CN110610233B - Fitness running heart rate prediction method based on domain knowledge and data driving - Google Patents
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- A63B22/00—Exercising apparatus specially adapted for conditioning the cardio-vascular system, for training agility or co-ordination of movements
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Abstract
The invention relates to a fitness running heart rate prediction method based on domain knowledge and data driving, which comprises the following steps of: 1) Collecting body-building running data of a sporter to obtain a training data set; 2) Establishing a domain model DM reflecting the relation between the heart rate and the movement speed and the gradient; 3) Establishing a data-driven autoregressive model ARM reflecting the relationship between the heart rate and the movement speed, the gradient and the movement time; 4) Determining parameters in the domain model and the autoregressive model by adopting a training data set to obtain an individualized domain model and an autoregressive model; 5) And (3) taking the heart rate values predicted by the autoregressive model and the field model, namely the outputs of the autoregressive model and the field model as the input of the integrated learning algorithm, and predicting through the integrated learning algorithm to obtain the final heart rate predicted value. The method is beneficial to improving the precision of the established personalized body-building running model.
Description
Technical Field
The invention relates to the technical field of fitness running modeling, in particular to a fitness running heart rate prediction method based on domain knowledge and data driving.
Background
Exercising on an indoor treadmill is not affected by seasonal weather and is simple and efficient, and becomes an important fitness mode. Most of the running machines on the market currently provide some optional exercise schemes constructed by applying domain knowledge to provide certain guidance for an exerciser in exercising and building up body. However, these exercise schemes are often established on the basis of running models of specific population or age group, and individual differences of physical functions of different exercisers are neglected, which easily results in problems of low model accuracy and inaccurate predicted heart rate value.
Disclosure of Invention
The invention aims to provide a fitness running heart rate prediction method based on domain knowledge and data driving, and the method is favorable for improving the accuracy of fitness running heart rate prediction.
In order to realize the purpose, the technical scheme of the invention is as follows: a fitness running heart rate prediction method based on domain knowledge and data driving comprises the following steps:
1) Collecting body-building running data of a sporter to obtain a training data set;
2) Establishing a domain model DM reflecting the relation between the heart rate and the movement speed and the gradient;
3) Establishing a data-driven autoregressive model ARM reflecting the relation between the heart rate and the exercise speed, the gradient and the exercise time;
4) Determining parameters in the domain model and the autoregressive model by adopting a training data set to obtain an individualized domain model and an autoregressive model;
5) And obtaining a t-time heart rate value predicted by the autoregressive model and a t-time heart rate value predicted by the field model, namely the outputs of the autoregressive model and the field model, by taking historical data before t time as the inputs of the autoregressive model and the field model, then taking the outputs of the autoregressive model and the field model as the inputs of an integrated learning algorithm, and predicting by the integrated learning algorithm to obtain a final heart rate predicted value.
Further, in step 2), the calculation formula of the domain model is as follows:
HR＝a((3.5+0.2V+0.9VA)/3.5)+b (1)
wherein HR represents heart rate, V represents exercise speed, A represents gradient, and a and b are two undetermined parameters corresponding to different runners.
Further, in step 3), the calculation formula of the data-driven autoregressive model is as follows:
wherein, a 0 Representing resting heart rate without exercise, m representing the order of the regression term, a 0 ,……,a m , b 0 ,……,b m ,c 0 ,……,c m Respectively representing the undetermined parameters before the heart rate term, the velocity term and the degree term.
Further, in step 4), the method for determining the parameters a and b slope of the domain model includes: and (4) taking the motion speed and the gradient of the two tests and the corresponding measured heart rate from the training data set, substituting the motion speed and the gradient and the corresponding measured heart rate into a calculation formula of the field model, and then solving the parameters a and b.
Further, in step 4), based on the training data set, the minimum description length criterion is adopted to calculate the value of m, and the undetermined parameter a is determined by the Newton method according to the principle of minimum error 0 ,……,a m ,b 0 ,……,b m ,c 0 ,……,c m 。
Further, in the step 5, the machine learning algorithm is a random forest algorithm RF.
Compared with the prior art, the invention has the beneficial effects that: the heart rate value is predicted through the autoregression model and the field model on the basis of establishing the field model and the data-driven autoregression model, then the output of the autoregression model and the field model is used as the input of the integrated learning algorithm, and the heart rate prediction is carried out through the integrated learning algorithm, so that the accuracy of the personalized fitness running heart rate prediction is improved.
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FIG. 1 is a flow chart of an implementation of an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and specific embodiments.
The invention provides a fitness running heart rate prediction method based on domain knowledge and data driving, which comprises the following steps as shown in figure 1:
1) The body-building running data of the sporter is collected to obtain a training data set.
2) And establishing a domain model DM reflecting the relation between the heart rate and the movement speed and the gradient.
The calculation formula of the domain model is as follows:
HR＝a((3.5+0.2V+0.9VA)/3.5)+b (1)
wherein, HR represents heart rate in bpm, V represents movement speed in m/min, A represents gradient in%, and a and b represent two undetermined parameters corresponding to different runners.
3) And establishing a data-driven autoregressive model ARM reflecting the relation between the heart rate and the exercise speed, the gradient and the exercise time.
The calculation formula of the data-driven autoregressive model is as follows:
wherein, a 0 Representing the resting heart rate without exercise, and the heart rate during exercise increases with increasing intensity on the basis of the resting heart rate, m representing the order of the regression term, a 0 ,……,a m ,b 0 ,……,b m ,c 0 ,……,c m Respectively representing the undetermined parameters before the heart rate term, the velocity term and the degree term.
4) And determining parameters in the domain model and the autoregressive model by adopting the training data set to obtain an individualized domain model and an autoregressive model.
The method for determining the parameters a and b of the field model comprises the following steps: and (4) taking the motion speed and the gradient of the two tests and the corresponding measured heart rate from the training data set, substituting the motion speed and the gradient and the corresponding measured heart rate into a calculation formula of the field model, and then solving the parameters a and b. Based on a training data set, solving the value of m by adopting a minimum description length criterion (MDL), and determining the undetermined parameter a by adopting a Newton method according to the principle of minimum error 0 ,……,a m ,b 0 ,……,b m ,c 0 ,……,c m 。
5) And obtaining a t-time heart rate value predicted by the autoregressive model and a t-time heart rate value predicted by the field model, namely the outputs of the autoregressive model and the field model, by taking historical data before t time as the inputs of the autoregressive model and the field model, then taking the outputs of the autoregressive model and the field model as the inputs of an integrated learning algorithm, and predicting by the integrated learning algorithm to obtain a final heart rate predicted value.
The ensemble learning algorithm may be a feedforward neural network (BPNN), a Support Vector Regression (SVR) or a random forest algorithm (RF). In this embodiment, the ensemble learning algorithm is a random forest algorithm RF.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (4)
1. A fitness running heart rate prediction method based on domain knowledge and data driving is characterized by comprising the following steps:
1) Acquiring body-building running data of a sporter to obtain a training data set;
2) Establishing a domain model DM reflecting the relation between the heart rate and the movement speed and the gradient;
3) Establishing a data-driven autoregressive model ARM reflecting the relationship between the heart rate and the movement speed, the gradient and the movement time;
4) Determining parameters in the domain model and the autoregressive model by adopting a training data set to obtain an individualized domain model and an autoregressive model;
5) Obtaining a t-time heart rate value predicted by the autoregressive model and a t-time heart rate value predicted by the domain model, namely the outputs of the autoregressive model and the domain model, by taking historical data before t time as the inputs of the autoregressive model and the domain model, then taking the outputs of the autoregressive model and the domain model as the inputs of an integrated learning algorithm, and predicting by the integrated learning algorithm to obtain a final heart rate predicted value;
in step 2), the calculation formula of the domain model is as follows:
HR＝a((3.5+0.2V+0.9VA)/3.5)+b (1)
wherein HR represents heart rate, V represents movement speed, A represents gradient, and a and b are two undetermined parameters corresponding to different runners;
in step 3), the calculation formula of the data-driven autoregressive model is as follows:
wherein, a 0 Representing resting heart rate without exercise, m representing the order of the regression term, a 0 ,……,a m ,b 0 ,……,b m ,c 0 ,……,c m Respectively representing the undetermined parameters before the heart rate term, the speed term and the gradient term.
2. The fitness running heart rate prediction method based on domain knowledge and data driving according to claim 1, wherein in the step 4), the parameters a and b of the domain model are determined by: and (4) taking the motion speed and the gradient of the two tests and the corresponding measured heart rate from the training data set, substituting the motion speed and the gradient and the corresponding measured heart rate into a calculation formula of the field model, and then solving the parameters a and b.
3. The fitness running heart rate prediction method based on domain knowledge and data driving of claim 1, wherein in the step 4), m is obtained by using a minimum description length criterion based on a training data set, and the parameter a to be determined is determined by Newton's method according to a principle of minimum error 0 ,……,a m ,b 0 ,……,b m ,c 0 ,……,c m 。
4. The fitness running heart rate prediction method based on domain knowledge and data driving of claim 1, wherein in the step 5, the ensemble learning algorithm is a random forest algorithm.
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