CARD (chronic obstructive pulmonary disease) data processing method and system based on millimeter wave radar

Abstract

The invention relates to a chronic obstructive pulmonary disease data processing method and system based on a millimeter wave radar, and the method comprises the steps: obtaining an echo signal of the millimeter wave radar which carries out a step test based on a target, and determining the motion state of the target in real time according to the echo signal; adjusting a filtering method of the echo signal according to the motion state of the target; through a variational mode decomposition method, sign data of the target are extracted from the filtered echo signals, and the sign data comprise respiration signals and heartbeat signals. According to the method, target sign data are measured through the echo and variational mode decomposition method of the millimeter wave radar, so that the cost of collecting and processing the chronic obstructive pulmonary disease data is reduced, and the collecting process is unattended.

Description

Technical Field

[0001] The present invention belongs to the technical field of millimeter-wave radar and physiological data, and particularly relates to a method and system for processing chronic obstructive pulmonary disease (COPD) data based on millimeter-wave radar. Background Art

[0002] Chronic obstructive pulmonary disease is the third leading cause of death in China, which can lead to dyspnea and has problems such as low awareness rate and low diagnosis rate. There are several main methods for the initial detection of existing COPD patients, including patients filling out questionnaires based on their own symptoms and exercise ability, and the risk of getting the disease can be evaluated through scores, but the sensitivity and specificity are insufficient; in the early stage of COPD, small airway lesions and alveolar destruction will occur, and chest CT can identify and quantify them, but it involves radiation exposure, and frequent examinations may increase the risk of patients receiving radiation, and the examinations are relatively expensive; measuring the forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) can evaluate the condition of airflow limitation. The FEV1 / FVC ratio is the gold standard for the diagnosis of COPD and can be measured by a spirometer, but the measurement requires the patient's close cooperation, such as forced exhalation, etc., which may be a challenge for some patients (such as children), and the requirements for operators are relatively high. Summary of the Invention

[0003] In order to reduce the cost of collecting and processing COPD data and realize unattended collection, in the first aspect of the present invention, a method for processing COPD data based on millimeter-wave radar is provided, including: obtaining the echo signal of the millimeter-wave radar based on the target doing a step test, and determining the motion state of the target in real time according to the echo signal; adjusting the filtering method of the echo signal according to the motion state of the target; extracting the physical sign data of the target from the filtered echo signal through the variational mode decomposition method, and the physical sign data includes respiratory signal and heartbeat signal.

[0004] In some embodiments of the present invention, the adjusting the filtering method of the echo signal according to the motion state of the target includes: if the target is stationary, filtering the phase and frequency of the echo signal.

[0005] In some embodiments of the present invention, the extracting the physical sign data of the target from the filtered echo signal through the variational mode decomposition method includes: decomposing the filtered echo signal into multiple components, and establishing a constrained variational model according to the multiple components; converting the constrained variational model into an unconstrained variational model through a quadratic penalty term and a Lagrange multiplier; based on the quadratic penalty term and the Lagrange multiplier, iterating the unconstrained variational model multiple times until a preset convergence condition is reached, and obtaining the optimal solution of the unconstrained variational model; separating the respiratory signal and the heartbeat signal based on the optimal solution.

[0006] Further, the constrained variational model is expressed as:

[0007] Among them, represents the unit impulse function, represents the modal function, represents the center frequency, f(t) represents the echo signal.

[0008] Furthermore, the unconstrained variational model is expressed as: , wherein, represents the Lagrangian function, represents the penalty factor, represents the Lagrange multiplier.

[0009] In the above embodiment, the real-time determination of the motion state of the target based on the echo signal includes: mixing and filtering the echo signal and the transmitted signal of the millimeter radar wave to obtain an intermediate frequency signal; performing Fourier transform and spectrum analysis on the intermediate frequency signal to obtain the distance data and speed data of the target; and determining the motion state of the target based on the distance data and the speed data.

[0010] In the second aspect of the present invention, there is provided a chronic obstructive pulmonary disease (COPD) data processing system based on a millimeter wave radar, including: a determination module, configured to obtain the echo signal of the millimeter wave radar based on a target performing a step test, and determine the motion state of the target in real time according to the echo signal; an adjustment module, configured to adjust the filtering method of the echo signal according to the motion state of the target; and an extraction module, configured to extract the physical sign data of the target from the filtered echo signal through a variational mode decomposition method, where the physical sign data includes a respiration signal and a heartbeat signal.

[0011] Furthermore, a building unit is configured to decompose the filtered echo signal into multiple components, and establish a constrained variational model according to the multiple components; a transformation unit is configured to transform the constrained variational model into an unconstrained variational model through a quadratic penalty term and a Lagrange multiplier; a solving unit is configured to perform multiple iterations on the unconstrained variational model based on the quadratic penalty term and the Lagrange multiplier until a preset convergence condition is reached, to obtain the optimal solution of the unconstrained variational model; and a separation unit is configured to separate the respiration signal and the heartbeat signal based on the optimal solution.

[0012] In the third aspect of the present invention, there is provided an electronic device, including: one or more processors; a storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the method for processing COPD data based on a millimeter wave radar provided in the first aspect of the present invention.

[0013] In a fourth aspect of the present invention, there is provided a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the method for processing chronic obstructive pulmonary disease (COPD) data based on a millimeter-wave radar provided in the first aspect of the present invention.

[0014] The beneficial effects of the present invention are as follows: The present disclosure simplifies the complexity of collecting chronic obstructive pulmonary disease data, improves test safety, and at the same time inputs the millimeter-wave radar information back to the voice module for broadcasting, which can timely feedback the test progress; after the tester completes all the exercise cycle tests, they rest at a set position for two minutes to observe the changes in heart rate and respiratory values and collect all the information during the test; on the other hand, it can be carried out unattended, and the patient can test by themselves while ensuring the safety of the tester. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the basic process of the method for processing chronic obstructive pulmonary disease data based on a millimeter-wave radar in some embodiments of the present invention; Figure 2 is a schematic diagram of the signal processing principle of the method for processing chronic obstructive pulmonary disease data based on a millimeter-wave radar in some embodiments of the present invention; Figure 3 is a schematic diagram of the structure of the system for processing chronic obstructive pulmonary disease data based on a millimeter-wave radar in some embodiments of the present invention; Figure 4 is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0017] Refer to Figure 1 and Figure 2 , in a first aspect of the present invention, there is provided a method for processing chronic obstructive pulmonary disease data based on a millimeter-wave radar, including: S100. obtaining an echo signal of a millimeter-wave radar for a target to perform a step test, and determining the motion state of the target in real time according to the echo signal; S200. adjusting the filtering method of the echo signal according to the motion state of the target; S300. extracting the physical sign data of the target from the filtered echo signal by a variational mode decomposition method, where the physical sign data includes a respiratory signal and a heartbeat signal.

[0018] Schematically, the basic process of the step test is as follows: The test subject stands behind the starting line. After the radar detects the target, the voice module gives an instruction: "Start the test. Each time you go up and down the stairs is regarded as an activity cycle. You need to complete ten activity cycles. Please keep walking during the test and pay attention to your safety under your feet." Then the test subject starts to climb the stairs. The radar detects the motion mode, can monitor the motion trajectory of the target, and at the same time calculates the speed and acceleration information and uploads it to the random forest model as input. The test subject will receive a voice prompt every time an odd number of activity cycles is completed: "You have completed 1 (3, 5, 7, 9) activity cycles. Come on!" After every even number of activity cycles, rest for 30 seconds at the designated position and receive a voice prompt: "You have completed 2 (4, 6, 8) activity cycles. Please stand within the red area and rest in place for 30 seconds." "You have completed 10 activity cycles. Please stand within the red area and rest in place for two minutes." When resting, the radar detects the stationary mode, measures the heart rate and respiratory value of the test target, and uploads it to the random forest model as input, and at the same time feeds it back to the voice module. According to the preset threshold, it is judged whether the test subject can continue the test. If so, the voice broadcasts: "You can continue the test." If not, the voice broadcasts: "Please pause the test!" At this time, it is identified as an abnormal situation and uploaded to the test platform. According to the actual situation, the threshold is set to 60 breaths per minute for respiration and 160 beats per minute for heart rate; repeat the test until ten activity cycles are completed. During this period, the voice module randomly broadcasts "Not bad, keep it up." "You're doing great." "Very good! Hold on a little longer." After the test is completed, the voice module gives an instruction: "You have completed the test. Please leave the test site." The collected test data is classified to determine whether the test subject is a chronic obstructive pulmonary disease patient, and the result is uploaded to the test platform. The test subject further checks according to the test result.

[0019] In step S100 of some embodiments of the present invention, the real-time determination of the motion state of the target according to the echo signal includes: S101. Mix and filter the echo signal and the transmitted signal of the millimeter radar wave to obtain an intermediate frequency signal; Specifically, a millimeter wave radar based on frequency modulated continuous wave (FMCW) is used. The antenna serves as a transceiver. The radar module transmits a linear frequency modulated pulse signal (TX) whose frequency changes linearly with time. After being reflected by the target, an echo signal (RX) is obtained. The original signal and the echo signal can obtain an intermediate frequency signal (IF) through a mixer and a low-pass filter.

[0020] S102. Perform Fourier transform and spectral analysis on the intermediate frequency signal to obtain the distance data and speed data of the target; Specifically, the frequency of the intermediate-frequency signal is the frequency difference between TX and RX, and the initial phase is the phase difference at the current moment. The radar time-domain signal is obtained by ADC (analog-to-digital conversion) sampling of the intermediate-frequency signal. The original data of the ADC-sampled signal is subjected to multi-pulse non-coherent accumulation processing for each Chirp to form a matrix of size N×M. Spectral analysis is performed on the M-frame fast-time sampling points by distance-dimensional FFT to obtain distance features. The frequency corresponding to the peak extracted after Fourier transform is proportional to the distance, and the frequency can be converted into distance. Since the time of only one frame is too short, the speed information of a certain distance is selected for calculation. It is set that every 0.5 m of movement is a node. The distance feature combined with the frame time and the number of frames can calculate the speed. Finally, the acceleration is obtained by performing time differentiation on the speed data, and an acceleration is calculated every 5 m of movement.

[0021] S103. Based on the distance data and the speed data, determine the motion state of the target.

[0022] Specifically, the distance feature combined with the speed feature can determine whether the target is in a stationary state or a moving state. When the speed is 0 and the distance is less than 1 m (within the red area), the radar determines it as the stationary mode and tests the heart rate and breathing information of the target. When the distance is greater than 2 m (outside the starting line), the radar determines it as the moving mode and tests the speed and acceleration information of the target.

[0023] In step S200 of some embodiments of the present invention, the method for adjusting the filtering method of the echo signal according to the motion state of the target includes: S201. If the target is stationary, filter the phase and frequency of the echo signal; When the subject is in a stationary state, the human heartbeat and breathing information can be extracted from the intermediate-frequency signal, and the position of the target can be determined by the extracted distance feature. However, since the displacement change is very small during chest vibration, the chest displacement range caused by breathing is 1 - 12 mm, and the chest displacement caused by heartbeat is 0.1 - 0.5 mm, which cannot be calculated by the intermediate-frequency signal frequency information, and the phase is greatly affected by it. Therefore, the phase is processed to extract the physical sign information. The specific phase is obtained through the arctangent function. Since when the chest moves greatly, the absolute value of the phase difference between adjacent sampling points is greater than π, the phase unwrapping method of adding or subtracting 2π is used to process the discontinuous phase. Then, the continuous phase values are subtracted by phase difference to enhance the target signal and suppress phase drift; Specifically, an FIR band-pass filter is used to screen out the respiratory signal and the heartbeat signal. Since both respiration and heart rate are higher than normal after exercise, and the respiration and heart rate of patients are also higher than those of non-patients, the passband frequency ranges are respectively set to 0.2~1.2 Hz and 1.0~3.0 Hz. The rest time is set to 30 s, and the adaptive variational mode decomposition algorithm is used to decompose the respiratory signal and the heartbeat signal. The intrinsic mode components can be regarded as intrinsic mode signals with a central frequency and a finite bandwidth. By continuously iterating and updating, the final intrinsic mode function components can be obtained, which can well avoid the mode mixing phenomenon.

[0024] In step S300 of some embodiments of the present invention, by using the variational mode decomposition method to extract the physical sign data of the target from the filtered echo signal, the physical sign data includes the respiratory signal and the heartbeat signal, including: S301. Decompose the filtered echo signal into multiple components, and establish a constrained variational model according to the multiple components; Specifically, decompose the original signal into k components, and establish a constrained variational model: , wherein, is the unit impulse function; to solve the constrained optimization problem, the constrained variational problem is transformed into an unconstrained variational problem, and by utilizing the advantages of the quadratic penalty term and the Lagrange multiplier method, an augmented Lagrangian function is introduced: S302. Transform the constrained variational model into an unconstrained variational model through the quadratic penalty term and the Lagrange multiplier; Specifically, ; wherein, is the penalty factor, is the Lagrange multiplier; through the alternating direction multiplier algorithm iteration, for all , update the functional until the convergence condition is satisfied.

[0025] S303. Based on the quadratic penalty term and the Lagrange multiplier, perform multiple iterations on the unconstrained variational model until the preset convergence condition is reached to obtain the optimal solution of the unconstrained variational model; S3014. Based on the optimal solution, separate the respiratory signal and the heartbeat signal.

[0026] Specifically, the convergence condition: , , wherein, the superscript n is the iteration number, is the component relative error, is the component absolute error. When both equations are satisfied, the saddle point of the augmented Lagrangian model is obtained, and the optimal solution of the model is calculated. According to the component to determine the corresponding signal, and realize the separation of the respiratory signal and the heartbeat signal.

[0027] It can be understood that the VMD algorithm determines the frequency and amplitude of each mode function through continuous optimization to achieve effective decomposition of the signal. A real signal is decomposed into a series of mode functions , each of which has a spectrum concentrated around its central frequency . Through iterative updates, including updating the mode signal and the central frequency, the algorithm makes the sum of all mode functions have the minimum error with the original signal while maintaining the smoothness of the mode signal. VMD requires the mode functions to meet two conditions: the sum of the mode bandwidths is the minimum, and the sum of the modes is equal to the original signal; VMD transforms the objective function into a Lagrangian function with constraints and optimizes it through the variational method.

[0028] Embodiment 2 Referring to Figure 3 , in the second aspect of the present invention, a data processing system 1 for chronic obstructive pulmonary disease based on millimeter-wave radar is provided, including: a determination module 11, configured to obtain the echo signal of the millimeter-wave radar based on the target doing a step test, and determine the motion state of the target in real time according to the echo signal; an adjustment module 12, configured to adjust the filtering method of the echo signal according to the motion state of the target; an extraction module 13, configured to extract the physical sign data of the target from the filtered echo signal through the variational mode decomposition method, where the physical sign data includes a respiratory signal and a heartbeat signal.

[0029] Further, the extraction module 13 includes: a establishment unit, configured to decompose the filtered echo signal into multiple components, and establish a constrained variational model according to the multiple components; a transformation unit, configured to transform the constrained variational model into an unconstrained variational model through a quadratic penalty term and a Lagrange multiplier; a solution unit, configured to perform multiple iterations on the unconstrained variational model based on the quadratic penalty term and the Lagrange multiplier until a preset convergence condition is reached, and obtain the optimal solution of the unconstrained variational model; a separation unit, configured to separate the respiratory signal and the heartbeat signal based on the optimal solution.

[0030] Embodiment 3 Referring to Figure 4 , in the third aspect of the present invention, an electronic device is provided, including: one or more processors; a storage device, configured to store one or more programs, when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the millimeter-wave radar-based chronic obstructive pulmonary disease data processing method of the first aspect of the present invention.

[0031] The electronic device 500 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0032] Generally, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 4 an electronic device 500 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had. Figure 4 Each block shown in

[0033] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, the above-described functions defined in the methods of embodiments of the present disclosure are performed. It should be noted that the computer-readable medium described in embodiments of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0034] The above computer-readable medium can be included in the above electronic device; or it can exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more computer programs, and when the above one or more programs are executed by the electronic device, the electronic device is caused to: Computer program code for performing the operations of the embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, Python, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0035] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0036] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for processing COPD data based on millimeter wave radar, characterized in that: include: Acquire the echo signal of the millimeter-wave radar based on the step test of the target, and determine the motion state of the target in real time according to the echo signal; Adjust the filtering method of the echo signal according to the target's motion state; Vital sign data of the target are extracted from the filtered echo signal by using a variational mode decomposition method, wherein the vital sign data includes a breathing signal and a heartbeat signal.

2. The method for processing COPD data based on millimeter wave radar according to claim 1, characterized in that: The filtering method for adjusting the echo signal according to the motion state of the target includes: If the target is stationary, the phase and frequency of the echo signal are filtered.

3. The method for processing COPD data based on millimeter wave radar according to claim 1, characterized in that: The step of extracting the target's vital sign data from the filtered echo signal by using a variational mode decomposition method comprises: Decomposing the filtered echo signal into multiple components, and establishing a constrained variational model based on the multiple components; The constrained variational model is transformed into an unconstrained variational model through quadratic penalty terms and Lagrange multipliers; Based on the quadratic penalty term and Lagrange multiplier, the unconstrained variational model is iterated multiple times until the preset convergence condition is reached, and the optimal solution of the unconstrained variational model is obtained; Based on the optimal solution, the breathing signal and the heartbeat signal are separated.

4. The method for processing COPD data based on millimeter wave radar according to claim 3, characterized in that: The constrained variational model is expressed as: ， in, represents the unit impulse function, represents the modal function, represents the center frequency, f(t) Indicates the echo signal.

5. The method for processing COPD data based on millimeter wave radar according to claim 4, characterized in that: The unconstrained variational model is expressed as: ， in, represents the Lagrangian function, represents the penalty factor, represents the Lagrange multiplier.

6. The method for processing COPD data based on millimeter wave radar according to claim 1, characterized in that: Determining the motion state of the target in real time according to the echo signal comprises: Mix and filter the echo signal and the transmission signal of the millimeter radar wave to obtain an intermediate frequency signal; Performing Fourier transform and spectrum analysis on the intermediate frequency signal to obtain distance data and speed data of the target; Based on the distance data and the speed data, a motion state of the target is determined.

7. A COPD data processing system based on millimeter wave radar according to claim 1, characterized in that: include: A determination module, used to obtain an echo signal of a millimeter-wave radar based on a step test of a target, and determine the motion state of the target in real time according to the echo signal; An adjustment module, used for adjusting the filtering method of the echo signal according to the motion state of the target; The extraction module is used to extract the target's vital sign data from the filtered echo signal by using a variational mode decomposition method, wherein the vital sign data includes a breathing signal and a heartbeat signal.

8. The COPD data processing system based on millimeter wave radar according to claim 7, characterized in that: The extraction module comprises: An establishing unit, used for decomposing the filtered echo signal into a plurality of components, and establishing a constrained variational model according to the plurality of components; A conversion unit, used to convert the constrained variational model into an unconstrained variational model through a quadratic penalty term and a Lagrange multiplier; A solution unit, used for performing multiple iterations on the unconstrained variational model based on a quadratic penalty term and a Lagrange multiplier until a preset convergence condition is reached, thereby obtaining an optimal solution of the unconstrained variational model; The separation unit is used to separate the respiratory signal and the heartbeat signal based on the optimal solution.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the COPD data processing method based on millimeter wave radar as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method for processing COPD data based on millimeter-wave radar as described in any one of claims 1 to 6 is implemented.

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