Maximum oxygen uptake evaluation method and system

Abstract

The embodiment of the invention provides a maximum oxygen uptake evaluation method and system. The maximum oxygen uptake evaluation method comprises the steps that physiological information of a user is obtained; based on a heart rate sensor assembly and an inertial sensor assembly, motion information and heart rate information of the user in the test motion execution period are collected; calculating the metabolic equivalent weight of the user based on the physiological information, the motion information and the heart rate information; and calculating and determining the maximum oxygen uptake of the user based on the metabolic equivalent and the heart rate information. The maximum oxygen uptake evaluation method provided by the invention is used for quickly and accurately evaluating and determining the maximum oxygen uptake of the user.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of maximum oxygen uptake assessment, and in particular to a maximum oxygen uptake assessment method and system. Background Art

[0002] Maximum oxygen uptake (VO2max) refers to the amount of oxygen that the body can take in when the human body is exercising at the highest intensity and the functions of various organs and systems reach their highest levels. As one of the important selection criteria for endurance athletes, VO2max is an important indicator of the body's aerobic exercise capacity. A high level of VO2max is the basis for a high level of aerobic exercise capacity.

[0003] Existing methods for measuring VO2max are divided into direct measurement and indirect measurement. The direct measurement method is also called laboratory measurement. During the test, the subject is asked to wear a special instrument and run on a treadmill. The treadmill speed level is adjusted to make the subject exercise to exhaustion. Then the gas exhaled by the subject is collected with a VO2max tester, and the concentration of oxygen and carbon dioxide is analyzed with a gas analyzer to calculate the VO2max. The indirect test method estimates the heart rate during exercise. The oxygen consumption of the human body is closely related to the power completed by the body and the heart rate during exercise. First, the subject is asked to exercise at a certain intensity, then the heart rate after exercise is measured, and then the VO2max is calculated based on the heart rate.

[0004] The direct test method is highly accurate, but it requires professional instruments, is expensive, and is complicated to operate. In addition, there is a risk of injury when the subject performs high-intensity exercise. The indirect test method is convenient to measure and uses simple equipment, but it is affected by individual differences. Some people experience myocardial inhibition and bradycardia due to the accumulation of lactic acid under the endocardium when they are in a hypoxic state. If the maximum oxygen uptake is calculated based on the heart rate at this moment, the value will be significantly higher. Summary of the invention

[0005] The present invention provides a maximum oxygen uptake assessment method for quickly and accurately assessing and determining a user's maximum oxygen uptake.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a maximum oxygen uptake assessment method, comprising:

[0007] Obtaining the user's physiological information;

[0008] Collecting the motion information and heart rate information of the user during the test motion based on the heart rate sensor component and the inertial sensor component;

[0009] Calculating the metabolic equivalent of the user based on the physiological information, exercise information and heart rate information;

[0010] The maximum oxygen uptake of the user is calculated and determined based on the metabolic equivalent and heart rate information.

[0011] As an optional embodiment, obtaining the user's physiological information includes:

[0012] Gender, age, weight.

[0013] As an optional embodiment, the heart rate sensor assembly includes a heart rate sensor arranged at the left chest of a human body and a first wireless data communication module connected to the heart rate sensor.

[0014] As an optional embodiment, the inertial sensor assembly includes a plurality of inertial sensors respectively arranged on the human pelvis, left upper arm, left lower arm, right upper arm, right lower arm, left thigh, left calf, right thigh, and right calf, and a second wireless data communication module connected to each of the inertial sensors.

[0015] As an optional embodiment, collecting the heart rate information of the user during the test exercise based on the heart rate sensor component includes:

[0016] Collecting first heart rate information of the user in a resting state;

[0017] Performing mean processing on the first heart rate information to obtain a resting heart rate;

[0018] Collecting second heart rate information of the user when in motion;

[0019] Performing mean processing on the second heart rate information to obtain an exercise heart rate;

[0020] Wherein, collecting the second heart rate information of the user in a motion state includes:

[0021] The second heart rate information of the user in the target second period after the first exercise period and the second exercise period is collected respectively.

[0022] As an optional embodiment, collecting the motion information of the user during the test motion based on the inertial sensor assembly includes:

[0023] Based on the inertial sensor component, the acceleration information and angular velocity information of the user in the target second period after the first exercise period and the second exercise period are respectively collected, and the acceleration information and angular velocity information can be used to determine the user's exercise posture.

[0024] As an optional embodiment, the calculating the metabolic equivalent of the user based on the physiological information, the motion information and the heart rate information includes:

[0025] Determining the motion posture of the user at different time points based on the motion information;

[0026] Processing the motion posture and heart rate information based on a bandpass filtering algorithm and a principal component analysis algorithm to obtain a processing result;

[0027] combining the processing result with physiological information to generate input information;

[0028] The input information is input into a pre-trained computing model to obtain the metabolic equivalent of the user.

[0029] As an optional embodiment, the processing of the motion posture and heart rate information based on the bandpass filtering algorithm and the principal component analysis algorithm to obtain the processing result includes:

[0030] Determine the passband cutoff frequency and stopband cutoff frequency of the bandpass filter;

[0031] Calculate and determine the sampling frequency of the heart rate information based on the passband cutoff frequency and the stopband cutoff frequency;

[0032] Normalizing the heart rate information based on the sampling frequency;

[0033] The motion posture and the processed heart rate information form a target matrix;

[0034] Performing zero mean processing on each row element in the target matrix, and calculating the covariance matrix of the processed target matrix;

[0035] Calculate and determine the eigenvalues ​​and corresponding eigenvectors of the covariance matrix;

[0036] A data matrix as a result of the processing is constructed based on the eigenvalues ​​and the eigenvectors.

[0037] As an optional embodiment, the calculating and determining the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information includes:

[0038]

[0039] Among them, GrossVO2 is the metabolic equivalent, HR1 and HR2 are two heart rate values ​​collected by the user during exercise, HRmax is the maximum heart rate, and HRrest is the resting heart rate.

[0040] Another embodiment of the present invention also provides a maximum oxygen uptake assessment system, including:

[0041] An acquisition module, used to obtain the user's physiological information;

[0042] A collection module, used for collecting the motion information and heart rate information of the user during the test motion through a heart rate sensor component and an inertial sensor component;

[0043] A first calculation module, used to calculate the metabolic equivalent of the user according to the physiological information, exercise information and heart rate information;

[0044] The second calculation module is used to calculate and determine the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information.

[0045] Based on the disclosure of the above embodiments, it can be known that the beneficial effects of the embodiments of the present invention include obtaining the physiological information of the user, and collecting the motion information and heart rate information of the user during the test exercise based on the heart rate sensor component and the inertial sensor component; then calculating the metabolic equivalent of the user based on the physiological information, motion information and heart rate information, and then determining the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information. Compared with the existing method of directly measuring the maximum oxygen uptake of the user, the evaluation method in the present application can effectively avoid the inconvenience and burden brought by the respiratory test instrument to the user's exercise, is more convenient for the subject to measure independently, and has the same accuracy as the direct measurement method, and has a high reference value.

[0046] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0049] Figure 1 4 is a flow chart of a method for evaluating maximum oxygen uptake in an embodiment of the present invention.

[0050] Figure 2 4 is an application flow chart of the maximum oxygen uptake evaluation method in an embodiment of the present invention.

[0051] Figure 3 4 is a structural block diagram of a maximum oxygen uptake evaluation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but are not intended to limit the present invention.

[0053] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0054] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0055] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the accompanying drawings.

[0056] It should also be understood that, although the invention has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the invention that have the characteristics recited in the claims and are therefore within the scope of protection defined thereby.

[0057] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0058] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0059] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0060] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a maximum oxygen uptake assessment method, comprising:

[0062] S1: Obtain user's physiological information;

[0063] S2: collecting the exercise information and heart rate information of the user during the test exercise based on the heart rate sensor component and the inertial sensor component;

[0064] S3: Calculating the metabolic equivalent of the user based on the physiological information, exercise information and heart rate information;

[0065] S4: Calculate and determine the user's maximum oxygen uptake based on the metabolic equivalent and heart rate information.

[0066] For example, the method of this embodiment is applied to a detection system, which includes an acquisition device and a data processing device, which may include but is not limited to a heart rate sensor, an inertial sensor, a computer, etc. By obtaining the user's physiological information and guiding the user to perform a detection process, including performing detection exercises, the user's motion information and heart rate information when performing the detection process are collected. The data processing device then obtains the motion information, heart rate information, and physiological information, and calculates the user's metabolic equivalent based on the various types of information obtained. After the metabolic equivalent is determined, the user's maximum oxygen uptake is determined based on the metabolic equivalent and heart rate information. Compared with the existing method of directly measuring the user's maximum oxygen uptake, the evaluation method in this embodiment can effectively avoid the inconvenience and burden of the respiratory test instrument on the user's exercise, is time-saving, and is more convenient for the subject to measure independently. It has the same accuracy as the direct measurement method and has a high reference value.

[0067] Specifically, the physiological information of the user obtained in this embodiment includes gender, age, and weight. This information can be actively input by the user into the data processing device to obtain.

[0068] For example, the data processing device can be a user's mobile terminal, such as a mobile phone, a laptop, a tablet computer, etc., or other types of devices with data processing capabilities, which are not unique. An application can be installed on the device, and the user can input his physiological information based on the input interface provided by the program. In addition, the user can also start the detection function based on the application. For example, after the user starts the detection function, the system will interact with the acquisition device to determine whether the contact connection is completed with the user's body. If completed, the program will output guidance information to guide the user to gradually enter the motion state, that is, enter the detection motion stage, which includes a resting stage and a motion stage to cover the user's heart rate information in different motion states. Among them, the motion stage can be but not limited to guiding the user to perform a running test in the target period, such as a 6-minute running test. Of course, according to the user's physiological information, the specific content of the detection motion can be different, that is, the content of the detection motion, that is, the test content can be different according to the user's physiological information, so that the heart rate information of the user in different states can be collected more comprehensively.

[0069] In this embodiment, the inertial sensor is used to collect the user's motion information, including the acceleration and angular velocity of the user's limbs during the user's motion. The heart rate sensor is used to collect the user's heart rate information during the test motion.

[0070] Specifically, the heart rate sensor assembly in this embodiment includes a heart rate sensor disposed at the left chest of a human body and a first wireless data communication module connected to the heart rate sensor.

[0071] The inertial sensor assembly includes a plurality of inertial sensors respectively arranged on the human pelvis, left upper arm, left lower arm, right upper arm, right lower arm, left thigh, left calf, right thigh, and right calf, and a second wireless data communication module connected to each of the inertial sensors.

[0072] All sensor components adopt the MEMS packaging method and are set close to the user's skin. That is, all sensor components are set closely against the user's skin to accurately collect the user's movement information.

[0073] In this embodiment, the module for realizing wireless data communication is a host module and ten slave modules, and the slave modules are connected to each sensor, that is, they are used to form a first wireless data communication module and a second wireless data communication module. The host module and the slave module follow the same communication channel, communication rate and verification method. The host module sends data packets to the maximum oxygen uptake assessment module in the data processing device by setting different receiving addresses to realize data interaction between the system and the acquisition device. In addition, a single-chip microcomputer processing unit, i.e., a wireless transmitting device, can also be set in the device. The single-chip microcomputer processing unit is used to pre-process the raw data uploaded by the inertial sensor and the heart rate sensor to obtain the acceleration, angular velocity posture data and heart rate data. The wireless transmitting device is used to send the data to the maximum oxygen uptake assessment module receiving end. In this embodiment, the wireless transmitting device is connected to the single-chip microcomputer processing unit through the SPI communication mode, and the inertial flyer unit and the heart rate sensor unit communicate with the single-chip microcomputer through I2C serial communication, and of course, they can also be connected in other ways. The data processing device in this embodiment can be but is not limited to a host computer, and the maximum oxygen uptake assessment module provided therein is used to receive, store and further process the heart rate data, and further process the acceleration and angular velocity data to determine the user's motion posture data, and finally accurately assess the maximum oxygen uptake in combination with the input user's physiological information.

[0074] Furthermore, in this embodiment, collecting the heart rate information of the user during the test exercise based on the heart rate sensor component includes:

[0075] S5: Collecting first heart rate information of the user in a resting state;

[0076] S6: performing mean processing on the first heart rate information to obtain a resting heart rate;

[0077] S7: collecting second heart rate information of the user in a motion state;

[0078] S8: performing mean processing on the second heart rate information to obtain an exercise heart rate;

[0079] Wherein, collecting the second heart rate information of the user in a motion state includes:

[0080] S9: Collect the second heart rate information of the user in the target second period after the first exercise period and the second exercise period respectively.

[0081] For example, the test exercise includes a resting phase and an exercise phase. When the test starts, the system will guide the user to remain resting for 1 minute, at which time the heart rate sensor will begin to collect the user's resting heart rate. Subsequently, the user is guided to perform a 6-minute running test, during which the user's motion posture data is collected, including acceleration, angular velocity data, and exercise heart rate data measured by the sensor during running. At the same time, after 2 minutes of exercise, the system will adaptively make matching requirements to the user based on the collected heart rate information, informing the user that they need to exercise vigorously so that their heart rate is not less than 80% of the maximum heart rate percentage, thereby ensuring the effectiveness of the test.

[0082] Specifically, in the resting state, the system will control the heart rate sensor to collect heart rate information at a frequency of once every 10 seconds within 1 minute, and finally take the average of 6 heart rates as the resting heart rate. After the start of exercise, the system will collect heart rate information in two time periods, the last 10 seconds of the 4th minute and the last 10 seconds of the 6th minute, and calculate the average heart rate respectively. The above two time periods are not specific, and you can select any time period in the initial stage of exercise and any time period near the end of exercise.

[0083] At the same time, the system controls the inertial sensor to collect the user's motion information, for example, based on the inertial sensor component, the user's motion information during the test motion is collected, including:

[0084] S10: Based on the inertial sensor assembly, the acceleration information and angular velocity information of the user in the target second period after the first exercise period and the second exercise period are respectively collected, and the acceleration information and angular velocity information can be used to determine the user's exercise posture.

[0085] That is, when collecting the heart rate information during exercise, the system will control the inertial sensor to simultaneously collect the acceleration information and angular velocity information of the user in the two time periods, and then determine the movement posture of the user in the two time periods.

[0086] After obtaining the exercise information and heart rate information, the system will calculate the user's metabolic equivalent based on the physiological information, exercise information and heart rate information, including:

[0087] S11: determining the motion posture of the user at different time points based on the motion information;

[0088] S12: Processing the motion posture and heart rate information based on a bandpass filtering algorithm and a principal component analysis algorithm to obtain a processing result;

[0089] S13: Combining the processing result with the physiological information to generate input information;

[0090] S14: Inputting the input information into a pre-trained computing model to obtain the metabolic equivalent of the user.

[0091] The processing of the motion posture and heart rate information based on the bandpass filtering algorithm and the principal component analysis algorithm to obtain the processing result includes:

[0092] S15: Determine the passband cutoff frequency and the stopband cutoff frequency of the bandpass filter;

[0093] S16: Determine the sampling frequency of the heart rate information based on the passband cutoff frequency and the stopband cutoff frequency;

[0094] S17: performing normalization processing on the heart rate information based on the sampling frequency;

[0095] S18: Combining the motion posture and the processed heart rate information into a target matrix;

[0096] S19: performing zero mean processing on each row element in the target matrix, and calculating the covariance matrix of the processed target matrix;

[0097] S20: Calculate and determine the eigenvalues ​​and corresponding eigenvectors of the covariance matrix;

[0098] S21: Constructing a data matrix as the processing result based on the eigenvalues ​​and eigenvectors.

[0099] That is, the system will process the user's motion posture data and exercise heart rate data using bandpass filtering and principal component analysis (PCA), and combine the user's age, gender, and weight data to calculate the user's metabolic equivalent. Specifically, in the present embodiment, bandpass filtering and principal component analysis are used to process the motion posture data and heart rate data, extract age, gender, weight, resting heart rate, exercise heart rate in two time periods, acceleration, and angular velocity data as feature information, and then bring them into the artificial neural network model (ANN) formed by pre-training in the system, so as to calculate the user's metabolic equivalent (also known as Metabolo) based on the pre-training model. In the input parameters of the ANN model, the importance of each feature is represented by a weight, and there are three calculation layers in the model, which are all formed by neuron arrangement, and these layers are connected by transfer functions. Before the initial model training, each layer of network weights is assigned a smaller random value, and after training, each weight is determined. And in order to improve the estimation error, the ANN model in the present embodiment, its final output is the metabolic equivalent within a period of time, i.e., a mean, rather than the metabolic equivalent at a certain moment.

[0100] Further, in this embodiment, the training process of the ANN model is implemented by a feedforward / backward propagation algorithm (BP neural network algorithm). Since the measured feature vectors include: age, gender, weight, resting heart rate, exercise heart rate in two time periods, acceleration, angular velocity, a total of 10 feature vectors. Therefore, the model adopts a three-layer network structure with an input layer containing 10 nodes, a hidden layer containing 6 nodes, and an output layer containing 1 node for structural setting. The importance of each feature in the input parameter is represented by a weight. Before training, a small random value is assigned to each layer of network weight, and the mean value = 0; variance = 1; the hyperbolic tangent curve function (Tanh function) with an implicit range of [-1,1] is selected as the activation function of the input layer and the hidden layer, and then forward propagation training is performed, that is, the input data is passed through the neural network, and the output result mode is obtained for training, and the error between the output result and the true result is calculated to obtain the loss function, and then the contribution of each neuron to the error is calculated according to the loss function, and the weight is updated based on the calculated contribution value. Repeat the above series of steps (forward propagation - calculation of loss function - back propagation) until the preset stop condition is reached to complete the training and learning of the model. Among them, the data training adopts the gradient descent method, and the learning rate is set to 0.0001. Of course, there is no specific limitation. It is only introduced here as an optional embodiment.

[0101] When using the bandpass filter algorithm to process data, first determine the passband cutoff frequency and stopband cutoff frequency of the bandpass filter. For example, half of the sampling frequency of each sensor is defined as the normalized frequency "1", and a value less than "1" (for example, 0.2) is used to define the passband frequency or stopband cutoff frequency of the digital filter (equivalent to defining the ratio of the cutoff frequency to half the sampling rate). Taking the measurement frequency of the heart rate as 1khz as an example, its normalized frequency is 500hz, the passband cutoff frequency: 1000Hz*0.2=200Hz, and the stopband cutoff frequency: 1000Hz*0.3=300Hz.

[0102] For the principal component analysis (PCA) method, when it is used to process heart rate information and motion information, the algorithm steps include: assuming that there are m n-dimensional data to be processed, including heart rate information and motion information, then the original data are organized into an n-row and m-column matrix X by column, each row of X is zero-meaned, that is, the mean of this row is subtracted, the covariance matrix is ​​calculated, the eigenvalues ​​of the covariance matrix and the corresponding eigenvectors are calculated, the eigenvectors are arranged into a matrix by row from top to bottom according to the corresponding eigenvalue size, and the first k rows are taken to form a matrix P, which is a new matrix. The data in the new matrix is ​​the data after dimensionality reduction to k dimensions, that is, the processing result.

[0103] After calculating the metabolic equivalent, the system will calculate the user's maximum oxygen uptake based on the metabolic equivalent and heart rate information, including using the following formula to evaluate the maximum oxygen uptake:

[0104]

[0105] Among them, GrossVO2 is the metabolic equivalent, HR1 and HR2 are two heart rate values ​​collected by the user during exercise, HRmax is the maximum heart rate, and HRrest is the resting heart rate.

[0106] like Figure 3 As shown, another embodiment of the present invention also provides a maximum oxygen uptake assessment system 100, including:

[0107] An acquisition module, used to obtain the user's physiological information;

[0108] A collection module, used for collecting the motion information and heart rate information of the user during the test motion through a heart rate sensor component and an inertial sensor component;

[0109] A first calculation module, used to calculate the metabolic equivalent of the user according to the physiological information, exercise information and heart rate information;

[0110] The second calculation module is used to calculate and determine the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information.

[0111] As an optional embodiment, obtaining the user's physiological information includes:

[0112] Gender, age, weight.

[0113] As an optional embodiment, the heart rate sensor assembly includes a heart rate sensor arranged at the left chest of a human body and a first wireless data communication module connected to the heart rate sensor.

[0114] As an optional embodiment, the inertial sensor assembly includes a plurality of inertial sensors respectively arranged on the human pelvis, left upper arm, left lower arm, right upper arm, right lower arm, left thigh, left calf, right thigh, and right calf, and a second wireless data communication module connected to each of the inertial sensors.

[0115] As an optional embodiment, collecting the heart rate information of the user during the test exercise based on the heart rate sensor component includes:

[0116] Collecting first heart rate information of the user in a resting state;

[0117] Performing mean processing on the first heart rate information to obtain a resting heart rate;

[0118] Collecting second heart rate information of the user when in motion;

[0119] Performing mean processing on the second heart rate information to obtain an exercise heart rate;

[0120] Wherein, collecting the second heart rate information of the user in a motion state includes:

[0121] The second heart rate information of the user in the target second period after the first exercise period and the second exercise period is collected respectively.

[0122] As an optional embodiment, collecting the motion information of the user during the test motion based on the inertial sensor assembly includes:

[0123] Based on the inertial sensor component, the acceleration information and angular velocity information of the user in the target second period after the first exercise period and the second exercise period are respectively collected, and the acceleration information and angular velocity information can be used to determine the user's exercise posture.

[0124] As an optional embodiment, the calculating the metabolic equivalent of the user based on the physiological information, the motion information and the heart rate information includes:

[0125] Determining the motion posture of the user at different time points based on the motion information;

[0126] Processing the motion posture and heart rate information based on a bandpass filtering algorithm and a principal component analysis algorithm to obtain a processing result;

[0127] combining the processing result with physiological information to generate input information;

[0128] The input information is input into a pre-trained computing model to obtain the metabolic equivalent of the user.

[0129] As an optional embodiment, the processing of the motion posture and heart rate information based on the bandpass filtering algorithm and the principal component analysis algorithm to obtain the processing result includes:

[0130] Determine the passband cutoff frequency and stopband cutoff frequency of the bandpass filter;

[0131] Calculate and determine the sampling frequency of the heart rate information based on the passband cutoff frequency and the stopband cutoff frequency;

[0132] Normalizing the heart rate information based on the sampling frequency;

[0133] The motion posture and the processed heart rate information form a target matrix;

[0134] Performing zero mean processing on each row element in the target matrix, and calculating the covariance matrix of the processed target matrix;

[0135] Calculate and determine the eigenvalues ​​and corresponding eigenvectors of the covariance matrix;

[0136] A data matrix as a result of the processing is constructed based on the eigenvalues ​​and the eigenvectors.

[0137] As an optional embodiment, the calculating and determining the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information includes:

[0138]

[0139] Among them, GrossVO2 is the metabolic equivalent, HR1 and HR2 are two heart rate values ​​collected by the user during exercise, HRmax is the maximum heart rate, and HRrest is the resting heart rate.

[0140] Another embodiment of the present invention further provides an electronic device, including:

[0141] at least one processor; and,

[0142] a memory communicatively coupled to the at least one processor;

[0143] The memory stores instructions executable by the at least one processor, wherein the instructions are configured to execute the maximum oxygen uptake assessment method as described in any one of the embodiments above.

[0144] Another embodiment of the present invention further provides a storage medium, which includes a stored program, wherein when the program is run, a device including the storage medium is controlled to execute the maximum oxygen uptake assessment method as described in any of the embodiments above.

[0145] The embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions, and when the computer-executable instructions are executed, at least one processor executes a maximum oxygen uptake assessment method such as in the above-described embodiment. It should be understood that each solution in this embodiment has the corresponding technical effects in the above-described method embodiment, which will not be repeated here.

[0146] It should be noted that the computer storage medium of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program configured to be used by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, optical cable, RF, etc., or any suitable combination of the above.

[0147] The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A method for evaluating maximum oxygen uptake, characterized in that: include: Obtaining the user's physiological information; Collecting the motion information and heart rate information of the user during the test motion based on the heart rate sensor component and the inertial sensor component; Calculating the metabolic equivalent of the user based on the physiological information, exercise information and heart rate information; The maximum oxygen uptake of the user is calculated and determined based on the metabolic equivalent and heart rate information.

2. The maximum oxygen uptake evaluation method according to claim 1, characterized in that: The obtaining of the user's physiological information includes: Gender, age, weight.

3. The maximum oxygen uptake evaluation method according to claim 1, characterized in that: The heart rate sensor assembly comprises a heart rate sensor arranged at the left chest of a human body and a first wireless data communication module connected to the heart rate sensor.

4. The maximum oxygen uptake evaluation method according to claim 1, characterized in that: The inertial sensor assembly includes a plurality of inertial sensors respectively arranged on the human pelvis, left upper arm, left lower arm, right upper arm, right lower arm, left thigh, left calf, right thigh, and right calf, and a second wireless data communication module connected to each of the inertial sensors.

5. The method for evaluating maximum oxygen uptake according to claim 1, characterized in that: Collecting the heart rate information of the user during the test exercise based on the heart rate sensor component includes: Collecting first heart rate information of the user in a resting state; Performing mean processing on the first heart rate information to obtain a resting heart rate; Collecting second heart rate information of the user when in motion; Performing mean processing on the second heart rate information to obtain an exercise heart rate; Wherein, collecting the second heart rate information of the user in a motion state includes: The second heart rate information of the user in the target second period after the first exercise period and the second exercise period is collected respectively.

6. The method for evaluating maximum oxygen uptake according to claim 1, characterized in that: Collecting the motion information of the user during the test motion based on the inertial sensor assembly includes: Based on the inertial sensor component, the acceleration information and angular velocity information of the user in the target second period after the first exercise period and the second exercise period are respectively collected, and the acceleration information and angular velocity information can be used to determine the user's exercise posture.

7. The maximum oxygen uptake evaluation method according to claim 1, characterized in that: The calculating the metabolic equivalent of the user based on the physiological information, the exercise information and the heart rate information includes: Determining the motion posture of the user at different time points based on the motion information; Processing the motion posture and heart rate information based on a bandpass filtering algorithm and a principal component analysis algorithm to obtain a processing result; combining the processing result with physiological information to generate input information; The input information is input into a pre-trained computing model to obtain the metabolic equivalent of the user.

8. The method for evaluating maximum oxygen uptake according to claim 7, characterized in that: The processing of the motion posture and heart rate information based on the bandpass filtering algorithm and the principal component analysis algorithm to obtain the processing result includes: Determine the passband cutoff frequency and stopband cutoff frequency of the bandpass filter; Calculate and determine the sampling frequency of the heart rate information based on the passband cutoff frequency and the stopband cutoff frequency; Normalizing the heart rate information based on the sampling frequency; The motion posture and the processed heart rate information form a target matrix; Performing zero mean processing on each row element in the target matrix, and calculating the covariance matrix of the processed target matrix; Calculate and determine the eigenvalues ​​and corresponding eigenvectors of the covariance matrix; A data matrix as a result of the processing is constructed based on the eigenvalues ​​and the eigenvectors.

9. The method for evaluating maximum oxygen uptake according to claim 1, characterized in that: The calculating and determining the maximum oxygen uptake of the user based on the metabolic equivalent and the heart rate information includes: Among them, GrossVO2 is the metabolic equivalent, HR1 and HR2 are two heart rate values ​​collected by the user during exercise, HRmax is the maximum heart rate, and HRrest is the resting heart rate.

10. A maximum oxygen uptake assessment system, characterized in that: include: An acquisition module, used to obtain the user's physiological information; A collection module, used for collecting the motion information and heart rate information of the user during the test motion through a heart rate sensor component and an inertial sensor component; A first calculation module, used to calculate the metabolic equivalent of the user according to the physiological information, exercise information and heart rate information; The second calculation module is used to calculate and determine the maximum oxygen uptake of the user based on the metabolic equivalent and heart rate information.

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