Knee joint osteotomy mode identification method oriented to swing saw high-frequency vibration working condition

Abstract

The invention belongs to the field of knee-joint osteotomy surgical robot control, and particularly relates to a knee-joint osteotomy modal identification method under a swing saw high-frequency vibration working condition, which comprises the following steps of: 1) constructing a knee-joint osteotomy modal identification system, defining original force information of an osteotomy swing saw measured by a force sensor as F0, the current measured by the current acquisition module is defined as i; (2) fitting a mapping curve of the frequency and the current of the swing saw in a no-load state, and further judging the state of the swing saw; 3) performing compliant filtering on the original force information to obtain a force signal after filtering processing; (4) differential derivation is conducted on the force signals obtained after filtering processing, a result is compared with a set judgment threshold value Th, different control strategies are executed according to the obtained different states of the swing saw, and corresponding control instructions are issued to a mechanical arm servo interface; and 5) transmitting the working current of the swing saw obtained in real time to a damping adjustment module of the control system for adjustment, thereby realizing stable identification of the knee joint osteotomy mode.

Description

Technical Field

[0001] The invention belongs to the field of knee joint osteotomy surgical robot control, and in particular is a knee joint osteotomy modal recognition method under a high-frequency vibration working condition of an oscillating saw. Background Art

[0002] In the field of orthopedic surgery, especially in knee replacement and other surgeries involving bone cutting, accurate bone cutting plays a vital role in the success of the surgery and the patient's postoperative recovery. As a commonly used bone cutting tool, the oscillating saw is in a high-frequency vibration state when working, so as to efficiently cut bone tissue.

[0003] The traditional method is to control the oscillating saw to cut bones only by visual observation, hand feel and familiarity with the anatomical structure. However, this method has many disadvantages. On the one hand, the high-frequency vibration of the oscillating saw will generate a large reaction force feedback to the doctor's hand, which can easily cause the doctor's hand fatigue. In addition, it is difficult to continuously and stably control the saw blade's cutting angle and depth during a long operation, which reduces the accuracy of bone cutting and may cause uneven bone cutting surface, affecting the fit of the implanted prosthesis and increasing the risk of complications such as postoperative loosening and pain.

[0004] Some existing technologies attempt to stabilize the oscillating saw with the help of simple mechanical auxiliary structures, but these structures often only focus on the static stability of the oscillating saw and fail to fully consider the changes in the dynamic characteristics of the bone itself under high-frequency vibration conditions. Bones are not rigid bodies. Under the high-frequency impact vibration of the oscillating saw, their internal structures will induce complex modal responses, including vibration deformation modes at different frequencies. Due to the lack of accurate identification of the knee joint bone cutting mode under such high-frequency vibration conditions, it is impossible to understand the current dynamic characteristics of the knee joint bones in real time, and it is difficult to flexibly adjust the bone cutting strategy according to the real-time state of the bones.

[0005] In addition, some monitoring methods focus on the collection of vibration parameters of the oscillating saw itself, such as amplitude and frequency, but ignore the unique modal information transmission formed during the interaction between bones and the oscillating saw. This isolated data monitoring cannot build a complete dynamic model of the bone cutting process, cannot provide doctors with comprehensive and intuitive bone cutting guidance, and cannot meet the increasing demand for precision medicine. An innovative method that can accurately identify the knee bone cutting mode under the high-frequency vibration of the oscillating saw is urgently needed to fill the gap in existing technologies and improve the accuracy and safety of knee bone cutting surgery. Summary of the invention

[0006] The purpose of the present invention is to provide a method for identifying the osteotomy modality of the knee joint under the high-frequency vibration condition of an oscillating saw. In this method, the correct identification of the osteotomy modality is achieved based on the mapping relationship between the differential information of the force signal measured by the six-dimensional force sensor and the current information of the oscillating saw to the vibration frequency, which provides a basis for realizing the automatic switching of different control strategies of the robot arm during the osteotomy process.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a knee joint bone cutting modal identification method under the high-frequency vibration working condition of an oscillating saw, comprising the following steps:

[0008] 1) Construct a knee joint bone cutting modal recognition system, define the original force information of the bone cutting oscillating saw measured by the force sensor as F0, and define the current measured by the current acquisition module as i;

[0009] 2) Under no-load condition, a mapping curve between the frequency and current of the oscillating saw is fitted to distinguish the load and no-load conditions of the subsequent oscillating saw at different frequencies, and then determine the state of the oscillating saw;

[0010] 3) Performing smooth filtering on the original force information to obtain a filtered force signal;

[0011] 4) Differentiate the filtered force signal, compare the result with the set judgment threshold Th, execute different control strategies according to the different states of the swing saw, and send the corresponding control instructions to the robot arm servo interface;

[0012] 5) The working current i of the oscillating saw is obtained in real time through the current acquisition module, and the collected current data is transmitted to the damping adjustment module of the control system for adjustment to achieve stable identification of the knee joint bone cutting mode.

[0013] The construction of the knee joint osteotomy modality recognition system is specifically as follows:

[0014] The force sensor is placed between the bone cutting oscillating saw and the end of the robot arm, and a current acquisition module is welded in the power supply circuit of the bone cutting oscillating saw to monitor the working current of the oscillating saw in real time. A vibration isolation pad is installed between the force sensor and the bone cutting oscillating saw;

[0015] Among them, the force sensor is a six-dimensional force sensor; and it is connected to the host computer, and is used to transmit force information to the host computer for processing; the vibration isolation pad is a polyurethane vibration isolation pad or a rubber vibration isolation pad, which is used to isolate high-frequency vibration signals.

[0016] The mapping curve between the frequency and current of the oscillating saw is fitted as follows:

[0017] When the oscillating saw is in a no-load state, the operating frequency f of the oscillating saw is adjusted in sequence, and the corresponding current i is recorded. After collecting multiple sets of corresponding data of f and i, the least squares method is used to fit the mapping curve of the oscillating saw frequency and current: i=g(f).

[0018] The smooth filtering of the original force information is specifically as follows:

[0019] 3-1) Use a convolution smoothing filter and select an average filter as the convolution kernel to perform smooth filtering on the original force information, that is:

[0020]

[0021] Among them, y[n] is the value of the filtered signal at the nth sampling point, x[nk] is the value of the original input signal at the nkth sampling point, and M is the half width of the convolution kernel, which determines that the length of the kernel function is 2M+1;

[0022] 3-2) Define the force signal F after filtering; where F = (F x ,F y ,F z ,T x ,T y ,T z ), where F x ,F y ,F z are the forces in the three coordinate axes of the force sensor coordinate system, T x ,T y ,T z are the moments in the three coordinate axis directions respectively.

[0023] The step 4) is specifically:

[0024] 4-1) Perform first-order differentiation on the filtered smooth force signal F to calculate F', the formula is:

[0025]

[0026] 4-2) setting a threshold value Th to judge the state of the swing saw; wherein the threshold value Th is set based on: the maximum value of the derivative of the force generated by manually dragging the swing saw;

[0027] 4-3) When F'≤Th, it indicates that the oscillating saw is not in the bone cutting state, that is, the oscillating saw is not turned on or does not contact the bone surface; according to the mode, switch to the corresponding control strategy, and send the corresponding control command to the servo interface of the robot arm;

[0028] 4-4) When F'>Th, it indicates that the oscillating saw has been turned on. The mapping curve of the oscillating saw frequency and current is fitted to determine whether the oscillating saw is in the bone cutting state. According to the mode, the corresponding control strategy is switched and the corresponding control instruction is sent to the robot arm servo interface;

[0029] 4-5) If the current current i is in the load range at the current frequency, it is determined that the oscillating saw is in the bone cutting mode or the oscillating saw is turned on and contacts the bone surface; according to the mode, switch to the corresponding control strategy and send the corresponding control instruction to the servo interface of the robot arm.

[0030] In step 4-4), whether the bone cutting state is in progress is determined as follows:

[0031] According to the fitted mapping curve of the oscillating saw frequency and current, combined with the real-time current i of the current oscillating saw, the working state of the oscillating saw is judged: if the current current i is in the no-load interval at the current frequency, it is judged that the oscillating saw is in the non-bone cutting mode or the oscillating saw is turned on but not in contact with the bone surface; otherwise, it is judged that the oscillating saw is in the bone cutting mode, or the oscillating saw is turned on and in contact with the bone surface.

[0032] The step 5) is specifically:

[0033] 5-1) Using nonlinear mapping, define the relationship between current i and damping coefficient B, that is:

[0034] D(i)=D min +k i ·(ii min ) b

[0035] Among them, D min is the minimum damping coefficient, which is used in low current state, that is, when the current is less than or equal to the set minimum current value, i min is the set minimum current value, k is the proportional coefficient, which indicates the intensity of the influence of current on damping, and b is the nonlinear adjustment parameter, which controls the speed of damping growth;

[0036] 5-2) Set the upper and lower limits of the damping coefficient to ensure the safety of the robot arm operation:

[0037] D min ≤D(i)≤D max

[0038] Among them, D max It is the maximum damping coefficient that the system can withstand to prevent the robot arm from responding slowly or losing control due to excessive damping.

[0039] A knee joint bone cutting modal identification system for high-frequency vibration working conditions of an oscillating saw comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement a knee joint bone cutting modal identification method for high-frequency vibration working conditions of an oscillating saw when executing the computer program.

[0040] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a knee joint bone cutting modal recognition method under high-frequency vibration conditions of an oscillating saw is implemented.

[0041] The present invention has the following beneficial effects and advantages:

[0042] 1. The present invention proposes a bone cutting mode recognition method based on force information and current information, which can effectively identify the bone cutting mode under high-frequency vibration conditions.

[0043] 2. The present invention establishes the relationship between the current and the vibration frequency of the oscillating saw, providing multi-dimensional information input for system control.

[0044] 3. The present invention installs a polyurethane vibration isolation pad between the bone cutting oscillating saw and the six-dimensional force sensor, which can effectively reduce the amplitude of the force transmitted to the force sensor by the high-frequency vibration of the oscillating saw, protect the force sensor from high-frequency strong load impact, extend the service life of the six-dimensional force sensor, and isolate some interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the method of the present invention;

[0046] Figure 2 This is a schematic diagram of the arrangement of the robotic arm, six-dimensional force sensor, vibration isolation pad, etc. in the present invention. DETAILED DESCRIPTION

[0047] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0048] like Figure 1 The present invention is a schematic flow chart of the present invention. This embodiment provides a knee joint osteotomy modality recognition method under high-frequency vibration conditions of an oscillating saw, comprising the following steps:

[0049] Step 1: If Figure 2 As shown in the figure, the six-dimensional force sensor is installed between the bone cutting oscillating saw and the end of the robot arm to ensure that the sensor can accurately measure the force information during the operation of the oscillating saw. A current acquisition module is welded in the power supply circuit of the bone cutting oscillating saw to monitor the working current of the oscillating saw in real time. In order to reduce the influence of the high-frequency vibration of the oscillating saw on the measurement accuracy of the six-dimensional force sensor, polyurethane vibration isolation pads and rubber vibration isolation pads are installed between the six-dimensional force sensor and the bone cutting oscillating saw. After comparison, it is found that the amplitude of the interference force is reduced more after the polyurethane vibration isolation pad is installed, so polyurethane is used to isolate high-frequency vibration signals.

[0050] According to step 1, the knee joint bone cutting modal recognition system is constructed, and the original force information of the bone cutting oscillating saw measured by the force sensor is defined as F0, and the current measured by the current acquisition module is defined as i;

[0051] Step 2: When the oscillating saw is in the no-load state, adjust the working frequency f of the oscillating saw in turn and record the corresponding current i. After collecting multiple sets of corresponding data of f and i, a nonlinear fitting method is used to obtain the mapping relationship between the oscillating saw current i and the frequency f i=g(f). This mapping curve is used to distinguish the load and no-load states of the oscillating saw at different frequencies.

[0052] Step 3: Smooth the original force signal F0 collected by the six-dimensional force sensor. Use a convolution smoothing filter to smoothly filter the signal. The specific filtering formula is:

[0053]

[0054] Among them, F[n] is the value of the filtered signal at the nth sampling point, F0[nk] is the value of the original input signal at the nkth sampling point, and M is the half width of the convolution kernel, which determines that the length of the filter kernel function is 2M+1. After filtering, a smooth force signal F is obtained, which reduces noise interference and provides a reliable basis for subsequent modal judgment.

[0055] Step 4: Perform the first-order differentiation of the smooth force signal F obtained in step 3 to calculate F', which is:

[0056]

[0057] The threshold Th is set to judge the state of the oscillating saw. When F'≤Th, it indicates that the oscillating saw is not in the bone cutting state (i.e., the oscillating saw is not turned on or does not contact the bone surface). At this time, the current mode is judged to be the non-bone cutting mode, and the control strategy 1 is switched according to the mode, and the corresponding control command is sent to the servo interface of the robot arm.

[0058] Step 5: When F'>Th, it indicates that the oscillating saw has been turned on, but it has not yet been determined whether it is in the bone cutting state. At this time, according to the mapping curve i=g(f) of the oscillating saw frequency and current fitted in step 2, combined with the current real-time current i of the oscillating saw, the working state of the oscillating saw is judged: if the current current i is in the no-load interval at the current frequency, it is judged that the oscillating saw is in the non-bone cutting mode (the oscillating saw is turned on but not in contact with the bone surface). According to this mode, switch to control strategy 2, and send the corresponding control command to the robot arm servo interface.

[0059] Step 6: If the current current i is within the load range at the current frequency, it is determined that the oscillating saw is in the bone cutting mode (the oscillating saw is turned on and in contact with the bone surface). According to this mode, the control strategy 3 is switched and the corresponding control command is sent to the servo interface of the robot arm.

[0060] In steps 4-6, control strategy 1-3 is not a special control method, but an operation mode further set according to human needs for the current bone cutting mode of the oscillating saw.

[0061] Step 7: Obtain the working current i of the oscillating saw in real time through the current acquisition module. Transmit the collected current data to the damping adjustment module of the control system. Define the relationship between the current i and the damping coefficient B. Use linear or nonlinear function mapping. In this embodiment, nonlinear mapping is used to make the system more sensitive to current changes. The formula is as follows:

[0062] D(i)=D min +k i ·(ii min ) b

[0063] Among them, D min is the minimum damping coefficient, used in low current state, i min is the minimum current value, k is the proportional coefficient, which indicates the influence of current on damping, and b is the nonlinear adjustment parameter, which controls the speed of damping growth. In order to ensure the safety of the robot operation, the upper and lower limits of the damping coefficient are set:

[0064] D min ≤D(i)≤D max

[0065] D max This is the maximum damping coefficient that the system can withstand, to avoid excessive damping that may cause the robot arm to respond slowly or lose control.

[0066] In step 2, this embodiment fits the mapping curve between the frequency and the current of the oscillating saw, and the least square method may be used.

[0067] In step 3, F = (F x ,F y ,F z ,T x ,T y ,T z ), where F x ,F y ,F z are the forces in the three coordinate axes of the force sensor coordinate system, T x ,T y ,T z are the moments in the three coordinate axis directions respectively.

[0068] In step 4, the threshold value Th is set based on the maximum value of the derivative of the force generated by the doctor manually dragging the oscillating saw during surgery, which is measured experimentally.

[0069] The above embodiment constructs a knee joint osteotomy modal recognition system, and uses a flexible convolution filtering algorithm to smooth the collected force signal to effectively suppress the high-frequency noise in the signal and extract the main features of the force signal. A vibration isolation pad is arranged between the oscillating saw and the six-dimensional force sensor to reduce false signals and noise caused by high-frequency vibration. At the same time, according to the relationship between the working frequency and current of the oscillating saw, the current data at different working frequencies of the oscillating saw are collected, and the mapping curve of the current and the vibration frequency is fitted to provide key parameter support for modal recognition. During the operation, the force signal after the flexible filtering is derived, and the derivative value is compared with the preset threshold, combined with the real-time current data of the oscillating saw, to achieve accurate recognition of the osteotomy modality. According to different modal characteristics, the corresponding control strategy is selected to dynamically adjust the flexibility and cutting trajectory of the robot arm to ensure the stability and accuracy of the osteotomy process.

[0070] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0071] The above description is only a specific example of the present invention and does not constitute any limitation to the present invention. It is obvious that for professionals in this field, after understanding the content and principles of the present invention, it is possible to make various modifications and changes in form and details without departing from the principles and structures of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

[0072] The present invention is intended to include these changes and modifications as long as they fall within the scope of the present invention and its equivalent technology.

Claims

1. A knee joint osteotomy modal identification method for high-frequency vibration of an oscillating saw, characterized in that: The following steps are involved: 1) Construct a knee joint bone cutting modal recognition system, define the original force information of the bone cutting oscillating saw measured by the force sensor as F0, and define the current measured by the current acquisition module as i; 2) Under no-load condition, a mapping curve between the frequency and current of the swing saw is fitted to distinguish the load and no-load conditions of the subsequent swing saw at different frequencies, and then determine the state of the swing saw; 3) Performing smooth filtering on the original force information to obtain a filtered force signal; 4) Differentiate the filtered force signal, compare the result with the set judgment threshold Th, execute different control strategies according to the different states of the swing saw, and send the corresponding control instructions to the robot arm servo interface; 5) The working current i of the oscillating saw is obtained in real time through the current acquisition module, and the collected current data is transmitted to the damping adjustment module of the control system for adjustment to achieve stable identification of the knee joint bone cutting mode.

2. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 1, characterized in that: The construction of the knee joint osteotomy modality recognition system is specifically as follows: The force sensor is placed between the bone cutting oscillating saw and the end of the robot arm, and a current acquisition module is welded in the power supply circuit of the bone cutting oscillating saw to monitor the working current of the oscillating saw in real time. A vibration isolation pad is installed between the force sensor and the bone cutting oscillating saw; Among them, the force sensor is a six-dimensional force sensor; and it is connected to the host computer, and is used to transmit force information to the host computer for processing; the vibration isolation pad is a polyurethane vibration isolation pad or a rubber vibration isolation pad, which is used to isolate high-frequency vibration signals.

3. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 1, characterized in that: The mapping curve between the frequency and current of the oscillating saw is specifically fitted as follows: When the oscillating saw is in a no-load state, the operating frequency f of the oscillating saw is adjusted in sequence, and the corresponding current i is recorded. After collecting multiple sets of corresponding data of f and i, the least squares method is used to fit the mapping curve of the oscillating saw frequency and current: i=g(f).

4. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 1, characterized in that: The smooth filtering of the original force information is specifically as follows: 3-1) Use a convolution smoothing filter and select an average filter as the convolution kernel to perform smooth filtering on the original force information, that is: Among them, y[n] is the value of the filtered signal at the nth sampling point, x[nk] is the value of the original input signal at the nkth sampling point, and M is the half width of the convolution kernel, which determines that the length of the kernel function is 2M+1; 3-2) Define the force signal F after filtering; where F = (F x ,F y ,F z ,T x ,T y ,T z ), where F x ,F y ,F z are the forces in the three coordinate axes of the force sensor coordinate system, T x ,T y ,T z are the moments in the three coordinate axis directions respectively.

5. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 1, characterized in that: The step 4) is specifically: 4-1) Perform first-order differentiation on the filtered smooth force signal F to calculate F', the formula is: 4-2) setting a threshold value Th to judge the state of the swing saw; wherein the threshold value Th is set based on: the maximum value of the derivative of the force generated by manually dragging the swing saw; 4-3) When F'≤Th, it indicates that the oscillating saw is not in the bone cutting state, that is, the oscillating saw is not turned on or does not contact the bone surface; according to the mode, switch to the corresponding control strategy, and send the corresponding control command to the servo interface of the robot arm; 4-4) When F'>Th, it indicates that the oscillating saw has been turned on. The mapping curve of the oscillating saw frequency and current is fitted to determine whether the oscillating saw is in the bone cutting state. According to the mode, the corresponding control strategy is switched and the corresponding control instruction is sent to the robot arm servo interface; 4-5) If the current current i is in the load range at the current frequency, it is determined that the oscillating saw is in the bone cutting mode or the oscillating saw is turned on and contacts the bone surface; according to the mode, switch to the corresponding control strategy and send the corresponding control instruction to the servo interface of the robot arm.

6. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 5, characterized in that: In step 4-4), whether the bone cutting state is in progress is determined as follows: According to the fitted mapping curve of the oscillating saw frequency and current, combined with the real-time current i of the current oscillating saw, the working state of the oscillating saw is judged: if the current current i is in the no-load interval at the current frequency, it is judged that the oscillating saw is in the non-bone cutting mode or the oscillating saw is turned on but not in contact with the bone surface; otherwise, it is judged that the oscillating saw is in the bone cutting mode, or the oscillating saw is turned on and in contact with the bone surface.

7. The knee joint osteotomy modal identification method under high-frequency vibration conditions of an oscillating saw according to claim 1, characterized in that: The step 5) is specifically: 5-1) Using nonlinear mapping, define the relationship between current i and damping coefficient B, that is: D(i)=D min +k i ·(i-i min ) b Among them, D min is the minimum damping coefficient, which is used in low current state, that is, when the current is less than or equal to the set minimum current value, i min is the set minimum current value, k is the proportional coefficient, which indicates the intensity of the influence of current on damping, and b is the nonlinear adjustment parameter, which controls the speed of damping growth; 5-2) Set the upper and lower limits of the damping coefficient to ensure the safety of the robot arm operation: D min ≤D(i)≤D max Among them, D max It is the maximum damping coefficient that the system can withstand to prevent the robot arm from responding slowly or losing control due to excessive damping.

8. A knee joint osteotomy modal recognition system for high-frequency vibration of an oscillating saw, characterized in that: It comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement a knee joint bone cutting modal identification method under a high-frequency vibration condition of an oscillating saw as described in any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, a knee joint bone cutting modal recognition method under high-frequency vibration conditions of an oscillating saw as described in any one of claims 1 to 7 is implemented.

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