CN113749644A - Intelligent garment capable of monitoring lumbar movement of human body and automatically correcting posture - Google Patents
Intelligent garment capable of monitoring lumbar movement of human body and automatically correcting posture Download PDFInfo
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Abstract
The invention relates to an intelligent garment capable of monitoring lumbar vertebra movement of a human body and automatically correcting posture, which specifically comprises a garment body, a recognition monitoring module and a vibration reminding module. The system firstly obtains the real-time motion state and posture of a user through a human motion state and posture recognition module, and obtains a reasonable threshold value of the real-time waist bending Cobb angle of the user based on a posture evaluation system constructed in advance. Then, the system obtains the Cobb angle of the real-time waist bending of the user through the waist movement angle measuring module, and simultaneously judges whether the angle exceeds a reasonable threshold value for too long time and determines whether to remind the user of paying attention to the waist posture through a vibrating device. The invention replaces the original product blind and powerful correction method by the modes of motion state and posture recognition, vibration reminding and autonomous correction in the aspect of lumbar vertebra correction, can effectively reduce the uncomfortable feeling of the lumbar vertebra sick people in the correction process, and helps the wide waist-using people to develop good waist-using habits.
Description
Technical Field
The invention belongs to the technical field of human posture monitoring, and particularly relates to an intelligent garment capable of monitoring human lumbar vertebra movement and autonomously correcting postures.
Background
With the development and popularization of the internet, the activities of entertainment, learning, working and the like of modern young people are obviously changed, and a series of lumbar health problems are caused by the living habits of long-term lowering of the head to play mobile phones and long-time sitting on computers. There is relevant data showing that lumbar disease is gradually trending toward the public and younger.
Most young people will use an auxiliary correction belt called a lumbar fixator. The device prevents the unreasonable waist-wearing situation by restraining the waist movement in a strong fixing way. However, the correction method is very easy to cause the rejection feeling of the user due to the problems of overlarge binding to the waist, poor wearing comfort, unfavorable physical activity and the like. Moreover, long-term use of such devices by patients may stress blood vessels and nerves, which is not conducive to blood backflow, but may also cause some nerve damage.
In the field of intelligent medical treatment, there are also a great number of human posture correction devices based on human posture recognition. The main characteristic of these devices is that they can recognize the different concentrated postures of the human body and perform the reminding. However, under the same motion state of the human body, different instantaneous postures have different influences on the lumbar of the human body; and the influence of the same instantaneous posture on the lumbar of the human body is different under different motion states of the human body. Therefore, the reasonableness of the human waist posture needs to be evaluated simultaneously in combination with the motion state of the human body and the instantaneous posture in the motion state. Secondly, some existing correction devices only comprise a reminding function, and no specific scheme is provided on the aspects of how to correct and adjust the posture and the like. Furthermore, the existing orthotic devices, as a means, cannot be worn by the user during routine work and learning, are bulky and lack aesthetics.
Therefore, in order to alleviate and treat the lumbar diseases which are gradually popularized and younger, researchers need to consider the problems of not only the effectiveness of the lumbar auxiliary correction device, but also the correction reasonableness, wearing comfort and negative effects caused in the correction process under different motion states and different postures, normal wearing and use of users in daily work and study, and the like.
The lumbar auxiliary correction device comprises an ergonomic flexible correction function and a human motion state and posture recognition function, can make up the defects of the existing lumbar auxiliary correction device in the aspects to a certain extent through the flexible correction and vibration reminding modes, and enables a user to independently develop a good waist using habit.
Disclosure of Invention
The invention aims to provide an intelligent garment capable of monitoring the movement of the lumbar vertebrae of a human body and correcting the posture of the human body independently.
The intelligent garment comprises a garment body, an identification monitoring module and a vibration reminding module, wherein the identification monitoring module and the vibration reminding module are arranged on the garment body, the identification monitoring module is used for collecting motion state and body posture information of a human body, a human body life posture evaluation model is established, a reasonable value range of a Cobb angle under the current motion state and posture is obtained through the human body life posture evaluation model, the real-time waist bending Cobb angle of a user is calculated by using a posture evaluation system, whether the Cobb angle is in the reasonable value range is judged, if not, the vibration reminding module is started, and the vibration reminding module is used for reminding the user to pay attention to the waist posture through vibration.
Furthermore, the identification monitoring module comprises a depth sensor and a three-axis angular acceleration sensor, the depth sensor comprises a right shoulder joint depth sensor, a left elbow joint depth sensor and a right elbow joint depth sensor which are embedded in the arm and arm joints of the garment, the four depth sensors all adopt an ultrasonic ranging principle, the relative distance between the garment joints is acquired across obstacles, the three-axis angular acceleration sensor comprises a first three-axis angular acceleration sensor arranged at the back collar of the garment body, a second three-axis angular acceleration sensor embedded in the first lumbar vertebra of the garment body and a third three-axis angular acceleration sensor embedded in the fifth lumbar vertebra of the garment body, the first three-axis angular acceleration sensor is used for identifying the motion state of the human body, and the second three-axis angular acceleration sensor and the third angular acceleration sensor are respectively used for acquiring the vertical ground-to-angle angular acceleration sensors of the first lumbar vertebra and the fifth lumbar vertebra And (5) acceleration, and then real-time monitoring is carried out on the physiological curvature of the lumbar of the human body.
Further, the real-time waist bending Cobb angle of the user is calculated by respectively measuring the vertical ground angle alpha of the first lumbar vertebra and the vertical ground angle beta of the fifth lumbar vertebra in a manner of integrating angular acceleration with time; and subtracting the vertical ground angle beta of the fifth lumbar vertebra from the vertical ground angle alpha of the first lumbar vertebra to obtain a difference value of the beta and the alpha, wherein the difference value is the Cobb angle of the lumbar vertebra measured by the sensor.
Further, the waistband can be dismantled and set up on the clothing body, and the waistband includes waistband body and composite bed, the composite bed sets up at the waistband middle part, the composite bed includes combined material overburden, first flexible buffer layer, medical nylon supporting layer and the flexible buffer layer of second that sets up from outside to inside in order.
Further, the method for establishing the human body life posture evaluation system comprises the following steps:
and 6, classifying 6 basic motion states of the human body, such as standing posture, sitting posture, lying posture, walking, going upstairs and downstairs by adopting a hierarchical classification method, taking data containing time domain feature vectors and frequency domain feature vectors as a training set in each layer, inputting the data into a random forest classification model, and training to obtain a human body living posture evaluation model.
Further, the hierarchical classification method specifically includes the following steps:
step 6.1, classifying the static posture and the dynamic posture at the 1 st level, firstly, uniformly specifying a target prediction set corresponding to samples with the motion states of standing posture, sitting posture and lying posture as the static posture and converting the static posture and the sitting posture into the same value; uniformly specifying a target prediction set corresponding to samples with motion states of walking, going upstairs and going downstairs as a dynamic attitude and converting the dynamic attitude into another same value through datamation, then performing data prediction on the digitalized target prediction set by using a random forest algorithm to obtain a prediction classification result, and entering a 2 nd level after the prediction classification is finished;
step 6.2, classifying walking and going upstairs and downstairs in the dynamic posture at the 2 nd level, firstly, screening out samples with a target prediction set as a dynamic posture in an original data set, and then uniformly specifying the target prediction set corresponding to the samples with the dynamic posture as the walking posture and carrying out datamation; uniformly specifying a target prediction set corresponding to the upstairs and downstairs samples in the dynamic postures as the upstairs and downstairs postures, converting the target prediction set into the same value, finally performing data training by using a random forest algorithm, wherein the training process is the same as that in the step 6.1, and entering a 3 rd level after the prediction and classification are completed;
6.3, classifying upstairs and downstairs in the upstairs and downstairs getting-down postures in the 3 rd level, firstly, screening out samples with a target prediction set as the upstairs or downstairs in an original data set, then inputting the samples for the upstairs or downstairs into a random forest algorithm for prediction classification, and entering the 4 th level after the prediction classification is finished;
6.4, classifying the standing posture and the sitting posture in the static posture at the 4 th level, firstly, screening samples of which the target prediction sets are static postures in the original data sets, and then uniformly specifying the target prediction sets corresponding to the samples with the static postures as the standing postures and converting the target prediction sets into the same value through data; uniformly specifying a target prediction set corresponding to standing and sitting samples in the static posture as a sitting and sleeping posture and converting the target prediction set into another same value; then, performing data prediction on the digitalized target prediction set by using a random forest algorithm to obtain a prediction classification result, and entering a 5 th level after the prediction classification is finished;
and 6.5, classifying sitting postures and lying postures in sitting and lying at the 5 th level, firstly, screening out samples with a target prediction set of sitting postures or lying postures in an original data set, then inputting the samples of the sitting postures and the lying postures into a random forest algorithm for prediction and classification to obtain a final prediction and classification result, and completing the data training process and obtaining a human body living posture evaluation model by prediction and classification.
Furthermore, the reasonable value range of the Cobb angle under the current motion state and posture is obtained by the human body life posture evaluation model according to the current medical standard after the current motion state and posture is obtained.
Further, the method for predicting data by using the random forest algorithm comprises the following steps:
6.11, carrying out replaceable random sampling on the training set of the data containing the time domain characteristic vectors and the frequency domain characteristic vectors, randomly taking out m characteristic vectors each time, and sampling for n times;
6.12, generating 1 decision tree for the eigenvector matrix obtained by sampling each time, and generating n decision trees together;
6.13, forming n decision tree prediction models by an algorithm based on the calculation and judgment modes of the decision tree information gain and the information gain rate, and predicting a target prediction set of the sample;
and 6.14, determining a final prediction classification result in a mode of voting by n decision trees.
The invention has the beneficial effects that: 1. in the process of realizing the correction functionality, the invention adopts a mode of combining moderate correction and autonomous correction. The mode of combining the vibration reminding of the system and the flexible fixation of the waist belt of the garment helps a user to use the waist correctly and form a good waist using habit. 2. In the invention, the mode of combining human motion state recognition and human posture recognition is adopted in the recognition of the human waist posture, so that the precision of the recognition of the human waist posture is improved, and the correction reasonability of the clothes in the using process is ensured. 3. In the measurement of the physiological curvature of the lumbar of the human body, the Cobb angle is adopted as a main index for monitoring the physiological curvature of the lumbar of the human body by the system. The finger has strong correlation with the physiological curvature of the lumbar of a human body in standard medicine and is easier to measure. The process of the reasonability evaluation of the physiological curvature of the lumbar of the human body is simplified. 4. The Bluetooth data transceiver module can upload the measured Cobb angle data to the mobile terminal in real time. By analyzing the data, the daily waist habit information of the user can be obtained, and the disease severity and the recurrence probability of the user can be evaluated. 5. The intelligent clothing takes the clothing as a carrier, and the human motion state recognition system, the human posture recognition system, the human lumbar physiological curvature monitoring system and the signal transceiving module are all arranged in the intelligent clothing, so that a user can conveniently carry the intelligent clothing in daily work and study, and the aesthetic requirements of the user in daily activities are met. 6. The human body life posture evaluation model is independently designed, the human body posture can be effectively classified, and the accuracy is high.
Drawings
FIG. 1 is a schematic front view of an intelligent garment for monitoring lumbar movements and correcting an autonomous posture of a human body according to the present invention;
FIG. 2 is a schematic back view of the intelligent garment for monitoring lumbar movements and correcting the autonomous posture of the human body according to the present invention;
FIG. 3 is a schematic view of the front waist shape of the intelligent garment for monitoring the movement of the lumbar vertebrae and correcting the posture of the human body autonomously according to the present invention;
FIG. 4 is a schematic view of the back modeling of the waist of the intelligent garment for monitoring the movement of the lumbar vertebrae and correcting the posture of the human body autonomously of the invention;
FIG. 5 is a schematic view of the internal structure of the waist of the intelligent garment for monitoring the movement of the lumbar vertebrae and correcting the posture of the human body autonomously of the invention;
FIG. 6 is a schematic diagram of the principle of lumbar curvature angle measurement of the intelligent garment for monitoring the movement of the lumbar vertebrae and correcting the posture of the user independently according to the present invention;
FIG. 7 is a flow chart of the functional implementation of the intelligent garment for monitoring the movement of the lumbar vertebrae of a human body and correcting the posture of the human body autonomously according to the present invention;
FIG. 8 is a flowchart of a human motion state identification method of the present invention;
FIG. 9 is a flow chart of the human motion state recognition hierarchical classification method of the present invention;
FIG. 10 is a data distribution diagram illustrating various motion states in a human motion state recognition training set according to the present invention;
FIG. 11 is a data distribution diagram illustrating various motion states in a human motion state identification test set according to the present invention;
fig. 12 is a data distribution diagram of a feature vector angle (X) of each motion state in a certain time domain in a test set for human motion state identification according to the present invention;
FIG. 13 is a diagram showing a data distribution of a feature vector tGravityAcc-min () -X of each motion state in a certain time domain in a test set for human motion state identification according to the present invention;
FIG. 14 is a diagram showing a data distribution of a feature vector tBodyAcc-energy () -X of each motion state in a certain time domain in a test set for human motion state identification according to the present invention;
FIG. 15 is a diagram showing a data distribution of the feature vector TBodyAcc-mean () -X of each motion state in a certain time domain in the test set for human motion state identification according to the present invention;
FIG. 16 is a diagram showing the result of clustering different motion states of a human body after a high-dimensional manifold is mapped to a two-dimensional space by T-SNE manifold learning in the intelligent garment for monitoring lumbar movements and correcting an autonomous posture of a human body of the invention;
FIG. 17 is a graph showing the results of testing the accuracy of the intelligent garment for monitoring lumbar movements and correcting autonomic posture of a human body in accordance with the present invention for different movement status test sets;
fig. 18 is a result chart of the accuracy test of the intelligent garment for monitoring the lumbar movement of the human body and correcting the autonomous posture of the human body to different posture test sets of the human body.
The list of parts represented by the various reference numbers in the drawings is as follows:
30. a garment body; 1. a right shoulder joint depth sensor; 2. a left shoulder joint depth sensor; 3. a left elbow joint depth sensor; 4. a right elbow joint depth sensor; 5. the right waist part is fixed with a thread gluing belt surface; 6. the left waist part is fixed with a thread gluing belt surface; 7. a first axial angular acceleration sensor; 8. a waistband body; 9. a second triaxial angular acceleration sensor; 10. a third triaxial angular acceleration sensor; 11. a power switch; 12. the left waist is fixed with a female surface of a fastening tape; 13. the right waist part is fixed with a female surface of a fastening belt; 14. a waistband breathable layer; 15. a waistband composite cover layer; 16. a waistband flexible buffer layer A; 17. a medical nylon support layer; 18. a flexible buffer layer B; 19. a micro pressurized vibration unit; 20. a flexible signal transmission line; 21. a flexible master control circuit; 22. a Bluetooth data transceiver module; 23. a storage battery; 24. the first lumbar vertebra; 25. the fifth lumbar vertebra; 26. the first lumbar vertebra is vertically aligned with the ground angle alpha; 27. the fifth lumbar vertebra is vertically opposite to the ground by an angle beta; 28. the difference between β and α; 29. lumbar Cobb angle.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
fig. 1 is a schematic front view of a garment body 30 of the present invention. A right shoulder joint depth sensor 1, a left shoulder joint depth sensor 2, a left elbow joint depth sensor 3 and a right elbow joint depth sensor 4 are embedded in the four positions of the arms and the arm joints of the garment respectively. The depth sensors at four positions all adopt an ultrasonic ranging principle, and the relative distance between the clothes joints can be acquired by crossing obstacles. The front waist of the garment is respectively provided with a right waist fixing thread gluing belt surface 5 and a left waist fixing thread gluing belt surface 6 which are used for fixing the waistband body 8.
Fig. 2 is a schematic view of the back of the garment body 30 of the present invention. A first triaxial angular acceleration sensor 7 is embedded in the back collar part of the garment body 30, and the model of the sensor is mpu 6050. The mpu6050 sensor can acquire angular velocity data and acceleration data of the human body in three directions of X, Y, Z axis. The waist of the garment body 30 is provided with a waistband body 8 which accords with human engineering and is used for slightly correcting the posture of the waist of a human body and monitoring the physiological curvature of the lumbar in real time.
Fig. 3 is a front view of the waistband body 8 of the invention. The left and right sides of the front surface of the waistband body 8 are respectively provided with a left waist fixing fastening tape female surface 12 and a right waist fixing fastening tape female surface 13. The two sides of the middle part of the waistband body 8 are provided with a waistband ventilating layer 14 for ensuring that the waistband has good ventilation.
Fig. 4 shows a back view of the waistband body 8 according to the invention. The outermost layer of the middle part of the waistband body 8 is a waistband composite material covering layer 15. The covering layer conforms to the contour of the waist structure of the human body and can be tightly attached to the skin of the waist of the human body. The second layer in the middle of the waistband body 8 is a waistband flexible buffer layer A16 which plays a role of reducing the waistband constraint and the human waist pressure together with the waistband flexible buffer layer B18 in the innermost layer in the middle of the waistband body. The third layer in the middle of the waistband body 8 is a medical nylon supporting layer 17 which is used for properly correcting the waist posture.
Fig. 5 is a schematic view showing the internal structure of the waistband body 8 according to the invention. The upper side and the lower side of the central axis of the internal structure of the waistband body 8 are respectively embedded with a second triaxial angular acceleration sensor 9 and a third triaxial angular acceleration sensor 10, and the models are mpu 6050. The upper and lower triaxial angular acceleration sensors are respectively used for acquiring vertical ground angular acceleration of the first lumbar vertebra and the fifth lumbar vertebra. A micro pressurizing and vibrating unit 19 is embedded in the middle of the central axis of the internal structure of the waistband body 8. The unit can be used for unreasonable waist reminding for a user in a vibration mode. The right part of the internal structure of the waistband body 8 is provided with a flexible signal transmission line 20 which is connected with a micro pressure vibration unit 19 and a flexible main control circuit 21. The signal transmission line adopts a DuPont line and is used for signal transmission between the second triaxial angular acceleration sensor 9, the third triaxial angular acceleration sensor 10 and the micro pressurizing vibration unit 19 and the flexible main control circuit 21. The flexible main control circuit 21 comprises a single chip microcomputer with a specific structure and is used for controlling various electronic elements of the waistband. The left side of the flexible main control circuit 21 is connected with the power switch 11 and the Bluetooth data transceiver module 22. The Bluetooth data transceiver module can send monitoring data to the mobile terminal and receive various instructions of the mobile terminal. The lower part of the flexible main control circuit 21 is connected with a storage battery 23. The storage battery 23 can be charged and discharged and is used for supplying power to the flexible main control circuit.
Fig. 6 is a schematic view showing the principle of measuring the bending angle of the lumbar vertebrae of the belt body 8 according to the present invention. The first lumbar vertebra 24 and the fifth lumbar vertebra 25 are respectively attached to the second triaxial angular acceleration sensor 9 and the third triaxial angular acceleration sensor 10 in a point-to-point manner. The upper and lower sensors can measure the vertical ground angle acceleration of the first lumbar vertebra 24 and the fifth lumbar vertebra 25 respectively, and measure the vertical ground angle alpha 26 of the first lumbar vertebra and the vertical ground angle beta 27 of the fifth lumbar vertebra respectively in a mode of integrating the angular acceleration with time. By subtracting the vertical ground angle β 27 at the fifth lumbar vertebra from the vertical ground angle α 26 at the first lumbar vertebra, the algorithm can obtain a difference 28 between β and α, and the difference is used as the lumbar Cobb angle 29 measured by the sensor.
The function of the invention is realized in the following steps:
as shown in fig. 7, the present invention first uses the first triaxial angular acceleration sensor 7 embedded in the back collar of the garment body 30 to obtain angular velocity and acceleration data of the human body in X, Y, Z three axial directions, and obtains corresponding angular and acceleration derived data through certain data processing (the acquisition of the derived data will be specifically described in further description of the technical solution of the present invention). And carrying out data training on the data by using a random forest algorithm to finally obtain a corresponding human motion state prediction model. Based on the model, the system can identify the real-time motion state of the human body. Then, the invention can acquire the relative distance of the four sensors by using the depth sensors 1, 2, 3 and 4 of the garment body 8 at the four joints. The system adopts a coordinate method and a distance method and utilizes a convolutional neural network algorithm to carry out data training on relative distance data to construct a corresponding human body posture prediction model. Based on the model, the system can identify the real-time posture of the human body, and finally obtain the specific posture of the user in the specific motion state. After the motion body and the posture information of the human body are obtained, the system obtains a reasonable value range of the Cobb angle under the current motion state and posture through a human body living posture evaluation system established by a cloud database. (the lumbar lordosis angle (Cobb angle) of people with different sexes and ages has a conventional reasonable standard in medicine under different motion states and postures, and the human body living posture evaluation system is established on the medical standard. the specific medical standard is shown in table 1 (the table only lists the lumbar lordosis angle reasonable value range under partial postures))
TABLE 1 average lumbar lordosis angle (Cobb angle) measured in different cases
*Bending the hip and knee.
1The subject is asked to lean slightly obliquely on the table while performing vertical Magnetic Resonance Imaging (MRI) and to place the arms on the cross bar to ensure inactivity.
Meanwhile, the invention utilizes the second triaxial angular acceleration sensor 9 and the third triaxial angular acceleration sensor 10 on the upper side and the lower side of the middle shaft in the waistband body 8 to measure the Cobb angle of the human body at the moment and judge whether the angle is within a reasonable threshold value. When the measured Cobb angle is in a reasonable range, the micro pressurizing vibration unit 19 at the central axis inside the waistband body 8 does not vibrate; when the measured Cobb angle exceeds a reasonable range and exceeds a long time, the micro pressurizing and vibrating unit 19 vibrates and prompts the user to adjust the waist posture autonomously. In addition, the waistband body 8 of the invention has flexible correction function, and can properly correct the waist posture through the medical nylon support layer 17. Meanwhile, the Bluetooth data transceiver module in the internal structure of the belt body 8 sends Cobb angle data of the user to the mobile terminal in real time, so that the real-time monitoring of the physiological curvature of the lumbar of the human body is realized
The following is a further description of the technical solution of the present invention.
As shown in fig. 8, the acquisition of angular acceleration derived data and the identification of human motion state in the present invention comprises the following steps:
firstly, a three-axis acceleration sensor embedded in the back collar of the garment acquires 3-axis original signals tac-XYZ and tGyro-XYZ of an accelerometer and a gyroscope. These time domain reflected signals (the prefix "t" denotes time) are captured at a constant frequency of 50 hz.
The system then performs filtering using a median filter and a third order low pass butterworth filter with an angular frequency of 20hz to remove noise. At the same time, the system uses another low-pass Butterworth filter with an angular frequency of 0.3Hz, and the acceleration signal is split into body and gravitational acceleration signals (BodyAcc-XYZ and tGravityAcc-XYZ).
Then, the system deduces the linear acceleration and the angular velocity of the body in time, and obtains reflection signals, namely a body acceleration time domain reflection signal (tBodyAccJerk-XYZ) returned by the accelerometer and a body angular velocity time domain return signal (tBodyGyroJerk-XYZ) returned by the gyroscope. The amplitudes of these three-dimensional signals are calculated using euclidean norms. The amplitudes are represented as a body acceleration time-domain amplitude signal (tBodyAccMag), a gravity acceleration time-domain amplitude signal (tgevityaccmag), a body acceleration time-domain amplitude reflection signal (tBodyAccJerkMag), a body angular velocity time-domain amplitude signal (tbodygyro mag), and a body angular velocity time-domain amplitude reflection signal (tbodygyro jerkmag).
Finally, the system performs Fast Fourier Transform (FFT) on the generated body acceleration frequency domain signal, body acceleration frequency domain reflection signal, body angular velocity frequency domain signal, body acceleration frequency domain amplitude reflection signal, body angular velocity frequency domain amplitude reflection signal (i.e., fBodyAcc-XYZ, fBodyAccJerk-XYZ, fbodygyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag). ("f" denotes a frequency domain signal). These signals can be used to estimate the variance of the feature vector in each mode. These signals include a body acceleration time domain signal tBodyAcc-XYZ, a gravity acceleration time domain signal tgravityac-XYZ, a body acceleration time domain reflection signal tBodyAccJerk-XYZ, a body angular velocity time domain signal tBodyGyro-XYZ, a body angular velocity time domain reflection signal tBodyGyro jerk-XYZ, a body acceleration time domain amplitude signal tBodyAccMag, a gravity acceleration time domain amplitude signal tgravityacmag, a body acceleration time domain amplitude reflection signal tBodyAccJerk mag, a body angular velocity time domain amplitude signal tBodyGyro mag, the body angular velocity time-domain amplitude reflection signal tBodyGyroJerkMag, the body acceleration frequency-domain signal fBodyAcc-XYZ, the body acceleration frequency-domain reflection signal fBodyAccJerk-XYZ, the body angular velocity frequency-domain signal fBodyGyro-XYZ, the body acceleration frequency-domain amplitude signal fBodyAccMag, the body acceleration frequency-domain amplitude reflection signal fBodyAccJerkMag, the body angular velocity frequency-domain amplitude signal fBodyGyroMag, the body angular velocity frequency-domain amplitude reflection signal fBodyGyroJerkMag. Where "-XYZ" is used to represent X, Y and the 3-axis signal in the Z direction.
The variable set obtained by the system through the signals comprises time domain characteristics such as entropy, rotation angle, elevation angle, mean value, absolute deviation, quartile, kurtosis, median, standard deviation and variance, and frequency domain characteristics such as energy, entropy and DC mean. Finally, the system obtains 561 feature vectors containing time domain and frequency domain variables in total, and records the motion state information of the human body in different time domains and frequency domains.
Finally, the system classifies 6 basic motion states of the human body by adopting a hierarchical classification method. In each layer, 561 feature vector data including time domain and frequency domain variables are used as a training set and input into a random forest classification algorithm for data training, and a human motion state recognition model is obtained.
As shown in fig. 9, the hierarchical classification process in the human motion state of the present invention comprises the following steps:
the system preliminarily divides the human motion state into 6 basic postures, wherein the basic postures comprise 3 static postures (standing posture, sitting posture and lying posture) and 3 dynamic postures (walking, going upstairs and going downstairs), and the identification process is divided into 5 levels.
The 2 nd level is used to classify walking and going upstairs and downstairs in dynamic gestures. Firstly, screening out a target prediction set in an original data set as a sample of a dynamic attitude. Then, uniformly defining a target prediction set corresponding to the sample with the walking dynamic posture as the walking posture and carrying out data processing (the data processing itself is the same value); and uniformly defining the target prediction sets corresponding to the samples of the dynamic postures of going upstairs and going downstairs as the postures of going upstairs and going downstairs, and converting the postures of going upstairs and going downstairs into the same value. And finally, training data by using a random forest algorithm, wherein the training process is the same as the above. And entering a 3 rd level after the prediction classification is finished.
The 3 rd level is used for classifying the upstairs and downstairs in the upstairs and downstairs. Firstly, screening out samples of which the target prediction set in the original data set is upstairs and downstairs. Then, since the data of the first floor and the second floor are the same, the random forest algorithm data training can be directly carried out, and the training process is the same as the above. And entering the 4 th layer after the prediction classification is finished.
The 4 th level is used to classify standing and sitting postures in static postures. First, a sample with a static posture of the target prediction set in the original data set is screened. Then, uniformly defining a target prediction set corresponding to the sample with the static posture as the standing posture and carrying out data processing (the data processing itself is the same value); and uniformly defining the target prediction sets corresponding to the standing and sitting samples in the static posture as the sitting and sleeping postures, and converting the target prediction sets into the same value. Then, a random forest algorithm is used for data training, and the training process is the same as above. And entering the 5 th layer after the prediction classification is finished.
The 5 th level is used to classify sitting and lying positions in sitting and lying. Firstly, screening out a target prediction set in an original data set as a sample for sitting and lying. Then, because the sitting posture and the lying posture are respectively subjected to the same value after being subjected to the data conversion at the layer, the random forest algorithm data training can be directly carried out, and the training process is the same as that of the random forest algorithm data training. And (5) finishing the data training process by predicting and classifying to obtain a human motion posture recognition model. For newly collected samples, the identification of the motion state type of the newly collected samples is realized through a hierarchical classification process.
As shown in fig. 10 and 11, 10299 pieces of data are measured in the construction of the human motion state identification model, wherein test set 7352 pieces and test set 2947 pieces are measured in the invention.
As shown in fig. 12, 13, 14 and 15, in the construction of the human motion state identification model, the present invention shows the data distribution of the feature vectors angle (X) and tgravitycac-min () -X, tBodyAcc-energy () -X, TBodyAcc-mean () -X in each motion state in a certain time domain. It can be found that the feature vectors in the respective motion states have a relatively obvious difference in the selected time domain.
As shown in FIG. 16, in the construction of the human motion state recognition model, the invention maps the high-dimensional manifold to the clustering results of different motion states of the human body after the two-dimensional manifold is mapped to the two-dimensional space through T-SNE manifold learning. It can be seen from the figure that, except that the clustering results of the sitting posture (movement state 3) and the standing posture (movement state 4) are not ideal, other movement states are clearly distinguished.
As shown in FIG. 17, in the identification of the motion state of the human body, the accuracy of the model obtained by data training is tested by using the sample test set. The abscissa in the figure represents the prediction result of the algorithm, and the ordinate represents the actual motion state. The diagonally overlapping areas represent the correct number of samples for different motion states. Through calculation, the prediction accuracy of the walking state (0) is 97.5%, the prediction accuracy of the going-upstairs state (1) is 92.5%, the prediction accuracy of the going-downstairs state (2) is 85.1%, the prediction accuracy of the sitting posture state (3) is 89.6%, the prediction accuracy of the standing posture state (4) is 97.1%, and the prediction accuracy of the lying posture state (5) is 100%. The overall prediction accuracy is as high as 93.7%.
As shown in fig. 18, in the recognition of the posture of the human body, the present invention provides the result of the accuracy test for predicting 5 postures of the upper limb of the human body. Wherein, the gesture A, C, D, E can be effectively recognized, and the recognition accuracy of the gesture B is up to 90 percent.
In conclusion, the intelligent garment for monitoring the movement of the lumbar vertebrae of the human body and correcting the autonomous posture has the following main characteristics:
first, the present invention employs a method of both moderate correction and autonomous correction in the process of achieving correction functionality. The mode of combining the vibration reminding of the system and the flexible fixation of the waist belt of the garment helps a user to use the waist correctly and form a good waist using habit.
Secondly, in the recognition of the human body waist using posture, the invention adopts a mode of combining human body motion state recognition and human body posture recognition, thereby improving the precision of the waist using posture recognition and ensuring the correction rationality of the clothes in the using process.
Thirdly, in the measurement of the physiological curvature of the lumbar of the human body, the Cobb angle is adopted as a main index for monitoring the physiological curvature of the lumbar of the human body by the system. The finger has strong correlation with the physiological curvature of the lumbar of a human body in standard medicine and is easier to measure. The process of the reasonability evaluation of the physiological curvature of the lumbar of the human body is simplified.
Fourthly, the Bluetooth data transceiver module can upload the measured Cobb angle data to the mobile terminal in real time. By analyzing the data, the daily waist habit information of the user can be obtained, and the disease severity and the recurrence probability of the user can be evaluated.
Fifthly, the intelligent clothing takes the clothing as a carrier, and the human motion state identification system, the human posture identification system, the human lumbar physiological curvature monitoring system and the signal transceiving module are all arranged in the intelligent clothing, so that the intelligent clothing is convenient for a user to carry in daily work and study, and aesthetic requirements of the user in daily activities are met.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Any equivalent modifications made based on the teachings of the present invention are also within the scope of the present invention.
Claims (8)
1. The intelligent garment capable of monitoring the lumbar movement of a human body and autonomously correcting the posture is characterized by comprising a garment body, an identification monitoring module and a vibration reminding module, wherein the identification monitoring module and the vibration reminding module are arranged on the garment body, the identification monitoring module is used for collecting the movement state and the body posture information of the human body, establishing a human body life posture evaluation model, obtaining a reasonable value range of a Cobb angle under the current movement state and posture through the human body life posture evaluation model, calculating the real-time waist bending Cobb angle of a user by using a posture evaluation system, judging whether the Cobb angle is in the reasonable value range, and if not, starting the vibration reminding module which is used for reminding the user to pay attention to the waist posture through vibration.
2. The intelligent garment capable of monitoring the movement of the lumbar vertebrae of the human body and correcting the autonomous posture of the human body according to claim 1, wherein the recognition and monitoring module comprises a depth sensor and a three-axis angular acceleration sensor, the depth sensor comprises a right shoulder joint depth sensor, a left elbow joint depth sensor and a right elbow joint depth sensor which are embedded in the arms and the arm joints of the garment, the four depth sensors adopt the ultrasonic distance measurement principle to obtain the relative distance between the joints of the garment across obstacles, the three-axis angular acceleration sensor comprises a first three-axis angular acceleration sensor arranged at the back collar of the garment body, a second three-axis angular acceleration sensor embedded at the first lumbar vertebrae of the garment body and a third three-axis angular acceleration sensor embedded at the fifth lumbar vertebrae of the garment body, the first three-axis angular acceleration sensor is used for recognizing the movement state of the human body, the second triaxial angular acceleration sensor and the third triaxial angular acceleration sensor are respectively used for acquiring vertical ground angular acceleration of the first lumbar vertebra and the fifth lumbar vertebra, and then real-time monitoring is carried out on the physiological curvature of the human lumbar vertebra.
3. The intelligent garment capable of monitoring the movement of the lumbar vertebrae of the human body and autonomously correcting the posture of the human body according to claim 2, wherein the real-time Cobb angle of the bending of the waist of the user is calculated by measuring the vertical ground angle α at the first lumbar vertebra and the vertical ground angle β at the fifth lumbar vertebra respectively through an angular acceleration-time integration mode; and subtracting the vertical ground angle beta of the fifth lumbar vertebra from the vertical ground angle alpha of the first lumbar vertebra to obtain a difference value of the beta and the alpha, wherein the difference value is the Cobb angle of the lumbar vertebra measured by the sensor.
4. The intelligent garment capable of monitoring lumbar movements and autonomously correcting posture of a human body according to claim 2, wherein the waistband is detachably arranged on the garment body and comprises a waistband body and a composite layer, the composite layer is arranged in the middle of the waistband, and the composite layer comprises a composite material covering layer, a first flexible buffer layer, a medical nylon supporting layer and a second flexible buffer layer which are sequentially arranged from outside to inside.
5. The intelligent garment capable of monitoring the movement of the lumbar vertebrae of the human body and correcting the posture of the human body automatically as claimed in claim 2, wherein the method for establishing the human body living posture evaluation system comprises the following steps:
step 1, a first three-axis acceleration sensor acquires 3-axis original signals tac-XYZ and tGyro-XYZ of an accelerometer and a gyroscope;
step 2, filtering tac-XYZ and tGyro-XYZ by a median filter and a third-order low-pass Butterworth filter with an angular frequency of 20Hz, and dividing the acceleration signal tac-XYZ into a body acceleration signal body Acc-XYZ and a gravity acceleration signal tGravityAcc-XYZ by another low-pass Butterworth filter with an angular frequency of 0.3 Hz;
step 3, deducing the linear acceleration and the angular velocity of the body, obtaining reflection signals, namely a body acceleration time domain reflection signal tBodyAccJerk-XYZ returned by the accelerometer and a body angular velocity time domain return signal tBodyGyyroJerk-XYZ returned by the gyroscope, and calculating the amplitudes of the three-dimensional signals by using Euclidean norms, wherein the amplitudes are a body acceleration time domain amplitude signal tBodyAccMag, a gravity acceleration time domain amplitude signal tGravityAccMag, a body acceleration time domain amplitude reflection signal tBodyAccJerMag, a body angular velocity time domain amplitude signal tBodyyGyrJerMag and a body angular velocity time domain amplitude reflection signal tBodyyGyroJerMag respectively;
step 4, performing fast Fourier transform on the generated body acceleration frequency domain signal, body acceleration frequency domain reflection signal, body angular velocity frequency domain signal, body acceleration frequency domain amplitude reflection signal, body angular velocity frequency domain amplitude signal, and body angular velocity frequency domain amplitude reflection signal to obtain a body acceleration time domain signal tBodyAcc-XYZ, a gravity acceleration time domain signal tGravityAcc-XYZ, a body acceleration time domain reflection signal tBodyAccJerk-XYZ, a body angular velocity time domain signal tBodyGyro-XYZ, a body acceleration time domain amplitude signal tBodyAccMag, a gravity acceleration time domain amplitude signal tGraviyAccyMag, a body acceleration time domain amplitude reflection time domain reflection signal tBodyAccJerk Mag-XYZ, a body angular velocity time domain amplitude reflection signal tBodyGyro Mag-Mag, a body angular velocity time domain amplitude reflection signal tBodyGyGy JyGyJerMag, a frequency domain acceleration frequency domain signal fDY-AccfyFarby, a body angular velocity frequency domain reflection signal-BodyfFarby-Effy, A body angular velocity frequency domain signal fBodyGyro-XYZ, a body acceleration frequency domain amplitude signal fBodyAccMag, a body acceleration frequency domain amplitude reflection signal fBodyAccJerkMag, a body angular velocity frequency domain amplitude signal fbodyyromag and a body angular velocity frequency domain amplitude reflection signal fBodyGyroJerkMa, wherein "— XYZ" is used to represent X, Y and a 3-axis signal in the Z direction;
step 5, obtaining time domain characteristic vectors and frequency domain characteristic vectors through the signals in the steps 1-3, and recording motion state information of the human body in different time domains and frequency domains;
and 6, classifying 6 basic motion states of the human body, such as standing posture, sitting posture, lying posture, walking, going upstairs and downstairs by adopting a hierarchical classification method, taking data containing time domain feature vectors and frequency domain feature vectors as a training set in each layer, inputting the data into a random forest classification model, and training to obtain a human body living posture evaluation model.
6. The intelligent garment capable of monitoring the movement of the lumbar vertebrae of the human body and correcting the posture of the human body autonomously as claimed in claim 2, wherein the hierarchical classification method specifically comprises the following steps:
step 6.1, classifying the static posture and the dynamic posture at the 1 st level, firstly, uniformly specifying a target prediction set corresponding to samples with the motion states of standing posture, sitting posture and lying posture as the static posture and converting the static posture and the sitting posture into the same value; uniformly specifying a target prediction set corresponding to samples with motion states of walking, going upstairs and going downstairs as a dynamic attitude and converting the dynamic attitude into another same value through datamation, then performing data prediction on the digitalized target prediction set by using a random forest algorithm to obtain a prediction classification result, and entering a 2 nd level after the prediction classification is finished;
step 6.2, classifying walking and going upstairs and downstairs in the dynamic posture at the 2 nd level, firstly, screening out samples with a target prediction set as a dynamic posture in an original data set, and then uniformly specifying the target prediction set corresponding to the samples with the dynamic posture as the walking posture and carrying out datamation; uniformly specifying a target prediction set corresponding to the upstairs and downstairs samples in the dynamic postures as the upstairs and downstairs postures, converting the target prediction set into the same value, finally performing data training by using a random forest algorithm, wherein the training process is the same as that in the step 6.1, and entering a 3 rd level after the prediction and classification are completed;
6.3, classifying upstairs and downstairs in the upstairs and downstairs getting-down postures in the 3 rd level, firstly, screening out samples with a target prediction set as the upstairs or downstairs in an original data set, then inputting the samples for the upstairs or downstairs into a random forest algorithm for prediction classification, and entering the 4 th level after the prediction classification is finished;
6.4, classifying the standing posture and the sitting posture in the static posture at the 4 th level, firstly, screening samples of which the target prediction sets are static postures in the original data sets, and then uniformly specifying the target prediction sets corresponding to the samples with the static postures as the standing postures and converting the target prediction sets into the same value through data; uniformly specifying a target prediction set corresponding to standing and sitting samples in the static posture as a sitting and sleeping posture and converting the target prediction set into another same value; then, performing data prediction on the digitalized target prediction set by using a random forest algorithm to obtain a prediction classification result, and entering a 5 th level after the prediction classification is finished;
and 6.5, classifying sitting postures and lying postures in sitting and lying at the 5 th level, firstly, screening out samples with a target prediction set of sitting postures or lying postures in an original data set, then inputting the samples of the sitting postures and the lying postures into a random forest algorithm for prediction and classification to obtain a final prediction and classification result, and completing the data training process and obtaining a human body living posture evaluation model by prediction and classification.
7. The intelligent garment capable of monitoring the lumbar vertebra movement and autonomously correcting the posture of the human body as claimed in claim 2, wherein the reasonable range of the Cobb angle in the current movement state and posture is obtained by the human body living posture evaluation model according to the existing medical standard after the current movement state and posture is obtained.
8. The intelligent garment capable of monitoring the movement of the lumbar vertebrae of the human body and correcting the posture of the human body autonomously as claimed in claim 2, wherein the method for predicting the data by using the random forest algorithm comprises the following steps:
6.11, carrying out replaceable random sampling on the training set of the data containing the time domain characteristic vectors and the frequency domain characteristic vectors, randomly taking out m characteristic vectors each time, and sampling for n times;
6.12, generating 1 decision tree for the eigenvector matrix obtained by sampling each time, and generating n decision trees together;
6.13, forming n decision tree prediction models by an algorithm based on the calculation and judgment modes of the decision tree information gain and the information gain rate, and predicting a target prediction set of the sample;
and 6.14, determining a final prediction classification result in a mode of voting by n decision trees.
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