A method and apparatus for determining a training state

Abstract

The application discloses a method and device for determining a training state, which comprises the following steps: obtaining physiological data, wherein the physiological data comprises physical fitness data, body state data and training load; the physical fitness data is used for representing the physical fitness intensity of a human body; the training load is used for representing the fatigue degree of the human body caused by training with different intensities; determining a physical fitness level according to the physical fitness data, determining a body state level according to the body state data, and determining a training load level according to the training load; determining the training state according to the physical fitness level, the body state level and the training load level; and outputting prompt information of the human body movement ability according to the training state. The method provided by the application can scientifically and effectively evaluate the current training state and the current movement ability of a user.

Description

Technical Field

[0001] This application relates to the field of computers, and more particularly to a method and apparatus for determining training status. Background Technology

[0002] Effective sports training can significantly improve a user's health and athletic performance.

[0003] To improve a user's health or performance, training must be conducted at a certain intensity and frequency. The intensity and frequency of training should be matched to the user's athletic ability; excessive or insufficient training will not only fail to effectively improve performance but may also lead to sports injuries and poor training results.

[0004] Therefore, it is urgent to find a scientific and effective way to assess users' current training status and athletic ability. Summary of the Invention

[0005] This application provides a method and apparatus for determining training status, which can scientifically and effectively assess a user's current training status and current athletic ability.

[0006] In a first aspect, embodiments of this application provide a method for determining a training state. The method includes: acquiring physiological data, including physical fitness data, body state data, and training load, wherein the physical fitness data represents the intensity of physical fitness; the training load represents the degree of fatigue caused to the body by training of different intensities; determining a physical fitness level based on the physical fitness data, determining a body state level based on the body state data, and determining a training load level based on the training load; determining the training state based on the physical fitness level, the body state level, and the training load level; and outputting prompt information about the body's motor ability based on the training state.

[0007] For example, in the technical solution provided in this application, the training state can be the user's training state before starting training, during training, or after training. The training can be pre-competition training for competitive athletes, or daily physical exercise training for ordinary non-competitive athletes.

[0008] Generally, determining a user's training status and athletic ability doesn't consider their physical condition. This can lead to situations where a user is in poor physical condition but is shown as being at their peak, or vice versa, providing misleading guidance and causing a mismatch between training intensity and frequency and the user's actual ability, potentially resulting in injury. However, this embodiment of the application determines a user's training status and athletic ability by considering their fitness level, physical condition level, and training load level. It comprehensively considers physical strength, physical condition, and the degree of fatigue caused by training, providing a holistic assessment of the user's training status and athletic ability. This assessment method is scientific and effective. The evaluation results obtained using this method are practical and reliable, allowing users to tailor their training to their individual fitness levels and abilities, thereby improving training effectiveness.

[0009] In one possible implementation, determining the training state based on the fitness level, the physical condition level, and the training load level includes: when the fitness level, the physical condition level, and the training load level are all at the first level, determining the training state as the first training state.

[0010] For example, in the technical solution provided in this application, the first level can be understood as excellent, superb, good, or very good. When the physical fitness level, physical condition level, and training load level are all at the first level, it indicates that the user's physical strength, physical condition, and training load are all superb (i.e., the fatigue brought to the user by training is minimal). In this first training state, the user's training state and athletic ability are superb.

[0011] In this embodiment, under conditions of exceptionally high physical strength, excellent physical condition, and minimal fatigue from training, an assessment result is obtained indicating that the user's training state and athletic ability are both excellent. This assessment result helps to provide the user with optimal training opportunities. The user can then train at an appropriate intensity based on this assessment result, achieving optimal training results with minimal effort and improving training efficiency. The assessment method is scientifically effective, and the assessment results possess practicality and reliability.

[0012] In some possible implementations, the physical fitness data includes maximum oxygen uptake (VO2 max), the body condition data includes two or more of resting heart rate, heart rate variability, sleep index, or heart rate interval, the training load includes the heart rate and running speed during exercise, and determining the fitness level based on the fitness data includes: determining the fitness level as a first level if the value of the VO2 max is within a first VO2 max range; determining the training load level based on the training load includes: determining the training load as a first level if the heart rate is within a first heart rate range and the running speed is within a first speed range; determining the body condition level based on the body condition data includes: determining the body condition level as the first level if, among the at least two data points included in the body condition data, the number of data points belonging to the first level is greater than or equal to the number of data points belonging to the second level.

[0013] For example, determining the body state level as the first level when the number of data points of the first level among the at least two data points included in the body state data is greater than or equal to the number of data points of the second level includes: when the body state data includes any two of the resting heart rate, heart rate variability, sleep index, or heart rate interval, both of the corresponding two body state data levels are of the first level, or when one of the two data points is of the first level and the other is of the second level, the body state level is determined to be the first level. When the body state data includes any three of the resting heart rate, heart rate variability, sleep index, or heart rate interval, two or more of the corresponding three body state data levels are of the first level, or when one of the three data points is of the first level and the other two are of the second level, the body state level is determined to be the first level. When the physical condition data includes the resting heart rate, the heart rate variability, the sleep index, and the heart rate interval, the physical condition level is the first level if four of the following are at the first level: the level of resting heart rate, the level of heart rate variability, the level of sleep index, and the level of heart rate interval; any three are at the first level; or any two are at the first level and the other two are at the second level.

[0014] Understandably, maximal oxygen uptake (VO2max) represents the maximum amount of oxygen a user can ingest per minute during strenuous exercise. Under the same conditions, the more oxygen a user can ingest and utilize, the more sugar or fat they can burn, providing more energy for exercise. Understandably, with resting heart rate within the normal range, a lower resting heart rate indicates a higher stroke volume, resulting in greater nutrient supply during exercise and stronger exercise sustainability. Understandably, heart rate variability (HRV) refers to the variation in the difference between successive heartbeats. A low HRV indicates that the user is in a state of tension or anxiety; a high HRV indicates that the user has good stress resistance. Understandably, the sleep index refers to the user's sleep quality. Good sleep quality can eliminate fatigue, regulate mood, and relieve anxiety, thus benefiting users in physical training. Understandably, the heart rate interval (RRI) refers to the change in RRI during the interval between two consecutive beats (RR) when the user is at rest, i.e., the time between two heartbeats. Understandably, the training load refers to the degree of fatigue experienced by the user's body during training of different intensities. The lower the degree of fatigue experienced by the user during exercise, the more beneficial the training.

[0015] In this embodiment, the system comprehensively considers two or more of the following: maximum oxygen uptake (VO2 max) representing physical fitness, resting heart rate, heart rate variability, sleep index, or heart rate interval representing physical condition, and training load representing fatigue. Only after considering all these indicators and determining that the user's physical fitness level, physical condition level, and training load are all at the first level, is the user's training state determined to be the first training state. This method effectively avoids unnecessary exercise injuries and prevents situations where a user's training state appears excellent when a certain bodily function is on the verge of danger, leading to high-intensity training and potentially causing irreversible damage to that function.

[0016] In one possible implementation, the method further includes: determining a first level of the resting heart rate if the resting heart rate is within a first resting heart rate range; determining a first level of the heart rate variability if the heart rate variability is within a first heart rate variability range; determining a first level of the sleep index if the sleep index is within a first sleep index range; and determining a first level of the heart rate interval if the heart rate interval is within a first heart rate interval range.

[0017] Understandably, the first resting heart rate range refers to a resting heart rate range that is lower than the normal resting heart rate range. Within this first resting heart rate range, the user's stroke volume is higher, resulting in greater nutrient supply during exercise and stronger exercise sustainability. Understandably, within this first heart rate variability range, the user's heart rate variability is higher, indicating good stress resistance. Understandably, within this first sleep index, the user's sleep quality is excellent. Understandably, the heart rate interval refers to the change in the relative rhythm (RR) between two consecutive heartbeats (RRI) in a user's resting state, i.e., the change in the time interval between two heartbeats. When the heart rate interval is within the normal range, a higher heart rate interval indicates good stress resistance, making the user suitable for high-intensity training. When the heart rate interval is within the normal range, a lower heart rate interval indicates a state of tension and anxiety, making the user suitable for relatively low-intensity training.

[0018] In one possible implementation, determining the training state based on the fitness level, the physical condition level, and the training load level includes: determining the training state as a second training state if any two of the fitness level, the physical condition level, and the training load level are determined to be a first level and the other is determined to be a second level; and determining the training state as a third training state if two or more of the fitness level, the physical condition level, and the training load level are determined to be a third level.

[0019] For example, in the technical solution provided in this application, the second level can be understood as good, normal, or relatively good. The third level can be understood as poor, inferior, or very poor. It is understood that if any two of the fitness level, physical condition level, and training load level are at the first level and the other is at the second level, it indicates that two of the user's fitness level, physical condition, or training load are excellent and the other is good; that is, in this second training state, the user's training state and athletic ability are good, and they are suitable for training of the corresponding intensity. It is understood that if two or three of the fitness level, physical condition level, and training load are at the third level, it indicates that two or three of the user's fitness level, physical condition, or training state are poor in this third state; that is, in this third state, the user is in a very unhealthy state or is extremely weak; in this third state, the user is overtrained, has a very poor training state and very poor athletic ability, and is not suitable for continued training.

[0020] In this embodiment, a comprehensive assessment of the user's various physical functions is conducted to determine whether the user's functions are excellent, good, or very poor. This allows for targeted guidance in a second training state, where some functions are excellent while others are good, aiming to improve the user's health and training efficiency and stimulate their athletic potential. Alternatively, if most of the user's functions are found to be extremely poor, this allows for timely communication to the user in a third training state where most functions are poor, indicating a very unhealthy or extremely weak condition. This prevents the user from being unaware that they are overtraining and continuing to train, which could lead to injury.

[0021] In one possible implementation, the human motor ability in the first training state is greater than that in the second training state, and the human motor ability in the second training state is greater than that in the third training state.

[0022] Understandably, in the first training state, the user's training condition is excellent and their athletic ability is extremely strong. For example, in a real-world scenario, for competitive athletes, this first training state could indicate that the user is well-prepared for competition, and the training results achieved in the early stages are excellent. If there is still time for further training, the user can continue with further improvement training. In the second training state, the user's training condition is good and their athletic ability is relatively strong. For example, in a real-world scenario, for competitive athletes, this second training state could indicate that the user is basically well-prepared for competition, and the results achieved in the early stages of training are relatively good. If there is still time for further training, the user can engage in some low-intensity improvement training. In the third state, the user's training condition is extremely poor and their athletic ability is extremely poor. For example, in a real-world scenario, for competitive athletes, this third training state could indicate that the user is not well-prepared for competition, and the results achieved in the early stages of training are very poor. The user should stop training to recover their physical fitness, physical condition, and training load until their physical fitness, physical condition, and training load meet the training requirements before resuming training.

[0023] In one possible implementation, the step of outputting the prompt information on human motor ability based on the training state includes: when the training state is determined to be a first training state, outputting the prompt information that the human motor ability is excellent and ready for competition, and outputting the prompt information that the human motor ability meets the training requirements of the first improvement training.

[0024] In one possible implementation, the step of outputting prompts regarding human motor ability based on the training state includes: when the training state is determined to be the second training state, outputting prompts indicating that human motor ability is relatively good and that basic preparation for competition is achieved; and outputting prompts indicating that human motor ability meets the training requirements of the second enhancement training; wherein the training intensity of the first enhancement training is greater than the training intensity of the second enhancement training; when the training state is determined to be the third training state, outputting prompts indicating that human motor ability is extremely poor and that preparation for competition is not achieved; and outputting prompts to stop training.

[0025] In this embodiment, after determining the user's training status, corresponding prompts are output based on that status. These prompts inform the user of their training status and athletic ability, and also provide training suggestions that match that status and ability. This effectively meets the user's needs and solves their problems.

[0026] In one possible implementation, the acquisition of physiological data includes: measuring the maximum oxygen uptake using the Cooper 12-minute run method; measuring the resting heart rate of the human body in a resting state; analyzing the electrocardiogram data using a time-domain analysis method to obtain the heart rate variability; collecting and analyzing human sleep data to obtain the sleep index; and analyzing the electrocardiogram data to obtain the heart rate interval; and measuring the human body's heart rate and running speed during exercise.

[0027] Optionally, the method for measuring VO2 max can involve the user performing maximal exercise on a fitness device, with a breathing mask continuously monitoring the content and flow rate of exhaled gases to calculate the subject's VO2 max. Other methods for measuring VO2 max include step tests, 6-minute walks, and distance running.

[0028] Optionally, the method for measuring resting heart rate can be as follows: after waking up in the morning, the user stands and uses their index and middle fingers to feel the pulse in their other hand, measuring the number of pulses in one minute. This is repeated for three consecutive days, and the average of the three resting heart rates measured over the three days is taken as the user's resting heart rate. Alternatively, the method for measuring resting heart rate can be as follows: after waking up, the user stands and wears the device provided in this solution to determine training status. The heart rate is measured using the electrodes built into the device, and after the reading on the watch stabilizes, the heart rate is read. This is repeated for three consecutive days, and the average value is taken.

[0029] Optionally, the sleep index can be measured by the user taking the Pittsburgh Sleep Quality Index test. Alternatively, the device that determines the training state can collect and analyze the user's sleep data to determine whether the user's Apnea Index (AHI) is greater than 5, the average blood oxygen saturation is lower than 95%, and the minimum blood oxygen saturation is lower than 90%, thus obtaining the user's sleep index.

[0030] Optionally, the method for measuring the training load can be to detect the user's current first exercise heart rate and the user's usual second exercise heart rate under the same intensity of training, as well as the user's current first exercise speed and the user's usual second exercise speed. The training load level is determined based on the difference between the first and second exercise heart rates and the difference between the first and second exercise speeds. In addition to assessing heart rate and speed during exercise, the training load can also be assessed through subjective indicators such as the user's self-perception, complexion, perspiration, motor coordination, and attention; this application does not impose any limitations on these methods.

[0031] Optionally, the device for determining the training status receives physiological data such as VO2 max, resting heart rate, sleep index, heart rate variability, heart rate interval, and training load input by the user. Alternatively, these physiological data can be measured by the device for determining the training status provided in this application. Alternatively, the measurements can be performed by a first measuring device for VO2 max, a second measuring device for resting heart rate, sleep index, heart rate variability, and heart rate interval, and a third measuring device for training load, and the measurement results can be manually input into the device for determining the training status provided in this application for further determination of the training status; or, the device for determining the training status provided in this application can communicate with the first, second, and third measuring devices, and the first, second, and third measuring devices can send the measurement results to the device for determining the training status, which then receives the measurement results.

[0032] In the embodiments of this application, physiological data can be acquired through the device for determining the training state, certain dedicated measuring devices, or manual measurement. The data measurement methods are simple and highly versatile.

[0033] Secondly, embodiments of this application provide an apparatus for determining a training state, used to execute the method in the first aspect or any possible implementation thereof. The apparatus for determining the training state includes a unit capable of executing the method in the first aspect or any possible implementation thereof.

[0034] For example, the device for determining the training state includes an input / output unit and a processing unit.

[0035] Thirdly, embodiments of this application provide an apparatus for determining a training state. This apparatus includes a processor configured to execute the method shown in the first aspect or any possible implementation thereof. Alternatively, the processor may execute a program stored in a memory, and when the program is executed, the method shown in the first aspect or any possible implementation thereof is executed.

[0036] In this embodiment of the application, the processor and memory can also be integrated into a single device, that is, the processor and memory can be integrated together.

[0037] In one possible implementation, the apparatus for determining the training state further includes a transceiver for receiving or transmitting signals. For example, the transceiver may be used to receive physical fitness data, body state data, or training load.

[0038] In this embodiment of the application, the device for determining the training state can be a chip or the like.

[0039] Fourthly, embodiments of this application provide an apparatus for determining a training state. The apparatus includes a logic circuit and an interface, the logic circuit and the interface being coupled. The interface is used to acquire physiological data. The logic circuit is used to determine a physical fitness level based on the physical fitness data, a body state level based on the body state data, and a training load level based on the training load. The training state is determined based on the physical fitness level, the body state level, and the training load level.

[0040] It is understood that the description of the physical fitness data, the body condition data, and the training load, etc., can be referred to the description in the first aspect; or, reference can also be made to the various embodiments shown below, which will not be detailed here.

[0041] Fifthly, embodiments of this application provide a computer-readable storage medium for storing a computer program that, when run on a computer, causes the method shown in the first aspect or any possible implementation thereof to be executed.

[0042] In a sixth aspect, embodiments of this application provide a computer program product comprising a computer program or computer code that, when run on a computer, causes the method shown in the first aspect or any possible implementation thereof to be executed.

[0043] In a seventh aspect, embodiments of this application provide a computer program that, when run on a computer, executes the method shown in the first aspect or any possible implementation of the first aspect. Attached Figure Description

[0044] Figures 1A-1B A system architecture diagram for determining training state is provided in the embodiments of this application;

[0045] Figure 2 A flowchart illustrating a method for determining training status provided in an embodiment of this application;

[0046] Figures 3A-3B A schematic diagram illustrating the evaluation criteria for physical fitness level, physical condition level, and training load level provided in the embodiments of this application;

[0047] Figure 4 A schematic diagram illustrating a method for determining training status provided in an embodiment of this application;

[0048] Figure 5 A schematic diagram of a display unit for determining training status provided in an embodiment of this application;

[0049] Figures 6 to 8 This is a schematic diagram of the structure of a device for determining training status provided in an embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described below in conjunction with the accompanying drawings.

[0051] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used only to distinguish different objects and not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0052] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] In this application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c".

[0054] The present application will now be further described with reference to the accompanying drawings.

[0055] Please see Figure 1A , Figure 1A This application provides a system architecture diagram for determining training status according to an embodiment. The system for determining training status includes a device 100 for determining training status, which can be an electronic device such as a wearable device, a detector, or a monitor. This application does not impose any special limitations on the specific type and form of the device 100 for determining training status.

[0056] The device 100 for determining training status is used to acquire (measure) physical fitness data, body condition data, and training load, and to determine the physical fitness level, body condition level, and training load level based on the physical fitness data, body condition data, and training load. It is understood that the acquisition of the physical fitness data, body condition data, and training load by the device 100 for determining training status can also be understood as the measurement of the physical fitness data, body condition data, and training load.

[0057] For example, the device 100 for determining training status is used to acquire (measure) the maximum oxygen uptake (VO2 max) from physical fitness data, and if the value of the VO2 max is within a first VO2 max range, the physical fitness level is determined to be a first level. For example, the device 100 for determining training status is used to acquire (measure) two or more of the following from body state data: resting heart rate, heart rate variability, sleep index, or heart rate interval; if the number of data points belonging to the first level among the at least two data points in the body state data is greater than or equal to the number of data points belonging to the second level, the body state level is determined to be the first level. For example, the device 100 for determining training status is used to acquire (measure) the heart rate and running speed of a person under exercise conditions during training load; if the heart rate is within a first heart rate range and the running speed is within a first speed range, the training load is determined to be the first level.

[0058] For a detailed description of the first maximum oxygen uptake range, the first heart rate range, the first speed range, etc., please refer to other embodiments shown in this application, which will not be described in detail here.

[0059] For example, such as Figure 1B As shown, the system for determining the training status may further include a first measuring device 200, which may be an electronic device such as a wearable device, a detector, or a monitor.

[0060] Optionally, the first measuring device 200 can be used to acquire one or more of physical fitness data, body condition data, or training load. For example, the first measuring device 200 can be used to acquire (measure) physical fitness data, body condition data, and training load, and send the physical fitness data, body condition data, and training load to the device 100 for determining the training state. The device 100 for determining the training state receives (acquires) the physical fitness data, body condition data, and training load, and determines the physical fitness level, body condition level, and training load level based on the physical fitness data, body condition data, and training load.

[0061] For example, the first measuring device 200 can be used to acquire (measure) physical fitness data and body condition data, and the device 100 for determining training status is used to acquire (measure) training load. The first measuring device 200 sends the physical fitness data and body condition data to the device 100 for determining training status, and the device 100 for determining training status receives (acquires) the physical fitness data and body condition data, and determines the physical fitness level, body condition level, and training load level based on the physical fitness data, body condition data, and training load.

[0062] For example, the device determining the training state may receive (acquire) the value of the maximum oxygen uptake input by the user. Alternatively, the device determining the training state may measure the maximum oxygen uptake. Alternatively, the device determining the training state may establish a communication connection with a first measuring device corresponding to the maximum oxygen uptake measurement, thereby the first device determining the training state receiving (acquiring) the maximum oxygen uptake sent by the first measuring device.

[0063] For example, such as Figure 1B As shown, in addition to the device 100 for determining the training state and the first measuring device 200, the system for determining the training state may also include a second measuring device 300, which may be an electronic device such as a wearable device, a detector, or a monitor.

[0064] Optionally, the second measuring device 300 can be used to acquire (measure) one or more of physical condition data or training load. For example, the first measuring device 200 is used to acquire (measure) physical fitness data, and the second measuring device 300 is used to acquire (measure) both physical condition data and training load. The device 100 for determining training status receives (acquires) the physical fitness data sent by the first measuring device 200 and receives (acquires) the physical condition data and training load sent by the second measuring device 300, and determines the physical fitness level, physical condition level, and training load level based on the physical fitness data, physical condition data, and training load.

[0065] For example, the first measuring device 200 is used to acquire (measure) physical fitness data, the second measuring device 300 is used to acquire (measure) body condition data, and the device 100 for determining training status is used to acquire (measure) training load. The device 100 for determining training status receives (acquires) the physical fitness data sent by the first measuring device 200 and receives (acquires) the body condition data sent by the second measuring device 300, and determines the physical fitness level, body condition level, and training load level based on the physical fitness data, body condition data, and training load.

[0066] For example, the first measuring device 200 is used to acquire (measure) physical fitness data, the second measuring device 300 is used to acquire (measure) training load, and the device 100 for determining training status is used to acquire (measure) body status data. The device 100 for determining training status receives (acquires) the physical fitness data sent by the first measuring device 200 and receives (acquires) the training load sent by the second measuring device 300, and determines the physical fitness level, body status level, and training load level based on the physical fitness data, body status data, and training load.

[0067] For example, the device determining the training state can receive (acquire) the values ​​of resting heart rate, heart rate variability, sleep index, and heart rate interval input by the user. Alternatively, the device determining the training state can measure the values ​​of resting heart rate, heart rate variability, sleep index, and heart rate interval. Alternatively, the device determining the training state can establish a communication connection with a second measuring device that measures the resting heart rate, heart rate variability, sleep index, and heart rate interval, thereby the first determining training state receiving (acquiring) the values ​​of resting heart rate, heart rate variability, sleep index, and heart rate interval sent by the first measuring device.

[0068] For example, such as Figure 1BAs shown, in addition to the device 100 for determining the training state, the first measuring device 200, and the second measuring device 300, the system for determining the training state may also include a third measuring device 400. This second measuring device may be an electronic device such as a wearable device, a detector, or a monitor. This application embodiment does not impose any special limitations on the specific type and form of the aforementioned first measuring device 200, second measuring device 300, and third measuring device 400.

[0069] The third measuring device 400 is used to measure training load. For example, the first measuring device 200 is used to measure physical fitness data, the second measuring device 300 is used to measure body condition data, and the third measuring device 400 is used to measure training load. The device 100 for determining training status receives (acquires) the physical fitness data sent by the first measuring device 200, receives (acquires) the body condition data sent by the second measuring device 300, and receives (acquires) the training load sent by the third measuring device 400, and determines the physical fitness level, body condition level, and training load level based on the physical fitness data, body condition data, and training load.

[0070] For example, the device determining the training state can receive (acquire) the user's current heart rate during training at the same intensity, a second heart rate during the user's usual training, a first speed during the user's current exercise, and a second speed during the user's usual training. Alternatively, the device determining the training state can measure the first heart rate, the second heart rate, the first speed, and the second speed. Alternatively, the third measuring device can measure the first heart rate, the second heart rate, the first speed, and the second speed, and the device determining the training state receives (acquires) the heart rate, the second heart rate, the first speed, and the second speed sent by the third measuring device.

[0071] In other words, the aforementioned VO2 max, heart rate variability, resting heart rate, sleep index, heart rate interval, first exercise heart rate, second exercise heart rate, first exercise speed, and second exercise speed can be acquired by the same device or by multiple different devices. For example, the aforementioned VO2 max, heart rate variability, resting heart rate, sleep index, heart rate interval, first exercise heart rate, second exercise heart rate, first exercise speed, and second exercise speed can all be acquired (measured) by the device 100 that determines the training state; or, all can be acquired (measured) by the first measuring device 200, the second measuring device 300, or the third measuring device 400; or, some can be acquired (measured) by the device 100 that determines the training state, and the remaining portion can be acquired (measured) by the first measuring device 200, the second measuring device 300, or the third measuring device 400. This document does not impose any limitations on this.

[0072] Understandably, the aforementioned device 100 for determining the training state receives relevant physiological data sent by the first measuring device 200, the second measuring device 300, or the third measuring device 400, which can also be understood as the device 100 for determining the training state acquiring the relevant physiological data.

[0073] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for determining a training state according to an embodiment of this application. The method can be implemented using the apparatus for determining the training state provided in this application (specifically, the one described above). Figure 1A The device 100 shown for determining the training state executes the procedure. This device can be an electronic device such as a wearable device, a detector, or a monitor. This embodiment does not limit the scope of this application (the executing entity in other embodiments below is the same as that described here, and will not be detailed further). Figure 2 As shown: This method includes the following steps:

[0074] 201. Acquire physiological data, including physical fitness data, body condition data, and training load; the physical fitness data is used to represent the physical strength of the human body; the training load is used to represent the degree of fatigue brought to the human body by training of different intensities.

[0075] In this embodiment, physical fitness refers to the basic motor abilities of the human body, manifested through qualities such as strength, speed, endurance, coordination, flexibility, and agility, and is an important component of an athlete's competitive ability. Different sports have different competitive characteristics and different requirements for athletes' motor qualities. Weightlifting mainly tests strength, while long-distance running mainly tests endurance; flexibility has a significant impact on the range of motion of gymnasts' technical movements, and coordination is an important foundation for badminton players' tactical performance in constantly changing competitive sports. In this embodiment, physical fitness intensity can refer to strength intensity, speed intensity, endurance intensity, coordination intensity, flexibility intensity, or agility intensity. For ease of description, the endurance intensity of a long-distance runner will be used as an example to elaborate on physical fitness intensity. Understandably, physical state refers to the user's physical and mental state. On the one hand, whether the user's major organs are free from disease, whether the various systems of the body have good physiological functions, and whether they have strong physical activity and labor capacity are all representations of the user's physical state; on the other hand, whether the user has a good ability to resist stress is a representation of the user's mental state.

[0076] In this embodiment, the physical fitness data includes maximal oxygen uptake (VO2 max). During exercise, the most sustained energy-supplying exercise mode is called aerobic exercise. Reaching VO2 max signifies that aerobic output has reached its maximum. Increasing intensity beyond this point constitutes anaerobic exercise, which is unsustainable. Therefore, in endurance sports such as marathons, trail running, and triathlons, VO2 max represents, to some extent, the capacity for aerobic energy supply. The physical condition data includes two or more of the following: resting heart rate, heart rate variability, sleep index, or heart rate interval. The training load includes the body's heart rate and running speed during exercise.

[0077] Understandably, VO2 max represents the maximum amount of oxygen a user can take in per minute during strenuous exercise. Under the same conditions, the more oxygen a user can take in and use, the more sugar or fat they can burn, providing more energy for exercise. Therefore, a user's physical fitness can be measured by VO2 max.

[0078] Understandably, resting heart rate refers to the number of heartbeats per minute when a user is awake and inactive at rest. A slow or fast resting heart rate indicates a sub-healthy state. Within a normal range, a lower resting heart rate indicates a higher stroke volume, meaning fewer heartbeats are needed to achieve the same stroke volume. During intense exercise, users typically reach a peak heart rate of around 180 beats per minute. At this point, a lower resting heart rate indicates a higher stroke volume, resulting in greater nutrient supply and longer exercise duration. Therefore, resting heart rate can serve as one of the indicators for assessing a user's training status and athletic ability.

[0079] Understandably, heart rate variability (HRV) refers to the variation in the difference between successive heartbeat cycles. If a user's heartbeat is regular, their HRV will be low; if the intervals between heartbeats vary significantly, their HRV will be relatively high. This variation in heartbeat cycles is controlled by the user's autonomic nervous system. The autonomic nervous system is divided into two types: the sympathetic nervous system ("fight or flight") and the parasympathetic nervous system ("relaxation or digestion"). If a user is in a "fight or flight" mode dominated by the sympathetic nervous system, their HRV will be low. If a user is in a "relaxation or digestion" mode dominated by the parasympathetic nervous system, their HRV will be higher. Therefore, a low HRV indicates a state of tension and anxiety; a high HRV indicates good stress tolerance. Conversely, tension and anxiety are detrimental to physical training, while good stress tolerance is beneficial. Therefore, when a user's heart rate variability is relatively high, high-intensity training is more suitable; when a user's heart rate variability is relatively low, low-intensity training is more suitable.

[0080] Understandably, this sleep index refers to a user's sleep quality. Good sleep quality can eliminate fatigue, regulate mood, and alleviate anxiety, which is beneficial for users' sports training. Conversely, poor sleep quality can lead to poor training performance, poor concentration, low morale, easy fatigue, and anxiety, thus affecting training results. Furthermore, poor sleep quality also reflects a user's poor physical condition and accompanying anxiety and tension, indicating that the user is not physiologically or psychologically prepared for training or competition. Therefore, the sleep index can serve as one of the indicators for assessing a user's training status and athletic ability.

[0081] Understandably, the heart rate interval refers to the change in the relative rhythm (RR) between two consecutive heartbeats (RRI) in a user's resting state, i.e., the change in the time interval between two heartbeats. When the heart rate interval is within the normal range, a higher heart rate interval indicates a greater change in the time interval between two heartbeats, meaning the user is in a "relaxation or digestion" mode dominated by the parasympathetic nervous system. In this state, the user has good stress resistance and is suitable for high-intensity training. Conversely, when the heart rate interval is within the normal range, a lower heart rate interval indicates a smaller change in the time interval between two heartbeats, meaning the user is in a "fight or flight" mode dominated by the sympathetic nervous system. In this state, the user is in a tense and anxious state and is suitable for relatively low-intensity training.

[0082] Understandably, training load refers to the degree of fatigue experienced by the user's body at different training intensities. The lower the degree of fatigue experienced by the user during exercise, the more beneficial the training. The user's heart rate and speed during exercise together reflect the user's training load. If the user's heart rate during exercise is higher than the normal exercise heart rate and the speed is greater, it indicates that the user experiences very little fatigue at that training intensity.

[0083] 202. Determine the physical fitness level based on the physical fitness data, determine the physical condition level based on the physical condition data, and determine the training load level based on the training load.

[0084] In this embodiment of the application, determining the physical fitness level based on the physical fitness data includes: determining the physical fitness level as a first level if the value of the maximum oxygen uptake is within a first maximum oxygen uptake range; determining the physical fitness level as a second level if the value of the maximum oxygen uptake is within a second maximum oxygen uptake range; and determining the physical fitness level as a third level if the value of the maximum oxygen uptake is within a third maximum oxygen uptake range.

[0085] Optionally, the first level can be understood as excellent, superb, good, or very good. The second level can be understood as good, average, or fairly good. The third level can be understood as poor, subpar, or very bad.

[0086] In this embodiment of the application, before determining the physical fitness level based on the physical fitness data, determining the physical condition level based on the physical condition data, and determining the training load level based on the training load, the method further includes receiving user input regarding whether the user is a competitive athlete, the user's gender, and the user's age.

[0087] For example, for athletes, the evaluation criteria for this physical fitness level are as follows: Figure 3A As shown. When the user is male, the first maximum oxygen uptake range is VO2max ≥ 75 ml / kg.min, the second maximum oxygen uptake range is 75 ml / kg.min > VO2max ≥ 50 ml / kg.min, and the third maximum oxygen uptake range is VO2max < 50 ml / kg.min. When the user is female, the first maximum oxygen uptake range is VO2max ≥ 65 ml / kg.min, the second maximum oxygen uptake range is 65 ml / kg.min > VO2max ≥ 40 ml / kg.min, and the third maximum oxygen uptake range is VO2max < 40 ml / kg.min.

[0088] In this embodiment of the application, determining the training load level based on the training load includes: determining the training load as the first level when the heart rate is within a first heart rate range and the running speed is within a first speed range; determining the training load as the second level when the heart rate is within a second heart rate range or the running speed is within a second speed range; and determining the training load as the third level when the heart rate is within a third heart rate range and the running speed is within a third speed range.

[0089] Understandably, if a user's heart rate decreases and running speed increases under the same training intensity, it indicates that the current exercise intensity causes minimal fatigue. For example, reuse... Figure 3A For athletes, the evaluation criteria for this training load are as follows: Figures 3A-3BAs shown. When the user's current exercise training intensity is the same as their usual training intensity, the first heart rate range is defined as follows: the first exercise heart rate is greater than or equal to 15 beats / min less than the user's usual second exercise heart rate; the second heart rate range is defined as the first exercise heart rate is greater than -15 beats / min but less than 15 beats / min less than the user's usual second exercise heart rate; and the third heart rate range is defined as the first exercise heart rate is less than or equal to -15 beats / min less than the user's usual second exercise heart rate. Similarly, the first speed range is defined as the first exercise speed is greater than or equal to 5 m / s more than the user's usual second exercise speed; the second speed range is defined as the first exercise speed is greater than -5 m / s but less than 5 m / s more than the user's usual second exercise speed; and the third speed range is defined as the first exercise speed is less than or equal to 5 m / s more than the user's usual second exercise speed.

[0090] In this embodiment of the application, determining the body status level based on the body status data includes: if the number of data points of the first level is greater than or equal to the number of data points of the second level among the at least two data points included in the body status data, then the body status level is determined to be the first level.

[0091] For example, when the body status data includes any two of the following: resting heart rate, heart rate variability, sleep index, or heart rate interval, if both of the corresponding two body status data levels are at the first level, or if one of the two data points is at the first level and the other is at the second level, then the body status level is determined to be the first level. If both of the corresponding body status data levels are at the second level, or if one is at the first level and the other is at the second level, or if one is at the second level and the other is at the third level, then the user's body status level is the second level. If both of the corresponding body status data levels are at the third level, then the user's body status level is the third level.

[0092] For example, when the physical condition data includes any three of the following: resting heart rate, heart rate variability, sleep index, or heart rate interval, the physical condition level is the first level if two or more of the corresponding three physical condition data are at the first level, or if one of the three is at the first level and the other two are at the second level. The physical condition level is the first level if all three corresponding physical condition data are at the second level, any two are at the second level and the other is at the third level, or if any one is at the first level and one of the other two is at the second level and the other is at the third level. The physical condition level is the third level if any two or more of the corresponding three physical condition data are at the third level.

[0093] For example, when the physical condition data includes the resting heart rate, the heart rate variability, the sleep index, and the heart rate interval, the physical condition level is the first level if four of the following are at the first level: the resting heart rate level, the heart rate variability level, the sleep index level, and the heart rate interval level; any three of these are at the first level; or any two of these are at the first level and the other two are at the second level. The user's physical condition level is the third level if four of the following are at the third level: the resting heart rate level, the heart rate variability level, the sleep index level, and the heart rate interval level; any three of these are at the third level; or any two of these are at the third level and the other two are at the second level. Other combinations are at the second level.

[0094] In this embodiment, when the resting heart rate is within a first resting heart rate range, the level of the resting heart rate is determined to be a first level. When the heart rate variability is within a first heart rate variability range, the level of the heart rate variability is determined to be a first level. When the sleep index is within a first sleep index range, the level of the sleep index is determined to be a first level. When the heart rate interval is within a first heart rate interval range, the level of the heart rate interval is determined to be a first level. When the resting heart rate is within a second resting heart rate range, the level of the resting heart rate is determined to be a second level. When the heart rate variability is within a second heart rate variability range, the level of the heart rate variability is determined to be a second level. When the sleep index is within a second sleep index range, the level of the sleep index is determined to be a second level. When the heart rate interval is within a second heart rate interval range, the level of the heart rate interval is determined to be a second level. When the resting heart rate is within a third resting heart rate range, the level of the resting heart rate is determined to be a third level. If the heart rate variability falls within a third heart rate variability range, the level of the heart rate variability is determined to be the third level. If the sleep index falls within a third sleep index range, the level of the sleep index is determined to be the third level. If the heart rate interval falls within a third heart rate interval range, the level of the heart rate interval is determined to be the third level.

[0095] For example, reuse Figure 3A For athletes, the evaluation criteria for resting heart rate, heart rate variability, sleep index, and heart rate interval are as follows: Figure 3AAs shown. The first resting heart rate range is 55 bpm ≥ 45 bpm, the second resting heart rate range is 85 bpm ≥ > 55 bpm, and the third resting heart rate range is < 45 bpm or > 85 bpm. The first heart rate variability range is HRV within (141 ± 20) ms, the second heart rate variability range is 121 ms ≥ HRV > 102 ms or 180 ms > HRV ≥ 161 ms, and the third heart rate variability range is HRV ≤ 102 ms or HRV ≥ 180 ms. The sleep index is obtained based on a sleep index test used to evaluate the Pittsburgh Sleep Quality Index and the scores on the test. The first sleep index range is 8 ≥ 0, the second sleep index range is 15 ≥ > 8, and the third sleep index range is 21 ≥ > 15. For male athletes, the first heart rate interval is defined as an RRI within (827.67±123.34) ms; the second heart rate interval is defined as 704.33 ms ≥ RRI > 634.33 ms or 1021.01 ms > RRI ≥ 951.01 ms; and the third heart rate interval is defined as RRI ≤ 634.33 ms or RRI ≥ 1021.01 ms. For female athletes, the first heart rate interval is defined as an RRI within (839.94±70.85) ms; the second heart rate interval is defined as 769.09 ms ≥ RRI > 709.09 ms or 970.79 ms > RRI ≥ 910.79 ms; and the third heart rate interval is defined as RRI ≤ 709.09 ms or RRI ≥ 970.79 ms.

[0096] In this embodiment, the system comprehensively considers two or more of the following: maximum oxygen uptake (VO2 max) representing physical fitness, resting heart rate, heart rate variability, sleep index, or heart rate interval representing physical condition, and training load representing fatigue. Only after considering all these indicators and determining that the user's physical fitness level, physical condition level, and training load are all at the first level, is the user's training state determined to be the first training state. This method effectively avoids unnecessary exercise injuries and prevents situations where a user's training state appears excellent when a certain bodily function is on the verge of danger, leading to high-intensity training and potentially causing irreversible damage to that function.

[0097] 203. Determine the training status based on the physical fitness level, the physical condition level, and the training load level.

[0098] In this embodiment, if the fitness level, the physical condition level, and the training load level are all at the first level, the training state is determined to be a first training state. If any two of the fitness level, the physical condition level, and the training load level are at the first level and the other is at the second level, the training state is determined to be a second training state. If two or more of the fitness level, the physical condition level, and the training load level are at the third level, the training state is determined to be a third training state.

[0099] Understandably, if any two of the fitness level, physical condition level, and training load level are at level one and the other is at level two, it indicates that two of the user's fitness level, physical condition, or training load are excellent and the other is good. In other words, in this second training state, the user's training status and athletic ability are good, making them suitable for training at the corresponding intensity. Understandably, if two or three of the fitness level, physical condition level, and training load level are at level three, it indicates that two or three of the user's fitness level, physical condition, or training status are poor. In other words, in this third state, the user is in a very unhealthy state or extremely weak. This third state also indicates overtraining; the user's training status and athletic ability are very poor, making them unsuitable for continued training.

[0100] In this embodiment of the application, the human motor ability in the first training state is greater than that in the second training state, and the human motor ability in the second training state is greater than that in the third training state.

[0101] Understandably, in the first training state, the user's training condition is excellent and their athletic ability is extremely strong. For example, in a real-world scenario, for competitive athletes, this first training state could indicate that the user is well-prepared for competition, and the training results achieved in the early stages are excellent. If there is still time for further training, the user can continue with further improvement training. In the second training state, the user's training condition is good and their athletic ability is relatively strong. For example, in a real-world scenario, for competitive athletes, this second training state could indicate that the user is basically well-prepared for competition, and the results achieved in the early stages of training are relatively good. If there is still time for further training, the user can engage in some low-intensity improvement training. In the third state, the user's training condition is extremely poor and their athletic ability is extremely poor. For example, in a real-world scenario, for competitive athletes, this third training state could indicate that the user is not well-prepared for competition, and the results achieved in the early stages of training are very poor. The user should stop training to recover their physical fitness, physical condition, and training load until their physical fitness, physical condition, and training load meet the training requirements before resuming training.

[0102] For example, such as Figure 4 As shown, the upward arrow "↑" indicates the first level of the corresponding physical fitness data, physical condition data, or training load; the left arrow "→" indicates the second level; and the downward arrow "↓" indicates the third level. The determination of the training state based on the physical fitness level, the physical condition level, and the training load level is specifically as follows:

[0103] When the fitness level, the physical condition level, and the training load level are all at the first level, the training state is determined to be the first training state. Optionally, this first training state can also be understood as the first optimal training state. For example, the first training state is as follows: Figure 4 The first optimal training state is shown.

[0104] If any two of the fitness level, physical condition level, and training load level are determined to be at the first level, and the other is at the second level, then the training state is determined to be the second training state. Optionally, this second training state can also be understood as the second optimal training state. For example, this second training state is as follows: Figure 4 The second optimal training state is shown.

[0105] If any two of the physical fitness, physical condition, and training load are at the first level and the other is at the third level, the user's training state is the first high-efficiency training state.

[0106] If any one of the physical fitness, physical condition, and training load is at the first level and the other two are at the second level, the user's training state is the second high-efficiency training state.

[0107] If any one of the physical fitness, physical condition, and training load is at level 1, and the other two are at level 2 and level 3 respectively, the user's training state is a medium-to-high efficiency training state.

[0108] If all three of the physical fitness, physical condition, and training load are at level two, the user's training status is a medium-efficiency training status.

[0109] If any two of the physical fitness, physical condition, and training load are at level two and the other is at level three, the user's training state is an inefficient training state.

[0110] If two or more of the physical fitness level, physical condition level, and training load level are determined to be at the third level, the training state is determined to be the third training state. In this third training state, the user is in a state of overtraining.

[0111] In this embodiment, under conditions of exceptionally high physical strength, excellent physical condition, and minimal fatigue from training, an assessment result is obtained indicating that the user's training state and athletic ability are both excellent. This assessment result helps to provide the user with optimal training opportunities. The user can then train at an appropriate intensity based on this assessment result, achieving optimal training results with minimal effort and improving training efficiency. The assessment method is scientifically effective, and the assessment results possess practicality and reliability.

[0112] In this embodiment, a comprehensive assessment of the user's various physical functions is conducted to determine whether the user's functions are excellent, good, or very poor. This allows for targeted guidance in a second training state, where some functions are excellent while others are good, aiming to improve the user's health and training efficiency and stimulate their athletic potential. Alternatively, if most of the user's functions are found to be extremely poor, this allows for timely communication to the user in a third training state where most functions are poor, indicating a very unhealthy or extremely weak condition. This prevents the user from being unaware that they are overtraining and continuing to train, which could lead to injury.

[0113] 204. Output prompts about human motor ability based on the training status.

[0114] In this embodiment of the application, the step of outputting prompt information on human motor ability based on the training state includes: when the training state is determined to be the first training state, outputting prompt information that human motor ability is excellent and ready for competition, and outputting prompt information that human motor ability meets the training requirements of the first improvement training.

[0115] In this embodiment, the step of outputting prompts regarding human motor ability based on the training state includes: when the training state is determined to be the second training state, outputting prompts indicating that the human motor ability is relatively good and that basic preparation for competition is achieved; and outputting prompts indicating that the human motor ability meets the training requirements of the second improvement training. When the training state is determined to be the third training state, outputting prompts indicating that the human motor ability is extremely poor and that preparation for competition is not achieved; and outputting prompts to stop training.

[0116] For example, reuse Figure 4When the training state is determined to be the first training state (first optimal training state), the system outputs a prompt indicating excellent physical ability and readiness for competition, as well as a prompt indicating that the first improvement training can begin. When the training state is determined to be the second training state (second optimal training state), the system outputs a prompt indicating good physical ability and basic readiness for competition, as well as a prompt indicating that the second improvement training can begin. When the training state is determined to be the first high-efficiency training state, the system outputs a prompt indicating good physical ability and basic readiness for competition, as well as a prompt indicating that the third improvement training can begin. When the training state is determined to be the second high-efficiency training state, the system outputs a prompt indicating good physical ability and basic readiness for competition, as well as a prompt indicating that the fourth improvement training can begin. When the training state is determined to be the medium-high efficiency training state, the system outputs a prompt indicating good physical ability and basic readiness for competition, as well as a prompt indicating that the first interval training can begin. When the training state is determined to be the medium efficiency training state, the system outputs a prompt indicating poor physical ability and a prompt indicating that the second interval training can begin. If the training state is determined to be an inefficient training state, the system outputs a message indicating poor human motor ability and suggesting that a third interval training session can be initiated. If the training state is determined to be the aforementioned third training state (overtraining state), the system outputs a message indicating that overtraining has resulted in very poor human motor ability and suggests that training should be stopped.

[0117] Understandably, the training intensity of the first, second, third, and fourth enhancement training sessions, as well as the first, second, and third interval training sessions, decreases progressively. Specifically, factors such as the target group (users of different ages and / or genders), different training goals (achieving good results in competition or improving physical health through exercise), and coaches with varying levels of strictness towards users will be considered. Therefore, the training intensity of the first, second, third, and fourth enhancement training sessions, as well as the first, second, and third interval training sessions, will vary to some extent. This document does not impose restrictions on this.

[0118] In this embodiment, after determining the user's training status, corresponding prompts are output based on that status. These prompts inform the user of their training status and athletic ability, and also provide training suggestions that match that status and ability. This effectively meets the user's needs and solves their problems.

[0119] For example, in the technical solution provided in this application, the training state can be the user's training state before starting training, during training, or after training. The training can be pre-competition training for competitive athletes, training for athletes to improve their athletic ability, or daily physical exercise training for ordinary non-competitive athletes.

[0120] like Figure 5 As shown, the first measuring device provided in this application may further include a display unit. The display unit displays three parts: physical fitness, physical condition, and training load. An upward arrow 501 indicates the first level, a left arrow indicates the second level 502, and a downward arrow 503 indicates the third level. For example, for male athletes, the maximum circumference of the ring in the physical fitness section of the display unit represents a maximum oxygen uptake of 100 ml / kg·min. The area of ​​the black ring is equal to the product of the ratio of the user's maximum oxygen uptake to 100 ml / kg·min and the total area of ​​the ring.

[0121] Understandably, the method for determining training status provided herein is applicable to both competitive and non-competitive athletes. However, due to factors such as the target group (competitive or non-competitive athletes), individual differences among users (differences in gender, age, height, or weight, etc.), and varying degrees of strictness required by individual users, the specific evaluation criteria for fitness level, physical condition level, and training load level may differ. The evaluation criteria presented herein are merely examples. In practical applications, users may be allowed to make minor adjustments to the evaluation criteria based on their individual needs and factors such as gender, age, height, or weight. This application does not impose any limitations on this.

[0122] Understandably, the training intensity of the first, second, third, and fourth enhancement training sessions, as well as the first, second, and third interval training sessions, decreases progressively. Specifically, factors such as the target group (users of different ages and / or genders), different training goals (achieving good results in competition or improving physical health through exercise), and coaches with varying levels of strictness towards users will be considered. Therefore, the training intensity of the first, second, third, and fourth enhancement training sessions, as well as the first, second, and third interval training sessions, will vary to some extent. This document does not impose restrictions on this.

[0123] In this embodiment, the user's training status and athletic ability are determined by referencing three factors: the user's fitness level, physical condition level, and training load level. This comprehensive assessment considers factors such as physical strength, physical condition, and the degree of fatigue caused by training, resulting in a scientifically effective evaluation method. The evaluation results obtained using this method are practical and reliable, allowing users to tailor their training to their individual fitness levels and abilities, thereby improving training effectiveness.

[0124] From the above Figure 1A It is understood that the aforementioned physical fitness data, body condition data, and training load can be obtained by the aforementioned device for determining training status, or by the first measuring device, the second measuring device, or the third measuring device. Specifically, the aforementioned... Figure 2 In the method embodiment shown, the acquisition of physiological data includes: measuring the maximum oxygen uptake using the Cooper 12-minute run method; measuring the resting heart rate of the human body in a resting state; analyzing the electrocardiogram data using a time-domain analysis method to obtain the heart rate variability; collecting and analyzing human sleep data to obtain the sleep index; and analyzing the electrocardiogram data to obtain the heart rate interval; and measuring the human body's heart rate and running speed during exercise.

[0125] Optionally, the method for measuring VO2 max can involve the user performing maximal exercise on a fitness device, with a breathing mask continuously monitoring the content and flow rate of inhaled gases, and calculating the subject's VO2 max. Alternatively, the method for measuring VO2 max can also include a step test, a 6-minute walk, or a distance run.

[0126] Optionally, the method for measuring resting heart rate can be as follows: After waking up in the morning, the user stands and uses their index and middle fingers to feel the pulse in their other hand, measuring the number of pulses in one minute. This is repeated for three consecutive days, and the average of the three resting heart rates measured over the three days is taken as the user's resting heart rate. Alternatively, the method can be as follows: After waking up, the user stands and wears the device provided in this solution to determine training status. The heart rate is measured using the electrodes built into the device, and after the reading on the watch stabilizes, the heart rate is read. This is repeated for three consecutive days, and the average value is taken.

[0127] Optionally, the heart rate variability can be measured using time-domain analysis or frequency-domain analysis, and the heart rate and pulse data in the collected electrocardiogram (ECG) data can be processed to obtain the desired heart rate variability. For example, in time-domain analysis, the stroke peak interval in the ECG data needs to be calculated first, then the corresponding RR interval is derived based on the stroke peak interval, and finally, time-domain statistics are performed on the RR interval to obtain the time-domain parameters of the heart rate variability, from which the value of the heart rate variability is obtained.

[0128] Optionally, the sleep index can be measured by the user taking the Pittsburgh Sleep Quality Index test. Alternatively, the device determining the training state can collect and analyze the user's sleep data to determine whether the Anaspiratory Hypoxia Index (AHI) is greater than 5, the average blood oxygen saturation is lower than 95%, and the minimum blood oxygen saturation is lower than 90%, thus obtaining the user's sleep index. The sleep index level is determined as follows: if the AHI is not greater than 3, the average blood oxygen saturation is not lower than 96%, and the minimum blood oxygen saturation is not lower than 91%, the user's sleep index level is Level 1. If the AHI is greater than or equal to 3 and not greater than 5, the average blood oxygen saturation is not higher than 96% and not lower than 95%, and the minimum blood oxygen saturation is not higher than 91% and not lower than 90%, the user's sleep index level is Level 2. If the AHI is greater than 5, the average blood oxygen saturation is lower than 95%, or the minimum blood oxygen saturation is lower than 90%, the user's sleep index level is Level 3.

[0129] Understandably, the heart rate interval refers to the change in the relative rate (RR) between two consecutive heartbeats (peak heart rate) in a user's resting state, that is, the change in the time interval between two heartbeats. It can be obtained by measuring, collecting, and analyzing the user's electrocardiogram and pulse data through time-domain analysis or frequency-domain analysis methods.

[0130] Optionally, the method for measuring the training load can be to detect the user's current first exercise heart rate and the user's usual second exercise heart rate under the same intensity of training, as well as the user's current first exercise speed and the user's usual second exercise speed. The training load level is determined based on the difference between the first and second exercise heart rates and the difference between the first and second exercise speeds. In addition to assessing heart rate and speed during exercise, the training load can also be assessed through subjective indicators such as the user's self-perception, complexion, perspiration, motor coordination, and attention; this application does not impose any limitations on these methods.

[0131] In the embodiments of this application, physiological data can be acquired through the device for determining the training state, certain dedicated measuring devices, or manual measurement. The data measurement methods are simple and highly versatile.

[0132] The following describes the apparatus for determining the training state provided in the embodiments of this application.

[0133] This application divides the device for determining the training state into functional modules according to the above-described method embodiments. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this application is illustrative and represents only one logical functional division; in actual implementation, other division methods may be used. The following will combine... Figures 6 to 8 The apparatus for determining the training state according to embodiments of this application is described in detail.

[0134] Figure 6 This is a schematic diagram of the structure of a device for determining training status provided in an embodiment of this application, as shown below. Figure 6 As shown, the apparatus for determining the training state includes a processing unit 601 and an input / output unit 602. This apparatus for determining the training state can be used to perform steps or functions executed by the apparatus for determining the training state in the above method embodiments.

[0135] For example, the input / output unit 602 is used to acquire the physical fitness data, body condition data, or training load. Specifically, the input / output unit 602 receives the physical fitness data, body condition data, or training load input by the user. Alternatively, the input / output unit 602 measures the user's physical fitness data, body condition data, or training load. Or, the input / output unit 602 receives the physical fitness data, body condition data, or training load sent by a first measuring device, a second measuring device, or a third measuring device.

[0136] Processing unit 601 is used to determine the physical fitness level based on the physical fitness data, determine the physical condition level based on the physical condition data, and determine the training load level based on the training load.

[0137] The processing unit is further configured to determine the training state based on the physical fitness level, the physical condition level, and the training load level;

[0138] The input / output unit 602 is used to output prompt information about human motor ability based on the training state.

[0139] For example, the processing unit 601 is specifically used to determine the training state as a first training state when the physical fitness level, the physical condition level, and the training load level are all at the first level.

[0140] For example, the processing unit 601 is specifically configured to: determine the physical fitness level as a first level when the value of the maximum oxygen uptake is included in a first maximum oxygen uptake range; determine the training load as a first level when the heart rate is included in a first heart rate range and the running speed is included in a first speed range; and determine the physical condition level as the first level when, among the at least two data points included in the physical condition data, the number of data points of the first level is greater than or equal to the number of data points of the second level.

[0141] The processing unit 601 is further configured to: determine the level of the resting heart rate as a first level when the resting heart rate is within a first resting heart rate range; determine the level of the heart rate variability as a first level when the heart rate variability is within a first heart rate variability range; determine the level of the sleep index as a first level when the sleep index is within a first sleep index range; and determine the level of the heart rate interval as a first level when the heart rate interval is within a first heart rate interval range.

[0142] For example, the processing unit 601 is specifically configured to determine the training state as a second training state when any two of the fitness level, the physical condition level, and the training load level are determined to be a first level and the other is a second level; and to determine the training state as a third training state when two or more of the fitness level, the physical condition level, and the training load level are determined to be a third level.

[0143] For example, the input / output unit 602 is specifically used to output a prompt message indicating that the human body's motor ability is excellent and ready for competition when the training state is determined to be the first training state, and to output a prompt message indicating that the human body's motor ability meets the training requirements of the first improvement training.

[0144] For example, the input / output unit 602 is specifically configured to, when the training state is determined to be the second training state, output a prompt message indicating that the human body's motor ability is relatively good and that it is basically ready for competition, and output a prompt message indicating that the human body's motor ability meets the training requirements of the second improvement training; the training intensity of the first improvement training is greater than the training intensity of the second improvement training; when the training state is determined to be the third training state, output a prompt message indicating that the human body's motor ability is extremely poor and that it is not ready for competition, and output a prompt message indicating that training should be stopped.

[0145] Understandably, the input / output unit may include a display subunit, which can display the aforementioned prompt information. For example, the display subunit may be a monitor.

[0146] For example, the input / output unit 602 is specifically used to measure the maximum oxygen uptake using the Cooper 12-minute run method; measure the resting heart rate of a human body in a resting state; analyze the electrocardiogram data using a time-domain analysis method to obtain the heart rate variability; collect and analyze human sleep data to obtain the sleep index; and analyze the electrocardiogram data to obtain the heart rate interval; and measure the human body's heart rate and running speed in an exercise state.

[0147] It is understood that, in the embodiments of this application, the descriptions of physical fitness data, body condition data, training load, first training state, second training state, third training state, first maximum oxygen uptake range, first resting heart rate range, first sleep index range, first heart rate interval range, and first heart rate variability range can be referred to the method embodiments shown above, and will not be described in detail here.

[0148] It is understood that the specific descriptions of the input / output unit and processing unit shown in the embodiments of this application are merely examples. For the specific functions or execution steps of the input / output unit and processing unit, please refer to the above method embodiments, which will not be detailed here. For example, the input / output unit 602 is used to perform... Figure 2 The processing unit 601 is used to execute steps 201 and 204 shown. Figure 2 Steps 202 and 203 are shown.

[0149] In one possible implementation, Figure 6 In the apparatus for determining the training state shown, the processing unit 601 may be one or more processors, and the input / output unit 602 may be a transceiver. Alternatively, the input / output unit 602 may also be a transmitting unit and a receiving unit. The transmitting unit may be a transmitter, and the receiving unit may be a receiver. The transmitting unit and the receiving unit are integrated into a single device, such as a transceiver. In the embodiments of this application, the processor and the transceiver may be coupled, etc. The connection method between the processor and the transceiver is not limited in the embodiments of this application.

[0150] like Figure 7 As shown, the device 70 for determining the training state includes one or more processors 720 and transceivers 710.

[0151] For example, when the device for determining the training state is used to perform the steps, methods, or functions described above, the transceiver 710 is used to acquire physical fitness data, body state data, or training load, and to output prompt information about human motor ability based on the training state. The processor 720 is used to determine a physical fitness level based on the physical fitness data, a body state level based on the body state data, and a training load level based on the training load; and to determine the training state based on the physical fitness level, the body state level, and the training load level.

[0152] exist Figure 7 In various implementations of the apparatus for determining the training state, the transceiver may include a receiver for performing a receiving function (or operation) and a transmitter for performing a transmitting function (or operation). The transceiver is also used to communicate with other devices / appliances via a transmission medium.

[0153] Optionally, the device 70 for determining the training state may further include one or more memories 730 for storing program instructions and / or data. The memory 730 is coupled to the processor 720. The coupling in this embodiment is an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, for information exchange between devices, units, or modules. The processor 720 may operate in conjunction with the memory 730. The processor 720 may execute program instructions stored in the memory 730. Optionally, at least one of the one or more memories may be included in the processor. In this embodiment, the memory 730 may store a first maximum oxygen uptake range, a first resting heart rate range, a first sleep index range, a first heart rate variability range, and a first heart rate interval, etc.

[0154] This application embodiment does not limit the specific connection medium between the transceiver 710, processor 720, and memory 730. This application embodiment... Figure 7 The memory 730, processor 720, and transceiver 710 are connected via a bus 740, and the bus is in... Figure 7 The connections between other components are shown in bold and are for illustrative purposes only, not as limiting information. The bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0155] In the embodiments of this application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules within the processor.

[0156] In this application embodiment, the memory may include, but is not limited to, non-volatile memory such as hard disk drive (HDD) or solid-state drive (SSD), random access memory (RAM), erasable programmable read-only memory (EPROM), read-only memory (ROM), or compact disc read-only memory (CD-ROM), etc. Memory is any storage medium capable of carrying or storing program code in the form of instructions or data structures, and capable of being read and / or written by a computer (such as the device for determining training state shown in this application), but is not limited to this. The memory in this application embodiment may also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0157] It is understood that the apparatus for determining the training state shown in the embodiments of this application may also have more than Figure 7 This application does not limit the use of other components or other related elements. The methods performed by the processor and transceiver shown above are merely examples; the specific steps performed by the processor and transceiver can be found in the methods described above.

[0158] In another possible implementation Figure 8 In the device for determining the training state shown, the processing unit 601 can be one or more logic circuits, and the input / output unit 602 can be an input / output interface, or a communication interface, or an interface circuit, or an interface, etc. Alternatively, the input / output unit 602 can also be a transmitting unit and a receiving unit. The transmitting unit can be an output interface, and the receiving unit can be an input interface, with the transmitting unit and receiving unit integrated into one unit, such as an input / output interface. Figure 8 As shown, Figure 8The apparatus for determining the training state shown includes logic circuitry 801 and interface 802. That is, the processing unit 601 can be implemented using logic circuitry 801, and the input / output unit 602 can be implemented using interface 802. The logic circuitry 801 can be a chip, processing circuit, integrated circuit, or system-on-chip (SoC) chip, etc., and the interface 802 can be an input / output interface, pins, etc. For example, Figure 8 Taking the device for determining the training state as an example, the chip includes a logic circuit 801 and an interface 802.

[0159] In this embodiment, the logic circuit and the interface can also be coupled to each other. The specific connection method between the logic circuit and the interface is not limited in this embodiment.

[0160] For example, when the device for determining the training state is used to execute the method, function, or step described above, interface 802 is used to acquire physical fitness data, body state data, or training load, and to output prompt information about human motor ability based on the training state. Logic circuit 801 is used to determine a physical fitness level based on the physical fitness data, a body state level based on the body state data, and a training load level based on the training load; and to determine the training state based on the physical fitness level, the body state level, and the training load level.

[0161] Optionally, the device for determining the training state may further include a memory 803, which may store a first maximum oxygen uptake range, a first resting heart rate range, a first sleep index range, a first heart rate variability range, and a first heart rate interval, etc.

[0162] It is understood that the apparatus for determining the training state shown in the embodiments of this application can be implemented in hardware or software, and the embodiments of this application do not limit this.

[0163] for Figure 8 For specific implementations of the various embodiments shown, please refer to the above embodiments, which will not be described in detail here.

[0164] In addition, this application also provides a computer program for implementing the operations and / or processes performed by the device for determining the training state in the method provided in this application.

[0165] This application also provides a computer-readable storage medium storing computer code that, when executed on a computer, causes the computer to perform the operations and / or processes performed by the means for determining the training state in the method provided in this application.

[0166] This application also provides a computer program product comprising computer code or a computer program that, when run on a computer, causes the operations and / or processes performed by the means for determining the training state in the method provided in this application to be executed.

[0167] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0168] The units described above as separate components may or may not be physically separate. 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 units can be selected according to actual needs to achieve the technical effects of the solutions provided in the embodiments of this application.

[0169] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0170] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above claims.

Claims

1. A method for determining training status, characterized in that, include: Acquire physiological data, including physical fitness data, body condition data, and training load. The physical fitness data is used to represent the physical strength of the human body. The training load is used to represent the degree of fatigue brought to the human body by training of different intensities. The physical fitness data includes maximum oxygen uptake. The body condition data includes at least two of the following: resting heart rate, heart rate variability, sleep index, or heart rate interval. The training load includes the human body's heart rate and running speed during exercise. The physical fitness level is determined based on the physical fitness data, the physical condition level is determined based on the physical condition data, and the training load level is determined based on the training load. The training status is determined based on the physical fitness level, the physical condition level, and the training load level. Based on the training status, output prompts regarding the human body's motor abilities.

2. The method as described in claim 1, characterized in that, Determining the training status based on the physical fitness level, the physical condition level, and the training load level includes: If the physical fitness level, the physical condition level, and the training load level are all at the first level, then the training state is determined to be the first training state.

3. The method as described in claim 1, characterized in that, Determining the physical fitness level based on the physical fitness data includes: If the value of the maximum oxygen uptake is included within the first maximum oxygen uptake range, the physical fitness level is determined to be the first level; Determining the training load level based on the training load includes: If the heart rate is within a first heart rate range and the running speed is within a first speed range, the training load is determined to be the first level. Determining the body condition level based on the body condition data includes: If, among the at least two data points included in the physical condition data, the number of data points belonging to the first level is greater than or equal to the number of data points belonging to the second level, the physical condition level is determined to be the first level.

4. The method as described in claim 3, characterized in that, The method further includes: If the resting heart rate is within a first resting heart rate range, the level of the resting heart rate is determined to be a first level; if the heart rate variability is within a first heart rate variability range, the level of the heart rate variability is determined to be a first level; if the sleep index is within a first sleep index range, the level of the sleep index is determined to be a first level; if the heart rate interval is within a first heart rate interval range, the level of the heart rate interval is determined to be a first level.

5. The method according to any one of claims 1-4, characterized in that, Determining the training status based on the physical fitness level, the physical condition level, and the training load level includes: If any two of the fitness level, physical condition level, and training load level are determined to be the first level and the other is the second level, the training state is determined to be the second training state. If at least two of the physical fitness level, the physical condition level, and the training load level are determined to be at the third level, the training state is determined to be the third training state.

6. The method as described in claim 5, characterized in that, The human motor ability in the first training state is greater than that in the second training state, and the human motor ability in the second training state is greater than that in the third training state.

7. The method according to any one of claims 1-4, characterized in that, The prompt information for outputting human motor ability based on the training state includes: When the training state is determined to be the first training state, a prompt message is output indicating that the human body's motor ability is excellent and ready for competition, and a prompt message indicating that the human body's motor ability meets the training requirements of the first improvement training is output.

8. The method as described in claim 7, characterized in that, The prompt information for outputting human motor ability based on the training state includes: When the training state is determined to be the second training state, a prompt message is output indicating that the human body's motor ability is relatively good and it is basically ready for competition, and a prompt message is output indicating that the human body's motor ability meets the training requirements of the second improvement training; the training intensity of the first improvement training is greater than the training intensity of the second improvement training. If the training state is determined to be the third training state, output a prompt message indicating that the human motor ability is extremely poor and that the person is not ready for competition, and output a prompt message to stop training.

9. The method as described in claim 3 or 4, characterized in that, The acquisition of physiological data includes: The maximum oxygen uptake was measured using the Cooper 12-minute run method. The method involves measuring the resting heart rate of a human body in a resting state, analyzing the electrocardiogram data using time-domain analysis to obtain the heart rate variability, collecting and analyzing human sleep data to obtain the sleep index, and analyzing the electrocardiogram data to obtain the heart rate interval. It measures a person's heart rate and running speed during exercise.

10. A device for determining training states, characterized in that, include: The input / output unit is used to acquire physiological data, including physical fitness data, body condition data, and training load. The physical fitness data is used to represent the physical strength of the human body; the training load is used to represent the degree of fatigue brought to the human body by training of different intensities. The physical fitness data includes maximum oxygen uptake. The body condition data includes at least two of the following: resting heart rate, heart rate variability, sleep index, or heart rate interval. The training load includes the human body's heart rate and running speed during exercise. The processing unit is used to determine the physical fitness level based on the physical fitness data, determine the physical condition level based on the physical condition data, and determine the training load level based on the training load. The processing unit is further configured to determine the training state based on the physical fitness level, the physical condition level, and the training load level; The input / output unit is used to output prompt information about human motor ability based on the training status.

11. The apparatus as claimed in claim 10, characterized in that, The processing unit is specifically used to determine the training state as a first training state when the physical fitness level, the physical condition level, and the training load level are all at the first level.

12. The apparatus as claimed in claim 10, characterized in that, The processing unit is specifically configured to determine the physical fitness level as the first level when the value of the maximum oxygen uptake is included in the first maximum oxygen uptake range; If the heart rate is within a first heart rate range and the running speed is within a first speed range, the training load is determined to be the first level. Furthermore, if, among the at least two data items included in the body state data, the number of data items belonging to the first level is greater than or equal to the number of data items belonging to the second level, the body state level is determined to be the first level.

13. The apparatus as claimed in claim 12, characterized in that, The processing unit is further configured to: determine the level of the resting heart rate as a first level when the resting heart rate is within a first resting heart rate range; determine the level of the heart rate variability as a first level when the heart rate variability is within a first heart rate variability range; determine the level of the sleep index as a first level when the sleep index is within a first sleep index range; and determine the level of the heart rate interval as a first level when the heart rate interval is within a first heart rate interval range.

14. The apparatus according to any one of claims 10-13, characterized in that, The processing unit is specifically used to determine the training state as a second training state when any two of the physical fitness level, the physical condition level, and the training load level are determined to be the first level and the other one is the second level. Furthermore, if at least two of the physical fitness level, the physical condition level, and the training load level are determined to be at the third level, the training state is determined to be the third training state.

15. The apparatus as claimed in claim 14, characterized in that, The human motor ability in the first training state is greater than that in the second training state, and the human motor ability in the second training state is greater than that in the third training state.

16. The apparatus according to any one of claims 10-13, characterized in that, Specifically, the input / output unit is used to output a prompt message indicating that the human body's motor ability is excellent and ready for competition when the training state is determined to be the first training state, and to output a prompt message indicating that the human body's motor ability meets the training requirements of the first improvement training.

17. The apparatus as claimed in claim 16, characterized in that, The input / output unit is specifically used to output a prompt message indicating that the human body's motor ability is good and it is basically ready for competition when the training state is determined to be the second training state, and to output a prompt message indicating that the human body's motor ability meets the training requirements of the second improvement training. The training intensity of the first enhancement training is greater than the training intensity of the second enhancement training; If the training state is determined to be the third training state, output a prompt message indicating that the human motor ability is extremely poor and that the person is not ready for competition, and output a prompt message to stop training.

18. The apparatus as claimed in claim 12 or 13, characterized in that, The input / output unit is specifically used to measure the maximum oxygen uptake using the Cooper 12-minute run method; The method involves measuring the resting heart rate of a human body in a resting state, analyzing the electrocardiogram data using time-domain analysis to obtain the heart rate variability, collecting and analyzing human sleep data to obtain the sleep index, and analyzing the electrocardiogram data to obtain the heart rate interval. In addition, it measures the heart rate and running speed of a person during exercise.

19. A device for determining training states, characterized in that, Including processor and memory; The memory is used to store computer-executed instructions; The processor is configured to execute the computer execution instructions to cause the method described in any one of claims 1-9 to be executed.

20. A device for determining training states, characterized in that, It includes logic circuits and interfaces, wherein the logic circuits and the interfaces are coupled; The interface is used to input and / or output code instructions, and the logic circuit is used to execute the code instructions to cause the method described in any one of claims 1-9 to be performed.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed, performs the method according to any one of claims 1-9.

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