# Dynamic Monitoring Method of Physical Training Intensity Based on Wearable Sensors

Authors: RuiHeng Li; Ying Huang; Wengang Ren; Jia Qiao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Physical training data are greatly affected by the human movement state, and the current monitoring method has the problem of low accuracy. A dynamic monitoring method of physical training intensity is designed based on the wearable sensor. The wearable sensor network is established, the quaternion is transformed into the corresponding Euler angle, and the measurement of the sensor is transformed into the human body coordinate system. In the process of human motion reconstruction, the human motion state parameters are estimated based on the wearable sensor data, the parameter eigenvalues are extracted, and the decision tree classification algorithm is used to identify the physical training state. According to the classification results, the dynamic monitoring model of three-dimensional energy training intensity is established, and the numerical index of training intensity is obtained. In terms of various physical training items, the average monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.12%, which is 3.70%, 4.59%, and 5.04% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology, respectively, so as to realize the accurate monitoring of sports state.


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## Body

## 1. Introduction

Physical training can promote the coordinated development of athletes’ strength, speed, coordination, flexibility, and other physical qualities. No data, no training has become the basic criterion of training, and the requirements for physical training are becoming more and more strict. The original qualitative evaluation cannot meet the basic needs of training, so it is necessary to make a quantitative evaluation of the training process. At this time, it is urgent to monitor the training process to obtain relevant training data. Scientific monitoring of sports training load is a necessary prerequisite for accurate diagnosis of athletes’ competitive ability [1]. Exercise intensity refers to the degree of internal response of the body caused by external stimulation in unit time during exercise. In the practice of sports training, sports intensity monitoring is one of the important links of training monitoring [2]. Regular physiological and biochemical function tests can help coaches, scientific researchers, and medical personnel to understand the functional state of athletes in a training course. However, there is no clear answer whether to achieve medium exercise intensity in the process of exercise. In order to enhance the improvement of bodily function, a low-intensity exercise programme is insufficient. The greater the workout intensity, the more harm the body will suffer. Long-term high-intensity exercise may tire the body and raise the risk of sports injury [3]. The sports training impact will not be maximised if the load intensity is too high or too low throughout the training procedure. Only within a particular range can the benefits of sports training be maximised. It follows the same fundamental trend as the “inverted U-shaped curve.” Athletes’ training state, training statistics, and other training information may be obtained in real time via dynamic monitoring of physical exercise intensity. It mainly monitors and records training, testing, and other relevant data to ensure the normal progress of the training plan. With the development of scientific training and the continuous improvement of the competitive sports level of various events, in order to adapt to the trend of accelerating the pace of competition and training, scholars have explored a variety of repeatable monitoring methods that can scientifically evaluate the sports’ intensity. Sansan uses the reflected interframe images to form a continuous virtual animation of athletes’ running training and defines the intensity of athletes’ running training [4]. Jinze et al. introduced adaptive filter factor and offset premeasurement in the calculation of filtered output user’s moving bone node position, taking into account the real-time output of node filter predicted position and angle smoothing value when the user’s action changes [5]. Yu and colleagues employ Internet of Things technology to monitor and assess the fitness impact, as well as provide scientific fitness counsel, in order to tackle the challenges that exist in fitness. [6]. The above monitoring methods can provide a reference for the formulation of physical training intensity and sports ability evaluation, but the monitoring data are greatly affected by the human motion state, resulting in low accuracy. Reasonable and effective monitoring of training load intensity helps to ensure the rationality and effectiveness of training methods and is of great significance to maintain and improve the competitive level of athletes. Therefore, this paper designs a dynamic monitoring method of physical training intensity based on the wearable sensor to quantitatively evaluate athletes’ physical training. The most suitable training intensity and amount for each athlete are found to ensure the functional state of athletes and achieve the best effect of physical training.


## 2. Dynamic Monitoring Method of Physical Training Intensity Based on Wearable Sensor

### 2.1. Building Wearable Sensor Networks

The position of the wearable sensor has a certain impact on the dynamic monitoring of physical training intensity. The signals measured at different positions are very different and are easily affected by high-speed movement. It is difficult to distinguish between fall and ADL and will increase the possibility of misjudgment. In this regard, wearable sensor networks are established. Wearable sensor networks are less intrusive to people’s privacy. At the same time, they have the advantages of small node volume and lightweight. They can now be readily worn on individuals and tracked thanks to technological advancements and the invention of implanted sensors [7]. Wearable sensor networks are generally simple to maintain on a daily basis, with the major maintenance requirements being on battery replacement [8, 9]. Chest sensors do not offer enough information to distinguish actions like sitting and standing. The data acquired by wearing the head sensor have the best classification detection accuracy and outstanding impact detection capabilities when compared to data produced by hip and wrist sensors [10, 11]. People wear different sorts of sensor nodes, which are equipped with various types of sensors, such as acceleration, temperature, sound, and light, in a wearable sensor network made of active sensor devices [12]. The data are sent to the sink node through wired, wireless, and other communication channels once the sensor node acquires the sensor reading. The sink node sends the data it receives to the processing node for further behavior analysis. Active sensor equipment has the benefit of having a robust sensor capability and the capacity to perceive a wide range of data. Pitch, roll, and heading angles, together known as Euler angles, are used in the construction of wearable sensor networks. In the process of moving target tracking, because the Euler angle has the problem of universal deadlock, that is, for the dynamic Euler angle, if it rotates around the object coordinate system, as long as the pitch angle is ±90°, no matter how many degrees the roll angle and heading angle are, there will be universal lock [13]. Therefore, in the process of obtaining the Euler angle, the quaternion is introduced to convert the Euler angle into quaternion, perform spherical linear interpolation on the quaternion, and then convert the quaternion into the corresponding Euler angle to act on the role. Although the calculation consumes memory, the equipment can rotate arbitrarily and has good flexibility. A four-dimensional vector is defined to represent a quaternion. The calculation formula is(1)u=a,b,c,dT,whereu represents quaternion; a,b,c,d are a four-dimensional vector element; and T represents the transpose matrix. According to the conversion from quaternion to Euler angle, the calculation formula of pitch angle is obtained as follows:(2)ϑ1=arcsin2ac−bd,where ϑ1 represents the pitch angle. Similarly, the calculation formula of rolling angle is(3)ϑ2=arctan2ab−cd1−2b2+c2,where ϑ2 represents the rolling angle. Finally, the heading angle can be obtained.(4)ϑ3=arctan2ad−bc1−2c2+d2,where ϑ3 represents heading angle. The original data of the wearable sensor, after calibration and filtering, data fusion are read to obtain the quaternion, and then, the quaternion is transformed into the attitude angle. The process of wearable sensor data acquisition is shown in Figure 1fig1.Figure 1
Data acquisition process of wearable sensor.The data are transmitted to the upper computer, and the data waveform is drawn for subsequent observation and analysis. Due to its rich types of sensors, the wearable sensor network can comprehensively perceive all kinds of data related to human behavior, including acceleration, temperature, sound, position, and object use. [14]. At the same time, it has the advantages of small privacy invasion, small volume, lightweight, small life interference, and simple maintenance. It has become a very advantageous technology of behavior perception.


### 2.2. Estimation of Human Motion State Parameters Based on Wearable Sensors

On the basis of constructing a wearable sensor network and collecting sensor data, the relative rotation matrix of the sensor measurement coordinate system relative to the human coordinate system is calculated through calibration steps, and then, the measurement of the sensor is converted to the human coordinate system [15]. In human motion reconstruction, the update of limb position not only needs the rotation angle of each joint but also needs to capture the limb parameters of the object. Wearable sensors are active. Their main characteristic is that they can autonomously provide power and detect physical quantities according to a predetermined programme and convey the sensed data in an active manner. Active sensor networks, which are the principal sensing technique utilised in behavior identification technology based on wearable sensor networks [16], offer more complete sensing, processing, and communication capabilities. We chose the shoulder joint as the calibration object because of the uniqueness of the human joint structure, and we obtained the joint angle of the shoulder joint using the unique posture of the upper limb. The difference between the two postures may be represented using the Riemannian metric. The Riemannian metric between two nearby posture measurements will be considerably significant if the limb’s posture is constantly changing. As a result, the Riemannian metric may be employed to express the amplitude of changes in limb posture. The Riemannian metric is first filtered using a moving average window, and the calibration posture is then identified and segmented using an amplitude and time double-threshold discrimination approach. This is the beginning point of the calibration posture when the Riemannian metric is always smaller than the amplitude threshold in the period beyond the time threshold. Similarly, the calibration pose’s endpoint is determined by a measurement larger than the threshold. Sensors that detect the amount of movements may be worn on various body areas [17]. Wearable sensors can capture fine-grained information related to human actions. The hand-eye calibration algorithm is used to calculate the wearable sensor human coordinate calibration matrix. The right shoulder joint is taken as an example. The rotational motion of the right shoulder joint can be calculated from the relative motion of the chest and the right upper arm.(5)P1Q1W=P2Q2,where P1 and P2 are the pose measurements of the wearable sensors of the chest and right upper arm, respectively; Q1 and Q2 are the relative rotation matrices of the chest and right upper arm sensor measurement coordinate system and human body coordinate system, respectively; W is the rotation matrix of the right shoulder joint. A different wearable sensor human body coordinate system calibration matrix can be obtained for each calibration pose, and one needs to be selected as the final result. The pose of the joint relative to the reference coordinate system can be calculated by using the wearable sensor human coordinate system calibration matrix. Thus, the human joint coordinates are obtained according to the wearable sensor data. Different motion states will produce different accelerations. By acquiring acceleration signals under different motion states, and carrying out corresponding preprocessing and feature recognition, the accurate identification of individual motion states can be realized. In order to reduce the impact of noise on recognition, effective preprocessing is needed before extracting data features. The primary purpose of acceleration data preprocessing is to filter the noise information in the original acceleration data and obtain better data for feature extraction. Smooth denoising filtering, data resampling, data normalisation, tilt correction of data collection direction, windowing of unified signal length, and other preprocessing techniques are already available. The human skeleton is modelled as a tree hierarchical joint chain. Joints link bones and connect bones. Only rotational motion degrees of freedom exist in all joints, with no translational motion degrees of freedom [18]. Human joint constraints are utilised to predict joint parameters and bone length in this work. Through the attitude, position, acceleration, angular rate, and angular acceleration rate information of the rigid body, the acceleration of the joint point relative to the reference coordinate system can be obtained.(6)v=Hα1+α2α3,where v represents acceleration; H represents the attitude matrix of the rigid body relative to the reference coordinate system; α1 represents the projection of acceleration in volume coordinate system; α2 represents the projection of angular rate in volume coordinate system; and α3 represents the projection of angular acceleration in volume coordinate system. According to the definition of the coordinate system, under the calibration posture, all human coordinate systems are parallel to each other; that is, the posture of all limbs relative to the reference coordinate system is equal [19].


### 2.3. Recognition of Physical Training State Based on Decision Tree Algorithm

During the dynamic monitoring of the wearer’s human posture and physical training intensity of the wearable sensor, the original data collected by the wearable sensor are processed and analyzed to obtain the current human motion state, and the judgment results are fed back to the wearer. The acceleration data of thex-axis, y-axis, and z-axis collected by the sensor are used to calculate the combined acceleration vector sum. The calculation formula of resultant acceleration is as follows:(7)ω=ωx2+ωy2+ωz2,where ω represents the resultant acceleration; ωx,ωy,ωz represent the acceleration of the wearable sensor on the x-axis, y-axis, and z-axis, respectively. In the process of human motion state recognition, the more the feature vectors are extracted, the more reliable the recognition results are [20]. However, with the addition of new motion features, the processing time for features will also increase greatly. When constructing the classification algorithm, too many feature dimensions lead to the complexity of the recognition algorithm, and the redundant information leads to the reduction of recognition accuracy. Moreover, some features are related, and the information contained is overlapped to a certain extent. The variance is maximised by orthogonal transformation based on the feature-based covariance matrix, and a collection of new, linear independent, and orthogonal vectors are employed as main components to replace the relevant original vectors, simplifying the information. The eigenvalues of combined acceleration and z-axis acceleration are extracted as standard deviation, mean value, inclination angle, peak interval, and peak trough, and the human physical training condition is detected and categorised using the decision tree classification method. Motion data are analyzed using peaks and troughs. Motion signals (acceleration, angular velocity, and attitude angle) are continually changing throughout human motion, and the crest is the greatest value in a given period of time [21]. The peak of a wave may show how the strength of movement increases over time. The peak of the wave is also varied depending on the activity frequency. As a result, the wave crest may accurately represent changes in human movement. The trough is the lowest value of a motion signal over time. The trough can better reflect the downward movement of the human body. Because the periodic frequency of each action is different, the time difference is different, which has a good effect in distinguishing periodic action from aperiodic action. The tree structure of the decision tree algorithm consists of a series of nodes, including root nodes, several internal branch nodes, and several leaf nodes. The path planning direction of the commonly used decision tree algorithm is from top to bottom. When classifying the characteristics of an object, the training data set is used as the root node to judge a characteristic attribute of the classification object. The leaf node is used to represent the classification results. In this paper, the node attributes are selected by using the information gain ratio, and the flow of the decision tree algorithm is shown in Figure 2.Figure 2
Decision tree algorithm flow.The calculation of information gain after the data set is divided according to attributes is as follows:(8)F=φA−∑c=1cAcAφAc,where F represents information gain; φ represents information entropy; A represents the sample value; and c indicates the quantity of a certain attribute value. The information gain rate is further calculated as follows:(9)f=F−∑c=1cAc/Alog2Ac/A,where f represents the information gain rate. The feature set is composed of five features: standard deviation, mean value, inclination, peak interval, and peak valley value of human triaxial acceleration signal, and the decision tree of physical training action is created to realize the effective recognition of the human motion state.


### 2.4. Build a Dynamic Monitoring Model of Three-Dimensional Energy Training Intensity

Based on the recognition of the human motion state, a dynamic monitoring model of three-dimensional energy training intensity is established. The recorded value obtained by digital to analog conversion of the electrical signal is induced by the wearable sensor. In general, the higher the value, the larger the acceleration created during physical activity, i.e., the higher the intensity of the activity [22]. The number of heartbeats per minute is referred to as heart rate. It is a vital physiological parameter that reflects human blood circulation activity and has become a crucial foundation for clinical assessment and illness monitoring in almost all disorders [23]. The heart rate has individual differences. Generally, the heart rate values of different people in a quiet state will be significantly different. People of different ages and genders also have different heart rate values. Even the same person has significant changes in heart rate at different time stages and different exercise states [24]. The calculation formula of physical training intensity can be expressed as(10)κ=γ1−γ3γ2−γ3,where κ represents exercise intensity; γ1 represents exercise heart rate; γ2 represents the maximum heart rate; and γ3 indicates resting heart rate. The exercise intensity is calculated through the measured exercise heart rate, and the exercise intensity is divided into three levels, as shown in Table 1.Table 1
Division of exercise intensity.
NumberExercise intensityValue range1Low0–50%2Medium51%–75%3High76%–100%The real-time detection of heart rate is helpful for people to control the intensity of exercise and keep fit scientifically. Firstly, the collected ECG signal is manually labeled with R-wave by experts to obtain the standard value of heart rate under exercise state. Moving average filtering algorithm is a simple and useful method for discrete digital signals. After filtering, the waveform will become smooth as a whole. Each sample yields fresh data, which is put at the tail end of the team, while the old data at the head of the team are destroyed, ensuring that the data in the window remain constant. The mean value of the data is calculated to obtain the new filtered result [25]. In heart rate detection, the sampling frequency is set to 50 Hz, 150 data are collected continuously each time, and then, the DC mean value of 150 IR data is calculated. The DC signal is removed from the original data, sliding average filtering is conducted with window 4, and the average value of the filtered signal is calculated. Then, all peaks are found greater than the threshold in the data, and the sampling interval is at least 4. The sampling point on the right edge corresponding to the peak is found, and the qualified peak should be greater than or equal to 2. The heart rate of this sampling is calculated by measuring the time difference between successive peaks. Then, the exercise heart rate is measured based on the R-wave detection algorithm. The gbdt algorithm in machine learning is used as the regression model to construct the training characteristics of ECG signal and train the regression model with the difference between the heart rate calibration value and the heart rate measurement value as the target value. The objective function of gbdt algorithm can be expressed as(11)G=g0+λ1η1m+λ2η2m+⋯+λnηnm,where G is the objective function; g0 represents the initial value given to the sample; λn is the weight coefficient; and ηn represents the prediction result of each tree; m represents the dataset. A regression tree is fitted to the data set, and the value of the leaf node region is estimated by linear search to minimize the loss function. Finally, the training model is used to predict the measured data, so as to modify the measurement results of exercise heart rate and improve the accuracy of heart rate measurement under exercise. In different exercise states, the heart rate of physical training athletes is calculated to obtain the numerical index of training intensity. So far, the design of the dynamic monitoring method of physical training intensity based on the wearable sensor is completed.


## 2.1. Building Wearable Sensor Networks

The position of the wearable sensor has a certain impact on the dynamic monitoring of physical training intensity. The signals measured at different positions are very different and are easily affected by high-speed movement. It is difficult to distinguish between fall and ADL and will increase the possibility of misjudgment. In this regard, wearable sensor networks are established. Wearable sensor networks are less intrusive to people’s privacy. At the same time, they have the advantages of small node volume and lightweight. They can now be readily worn on individuals and tracked thanks to technological advancements and the invention of implanted sensors [7]. Wearable sensor networks are generally simple to maintain on a daily basis, with the major maintenance requirements being on battery replacement [8, 9]. Chest sensors do not offer enough information to distinguish actions like sitting and standing. The data acquired by wearing the head sensor have the best classification detection accuracy and outstanding impact detection capabilities when compared to data produced by hip and wrist sensors [10, 11]. People wear different sorts of sensor nodes, which are equipped with various types of sensors, such as acceleration, temperature, sound, and light, in a wearable sensor network made of active sensor devices [12]. The data are sent to the sink node through wired, wireless, and other communication channels once the sensor node acquires the sensor reading. The sink node sends the data it receives to the processing node for further behavior analysis. Active sensor equipment has the benefit of having a robust sensor capability and the capacity to perceive a wide range of data. Pitch, roll, and heading angles, together known as Euler angles, are used in the construction of wearable sensor networks. In the process of moving target tracking, because the Euler angle has the problem of universal deadlock, that is, for the dynamic Euler angle, if it rotates around the object coordinate system, as long as the pitch angle is ±90°, no matter how many degrees the roll angle and heading angle are, there will be universal lock [13]. Therefore, in the process of obtaining the Euler angle, the quaternion is introduced to convert the Euler angle into quaternion, perform spherical linear interpolation on the quaternion, and then convert the quaternion into the corresponding Euler angle to act on the role. Although the calculation consumes memory, the equipment can rotate arbitrarily and has good flexibility. A four-dimensional vector is defined to represent a quaternion. The calculation formula is(1)u=a,b,c,dT,whereu represents quaternion; a,b,c,d are a four-dimensional vector element; and T represents the transpose matrix. According to the conversion from quaternion to Euler angle, the calculation formula of pitch angle is obtained as follows:(2)ϑ1=arcsin2ac−bd,where ϑ1 represents the pitch angle. Similarly, the calculation formula of rolling angle is(3)ϑ2=arctan2ab−cd1−2b2+c2,where ϑ2 represents the rolling angle. Finally, the heading angle can be obtained.(4)ϑ3=arctan2ad−bc1−2c2+d2,where ϑ3 represents heading angle. The original data of the wearable sensor, after calibration and filtering, data fusion are read to obtain the quaternion, and then, the quaternion is transformed into the attitude angle. The process of wearable sensor data acquisition is shown in Figure 1fig1.Figure 1
Data acquisition process of wearable sensor.The data are transmitted to the upper computer, and the data waveform is drawn for subsequent observation and analysis. Due to its rich types of sensors, the wearable sensor network can comprehensively perceive all kinds of data related to human behavior, including acceleration, temperature, sound, position, and object use. [14]. At the same time, it has the advantages of small privacy invasion, small volume, lightweight, small life interference, and simple maintenance. It has become a very advantageous technology of behavior perception.


## 2.2. Estimation of Human Motion State Parameters Based on Wearable Sensors

On the basis of constructing a wearable sensor network and collecting sensor data, the relative rotation matrix of the sensor measurement coordinate system relative to the human coordinate system is calculated through calibration steps, and then, the measurement of the sensor is converted to the human coordinate system [15]. In human motion reconstruction, the update of limb position not only needs the rotation angle of each joint but also needs to capture the limb parameters of the object. Wearable sensors are active. Their main characteristic is that they can autonomously provide power and detect physical quantities according to a predetermined programme and convey the sensed data in an active manner. Active sensor networks, which are the principal sensing technique utilised in behavior identification technology based on wearable sensor networks [16], offer more complete sensing, processing, and communication capabilities. We chose the shoulder joint as the calibration object because of the uniqueness of the human joint structure, and we obtained the joint angle of the shoulder joint using the unique posture of the upper limb. The difference between the two postures may be represented using the Riemannian metric. The Riemannian metric between two nearby posture measurements will be considerably significant if the limb’s posture is constantly changing. As a result, the Riemannian metric may be employed to express the amplitude of changes in limb posture. The Riemannian metric is first filtered using a moving average window, and the calibration posture is then identified and segmented using an amplitude and time double-threshold discrimination approach. This is the beginning point of the calibration posture when the Riemannian metric is always smaller than the amplitude threshold in the period beyond the time threshold. Similarly, the calibration pose’s endpoint is determined by a measurement larger than the threshold. Sensors that detect the amount of movements may be worn on various body areas [17]. Wearable sensors can capture fine-grained information related to human actions. The hand-eye calibration algorithm is used to calculate the wearable sensor human coordinate calibration matrix. The right shoulder joint is taken as an example. The rotational motion of the right shoulder joint can be calculated from the relative motion of the chest and the right upper arm.(5)P1Q1W=P2Q2,where P1 and P2 are the pose measurements of the wearable sensors of the chest and right upper arm, respectively; Q1 and Q2 are the relative rotation matrices of the chest and right upper arm sensor measurement coordinate system and human body coordinate system, respectively; W is the rotation matrix of the right shoulder joint. A different wearable sensor human body coordinate system calibration matrix can be obtained for each calibration pose, and one needs to be selected as the final result. The pose of the joint relative to the reference coordinate system can be calculated by using the wearable sensor human coordinate system calibration matrix. Thus, the human joint coordinates are obtained according to the wearable sensor data. Different motion states will produce different accelerations. By acquiring acceleration signals under different motion states, and carrying out corresponding preprocessing and feature recognition, the accurate identification of individual motion states can be realized. In order to reduce the impact of noise on recognition, effective preprocessing is needed before extracting data features. The primary purpose of acceleration data preprocessing is to filter the noise information in the original acceleration data and obtain better data for feature extraction. Smooth denoising filtering, data resampling, data normalisation, tilt correction of data collection direction, windowing of unified signal length, and other preprocessing techniques are already available. The human skeleton is modelled as a tree hierarchical joint chain. Joints link bones and connect bones. Only rotational motion degrees of freedom exist in all joints, with no translational motion degrees of freedom [18]. Human joint constraints are utilised to predict joint parameters and bone length in this work. Through the attitude, position, acceleration, angular rate, and angular acceleration rate information of the rigid body, the acceleration of the joint point relative to the reference coordinate system can be obtained.(6)v=Hα1+α2α3,where v represents acceleration; H represents the attitude matrix of the rigid body relative to the reference coordinate system; α1 represents the projection of acceleration in volume coordinate system; α2 represents the projection of angular rate in volume coordinate system; and α3 represents the projection of angular acceleration in volume coordinate system. According to the definition of the coordinate system, under the calibration posture, all human coordinate systems are parallel to each other; that is, the posture of all limbs relative to the reference coordinate system is equal [19].


## 2.3. Recognition of Physical Training State Based on Decision Tree Algorithm

During the dynamic monitoring of the wearer’s human posture and physical training intensity of the wearable sensor, the original data collected by the wearable sensor are processed and analyzed to obtain the current human motion state, and the judgment results are fed back to the wearer. The acceleration data of thex-axis, y-axis, and z-axis collected by the sensor are used to calculate the combined acceleration vector sum. The calculation formula of resultant acceleration is as follows:(7)ω=ωx2+ωy2+ωz2,where ω represents the resultant acceleration; ωx,ωy,ωz represent the acceleration of the wearable sensor on the x-axis, y-axis, and z-axis, respectively. In the process of human motion state recognition, the more the feature vectors are extracted, the more reliable the recognition results are [20]. However, with the addition of new motion features, the processing time for features will also increase greatly. When constructing the classification algorithm, too many feature dimensions lead to the complexity of the recognition algorithm, and the redundant information leads to the reduction of recognition accuracy. Moreover, some features are related, and the information contained is overlapped to a certain extent. The variance is maximised by orthogonal transformation based on the feature-based covariance matrix, and a collection of new, linear independent, and orthogonal vectors are employed as main components to replace the relevant original vectors, simplifying the information. The eigenvalues of combined acceleration and z-axis acceleration are extracted as standard deviation, mean value, inclination angle, peak interval, and peak trough, and the human physical training condition is detected and categorised using the decision tree classification method. Motion data are analyzed using peaks and troughs. Motion signals (acceleration, angular velocity, and attitude angle) are continually changing throughout human motion, and the crest is the greatest value in a given period of time [21]. The peak of a wave may show how the strength of movement increases over time. The peak of the wave is also varied depending on the activity frequency. As a result, the wave crest may accurately represent changes in human movement. The trough is the lowest value of a motion signal over time. The trough can better reflect the downward movement of the human body. Because the periodic frequency of each action is different, the time difference is different, which has a good effect in distinguishing periodic action from aperiodic action. The tree structure of the decision tree algorithm consists of a series of nodes, including root nodes, several internal branch nodes, and several leaf nodes. The path planning direction of the commonly used decision tree algorithm is from top to bottom. When classifying the characteristics of an object, the training data set is used as the root node to judge a characteristic attribute of the classification object. The leaf node is used to represent the classification results. In this paper, the node attributes are selected by using the information gain ratio, and the flow of the decision tree algorithm is shown in Figure 2.Figure 2
Decision tree algorithm flow.The calculation of information gain after the data set is divided according to attributes is as follows:(8)F=φA−∑c=1cAcAφAc,where F represents information gain; φ represents information entropy; A represents the sample value; and c indicates the quantity of a certain attribute value. The information gain rate is further calculated as follows:(9)f=F−∑c=1cAc/Alog2Ac/A,where f represents the information gain rate. The feature set is composed of five features: standard deviation, mean value, inclination, peak interval, and peak valley value of human triaxial acceleration signal, and the decision tree of physical training action is created to realize the effective recognition of the human motion state.


## 2.4. Build a Dynamic Monitoring Model of Three-Dimensional Energy Training Intensity

Based on the recognition of the human motion state, a dynamic monitoring model of three-dimensional energy training intensity is established. The recorded value obtained by digital to analog conversion of the electrical signal is induced by the wearable sensor. In general, the higher the value, the larger the acceleration created during physical activity, i.e., the higher the intensity of the activity [22]. The number of heartbeats per minute is referred to as heart rate. It is a vital physiological parameter that reflects human blood circulation activity and has become a crucial foundation for clinical assessment and illness monitoring in almost all disorders [23]. The heart rate has individual differences. Generally, the heart rate values of different people in a quiet state will be significantly different. People of different ages and genders also have different heart rate values. Even the same person has significant changes in heart rate at different time stages and different exercise states [24]. The calculation formula of physical training intensity can be expressed as(10)κ=γ1−γ3γ2−γ3,where κ represents exercise intensity; γ1 represents exercise heart rate; γ2 represents the maximum heart rate; and γ3 indicates resting heart rate. The exercise intensity is calculated through the measured exercise heart rate, and the exercise intensity is divided into three levels, as shown in Table 1.Table 1
Division of exercise intensity.
NumberExercise intensityValue range1Low0–50%2Medium51%–75%3High76%–100%The real-time detection of heart rate is helpful for people to control the intensity of exercise and keep fit scientifically. Firstly, the collected ECG signal is manually labeled with R-wave by experts to obtain the standard value of heart rate under exercise state. Moving average filtering algorithm is a simple and useful method for discrete digital signals. After filtering, the waveform will become smooth as a whole. Each sample yields fresh data, which is put at the tail end of the team, while the old data at the head of the team are destroyed, ensuring that the data in the window remain constant. The mean value of the data is calculated to obtain the new filtered result [25]. In heart rate detection, the sampling frequency is set to 50 Hz, 150 data are collected continuously each time, and then, the DC mean value of 150 IR data is calculated. The DC signal is removed from the original data, sliding average filtering is conducted with window 4, and the average value of the filtered signal is calculated. Then, all peaks are found greater than the threshold in the data, and the sampling interval is at least 4. The sampling point on the right edge corresponding to the peak is found, and the qualified peak should be greater than or equal to 2. The heart rate of this sampling is calculated by measuring the time difference between successive peaks. Then, the exercise heart rate is measured based on the R-wave detection algorithm. The gbdt algorithm in machine learning is used as the regression model to construct the training characteristics of ECG signal and train the regression model with the difference between the heart rate calibration value and the heart rate measurement value as the target value. The objective function of gbdt algorithm can be expressed as(11)G=g0+λ1η1m+λ2η2m+⋯+λnηnm,where G is the objective function; g0 represents the initial value given to the sample; λn is the weight coefficient; and ηn represents the prediction result of each tree; m represents the dataset. A regression tree is fitted to the data set, and the value of the leaf node region is estimated by linear search to minimize the loss function. Finally, the training model is used to predict the measured data, so as to modify the measurement results of exercise heart rate and improve the accuracy of heart rate measurement under exercise. In different exercise states, the heart rate of physical training athletes is calculated to obtain the numerical index of training intensity. So far, the design of the dynamic monitoring method of physical training intensity based on the wearable sensor is completed.


## 3. Experiment

### 3.1. Experimental Preparation

In order to test the effect of the dynamic monitoring method of physical training intensity based on the wearable sensor, physical training was carried out for students majoring in physical education in a school, and its training intensity was evaluated and analyzed. The subjects were healthy boys without vascular diseases and other diseases. The length of the experimenter’s upper and lower limbs was measured using a tape measure before the experiment, and the wearable sensors were placed on the chest, upper arm, forearm, thigh, and lower leg, respectively. To decrease the influence of muscle participation on posture assessment, it should be positioned as near to the bone as feasible and with as little muscular tissue as possible. The quaternion low-pass filter is used to filter the posture measurement of limbs in the preprocessing step of human motion data to reduce the impact of limb jitter in the motion process. To extract effective human motion segments, the sliding window threshold approach is used to segment the human motion signal. Push-ups, sit-ups, horizontal bar pull-ups, parallel bar arm flexion and extension, single-leg squat and stand-up, jogging, and combined exercise are among the physical training items examined by the individuals. The combined exercise is to complete 10 times of prone stretching and flexion, 2 times of turn back running within 5 meters, 5 times of push-ups, and 10 times of frog leaps in 5 minutes. The running event is the 3000 meter long run. Measure at the beginning of the movement and after the movement state is stable (about 3 min at the beginning of the movement), and relevant data are recorded.


### 3.2. Experimental Results and Analysis

The monitoring results of the dynamic monitoring method of physical training intensity based on the wearable sensor are compared with the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology. The number of experiments is set as 50–400 and the test results are counted under different numbers. Among them, the test results of sit-ups and single-leg squats and stands are shown in Figures3 and 4.Figure 3
Comparison of training intensity monitoring accuracy of sit-ups.Figure 4
Comparison of training intensity monitoring accuracy of single-leg squat and standing events.According to the test results of the training intensity of sit-ups, the monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 96.34%, which is 3.19%, 2.72%, and 3.51% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of things technology, respectively.According to the test results of the training intensity of single-leg squat and stand-up items, the monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.27%, which is 3.21%, 3.88%, and 4.32% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology, respectively. The monitoring accuracy of the four methods is counted in each item of physical training and further calculated the average monitoring accuracy of each item. The results are shown in Table2.Table 2
Comparison results of average monitoring accuracy of physical training (%).
Physical training programmeMethod based on wearable sensorMethod based on reflected interframe imageMethod based on median average filtering algorithmMethod based on Internet of Things technologyPush-up96.8494.4693.4693.48Abdominal curl96.3493.1593.6292.83Horizontal bar pull up97.3293.0492.5892.64Parallel bar arm flexion and extension97.4592.8791.3591.31One leg squat and stand up97.2794.0693.3992.95Run98.1894.4192.2491.04Combined motion96.4691.9291.0890.32According to the test results in Table2, according to the comprehensive physical training items, the average monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.12%, which is 3.70%, 4.59%, and 5.04% higher than that based on reflected interframe image, median average filtering algorithm, and Internet of Things technology. Therefore, the monitoring method designed in this paper has achieved good results. In the case of predicting the health status according to the monitored physiological data under the real-time motion state of the human body, it can be used to distinguish the changes of human parameters caused by individual motion and the changes caused by abnormal individual health. If it is extended to practical application scenarios, it has a certain application prospect based on a large number of accurate data.


## 3.1. Experimental Preparation

In order to test the effect of the dynamic monitoring method of physical training intensity based on the wearable sensor, physical training was carried out for students majoring in physical education in a school, and its training intensity was evaluated and analyzed. The subjects were healthy boys without vascular diseases and other diseases. The length of the experimenter’s upper and lower limbs was measured using a tape measure before the experiment, and the wearable sensors were placed on the chest, upper arm, forearm, thigh, and lower leg, respectively. To decrease the influence of muscle participation on posture assessment, it should be positioned as near to the bone as feasible and with as little muscular tissue as possible. The quaternion low-pass filter is used to filter the posture measurement of limbs in the preprocessing step of human motion data to reduce the impact of limb jitter in the motion process. To extract effective human motion segments, the sliding window threshold approach is used to segment the human motion signal. Push-ups, sit-ups, horizontal bar pull-ups, parallel bar arm flexion and extension, single-leg squat and stand-up, jogging, and combined exercise are among the physical training items examined by the individuals. The combined exercise is to complete 10 times of prone stretching and flexion, 2 times of turn back running within 5 meters, 5 times of push-ups, and 10 times of frog leaps in 5 minutes. The running event is the 3000 meter long run. Measure at the beginning of the movement and after the movement state is stable (about 3 min at the beginning of the movement), and relevant data are recorded.


## 3.2. Experimental Results and Analysis

The monitoring results of the dynamic monitoring method of physical training intensity based on the wearable sensor are compared with the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology. The number of experiments is set as 50–400 and the test results are counted under different numbers. Among them, the test results of sit-ups and single-leg squats and stands are shown in Figures3 and 4.Figure 3
Comparison of training intensity monitoring accuracy of sit-ups.Figure 4
Comparison of training intensity monitoring accuracy of single-leg squat and standing events.According to the test results of the training intensity of sit-ups, the monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 96.34%, which is 3.19%, 2.72%, and 3.51% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of things technology, respectively.According to the test results of the training intensity of single-leg squat and stand-up items, the monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.27%, which is 3.21%, 3.88%, and 4.32% higher than the methods based on reflected interframe image, median average filtering algorithm, and Internet of Things technology, respectively. The monitoring accuracy of the four methods is counted in each item of physical training and further calculated the average monitoring accuracy of each item. The results are shown in Table2.Table 2
Comparison results of average monitoring accuracy of physical training (%).
Physical training programmeMethod based on wearable sensorMethod based on reflected interframe imageMethod based on median average filtering algorithmMethod based on Internet of Things technologyPush-up96.8494.4693.4693.48Abdominal curl96.3493.1593.6292.83Horizontal bar pull up97.3293.0492.5892.64Parallel bar arm flexion and extension97.4592.8791.3591.31One leg squat and stand up97.2794.0693.3992.95Run98.1894.4192.2491.04Combined motion96.4691.9291.0890.32According to the test results in Table2, according to the comprehensive physical training items, the average monitoring accuracy of the dynamic monitoring method of physical training intensity based on the wearable sensor is 97.12%, which is 3.70%, 4.59%, and 5.04% higher than that based on reflected interframe image, median average filtering algorithm, and Internet of Things technology. Therefore, the monitoring method designed in this paper has achieved good results. In the case of predicting the health status according to the monitored physiological data under the real-time motion state of the human body, it can be used to distinguish the changes of human parameters caused by individual motion and the changes caused by abnormal individual health. If it is extended to practical application scenarios, it has a certain application prospect based on a large number of accurate data.


## 4. Conclusion

Physical training is one of the main ways to improve athletes’ special competitive ability and special performance. This paper presents a dynamic monitoring method of physical training intensity based on wearable sensing. In this study, only the physiological parameter of heart rate is selected. In the follow-up, it is necessary to expand the research on blood pressure and body temperature. The types of samples involved are few and the number is insufficient, and the changes of physiological parameters during exercise vary greatly according to age, body shape, and so on. In future research, we need to further increase the number of samples and the diversity and richness of samples to enhance the robustness of the dynamic monitoring method of physical training intensity.



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*Source: 8476595-2022-03-30.xml*