the user according to that guidance frequency. As another example, a wearable device acquires activity data detected by a set of sensors during a user's movement and then sends this activity data to an intermediate device or a server. The intermediate device or server analyzes and processes the activity data to obtain a guidance frequency and sends this guidance frequency to the wearable device, enabling the wearable device to provide frequency guidance to the user according to that guidance frequency. For example, the processor of a wearable device acquires activity data detected by a set of sensors during the user's movement, analyzes and processes the activity data to obtain the user's guidance frequency, and provides frequency guidance to the user based on the guidance frequency. Alternatively, the wearable device sends the guidance frequency to an intermediate device or another wearable device so that the intermediate device or another wearable device can provide frequency guidance to the user based on the guidance frequency.

[0119] It should be noted that the foregoing description is merely illustrative and is intended to enable those skilled in the art to better understand the technical solutions of the embodiments of this disclosure. This disclosure does not impose specific restrictions on the executing entities of each operation and / or step of the method for guiding the frequency of actions, and for ease of understanding, they are collectively referred to as electronic devices.

[0120] Figure 1 This is a schematic flowchart illustrating an example of a method for guiding motion frequency provided in some embodiments of this disclosure. See also Figure 1 The method includes steps S101 to S103.

[0121] S101, acquire activity data detected during the user's exercise, the activity data including physiological measurement data.

[0122] The activity data may include physiological measurement data. In some examples, physiological measurement data may include one or any combination of pulse wave data, heart rate data, blood oxygen data, skin conductance data, electromyography data, respiration data, electrocardiogram data, blood pressure data, blood glucose data, body temperature data, etc. The physiological measurement data may include raw data output from physiological sensors (referred to as raw physiological sensor data), or intermediate data obtained by processing the raw physiological sensor data through one or more processes, or at least one physiological parameter sequence obtained by processing the raw physiological sensor data, etc. Each physiological parameter sequence corresponds to a physiological parameter and includes multiple values ​​of that physiological parameter (e.g., values ​​at multiple sampling points, or values ​​at multiple times), which is not limited here. In some embodiments, the activity data may further include motion data, which may include at least one of accelerometer data, gyroscope data, and geomagnetic sensor data, or processed data obtained based on at least one of the above motion sensor data, such as one or any combination of motion distance, motion time, motion type, and movement frequency. In other embodiments, the activity data may also include other types of data, which are not limited here.

[0123] In some examples, wearable devices can acquire user activity data when worn by a user, for example, by acquiring physiological measurement data through at least one physiological sensor in a sensor set, or by acquiring motion data through at least one motion sensor in a sensor set. Alternatively, an intermediate device can acquire user activity data through its own sensor set when attached to or worn by a user. Or, an intermediate device or server can acquire user activity data from a wearable device. The method by which electronic devices acquire user activity data is not limited here.

[0124] In some examples, electronic devices can determine a user's activity state based on sensor data collected from their own or other devices' sensor sets, or based on user commands, and acquire the user's activity data when it is determined that the user is in motion. In other examples, electronic devices can determine the user's movement type based on sensor data collected from their sensor sets or user commands, and acquire the user's activity data when it is determined that the user is engaged in a specific movement type. This specific movement type can be preset, such as periodic movements (or repetitive movements) like walking, running, cycling, or swimming, or it can be pre-set by the user; the specific implementation of the specific movement type is not limited here. In still other examples, electronic devices can acquire user activity data when they detect that one or more of the user's movement parameters and / or physiological parameters meet specific conditions. For example, the electronic device acquires the user's activity data in response to determining that the user's movement speed remains stable. As another example, the electronic device acquires the user's activity data in response to the user's movement distance or time reaching a certain threshold. In other examples, electronic devices may acquire user activity data in response to determining that they or connected wearable or intermediate devices have entered a specific mode (such as frequency training mode, exercise guidance mode, exercise mode, or a specific type of exercise mode, etc.).

[0125] S102, Based on the physiological measurement data, determine the change information of at least one physiological parameter of the user within a first time period.

[0126] The physiological measurement data may include measurement data of one or more physiological parameters (such as heart rate, blood oxygen, blood pressure, body temperature, respiratory rate, etc.). The acquired physiological measurement data can be processed to obtain information on the changes of at least one physiological parameter within a first time period.

[0127] In some embodiments, the at least one physiological parameter may be preset, for example, the at least one physiological parameter may be predetermined based on knowledge of exercise physiology.

[0128] In other embodiments, the at least one physiological parameter may be determined based on the current type of exercise. For example, for running, the at least one physiological parameter may include at least one of heart rate (such as instantaneous heart rate, average heart rate, maximum heart rate, etc.) and blood oxygen saturation. For cycling, the at least one physiological parameter may include at least one of body temperature (such as core temperature or skin temperature) and blood oxygen saturation. For swimming, the at least one physiological parameter may include at least one of respiratory parameters (such as respiratory rate, respiratory rhythm, respiratory depth, expiratory time, inspiratory time, etc., or any combination thereof) and body temperature.

[0129] In other embodiments, the at least one physiological parameter can be determined based on the current mode. For example, in power-saving mode, to reduce device power consumption, monitoring of changes in some physiological parameters can be stopped. In this case, the corresponding at least one physiological parameter may be a subset of the at least one physiological parameter corresponding to the normal mode.

[0130] In other embodiments, the at least one physiological parameter can be determined based on user information, such as user attribute data. As an example, the user's current health status and exercise level can be determined based on user information, and the at least one physiological parameter can be determined based on this. As another example, the user information includes the user's historical exercise or activity data, and the at least one physiological parameter can be determined based on this data. For example, the user's historical exercise or activity data can be used to determine the characteristics of physiological parameter changes during exercise, and accordingly determine the at least one physiological parameter that needs to be monitored. As yet another example, the at least one physiological parameter can be determined based on user focus information. For example, if the user is focused on avoiding exercise injury, the at least one physiological parameter may include at least one of heart rate, respiratory parameters, blood oxygen saturation, blood pressure, and lactate level. As another example, if the user is focused on avoiding excessive heart rate, the at least one physiological parameter may include heart rate. As yet another example, if the user is focused on the effect of excessive sweating, the at least one physiological parameter may include body temperature. As yet another example, the at least one physiological parameter can be determined based on the user's expected training goals. For example, if a user's training goal is weight loss, the at least one physiological parameter could include heart rate. As another example, if a user's training goal is to increase exercise power, the at least one physiological parameter could include at least one of heart rate and respiratory parameters.

[0131] The length of the first time period can be preset, determined based on the user-set training time (or exercise time), or determined based on the user's historical exercise or activity data; no limitation is made here. In some examples, changes in physiological parameters can be monitored periodically. This monitoring period can be based on a fixed time, such as 5 minutes, 10 minutes, etc., or on a fixed distance, such as 50 meters, 200 meters, etc. In this case, the first time period can be any detection period; no limitation is made here regarding the specific implementation of the first time period.

[0132] In some embodiments, the information on changes in physiological parameters within a first time period can represent the changes in the physiological parameters within that time period, such as one or any combination of the following: trend of change, amount of change, proportion of change, rate of change, range of change, etc. For example, taking heart rate as an example, the trend of heart rate changes within the first time period can be determined based on physiological measurement data. Alternatively, the amount or proportion of heart rate changes within the first time period can be determined based on physiological measurement data. Or, the trend of heart rate changes and the amount or proportion of heart rate changes within the first time period can be determined based on physiological measurement data, and so on.

[0133] The change information of at least one physiological parameter of a user within a first time period can be determined in various ways. In some embodiments, the amount of change of at least one physiological parameter within the first time period can be determined. In some examples, the change information of a physiological parameter within the first time period can be determined based on the value of each physiological parameter at the beginning and end of the first time period. For example, the change information of a physiological parameter within the first time period includes the difference, ratio, or magnitude relationship between the value of the physiological parameter at the end of the first time period and the value at the beginning of the first time period, or the ratio of the difference to the length of the first time period, or any combination thereof. In other examples, the change information of a physiological parameter within the first time period can be determined based on the values ​​of a physiological parameter at the first few moments and the last few moments of the first time period. For example, the average value of the physiological parameter at the first few moments and the average value at the last few moments of the first time period can be calculated, and the change information of the physiological parameter within the first time period can be obtained based on the calculated average value. In still other examples, the change information of a physiological parameter within the first time period can be determined based on the time change curve of a physiological parameter within the first time period. For example, information on changes in physiological parameters includes the slope of the time-varying curve of the physiological parameter (such as the average slope, maximum slope, etc.), or the difference between the maximum and minimum values ​​of the time-varying curve, or the difference between the maximum or minimum value of the time-varying curve and the initial value, etc.

[0134] S103, determine the first guidance frequency for the user based on the change information of at least one physiological parameter of the user within a first time period.

[0135] The first guidance frequency is used to guide the user's movement frequency during a second time period following a first time period. The first guidance frequency includes at least one of a guidance movement frequency and a guidance beat frequency. The guidance movement frequency can be a recommended movement frequency for the user to perform repetitive (or periodic) movements during movement. The guidance beat frequency can be a beat frequency used to guide the user to perform repetitive movements at a specific rhythm during movement. For example, during user movement, the user can be guided to perform repetitive movements at the same movement frequency by outputting beat sounds at a specific frequency, or the user can be guided to adjust their movement frequency according to the changing trend of the beat frequency by outputting beat sounds at a dynamically changing frequency (e.g., increasingly faster or slower).

[0136] In this embodiment of the disclosure, by detecting the user's physiological measurement data during exercise, and determining the change information of at least one physiological parameter of the user based on the physiological measurement data, and then determining the user's first guidance frequency based on the change information of at least one physiological parameter within a first time period, the frequency guidance matches the user's physiological condition during exercise, which is beneficial to improving the scientific nature and training effect of the user's exercise training, and reducing the risk of sports injury.

[0137] In some embodiments, a frequency guidance model can be pre-set, and information on changes in one or more physiological parameters of the user can be input into the frequency guidance model to determine the user's first guidance frequency. This frequency guidance model can be based on architectures such as machine learning, neural networks, and reinforcement learning, or generated based on specific algorithms. The frequency guidance model can be universally applicable to all users, or generated or updated based on user information (i.e., personalized for the user), or generated or updated based on the user's user group (e.g., novice user group or experienced user group) (i.e., customized for a specific user group). In some examples, the frequency guidance model may also depend on a specific type of exercise; this is not limited here.

[0138] In other embodiments, a mapping relationship or rule between the change information of one or more physiological parameters and the guidance frequency can be set. In this case, the user's first guidance frequency can be determined based on the mapping relationship or rule and the change information of at least one physiological parameter in the first time period.

[0139] In some embodiments, the physiological parameter change information includes at least one of the physiological parameter change trend, change amount, and change rate. Accordingly, in S103, the first guidance frequency can be determined based on at least one of the physiological parameter change trend, change amount, and change rate during the first time period. The following will describe different scenarios in detail.

[0140] In the first scenario, the first guidance frequency can be determined based on the changing trend of at least one physiological parameter within a first time period.

[0141] In some embodiments, a first correspondence can be established between the changing trends of one or more physiological parameters and the guidance frequency. Based on this first correspondence and the changing trend of the physiological parameter within a first time period, a first guidance frequency is determined. For example, in response to an increasing trend in the changing trend of a certain physiological parameter within the first time period, the first guidance frequency is determined to be a first preset value. Conversely, in response to a decreasing or unchanged trend in the changing trend of the physiological parameter within the first time period (e.g., the change in the physiological parameter is within a certain threshold range), the first guidance frequency is determined to be a second preset value. The first and second preset values ​​can be fixed values, or they can be determined based on one or any combination of user attribute data, the user's user group, the user's historical activity or exercise data, user settings, etc. The magnitude relationship between the first and second preset values ​​is not limited here. For example, if the physiological parameter includes heart rate, the second preset value can be greater than the first preset value. As another example, if the physiological parameter includes blood oxygen saturation, the second preset value can be less than the first preset value. As another example, in response to the first physiological parameter changing trend being the first trend within the first time period, and the second physiological parameter changing trend being the second trend within the first time period, the first guidance frequency is determined to be the third preset value, and so on. The possible implementation methods will not be listed one by one here.

[0142] In other embodiments, a second correspondence can be established between the changing trends of one or more physiological parameters and preset step lengths. Based on this second correspondence and the changing trend of the at least one physiological parameter within a first time period, a corresponding preset step length is determined from multiple preset step lengths, and a first guidance frequency is determined based on this corresponding preset step length. As an example, the first guidance frequency can be determined based on the corresponding preset step length and the user's reference action frequency. For example, the user's reference action frequency can be adjusted based on the corresponding preset step length to obtain the first guidance frequency. For example, the sum / difference between the user's reference action frequency and the corresponding preset step length can be used as the first guidance frequency. As another example, the first guidance frequency can be determined based on the corresponding preset step length and the user's current action frequency. The user's current action frequency can be the user's average action frequency, highest action frequency, or other statistical results within the first time period, or the user's instantaneous action frequency at the end of the first time period, or the user's average action frequency in the last few moments of the first time period, etc. The implementation method of the user's current action frequency is not limited here. For example, the sum / difference between the corresponding preset step length and the user's current action frequency can be used as the first guidance frequency. As another example, the first guidance frequency can be determined based on the corresponding preset step size and the user's initial guidance frequency in the first time period. For instance, the initial guidance frequency can be adjusted based on the corresponding preset step size to obtain the first guidance frequency.

[0143] In the second scenario, the first guidance frequency can be determined based on the change in at least one physiological parameter within the first time period.

[0144] In some embodiments, the first guidance frequency for the user can be determined by comparing the change in at least one physiological parameter within a first time period with at least one preset threshold. For example, if the change in a physiological parameter within the first time period is greater than the first preset threshold, the first guidance frequency is determined to be a fourth preset value; if the change in the physiological parameter within the first time period is less than or equal to the first preset threshold, the first guidance frequency is determined to be a fifth preset value. As another example, two or more preset thresholds can be set for a physiological parameter: a first preset threshold and a second preset threshold, where the first preset threshold is greater than the second preset threshold. In this case, as an example, if the change in the physiological parameter within the first time period is greater than the first preset threshold, the first guidance frequency is determined to be a fourth preset value; if the change in the physiological parameter within the first time period is less than the second preset threshold, the first guidance frequency is determined to be a fifth preset value. As yet another example, if the change in the physiological parameter within the first time period is between the first and second preset thresholds, the first guidance frequency is determined to be a sixth preset value. The fourth, fifth, and sixth preset values ​​in the above examples can be fixed values, or determined based on one or any combination of user attribute data, the user's user group, the user's historical movement or activity data, user settings information, etc. There are no restrictions on the magnitude relationship between the fourth, fifth, and sixth preset values. For example, if the physiological parameter includes heart rate or blood oxygen saturation, the fourth preset value can be less than the fifth preset value, and the fifth preset value can be greater than or equal to the sixth preset value; no restrictions are imposed here. As another example, the changes in multiple physiological parameters within a first time period can be compared with their respective preset thresholds to determine the first guidance frequency.

[0145] In other embodiments, a corresponding step size can be determined based on the change of the at least one physiological parameter within a first time period, and a first guidance frequency can be determined based on the corresponding step size. For example, the corresponding step size can be determined from multiple preset step sizes based on the range of change of a certain physiological parameter within the first time period and the correspondence between a preset range and a preset step size. Another example is determining the corresponding step size based on the difference between the change of a certain physiological parameter within the first time period and a preset threshold. The implementation method for determining the corresponding step size is not limited here. After determining the corresponding step size, the implementation method for determining the first guidance frequency based on the corresponding step size can refer to the embodiments described above; for simplicity, it will not be repeated here.

[0146] In other embodiments, a preset algorithm can be used to process the changes in at least one physiological parameter within a first time period to obtain a first guidance frequency. As an example, the changes in at least one physiological parameter within the first time period can be weighted to obtain the first guidance frequency. For instance, the difference between the change in heart rate and a preset heart rate change threshold, and the difference between the change in respiratory rate and a preset respiratory rate change threshold can be weighted to obtain the first guidance frequency.

[0147] In other embodiments, the change in at least one physiological parameter within a first time period can be input into a frequency guidance model for processing to obtain a first guidance frequency.

[0148] In the third scenario, the first guidance frequency can be determined based on the trend and magnitude of change of at least one physiological parameter within a first time period. In this case, the first guidance frequency can be determined by combining various possible implementations of the first and second scenarios described above. For example, if the user's heart rate trend is an increase in heart rate and the magnitude of the change is greater than a preset heart rate change threshold, then the first guidance frequency is determined as a reference action frequency reduced by a specific step size. As another example, if the user's heart rate trend is an increase in heart rate and the magnitude of the change is less than or equal to a preset heart rate change threshold, then the first guidance frequency is determined as the user's current action frequency. Yet another example, if the user's heart rate trend is a constant heart rate, then the first guidance frequency is determined as a reference action frequency. The specific implementation is not limited here.

[0149] In some embodiments, the first guidance frequency can be determined by combining information on the changes of at least one physiological parameter within a first time period and other user information. This other information can be of various types. In some examples, it may include at least one of the following: the user's reference movement frequency, the user's current movement frequency, the user's initial guidance frequency within the first time period, the user's movement data within the first time period, information on the changes in the user's real-time movement frequency within the first time period, the user's attribute data, the user group to which the user belongs, the user's historical movement or activity data, and the user's training objective. This disclosure does not limit this; the following is merely an illustrative description.

[0150] In some embodiments, such as Figure 2 As shown, in S201, the user's reference action frequency is acquired. In S202, a first guidance frequency is determined based on the change information of at least one physiological parameter during the first time period and the user's reference action frequency.

[0151] The user's reference movement frequency can be determined in several ways. In some examples, the user's reference movement frequency can be a preset value, such as a value predetermined based on exercise physiology. In other examples, the user's reference movement frequency can be determined based on one or any combination of the user's attribute data, the user group to which the user belongs, the user's historical movement or activity data, the user's training goals, etc.

[0152] As an example, for a specific type of exercise, different reference movement frequencies can be set for different user groups, and the user's reference movement frequency can be determined based on the user group to which the user belongs. For example, for running, if the user belongs to the beginner user group, the reference movement frequency (i.e., reference cadence) is 180 times / minute, while if the user belongs to the experienced user group, the reference movement frequency is 185 times / minute.

[0153] As another example, historical movement frequency data of a user can be obtained from their historical movement or activity data, and a reference movement frequency can be determined based on this data. For example, the user's highest historical movement frequency or average historical movement frequency can be determined based on this data, and this highest historical movement frequency or average historical movement frequency can be used as the user's reference movement frequency. Alternatively, the user's reference movement frequency can be obtained by performing one or more calculations on the highest historical movement frequency or average historical movement frequency. Alternatively, the user's reference movement frequency can be determined based on the user's historical movement frequency data and a preset frequency threshold. This preset frequency threshold can be determined based on knowledge of exercise physiology or based on at least one of the user's attribute data, the user's user group, and the type of exercise. For example, if the user's historical average movement frequency is lower than or equal to the preset frequency threshold, the preset frequency threshold can be determined as the reference movement frequency for this exercise. As shown in Figure 3, taking running as an example, the preset frequency threshold is 180 steps / minute. If the user's historical average step frequency is 165 steps / minute, the user's reference movement frequency can be set to 180 steps / minute. As another example, if the user's historical average movement frequency is higher than the preset frequency threshold, then that historical average movement frequency can be determined as the reference movement frequency. Figure 4 As shown, if a user's historical average step frequency is 182 steps / minute, then a certain value can be added to this historical average step frequency, for example, the reference action frequency can be set to 186 steps / minute.

[0154] As another example, historical physiological measurement data detected synchronously with historical action frequency data can be obtained from the user's historical activity data. Based on this historical action frequency data and historical physiological measurement data, the user's reference action frequency can be determined. Optionally, the historical action frequency data can be filtered or processed based on the historical physiological measurement data. For example, action frequency data with at least one corresponding physiological parameter within a preset parameter range can be filtered from the historical action frequency data, and the reference action frequency can be obtained based on the filtered action frequency data. Optionally, the reference action frequency can be obtained by weighting multiple action frequencies included in the historical action frequency data or multiple action frequencies obtained by calculating the historical action frequency data (e.g., the highest multiple historical action frequencies) based on the historical physiological measurement data, such as by weighted averaging or weighted filtering.

[0155] As another example, the reference movement frequency (PRF) for a user can be determined based on their training goals. For instance, if the user's training goal is weight loss or health maintenance, the PRF would be set to a relatively low value. Conversely, if the user's training goal is improvement or competition, the PRF would be set to a relatively high value. The specific method for determining the user's PRF is not limited here.

[0156] In some embodiments, the user's reference action frequency can also be determined based on user input information. For example, the determined reference action frequency can be adjusted or otherwise processed using the user input information, or the user-input reference action frequency can be used as the user's reference action frequency. Alternatively, before determining the reference action frequency, the user's subjective intention regarding the reference action frequency can be solicited. In some examples, such as... Figure 3 As shown, the system can ask the user whether they agree to have a reference action frequency determined for them. In other examples, the system can ask the user to input a reference action frequency and determine the reference action frequency if the user refuses to input one. In other examples, such as... Figure 4 As shown, you can ask the user to confirm whether they want to further increase the frequency of actions. Etc., this is not limited here.

[0157] In some embodiments, after determining the reference action frequency, the user can be asked to confirm the determined reference action frequency. For example, the determined reference action frequency can be output to the user via an output device, and the user can be prompted whether they agree. If the user issues an instruction to agree (i.e., confirm the determined reference action frequency), the determined reference action frequency can be used as the user's reference action frequency. If the user issues an instruction to disagree (i.e., reject the determined reference action frequency), the user can be prompted to input a desired reference action frequency, and the user-input reference action frequency can be used as the user's reference action frequency. In other embodiments, such as... Figure 3 As shown, the user can also be asked to input the final reference action frequency based on a defined reference action frequency; this is not limited here.

[0158] Then, a first guidance frequency can be determined based on the user's reference movement frequency and the change information of at least one physiological parameter within a first time period. In some examples, the user's reference movement frequency can be adjusted based on the change information of at least one physiological parameter within a first time period to obtain the user's first guidance frequency.

[0159] As an example, referring to the description above, the corresponding step length can be determined based on at least one of the trends and magnitudes of change of at least one physiological parameter within the first time period, and the first guidance frequency can be determined based on the corresponding step length and the user's reference action frequency.

[0160] As another example, a frequency adjustment strategy or direction can be determined based on the changes in at least one physiological parameter within a first time period. This determined strategy or direction can then be used to adjust the reference action frequency to obtain a first guiding frequency. For instance, based on the changing trend of at least one physiological parameter of the user within the first time period and a preset mapping relationship, the frequency adjustment strategy or direction can be determined as increasing the frequency, decreasing the frequency, or keeping it unchanged. Then, the reference action frequency can be adjusted based on this strategy or direction to obtain the first guiding frequency. The adjustment step size can be preset, determined based on the change in at least one physiological parameter within the first time period, determined based on user-related information (e.g., user attribute data, user group to which the user belongs, user's historical movement or activity data, etc.), or determined through other means, which are not limited here.

[0161] In other examples, information on changes in at least one physiological parameter and a reference movement frequency can be input into a frequency guidance algorithm or model for processing to obtain a first guidance frequency. In other embodiments, multiple frequency guidance algorithms or models can be set, and based on information on changes in at least one physiological parameter within a first time period, a corresponding frequency guidance algorithm or model can be determined from multiple preset frequency guidance algorithms or models. The first guidance frequency is then determined based on the reference movement frequency and the corresponding frequency guidance algorithm or model. For example, if the heart rate change exceeds a certain heart rate change threshold, the corresponding frequency guidance algorithm is determined to be a linear function with the reference movement frequency as the input variable; if the heart rate change does not exceed the heart rate change threshold, the corresponding frequency guidance algorithm is determined to be an exponential function with the reference movement frequency as the input variable. As another example, the parameters of the frequency guidance algorithm or model can be determined based on information on changes in at least one physiological parameter within a first time period.

[0162] In some embodiments, such as Figure 5 As shown, in S501, information on changes in the user's historical movement or activity data is acquired. In S502, based on the user's historical movement or activity data and information on changes in at least one physiological parameter within a first time period, a first guidance frequency for the user is determined.

[0163] The historical exercise or activity data can be historical data of the user performing the same or different types of exercise. This historical exercise or activity data can be recently collected data (e.g., data within a certain time interval from the present) or include data from all periods; there is no limitation here. The historical exercise or activity data can include one or more of the following: historical exercise type, historical exercise time, historical movement frequency, historical exercise duration, historical exercise speed, historical exercise distance, historical energy consumption, historical exercise intensity, and historical exercise volume. The historical activity data can include at least one of historical exercise or activity data and historical physiological measurement data. The historical physiological measurement data can include physiological measurement data collected during previous exercise activities, or physiological measurement data collected when the user is not exercising, resting, or sleeping; there is no limitation here.

[0164] As an example, historical exercise or activity data can be preprocessed. Optionally, the historical exercise or activity data can be filtered or selected based on one or any combination of factors such as the current exercise type, time range, exercise performance parameters, and corresponding physiological parameters. For example, assuming the exercise type for this training session is running, the user's historical running data can be obtained from the historical exercise or activity data. Similarly, if the exercise type for this training session is cycling, the user's historical cycling data can be obtained from the historical exercise or activity data. There are no specific limitations here.

[0165] As an example, based on a user's historical movement or activity data, a statistical result of the user's movement frequency can be calculated. Based on this statistical result and the change information of at least one physiological parameter within a first time period, a first guidance frequency for the user can be determined. The statistical result of the movement frequency can include the average, median, range, maximum, etc., of the movement frequency. Alternatively, the statistical result of the movement frequency can be obtained by combining historical physiological measurement data. For example, based on historical physiological measurement data, the range or change of physiological parameters corresponding to each movement frequency included in the historical movement frequency data can be determined, and the movement frequencies in the historical movement frequency data can be statistically analyzed based on the range or change of physiological parameters. This is not limited here.

[0166] As another example, historical activity data includes historical physiological measurement data. Based on this data, a baseline for at least one physiological parameter of the user can be determined. Then, based on this baseline and the changes in that parameter within a first time period, the user's real-time physiological state during exercise can be determined, thereby establishing a first guidance frequency. The baseline of the physiological parameter may include a steady-state level or range. Optionally, the baseline may include resting heart rate. Based on the resting heart rate and the user's heart rate changes within the first time period, the first guidance frequency can be determined. For example, based on the user's heart rate changes within the first time period and their resting heart rate, it can be determined that the user's heart rate change within the first time period reaches a specific range of resting heart rate, and the preset frequency corresponding to this specific range can be defined as the first guidance frequency.

[0167] As another example, the mapping relationship between changes in physiological parameters and movement frequency can be determined based on historical physiological measurement data and historical movement or activity data included in the user's historical activity data, and a first guidance frequency can be determined based on the mapping relationship and the change information of at least one physiological parameter in a first time period.

[0168] In some embodiments, such as Figure 6 As shown, in S601, the user's motion data within a first time period is acquired. In S602, a first guidance frequency is determined based on the motion data within the first time period and information on changes in at least one physiological parameter.

[0169] As an example, the user's current movement frequency can be determined based on the user's movement data within a first time period. Then, based on the user's current movement frequency and the change information of at least one physiological parameter within the first time period, a first guidance frequency can be determined. Optionally, the user's current movement frequency and the change information of one or more physiological parameters can be input into a frequency guidance model or algorithm to obtain the user's first guidance frequency. Optionally, a mapping relationship can be set between the user's movement frequency and a preset change of at least one physiological parameter. Based on this mapping relationship, a preset change of at least one physiological parameter corresponding to the current movement frequency can be determined. Then, the first guidance frequency is determined by comparing the change information of the at least one physiological parameter within the first time period with the preset change. For example, if the change indicated by the change information exceeds a preset change threshold, the first guidance frequency is determined as a first preset frequency value, or the first guidance frequency is obtained based on the current movement frequency or reference movement frequency and a first step length. As another example, if the change indicated by the change information does not exceed the preset change threshold, the first guidance frequency is determined as a second preset frequency value greater than the first preset frequency value, or the first guidance frequency is obtained based on the current movement frequency or reference movement frequency and a second step length greater than the first step length.

[0170] Determining the user's first guidance frequency based on the user's current movement frequency and changes in at least one physiological parameter allows the first guidance frequency to be closer to the user's current movement status, making it more conducive for the user to achieve the expected movement frequency. This improves the rationality of the guidance frequency for the user, effectively reduces the possibility of user injury and the risk of user discomfort during exercise training, and enhances user experience and training effectiveness.

[0171] As another example, see Figure 7 In step S701, based on the motion data, information on the change in the user's real-time movement frequency within a first time period is obtained. This information may include one or any combination of the following: the amount of change in real-time movement frequency (or instantaneous movement frequency), the trend of change, the proportion of change, and the range of change. For example, based on the user's motion data within the first time period, the user's minimum and maximum movement frequencies can be determined, thereby determining the amount and range of change in the user's real-time movement frequency. As another example, the user's movement frequency data within the first time period can be fitted to obtain the trend of change in the user's real-time movement frequency.

[0172] In S702, the first guidance frequency of the user is determined based on the change information of the user's real-time action frequency during the first time period and the change information of the at least one physiological parameter during the first time period.

[0173] As an example, information on changes in the user's real-time movement frequency and changes in at least one physiological parameter can be input into a frequency guidance model or algorithm to obtain the user's first guidance frequency. As another example, the first guidance frequency can be determined based on a preset mapping relationship or rule, along with information on changes in the user's real-time movement frequency and changes in at least one physiological parameter.

[0174] Determining the first guidance frequency based on the changes in the user's movement frequency and physiological parameters during the first time period ensures that the guidance frequency during exercise matches the user's current movement and physiological condition, thereby improving training effectiveness, avoiding sports injuries, and enhancing the user experience.

[0175] As another example, the user's first guidance frequency can be determined by comparing the changes in the user's real-time action frequency within a first time period with the changes in the at least one physiological parameter within the first time period. For example, the first guidance frequency can be obtained based on the comparison result and preset rules, algorithms, or parameters. Alternatively, the first guidance frequency can be determined based on the comparison result and a reference action frequency. Or, the first guidance frequency can be determined based on the comparison result and the current action frequency. Or, the first guidance frequency can be determined based on the comparison result and the initial guidance frequency of the first time period.

[0176] Optionally, it can be determined whether the changing trends of the real-time action frequency and the physiological parameters within the first time period satisfy a preset trend relationship. The preset relationship is the correspondence between the changing trends of action frequency and physiological parameters. For example, this preset relationship includes: a constant real-time action frequency corresponds to a constant heart rate; a decrease in the real-time action frequency corresponds to a constant or decreased heart rate; and an increase in the real-time action frequency corresponds to an increase in the heart rate. In this case, if the user's real-time action frequency shows an increasing trend within the first time period, while the user's heart rate shows a constant trend, it can be considered that the user can adapt to a higher action frequency, thus setting the first guidance frequency to a relatively high value. If the user's real-time action frequency shows a constant trend within the first time period, while the user's heart rate shows an increasing trend, it can be considered that the user still needs time to adapt to the real-time action frequency, thus setting the first guidance frequency to a relatively low value. The specific implementation method for determining the first guidance frequency value can refer to the various possible embodiments described above, such as preset frequency values, preset parameters, preset rules, preset step sizes, etc., which will not be elaborated here.

[0177] Optionally, the change or percentage of real-time movement frequency within a first time period can be compared with the change or percentage of at least one physiological parameter within the same time period. If the change or percentage of at least one physiological parameter within the first time period is greater than the change or percentage of real-time movement frequency, it can be considered that changing the user's movement frequency within the first time period is somewhat strenuous for the user, and the first guidance frequency can be set to a relatively low value. Alternatively, if the change or percentage of at least one physiological parameter within the first time period is less than or equal to the change or percentage of real-time movement frequency, it can be considered that changing the user's movement frequency within the first time period is relatively easy for the user, and the first guidance frequency can be set to a relatively high value. For example, continuing with running as an example, if the user's current movement frequency is 182, and assuming the change in the user's real-time movement frequency within the first time period is 2, and the change in heart rate within the first time period is 5, the user's first guidance frequency can be obtained by reducing the current movement frequency by a certain value, or the first guidance frequency can be set to a preset frequency value, such as 180. For example, assuming the user's real-time movement frequency changes by 2 within a first time period, and the user's heart rate changes by 1 within the same time period, the user's first guidance frequency can be determined as the current movement frequency plus a certain value, or the first guidance frequency can be set to the current movement frequency 182 or a preset frequency value. Alternatively, the first guidance frequency can be determined based on the difference between the change or percentage of the real-time movement frequency within the first time period and the change or percentage of at least one physiological parameter within the same time period.

[0178] Optionally, if the change in action frequency indicated by the change information of the user's real-time action frequency within the first time period is less than or equal to the change in parameter indicated by the change information of the at least one physiological parameter within the first time period, then the user's first guidance frequency is determined to be the fifth frequency, and the fifth frequency is less than or equal to the current action frequency, the reference action frequency, or the initial guidance frequency of the first time period.

[0179] Optionally, if the change in the user's real-time action frequency indicated by the change information within the first time period is greater than the change in the physiological parameter indicated by the change information of the at least one physiological parameter within the first time period, then the user's first guidance frequency is determined to be the sixth frequency, and the sixth frequency is greater than the current action frequency, the reference action frequency, or the initial guidance frequency of the first time period.

[0180] In some embodiments, such as Figure 8As shown, in S801, the initial guidance frequency of the user in the first time period is obtained. In S802, the first guidance frequency of the user is determined based on the initial guidance frequency and the change information of the at least one physiological parameter in the first time period.

[0181] The initial guidance frequency for the first time period is used to guide the user's movement frequency during the exercise process within the first time period. This initial guidance frequency can be a fixed value, or it can be determined based on at least one of the following: user attribute data, the user's user group, the user's historical movement or activity data, movement or activity data detected in at least one prior time period (i.e., before the first time period) during the current exercise, and the exercise type. In some examples, the initial guidance frequency for the first time period can also be determined through the various possible implementations described above, the difference being that the activity data used is activity data detected in at least one prior time period; this is not limited here.

[0182] In some cases, information on changes in at least one physiological parameter and an initial guidance frequency can be input into a frequency guidance model or algorithm for processing to obtain a first guidance frequency.

[0183] In other examples, the initial guidance frequency can be adjusted based on changes in at least one physiological parameter to obtain a first guidance frequency. Optionally, the corresponding step size can be determined based on changes in at least one physiological parameter over a first time period, and the first guidance frequency is obtained based on the corresponding step size and the initial guidance frequency (e.g., the sum of the initial guidance frequency and the corresponding step size). For example, if the user's heart rate increases by more than or equal to 5 during the first time period, the corresponding step size is determined to be -1, and the first guidance frequency is reduced by 1 from the initial guidance frequency. As another example, if the user's heart rate increases by more than or equal to 0 and less than 5 during the first time period, the corresponding step size is determined to be +1, and the first guidance frequency is increased by 1 from the initial guidance frequency. As yet another example, if the user's heart rate decreases during the first time period, the corresponding step size is determined to be +2, and the first guidance frequency is increased by 2 from the initial guidance frequency. Optionally, a reference movement frequency or current movement frequency can also be determined, and the first guidance frequency is determined based on the corresponding step size, the initial guidance frequency, and the reference movement frequency or current movement frequency. For example, the sum of the initial guidance frequency and the corresponding step size can be compared with the reference motion frequency, and the first guidance frequency can be determined based on the comparison result. If the sum of the initial guidance frequency and the corresponding step size is greater than the reference motion frequency, then the sum of the initial guidance frequency and the corresponding step size can be used as the first guidance frequency. Alternatively, if the sum of the initial guidance frequency and the corresponding step size is less than or equal to the reference motion frequency, then the reference motion frequency can be used as the first guidance frequency, but this is not a limitation here.

[0184] The first guidance frequency is determined based on the changes in the user's physiological parameters in the first time period and the initial guidance frequency in the first time period. The first guidance frequency can be used to guide the user's movement frequency in the second time period after the first time period. This allows the guidance frequency to be adjusted according to the real-time changes in the user's physiological parameters during exercise, making the frequency guidance more consistent with the user's movement at each stage of exercise, thereby improving the accuracy of frequency guidance and enhancing training effectiveness.

[0185] In some embodiments, such as Figure 9 As shown, in S901, user attribute data is acquired. In S902, a first guidance frequency is determined based on the change information of at least one physiological parameter and the user attribute data. The user attribute data may include one or any combination of age, gender, height, weight, BMI (Body Mass Index), exercise habits (movement frequency, exercise duration, exercise time, exercise type, etc.), and health status. For example, at least one of customized strategies, parameters, thresholds, algorithms, models, and mapping relationships can be set based on the user attribute data. As another example, the change information of at least one physiological parameter within a first time period can be processed based on the user attribute data, such as filtering, screening, normalization, weighting, or one or more other processing methods, and the first guidance frequency for the user can be obtained based on the processed change information. As an example, a threshold range for at least one physiological parameter can be determined based on the user attribute data, and the first guidance frequency can be determined based on the threshold range of the at least one physiological parameter and the change information of the at least one physiological parameter within a first time period. For example, the user's maximum heart rate can be determined based on the user attribute data, and the first guidance frequency can be determined based on the user's maximum heart rate and the heart rate change information within a first time period.

[0186] In other embodiments, the user's physiological measurement data can be processed based on the user's attribute data, and the processed physiological measurement data can be used to obtain information on the change of at least one physiological parameter in a first time period, which is not limited here.

[0187] By combining user attribute data with information on changes in at least one of the user's physiological parameters to determine the first guidance frequency, the applicability and accuracy of the determined first guidance frequency for the user can be improved.

[0188] In some embodiments, the method for determining the first guidance frequency may also be as follows: Figure 10 As shown, Figure 10 A flowchart illustrating the determination of a first guidance frequency, as shown in an exemplary embodiment of this disclosure, includes:

[0189] S1001, Obtain the user group of the user, the user group includes experienced users or novice users.

[0190] S1002, based on the user's user group and the change information of the at least one physiological parameter during the first time period, determine the user's first guidance frequency.

[0191] The user group can be determined through one or any combination of user self-selection, user's historical exercise or activity data, user attribute data, user training goals, and the type of exercise trained. In some examples, the user group can be determined at least partially based on the user's current exercise level, such as the time taken to cover a specific distance or average pace during historical exercise. For another example, by comparing the cumulative exercise time in historical exercise or activity data with a preset time threshold, it can be determined whether the user is an experienced user or a beginner user. If the cumulative exercise time in historical exercise or activity data is greater than or equal to the preset time threshold, the user is identified as an experienced user; if the cumulative exercise time in historical exercise or activity data is less than the preset time threshold, the user is identified as a beginner user. In other examples, the user group can be at least partially based on age classifications included in the user's attribute information, such as elderly, youth, and children. For yet another example, by comparing body fat percentage and a body fat threshold in the attribute information, it can be determined whether the user is an experienced user or a beginner user. If the body fat percentage in the attribute information is greater than or equal to the body fat threshold, the user is identified as a beginner user; if the body fat percentage in the attribute information is less than the body fat threshold, the user is identified as an experienced user. In other examples, user groups can be categorized at least partially based on users' training goals, such as weight loss, health maintenance, fitness, and competition. In still other examples, user groups can be categorized at least partially based on the type of exercise being trained, such as running, swimming, cycling, extreme sports, indoor fitness, etc. The implementation of user groups is not limited here.

[0192] Different user groups have different physiological and motor characteristics, and the physiological parameter changes during exercise also differ among users in different groups. Therefore, the initial guidance frequency for users can be determined based on the user group and the changes in at least one physiological parameter within a first time period.

[0193] In some examples, mapping relationships can be established for different user groups based on their physiological and movement characteristics. Alternatively, different algorithms or models can be developed for different user groups, or different thresholds or parameters can be set for different user groups. For example, a lower heart rate variability threshold can be set for long-distance running or marathons, while a higher threshold can be set for sprinting or interval running. Another example is that a reference movement frequency can be set to a preset value for novice users, while for experienced users, their reference movement frequency can be determined based on their historical exercise or activity data. In other examples, an initial guidance frequency can be determined based on the user group to which the user belongs. Then, based on the initial guidance frequency and the change information of at least one physiological parameter within a first time period, a first guidance frequency can be determined. Taking running as an example, the initial guidance frequency for novice users could be 165, and for experienced users, it could be 180. In still other examples, the change information of at least one physiological parameter within a first time period can include the rate of change of at least one physiological parameter within that first time period. A rate of change threshold for at least one physiological parameter can be set based on the user group to which the user belongs, and the rate of change of at least one physiological parameter within a first time period can be compared with the corresponding rate of change threshold to determine the first guidance frequency. Wait, the specific implementation method is not limited here.

[0194] Determining the user's first guidance frequency based on the user group they belong to and the changes in their physiological parameters within the first time period can ensure that the determined first guidance frequency is closely related to the user's characteristics, which is beneficial to improving the user's training effect.

[0195] In some embodiments, the user's first guidance frequency can also be obtained based on any combination of the other parameters mentioned above and the change information of at least one physiological parameter within a first time period. As an example, the user's first guidance frequency can be determined based on the user group to which the user belongs, the user's current action frequency, and the change information of at least one physiological parameter within a first time period. For instance, a frequency adjustment strategy (e.g., determining to increase a specific value, keep it unchanged, or decrease a specific value) can be determined based on the user group to which the user belongs and the change information of at least one physiological parameter within a first time period, and the current action frequency can be adjusted according to this frequency adjustment strategy to obtain the first guidance frequency. As another example, based on the user group and the current action frequency, the expected change information of at least one physiological parameter of the user can be determined through a preset algorithm, model, or mapping relationship, and the first guidance frequency can be determined by comparing the change information of at least one physiological parameter of the user within a first time period with the expected change information. If the change information of at least one physiological parameter within a first time period exceeds the change corresponding to the expected change information, the first guidance frequency can be determined to be a relatively small value, such as a relatively small preset value, a small increase in value based on the current action frequency or reference action frequency, or a certain decrease in value based on the initial guidance frequency, etc. If the change in at least one physiological parameter within the first time period does not exceed the change corresponding to the expected change, the first guidance frequency can be determined as a relatively large value, such as a relatively large preset value, a large increase based on the current action frequency or reference action frequency, or maintaining the initial guidance frequency, etc. For example, the user group, the current action frequency, and the change information of at least one physiological parameter within the first time period can be input into a preset algorithm, model, or mapping relationship for processing to obtain the first guidance frequency.

[0196] In some embodiments, a first guidance frequency can be determined based on the user group to which the user belongs, the user's real-time action frequency changes within a first time period, and the changes in at least one physiological parameter within the first time period. For example, a specific threshold can be determined based on the user group to which the user belongs, and the first guidance frequency can be determined by comparing the difference between the user's real-time action frequency changes and the changes in at least one physiological parameter within the first time period with the specific threshold. As another example, a step size can be determined based on the user group to which the user belongs, and a frequency adjustment strategy (e.g., keeping it unchanged, increasing, or decreasing) can be determined by comparing the user's changes within the first time period with the changes in at least one physiological parameter within the first time period. Then, the initial guidance frequency, current action frequency, or reference action frequency can be adjusted according to the frequency adjustment strategy and the step size to determine the first guidance frequency. Alternatively, the first guidance frequency can be determined based on the frequency adjustment strategy, the step size, and the user's real-time action frequency changes within the first time period. As yet another example, based on the user group and the user's real-time action frequency changes within the first time period, a change standard for at least one physiological parameter of the user can be determined using algorithms, models, or mapping relationships. Then, the first guidance frequency can be determined by comparing the changes in the user's at least one physiological parameter within the first time period with the change standard.

[0197] The embodiments described above exemplify how a first guidance frequency can be determined by combining information on the changes of at least one physiological parameter within a first time period with other information. However, it is understood that in actual implementation, other types of information or other combinations of various information may also be used. For example, the first guidance frequency can be determined by combining the user's training objective, the user's current action frequency, and information on the changes of at least one physiological parameter within the first time period. For example, based on the user's training objective, a target action frequency can be determined, and then the first guidance frequency can be obtained based on the difference between the current action frequency and the target action frequency, as well as information on the changes of at least one physiological parameter within the first time period. As another example, a frequency adjustment strategy can be determined based on the user's training objective and information on the changes of at least one physiological parameter within the first time period, and the current action frequency, reference action frequency, or initial guidance frequency can be adjusted based on this frequency adjustment strategy to obtain the user's first guidance frequency.

[0198] It is understood that the first guidance frequency determined in the above embodiments may include a guidance action frequency or a guidance beat frequency, wherein the determined guidance beat frequency corresponds to the suggested action frequency, or may include both the guidance beat frequency and the guidance action frequency, wherein the guidance beat frequency and the guidance action frequency are consistent, and the guidance beat frequency may refer to the target frequency of the beat tone used to guide the action frequency. In some embodiments, where the first guidance frequency determined in the above embodiments includes the guidance action frequency, the guidance beat frequency may be further determined based on the determined guidance action frequency. In this case, the guidance beat frequency may refer to the target frequency of the beat tone used to guide the action frequency, or the beat frequency range of the beat tone, or the rhythm of the beat tone. For example, the guidance beat frequency may be determined based on the difference between the guidance action frequency and the current action frequency or the initial guidance frequency. As another example, the guidance beat frequency may be determined by comparing the guidance action frequency with a preset frequency threshold, and so on.

[0199] In some embodiments, after determining the first guidance frequency, motion frequency guidance information can also be provided to the user based on the first guidance frequency. This motion frequency guidance information can be used to guide the user to adjust the motion frequency. Optionally, the motion frequency guidance information may include at least one of the following: audio signals, visual objects, display backlight signals, flashlight light signals, vibration signals, etc.

[0200] In some examples, the first guidance frequency includes a guidance action frequency. Action frequency guidance information can be generated based on this guidance action frequency and output through one or more output devices. In this case, the action frequency guidance information can be used to instruct the user to adjust the action frequency to the guidance action frequency, or to adjust the action frequency in the direction of the guidance action frequency.

[0201] In other examples, the first guidance frequency includes a guidance beat frequency, from which a beat sound signal can be generated and output through an audio output device. Alternatively, other types of motion frequency guidance information can be generated based on the guidance beat frequency, such as a displayed visual object, a flashlight beam, a vibration signal, or one or more other types, and the beat sound signal and other types of motion frequency guidance information can be output synchronously. In this case, the motion frequency guidance information can be used to instruct the user to perform an action according to the guidance beat frequency, or to instruct the user to adjust the motion frequency according to the guidance beat frequency.

[0202] Optionally, an audio signal can be generated based on a first guidance frequency and output through an audio output device. This audio output device can be headphones, or an audio output component integrated into a wearable device, intermediate device, or other electronic device. In one example, the value of the first guidance frequency can be announced to the user, and the audio signal can be used to indicate the value of the first guidance frequency. In another example, the audio signal can include a beat tone whose beat frequency can be the first guidance frequency, or the beat frequency and / or other parameters of the beat tone can change at a specific rhythm (e.g., getting faster or slower) to instruct the user to adjust their movement frequency according to the rhythmic change of at least one parameter of the beat tone. Optionally, multiple playback strategies can be employed when playing the beat tone. For example, to make the beat frequency in the beat tone clearer and more prominent, only the beat tone can be played. Alternatively, if the user instructs to play specific audio (e.g., music, podcast, or audiobook), the beat tone can be mixed with the audio instructing the user to play to obtain a mixed audio, and then the mixed audio can be played. Alternatively, to increase the fun of exercise training, the beat sound can be superimposed with other default or user-defined audio and played, so that users can hear the content they want to listen to and also get the beat sound at the same time.

[0203] Optionally, a visualization object can be generated based on a first guidance frequency and displayed via a display device. Optionally, the display device can be integrated into a wearable device, middleware device, or other electronic device. In one example, the visualization object includes instruction information for instructing the user to adjust the action frequency to a guidance action frequency included in the first guidance frequency, or it may include suggestions for adjusting the action frequency, or it may include the first guidance frequency. For example, the visualization object may include a specific value of the first guidance frequency, or text, images, or virtual objects instructing the user to increase or decrease the action frequency. As another example, the visualization object may include color blocks, such as... Figure 11 The circular colored dot is shown. Optionally, the display parameters of the visualized object on the display device can be adjusted according to a first guiding frequency. These display parameters are used to control the display effect of the visualized object. For example, the visualized object includes... Figure 11 The circular colored dot shown is displayed with parameters used to control it to blink at a frequency that is the same as or matches the first guided frequency. Alternatively, the visualized object may include a virtual object, with display parameters used to control the virtual object to perform actions at a guided action frequency, or to perform actions in a manner that gradually increases or decreases the action frequency.

[0204] Optionally, at least one operating parameter of the flashlight can be determined based on a first guidance frequency, causing the flashlight to emit a light signal according to the at least one operating parameter. The at least one operating parameter may include, for example, one or any combination of frequency, intensity, wavelength, mode, or light-emitting element. Optionally, the flashlight can be integrated into a wearable device, intermediate device, or other electronic device. Optionally, the flashlight may include at least one light-emitting element for emitting a light signal of at least one wavelength, such as white light, green light, red light, blue light, etc. In one example, the flashlight's blinking frequency can be determined based on the first guidance frequency; for example, the flashlight blinks at the first guidance frequency, or the flashlight blinks at a specific changing rhythm to instruct the user to adjust the frequency of their action according to that changing rhythm. In other examples, the wavelength of the light signal emitted by the flashlight, or a combination of wavelengths, can be determined based on the first guidance frequency. For example, if the guided action frequency is greater than a specific frequency value (e.g., a specific frequency threshold, the user's initial guided action frequency, the user's current action frequency, or the action frequency at the end of the first time period), or if the user needs to increase their action frequency or their target action frequency (i.e., the action frequency the user expects to achieve during this frequency training or the maximum expected action frequency) is determined based on the first guided frequency, then the flashlight can emit red light. As another example, if the guided action frequency is less than the specific frequency value, or if the user needs to decrease their action frequency or their target action frequency is determined based on the first guided frequency, then the flashlight can emit green light. As yet another example, if the guided action frequency is equal to the specific frequency value, or if the user maintains their action frequency or their target action frequency remains unchanged based on the first guided frequency, then the flashlight can emit blue or white light. Furthermore, the wavelength switching frequency of the flashlight's emitted light signal can be determined based on the first guided frequency. In some examples, the flashlight's illumination mode can be determined based on the first guided frequency. The flashlight can enter a flashing mode or a constant-on mode, a long-flash mode or a short-flash mode, etc., based on the first guided frequency. For example, if the first guidance frequency is greater than a specific frequency value, or if the user's desired action frequency needs to be increased based on the first guidance frequency, or if the user's target action frequency is increased, the flashlight enters a flashing mode or a short flashing mode. As another example, if the first guidance frequency is less than a specific frequency value, or if the user's desired action frequency needs to be decreased based on the first guidance frequency, or if the user's target action frequency is reduced, the flashlight enters a constant-on mode or a long flashing mode, and so on. In other examples, the flashlight's luminous intensity can be determined based on the first guidance frequency. For example, the magnitude of the luminous intensity can indicate the magnitude of the first guidance frequency or its relationship to a specific frequency threshold, the user's initial guidance frequency, the current action frequency, or the real-time guidance frequency at the end of a first time period. Furthermore, changes in luminous intensity can instruct the user to increase or decrease their action frequency, and so on.In other examples, the flashlight includes at least one light-emitting element, which can be selectively illuminated based on a first guide frequency. For example, a guide swing rhythm of the user's arm can be determined based on the first guide frequency, and at least one light-emitting element of the flashlight can be selectively illuminated according to that guide swing rhythm. In other examples, two or more operating parameters of the flashlight can be determined based on the first guide frequency, such that the two or more operating parameters are used to indicate the first guide frequency or to provide an adjustment suggestion for the swing frequency. For example, one operating parameter may indicate the direction of adjustment of the swing frequency, and another operating parameter may indicate the magnitude of adjustment of the swing frequency, but this is not limited here.

[0205] Optionally, the vibration frequency of the vibrating component can be determined based on the first guidance frequency. The vibrating component of the wearable device, intermediate device or other electronic device can vibrate at the determined vibration frequency to instruct the user to perform actions at the vibration frequency, or to perform actions at an action frequency that matches the vibration frequency (e.g., in rhythm), or to adjust the action frequency according to the changing trend of the vibration frequency, etc.

[0206] Optionally, the action frequency guidance information may include at least two of the above and be output through at least two output components. For example, an audio signal may be output through an audio output device while a light signal is emitted through a flashlight. Another example is that an audio signal may be output through an audio output device while relevant information is displayed through a display device, and so on. Optionally, when the action frequency guidance information is output through at least two output methods, the information output or the amount of information contained in the different output methods may be the same or different. As an example, a beat tone may be output to indicate an increase or decrease in the action frequency, and detailed information about the first guidance frequency (e.g., a specific value of the first guidance frequency or action suggestions related to the first guidance frequency) may be displayed through a display device. For example, a higher beat tone indicates an increase in the action frequency, while a lower beat tone indicates a decrease in the action frequency. Another example is that a change in the rhythm of the beat tone from fast to slow indicates an increase in the action frequency, while a change in the rhythm of the beat tone from slow to fast indicates a decrease in the action frequency. As another example, at least one operating parameter of the flashlight may be used to indicate an increase or decrease in the action frequency, and detailed information about the first guidance frequency may be displayed through a display device. As another example, detailed information about the first guidance frequency can be indicated via audio signals, and the user can be instructed to speed up or slow down their movements via a flashlight, vibrating components, or visual objects. The implementation method for outputting movement frequency guidance information is not limited here.

[0207] By determining the movement frequency guidance information based on the first guidance frequency and providing this guidance information to the user to guide the user's movement, the scientific nature of the user's exercise training and the operability of adjusting the movement frequency are improved, thereby improving the exercise effect of the exerciser.

[0208] In some embodiments, such as Figure 12 As shown, one possible way to provide users with motion frequency guidance information is as follows.

[0209] In S1201, the difference between the user's current action frequency and the first guidance frequency is obtained.

[0210] In S1202, based on the difference, action frequency guidance information is provided to the user.

[0211] Action frequency guidance information can be generated based on the difference between the user's current action frequency and a first guidance frequency (such as a guided action frequency). Optionally, the action frequency guidance information can be determined by comparing the difference between the user's current action frequency and the first guidance frequency with a preset difference range.

[0212] In some examples, when the difference between the user's current action frequency and the first guided frequency is within a preset difference range, action frequency guidance information can be generated based on the first guided frequency. This action frequency guidance information can be used to instruct the user to adjust their action frequency to the first guided frequency. In other examples, when the difference between the current action frequency and the first guided frequency exceeds a preset difference range, action frequency guidance information can be generated based on the preset difference range. This action frequency guidance information can be used to instruct the user to adjust their action frequency to the preset difference range.

[0213] In other examples, the audio parameters of the beat can be adjusted by comparing the difference between the user's current movement frequency and the first guided frequency with a preset difference range. For example, if the difference between the current movement frequency and the first guided frequency exceeds the preset difference range, the playback volume of the beat can be increased. Increasing the playback volume of the beat makes the playback effect of the beat clearer, thereby prompting the user to follow the beat frequency for exercise training. In other examples, if the difference between the current movement frequency and the first guided frequency is within the preset difference range, the playback volume of the beat can be decreased. Figure 13In the example shown, the playback volume of the beat tone changes with the degree of match between the current action frequency and the first guided frequency. If the degree of match is less than a first specific threshold, the beat tone is played at a certain volume. If the degree of match is greater than the first specific threshold, as the degree of match increases, the difference between the current action frequency and the first guided frequency decreases. At this point, the playback volume of the beat tone can be controlled to gradually decrease as the degree of match increases, until the degree of match reaches a second specific threshold, at which point the playback volume decreases to zero, i.e., the beat tone stops playing.

[0214] In other examples, the action frequency guidance information can be adjusted or modified based on the difference between the user's current or real-time action frequency and the guided action frequency. For example, if the difference between the user's current or real-time action frequency and the guided action frequency is large, at least one parameter of at least one of the beat sound, the visual object, the flashlight, and the vibration signal can be adjusted, such as increasing the beat frequency of the beat sound, increasing the vibration frequency of the vibration signal, illuminating the first light-emitting element of the flashlight, or switching the flashlight mode to a short flashing mode, etc., to prompt the user to increase the frequency. As the difference between the user's current or real-time action frequency and the guided action frequency decreases, at least one parameter of at least one of the beat sound, the visual object, the flashlight, and the vibration signal can be adjusted, such as gradually decreasing the beat frequency of the beat sound, decreasing the vibration frequency of the vibration signal, illuminating the second light-emitting element of the flashlight, or switching the flashlight mode to a long flashing mode, etc., until the user's current or real-time action frequency matches the guided action frequency, at which point the output of the action frequency guidance information is stopped, such as stopping the output of the beat sound, stopping the flashlight from illuminating, stopping the output of the vibration signal, etc.

[0215] By controlling the audio playback parameters, the playback effect of the beat sound can be varied according to the degree of match between the user's current movement frequency and the first guided frequency. When the match between the current movement frequency and the first guided frequency is poor, the playback effect of the beat sound is enhanced to prompt the user to adjust the movement frequency, thereby improving the accuracy of guidance during exercise training.

[0216] In other examples, when the difference between the current action frequency and the first guided frequency is within a preset difference range, an adjustment period can be determined, and action frequency guidance information for that adjustment period can be generated. This action frequency guidance information can be used to instruct the user to adjust the action frequency to the first guided frequency. For example, the action frequency guidance information can be used to prompt the user to adjust the action frequency to the first guided frequency within the next preset duration. In other examples, when the difference between the current action frequency and the first guided frequency exceeds a preset difference range, multiple adjustment periods can be determined, and action frequency guidance information for each of the multiple adjustment periods can be generated, so that the user can gradually adjust the action frequency to the first guided frequency over multiple adjustment periods. For example, if the user's current action frequency is 168, the first guided frequency is 180, and the preset difference range is 5, it can be determined that the user needs to adjust the action frequency to the first guided frequency within the next three preset durations, and action frequency guidance information can be generated for the next three preset durations: the action frequency is adjusted to 172 within the first preset duration, the action frequency is adjusted to 176 within the second preset duration, and the action frequency is adjusted to 180 within the third preset duration. Optionally, at this time, the user's actual action frequency in the first adjustment cycle among multiple adjustment cycles can also be obtained, and action frequency guidance information for at least one subsequent second adjustment cycle can be adjusted based on the actual action frequency in the first adjustment cycle.

[0217] Optionally, action frequency guidance information can be determined by comparing the user's current action frequency with a first guided frequency. In some examples, if the difference between the current action frequency and the first guided frequency indicates that the user's current action frequency is higher than the first guided frequency, action frequency guidance information can be generated to instruct the user to slow down the pace of their actions. For example, an audio prompt such as "slow down" can be output to the user. Alternatively, the prompt message "slow down" can be displayed on the screen, or the display color can be changed to a first color, such as green. Another example is controlling the flashlight's intensity and color to a first light intensity and a first light color, etc. In other examples, if the difference between the current action frequency and the first guided frequency indicates that the current action frequency is lower than the first guided frequency, action frequency guidance information can be generated to instruct the user to speed up the pace of their actions. For example, an audio prompt such as "faster" can be output to the user. Another example is displaying the prompt message "faster" on the device, or changing the display color to a second color, such as red. Yet another example is controlling the flashlight's intensity and color to a second light intensity and a second light color, etc. This allows users to adjust their real-time motion frequency based on the motion frequency guidance information.

[0218] In some embodiments, action frequency guidance information can also be determined by comparing the difference between the user's current action frequency and a first guidance frequency. For example, playback parameters of a beat sound can be determined, which can be used to control the playback effects of the beat sound, such as volume and frequency.

[0219] In some embodiments, after determining the first guidance frequency, information on the changes of at least one physiological parameter of the user during a second time period following the first time period can also be obtained. Based on the information on the changes of the at least one physiological parameter during the second time period and the first guidance frequency, a second guidance frequency for the user is determined. This second guidance frequency can be used to guide the user's movement frequency in a third time period following the second time period.

[0220] Optionally, the second time period can be a time period following and adjacent to the first time period, i.e., the next time period after the first time period. For example, the first time period can be the current frequency guidance cycle, and the second time period can be the next frequency guidance cycle after the current frequency guidance cycle. Optionally, the second time period can also be any time period following the first time period, and the duration of the second time period can be the same as or different from the duration of the first time period.

[0221] The method for determining the second guidance frequency based on the change information of at least one physiological parameter in the second time period and the first guidance frequency can refer to the method embodiments described above. For example, by comparing the change amount of the at least one physiological parameter in the second time period with at least one preset threshold, the first guidance frequency is adjusted to obtain the user's second guidance frequency. Another example is that the first guidance frequency is adjusted based on the change trend of at least one physiological parameter in the second time period to obtain the second guidance frequency. Yet another example is that the change information of at least one physiological parameter in the second time period and the first guidance frequency are processed using a preset mapping relationship, algorithm, or model to obtain the second guidance frequency.

[0222] By detecting the user's movement frequency and physiological parameters at the first guidance frequency, the user's real-time movement status can be determined, and the user's movement frequency can be guided in real time based on changes in physiological parameters. This enables real-time guidance and dynamic adjustment of movement frequency, thereby improving the precision and accuracy of movement frequency guidance and further enhancing the effectiveness of the user's exercise training.

[0223] This disclosure also provides a device for guiding the frequency of movements. Optionally, such as Figure 14 As shown, the device can be used to perform the methods in the various embodiments described above.

[0224] The first acquisition unit 1401 is used to acquire activity data detected during the user's exercise, the activity data including physiological measurement data;

[0225] The first determining unit 1402 is used to determine, based on the physiological measurement data, the change information of at least one physiological parameter of the user within a first time period;

[0226] The second determining unit 1403 is used to determine the first guidance frequency of the user based on the change information of at least one physiological parameter of the user within a first time period. The first guidance frequency includes at least one of guidance action frequency and guidance beat frequency.

[0227] In some embodiments, the change information includes at least one of change trend, change amount, and change rate.

[0228] In some embodiments, the second determining unit 1403 is configured to determine the first guidance frequency based on at least one of the change trend, change amount, and change rate of the at least one physiological parameter during the first time period.

[0229] In some embodiments, the activity data may further include motion data.

[0230] In some embodiments, the activity data includes: real-time action frequency.

[0231] In some embodiments, the second determining unit 1403 is used to determine the first guidance frequency of the user based on the user's real-time action frequency in a first time period and the change information of the at least one physiological parameter in the first time period.

[0232] In some embodiments, the device further includes: a second acquisition unit, configured to acquire information on the change in the user's real-time movement frequency within a first time period based on the movement data included in the activity data. Optionally, the second determination unit 1403 is configured to determine a first guidance frequency for the user based on the information on the change in the user's real-time movement frequency within the first time period and the information on the change in the at least one physiological parameter within the first time period.

[0233] In some embodiments, the second determining unit 1403 is configured to determine the user's first guidance frequency by comparing the user's real-time action frequency change information and the at least one physiological parameter change information within the first time period.

[0234] In some embodiments, the apparatus further includes a third acquisition unit, configured to acquire the user's initial guidance frequency. Optionally, the second determination unit 1403 is configured to determine the user's first guidance frequency based on the initial guidance frequency and the change information of the at least one physiological parameter within a first time period.

[0235] In some embodiments, the apparatus further includes a fourth acquisition unit, configured to acquire the user group to which the user belongs, the user group including experienced users or novice users. Optionally, the second determining unit 1403 is configured to determine a first guidance frequency for the user based on the user group and the change information of the at least one physiological parameter within a first time period.

[0236] In some embodiments, the device further includes a frequency guidance unit for providing the user with motion frequency guidance information based on the first guidance frequency.

[0237] In some embodiments, the frequency guidance unit is configured to output a beat tone via an audio output device according to the first guidance frequency.

[0238] In some embodiments, the frequency guidance unit is used to modify the action frequency guidance information displayed by the display device according to the first guidance frequency.

[0239] In some embodiments, the frequency guidance unit is used to control at least one operating parameter of the flashlight of the third output device according to the first guidance frequency.

[0240] In some embodiments, the frequency guidance unit is configured to: obtain the difference between the user's current action frequency and the first guidance frequency, and provide the user with action frequency guidance information based on the difference.

[0241] In some embodiments, the apparatus further includes:

[0242] The fifth acquisition unit is used to acquire information on the changes of at least one physiological parameter of the user during a second time period, which is after the first time period.

[0243] The third determining unit is used to determine the user's second guidance frequency based on the change information of the at least one physiological parameter during the second time period and the first guidance frequency.

[0244] In some embodiments, the device can be used to perform the method for guiding the frequency of actions in the above embodiments. Accordingly, the various modules or units included in the device can be used to implement the steps and / or processes in various possible implementations of the above methods, which will not be described in detail here for the sake of brevity.

[0245] This disclosure also provides another method for guiding motion frequency, which can be applied to electronic devices. This method can be applied to the motion frequency guidance system described above, for example, to wearable devices such as smartwatches, wristbands, headphones, and smart glasses, or to intermediate devices such as mobile phones, or to servers, or other types of electronic devices; no limitation is made here. Please see [link to previous document]. Figure 15 The method may include S1501 to S1503.

[0246] S1501, Obtain the user's initial guidance frequency.

[0247] The initial guidance frequency is the guidance frequency when the user begins exercising, or the guidance frequency for other time periods prior to the first time period. This initial guidance frequency may include at least one of an initial guidance beat frequency and an initial guidance movement frequency. In some examples, the first guidance frequency determined according to any of the foregoing embodiments can be used as the user's initial guidance frequency. In other examples, a reference guidance frequency determined according to any of the preceding embodiments can be used as the user's initial guidance frequency. In still other examples, the initial guidance frequency can be a preset value; for example, for running, the initial guidance frequency for all users is uniformly set to 180 times / minute.

[0248] In other examples, the initial guidance frequency can be determined based on the user's historical movement or activity data. Optionally, the initial guidance frequency can be obtained based on historical action frequency data included in the historical movement or activity data. For example, the initial guidance frequency can be determined based on the average or maximum value of historical action frequencies included in the historical action frequency data.

[0249] In some examples, the initial guidance frequency can be determined based on user settings. In other examples, it can also be determined based on user attribute data. In still other examples, the initial guidance frequency can be determined based on the user's user group. The user group can be determined through user settings, based on the user's historical exercise or activity data, or based on the user's attribute data. For example, in running training, the initial guidance frequency could be 175 times / minute for long-distance runners and 180 times / minute for sprinters.

[0250] In other examples, the initial guidance frequency for a user can be determined based on their historical motion or activity data and their attribute data. For instance, if the guidance frequency determined based on the user's historical motion or activity data is 180 times / minute, and the guidance frequency determined based on the user's attribute data is 186 times / minute, these two guidance frequencies can be weighted to determine the user's initial guidance frequency. This disclosure does not limit the method used to determine this initial guidance frequency.

[0251] S1502, Obtain the user's real-time activity data, which includes real-time physiological measurement data.

[0252] It can obtain real-time activity data of users under the guidance of the initial guidance frequency.

[0253] In some embodiments, when a user begins exercising, they can be guided to perform warm-up exercises to bring their body into a training state that aligns with the exercise training goals. In some examples, real-time activity data can be acquired, and the user's physical state can be determined based on this data to ensure it meets training requirements. Optionally, it can be determined that the user's real-time physiological measurements correspond to physiological indicators that have reached preset ranges. For example, if the user's training goal is to improve metabolism, their heart rate can be guided to enter the second heart rate zone. Similarly, if the user's training goal is to improve endurance, their heart rate can be guided to enter the third heart rate zone. The second heart rate zone refers to 60-70% of the user's maximum heart rate, and the third heart rate zone refers to 70-80% of their maximum heart rate. In some examples, in response to determining that the user's physical state meets training requirements, an initial guidance frequency is provided. This guidance on movement frequency, given that the user's physical state is suitable for training, improves the safety of the guidance and thus enhances the user's exercise training effectiveness.

[0254] The method for obtaining real-time user activity data can be referred to the description of the method embodiment above, and will not be repeated here.

[0255] S1503, Based on the user's physiological measurement data, the initial guidance frequency is adjusted to obtain an updated guidance frequency.

[0256] During a user's exercise, real-time physiological measurement data can be collected through wearable devices or other electronic devices, and the guidance frequency can then be adjusted based on this data.

[0257] In some embodiments, the user's real-time physiological measurement data and an initial guidance frequency can be input into a frequency guidance model, algorithm, or mapping relationship to obtain an updated guidance frequency. The output of the frequency guidance model or algorithm can be an adjustment value relative to the initial guidance frequency or the updated guidance frequency. For example, if the user's real-time heart rate is 110, the guidance frequency corresponding to the real-time heart rate of 110 can be determined according to the mapping relationship. Then, an adjustment can be made based on the initial guidance frequency so that the updated guidance frequency is the same as the guidance frequency corresponding to the real-time heart rate of 110, or so that the updated guidance frequency is adjusted from the initial guidance frequency in the direction of the guidance frequency corresponding to the real-time heart rate of 110.

[0258] Once the user's real-time physiological measurement data is determined, their real-time physiological state can be assessed based on this data. Adjusting the initial guidance frequency according to the user's real-time physiological state allows the guidance frequency to align with the user's physiological state, helping the user adjust their training plan based on physiological responses, thereby improving the effectiveness and safety of exercise training.

[0259] In some embodiments, the change information of at least one physiological parameter of the user within a first time period can be determined based on real-time physiological measurement data, and the update guidance frequency can be obtained based on the change information of at least one physiological parameter of the user within the first time period.

[0260] In some examples, a user's frequency adjustment strategy can be determined based on changes in the physiological parameters over a first time period. This strategy can be to increase, maintain, or decrease the frequency. Alternatively, the frequency adjustment strategy can further include the adjusted value. Optionally, a mapping relationship can be established between changes in physiological parameters and the frequency adjustment strategy. For example, if the user's heart rate remains constant, the frequency adjustment strategy is to increase. As another example, if the user's blood oxygen saturation decreases, the corresponding frequency adjustment strategy is to decrease. Optionally, a range or threshold for the physiological parameters can be set, and the frequency adjustment strategy can be determined by comparing the range or threshold corresponding to real-time physiological measurement data with the range or threshold. For example, if the physiological parameter range or threshold is 2, and the user's heart rate changes by more than 2, the guided frequency needs to be decreased by 1.

[0261] In some embodiments, at least one of the user's attribute data, the user group to which the user belongs, and the real-time motion data included in the real-time activity data can be obtained, and the initial guidance frequency can be adjusted based on the user's attribute data, the user group to which the user belongs, the real-time motion data, and the real-time physiological measurement data to obtain an updated guidance frequency.

[0262] In some examples, the initial guidance frequency can be adjusted based on user groups and real-time physiological measurement data to obtain an updated guidance frequency. Different user groups may correspond to different ranges of real-time physiological measurement data. For example, for novice users, whose physical functions are relatively poor, their heart rate and respiratory rate may be faster during exercise. Optionally, after obtaining the user's real-time physiological measurement data, a corresponding range or threshold of physiological parameters can be determined based on the user's user group. The updated guidance frequency is then determined by comparing the user's real-time physiological measurement data with the corresponding range or threshold. Optionally, a guidance frequency adjustment strategy can be determined based on the real-time physiological measurement data, such as increasing, maintaining, or decreasing the frequency. A step size can be determined based on the user's user group, and the initial guidance frequency can be adjusted based on this step size and the guidance frequency adjustment strategy to obtain the updated guidance frequency. Optionally, a corresponding frequency guidance model, algorithm, or mapping relationship can be determined based on the user group, and the real-time physiological measurement data can be processed using this corresponding frequency guidance model, algorithm, or mapping relationship to obtain the updated guidance frequency.

[0263] In other examples, the initial guidance frequency can be adjusted based on the user's real-time motion data and real-time physiological measurement data to obtain an updated guidance frequency. Optionally, the user's real-time movement frequency or current movement frequency in a first time period can be determined based on the user's real-time motion data, and an updated guidance frequency can be obtained based on the real-time movement frequency or current movement frequency and real-time physiological measurement data. As an example, the corresponding physiological parameter range or threshold can be determined based on the user's real-time movement frequency or current movement frequency, and an updated guidance frequency can be obtained by comparing the real-time physiological measurement data with the physiological parameter range or threshold. For example, based on the user's real-time motion data, the user's current cadence can be determined to be 190, the corresponding body temperature range is 37.1 to 37.3 degrees Celsius, and the blood oxygen saturation range is 95% to 98%. In the user's real-time physiological measurement data, the body temperature is 37.2 degrees Celsius and the blood oxygen saturation is 98%. This indicates that the user is well adapted to the current cadence, and therefore the user's cadence can be further increased. A specific value can be added to the user's cadence based on the initial guidance frequency to obtain an updated guidance frequency. The specific value to be increased can be determined based on a preset mapping relationship, a default value, or the current cadence. Alternatively, the user's current cadence and real-time physiological measurement data can be input into the model or algorithm to determine the value. As another example, the user's real-time movement data can be used to determine the changes in the user's real-time movement frequency over a first time period, and the updated guidance frequency can be determined based on these changes in real-time movement frequency and the real-time physiological measurement data.

[0264] In other embodiments, the initial guidance frequency can be adjusted based on user groups, real-time motion data, and real-time physiological measurement data to obtain an updated guidance frequency. As an example, the correspondence between a user's movement frequency and physiological parameter range can be determined based on the user group. By comparing the user's real-time motion data, real-time physiological measurement data, and this correspondence, the updated guidance frequency can be determined. For example, an experienced user's cadence of 180 corresponds to a heart rate range of 115-125. Based on the user's real-time motion data, the user's current cadence is determined to be 180, and based on the real-time physiological measurement data, the user's real-time heart rate is determined to be 130. The comparison shows that the user's real-time heart rate exceeds the corresponding heart rate range, indicating that the user's current cadence has exceeded the body's tolerance. Therefore, it is necessary to reduce the user's cadence or maintain the cadence for a period of time. The updated guidance frequency can be obtained by reducing the initial guidance frequency by a certain value, or by adjusting the updated guidance frequency to the current cadence. As another example, the adjustment step size can be determined based on the user group, and a frequency adjustment strategy (e.g., increase, remain unchanged, or decrease) can be determined based on real-time motion data and real-time physiological measurement data. Then, the updated guidance frequency can be obtained based on this frequency adjustment strategy and the adjustment step size. For instance, based on real-time motion data, the changes in the user's real-time movement frequency within a first time period can be determined, and based on real-time physiological measurement data, the changes in at least one physiological parameter of the user within the first time period can be determined. Then, based on the changes in the real-time movement frequency and at least one physiological parameter within the first time period, a frequency adjustment strategy can be obtained.

[0265] In some embodiments, the guidance frequency can be determined periodically. This period can be a fixed time length, such as adjusting the guidance frequency every five minutes. The period can also be a fixed distance, such as determining the guidance frequency every 200 meters the exerciser moves, and so on. Optionally, real-time activity data of the user within the current period can be acquired, and the guidance frequency for the next period can be determined based on this data.

[0266] Determining the initial guidance frequency based on changes in physiological parameters is to keep the user's physiological parameters within a scientifically reasonable range, thereby ensuring the effectiveness of the user's exercise training and guaranteeing the user's physical safety.

[0267] In some embodiments, the initial guidance frequency is the initial guidance frequency in the method embodiments described above, and the updated guidance frequency is the first guidance frequency in the method embodiments described above.

[0268] In some embodiments, the initial guidance frequency is a first guidance frequency in the method embodiments described above, and the updated guidance frequency is a second guidance frequency in the method embodiments described above.

[0269] Various possible implementations of the above embodiments can be referred to the description of the embodiments above, and for the sake of brevity, they will not be repeated here.

[0270] Corresponding to the foregoing embodiments of the method for guiding motion frequency, this disclosure also provides an embodiment of another apparatus for guiding motion frequency.

[0271] Please refer to Figure 16 , Figure 16 This is a schematic block diagram of a device for guiding the frequency of movements, illustrating an embodiment of this disclosure. The device includes:

[0272] The sixth acquisition unit 1601 is used to acquire the user's initial guidance frequency;

[0273] The seventh acquisition unit 1602 is used to acquire the user's real-time activity data, which includes real-time physiological measurement data;

[0274] The update unit 1604 is used to adjust the initial guidance frequency based on the user's real-time physiological measurement data to obtain an updated guidance frequency.

[0275] In some embodiments, the sixth acquisition unit 1601 is configured to:

[0276] Acquire at least one of the user's historical movement or activity data, the user group to which the user belongs, and the user's attribute data, wherein the user group includes novice users or experienced users;

[0277] The initial guidance frequency is determined based on at least one of the historical movement or activity data, the user group to which the user belongs, and the user's attribute data.

[0278] In some embodiments, the update unit 1604 is configured to:

[0279] Obtain at least one of the following: the user's attribute data, the user group to which the user belongs, and the real-time action data included in the real-time activity data;

[0280] The initial guidance frequency is adjusted based on at least one of the user's attribute data, the user group to which the user belongs, and the real-time motion data, as well as the real-time physiological measurement data, to obtain an updated guidance frequency.

[0281] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0282] This disclosure also provides another method and corresponding apparatus for guiding movement frequency, the method being executed by an electronic device. The method includes: acquiring activity data detected by a user during exercise, the activity data including at least one of physiological measurement data and motion data; determining the user's guided movement frequency based on the activity data; and determining at least one operating parameter of a flashlight based on the guided movement frequency.

[0283] In some embodiments, a flashlight may include at least one light-emitting element for emitting light signals of at least one wavelength. For example, one or more of the following: white light, red light, green light, blue light, etc. Each of the at least one light-emitting element may support the emission of light signals of one wavelength, or support the emission of light signals of multiple wavelengths.

[0284] In some embodiments, the flashlight may include a first light-emitting element and a second light-emitting element. The first light-emitting element and the second light-emitting element may be used to emit light signals of different wavelengths, or the first light-emitting element and the second light-emitting element may be used to emit light signals of different intensities, or the first light-emitting element and the second light-emitting element may be used to emit light signals that flash at different frequencies, or the first light-emitting element and the second light-emitting element may be used to emit light signals in different modes, etc., without limitation.

[0285] In some embodiments, the flashlight may support at least one light emission mode, such as constant light mode, flashing mode, short flashing mode, long flashing mode, red light mode, white light mode, etc., or any combination thereof.

[0286] In some embodiments, at least one of the operating parameters of a flashlight may include at least one of the following: emission wavelength, emission intensity, emission mode, and selectively illuminated light-emitting element.

[0287] The flashlight's at least one operating parameter can be used to indicate the guided action frequency, or to instruct the user to adjust the action frequency in the direction of the guided action frequency.

[0288] In some embodiments, the flashlight may be integrated into the electronic device, in which case the method further includes: controlling the flashlight to emit a light signal according to at least one determined operating parameter. In other embodiments, the flashlight may be integrated into another electronic device. In this case, the method further includes: sending relevant information about the at least one operating parameter to the other electronic device, so that the other electronic device controls the flashlight to emit a light signal based on the relevant information.

[0289] The implementation of each step and / or process in this method can be found in the description of the above embodiments, and will not be repeated here for the sake of brevity.

[0290] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0291] The device for guiding action frequency provided in this disclosure can be applied to wearable devices, intermediate devices, servers, or other electronic devices. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by a processor reading the corresponding computer program instructions from non-volatile memory into memory and executing them. From a hardware perspective, such as... Figure 17 The diagram shown is a hardware structure diagram of an electronic device provided in an embodiment of this disclosure, except... Figure 17 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware depending on its actual function, which will not be described in detail here.

[0292] Based on the same inventive concept, this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for guiding the frequency of actions.

[0293] Based on the same inventive concept, this disclosure also provides an electronic device, the electronic device comprising:

[0294] Processor; memory for storing instructions executable by the processor;

[0295] The processor is configured to execute the method described above for guiding the frequency of actions.

[0296] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0297] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for guiding the frequency of movements, characterized in that, The method includes: Acquire activity data detected during the user's exercise process, including physiological measurement data; Based on the physiological measurement data, determine the change information of at least one physiological parameter of the user within a first time period; Based on the changes in at least one physiological parameter of the user during a first time period, a first guidance frequency for the user is determined, wherein the first guidance frequency includes at least one of guidance action frequency and guidance beat frequency.

2. The method according to claim 1, characterized in that, The change information includes at least one of change trend, change amount, and change rate; Determining the first guidance frequency for the user based on the change information of at least one physiological parameter of the user within a first time period includes: determining the first guidance frequency based on at least one of the change trend, change amount, and change rate of the at least one physiological parameter within the first time period.

3. The method according to claim 1 or 2, characterized in that, The activity data also includes: motion data; The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes: Based on the user's movement data during the first time period, the user's current action frequency is determined; The first guidance frequency for the user is determined based on the user's current action frequency and the changes in at least one physiological parameter within a first time period.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: obtaining information on the change in the user's real-time action frequency within a first time period based on the motion data included in the activity data; The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes: The first guidance frequency of the user is determined based on the changes in the user's real-time action frequency during the first time period and the changes in at least one physiological parameter during the first time period.

5. The method according to claim 4, characterized in that, The step of determining the user's first guidance frequency based on the user's real-time action frequency changes within a first time period and the changes in at least one physiological parameter within the first time period includes: The user's first guidance frequency is determined by comparing the changes in the user's real-time action frequency and the changes in at least one physiological parameter during the first time period.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: obtaining the user's initial guidance frequency during the first time period; The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes: The first guidance frequency for the user is determined based on the initial guidance frequency and the changes in at least one physiological parameter during the first time period.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: obtaining the user group to which the user belongs, the user group including experienced users or novice users; The step of determining the first guidance frequency for the user based on changes in at least one physiological parameter of the user within a first time period includes: Based on the user group to which the user belongs and the change information of the at least one physiological parameter during the first time period, the first guidance frequency of the user is determined.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Based on the first guidance frequency, action frequency guidance information is provided to the user.

9. The method according to claim 8, characterized in that, Providing the user with action frequency guidance information based on the first guidance frequency includes: According to the first guiding frequency, a beat tone is output through an audio output device; or Modify the action frequency guidance information displayed on the display device according to the first guidance frequency; or Based on the first guidance frequency, control at least one operating parameter of the flashlight of the third output device.

10. The method according to claim 8 or 9, characterized in that, Providing the user with action frequency guidance information based on the first guidance frequency includes: Obtain the difference between the user's current action frequency and the first guidance frequency; Based on the differences, the user is provided with guidance information on the frequency of actions.

11. The method according to any one of claims 1 to 10, characterized in that, The method further includes: Obtain information on the changes of at least one physiological parameter of the user during a second time period, which is after the first time period; The second guidance frequency for the user is determined based on the changes in the at least one physiological parameter during the second time period and the first guidance frequency.

12. A method for guiding the frequency of movements, characterized in that, The method includes: Obtain the initial guidance frequency from users; Acquire the user's real-time activity data, which includes real-time physiological measurement data; Based on the real-time physiological measurement data, the initial guidance frequency is adjusted to obtain an updated guidance frequency.

13. The method according to claim 12, characterized in that, The initial guidance frequency for obtaining users includes: The system acquires at least one of the user's historical movement or activity data, the user's attribute data, and the user group to which the user belongs, wherein the user group includes novice users or experienced users. The initial guidance frequency is determined based on at least one of the historical movement or activity data, the user's attribute data, and the user group to which the user belongs.

14. The method according to claim 12 or 13, characterized in that, The step of adjusting the initial guidance frequency based on the user's real-time physiological measurement data to obtain an updated guidance frequency includes: Obtain at least one of the following: the user's attribute data, the user group to which the user belongs, and the real-time motion data included in the real-time activity data; The initial guidance frequency is adjusted based on at least one of the user's attribute data, the user group to which the user belongs, and the real-time motion data, as well as the real-time physiological measurement data, to obtain an updated guidance frequency.

15. A method for guiding the frequency of movements, characterized in that, include: Acquire activity data detected during user exercise, wherein the activity data includes at least one of physiological measurement data and exercise data; Based on the activity data, determine the frequency of the user's guided actions; Based on the frequency of the guided actions, determine at least one operating parameter of the flashlight.

16. A device for guiding the frequency of movements, characterized in that, The device includes: At least one module or unit is used to perform the method of any one of claims 1 to 11, or the method of any one of claims 12 to 14, or the method of claim 15.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method of any one of claims 1 to 11, or the method of any one of claims 12 to 14, or the method of claim 15.

18. An electronic device, characterized in that, The electronic device includes: Processor; memory for storing instructions executable by the processor; The processor is configured to perform the method according to any one of claims 1 to 11, or The method of any one of claims 12 to 14, or the method of claim 15.

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