CN117796812B - Weight reduction auxiliary method and medium for bedside lower limb rehabilitation robot - Google Patents
Weight reduction auxiliary method and medium for bedside lower limb rehabilitation robot Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 40
- 210000003141 lower extremity Anatomy 0.000 title claims abstract description 28
- 239000013585 weight reducing agent Substances 0.000 title claims description 7
- 210000002414 leg Anatomy 0.000 claims abstract description 70
- 210000004394 hip joint Anatomy 0.000 claims abstract description 33
- 230000005484 gravity Effects 0.000 claims abstract description 32
- 210000000629 knee joint Anatomy 0.000 claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 16
- 230000003068 static effect Effects 0.000 claims abstract description 5
- 210000000689 upper leg Anatomy 0.000 claims description 24
- 244000309466 calf Species 0.000 claims description 17
- 210000000544 articulatio talocruralis Anatomy 0.000 claims description 10
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- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
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- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000004064 dysfunction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
- A61B5/224—Measuring muscular strength
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- A61H1/00—Apparatus for passive exercising; Vibrating apparatus; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
- A61H1/02—Stretching or bending or torsioning apparatus for exercising
- A61H1/0237—Stretching or bending or torsioning apparatus for exercising for the lower limbs
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- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/16—Physical interface with patient
- A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
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Abstract
The application discloses a weight-reducing auxiliary method and medium for a bedside lower limb rehabilitation robot, wherein the method comprises the following steps of: step 1, establishing a patient leg two-connecting-rod model; step 2, under the condition that the legs of the patient straighten, guiding the legs of the patient to move by any section of track, and recording the position information of the tail ends of the legs of the patient in the process; step 3, identifying the leg length and hip joint coordinates of the patient based on the leg two-connecting-rod model; step 4, converting the position information of the tail end of the leg of the patient into knee joint and hip joint angles of the patient through an inverse kinematics model based on the leg length and hip joint coordinates of the patient identified in the step 3; and 5, calculating the gravity of the leg at the current position based on the connecting rod statics model. According to the application, the force required by leg holding is solved to identify the gravity of the leg of the patient, so that the detection value of the force sensor on the bedside lower limb rehabilitation robot is compensated, thereby obtaining more accurate output of the patient and realizing accurate assessment of the muscle strength recovery condition of the patient.
Description
Technical Field
The application relates to the technical field of medical robots, in particular to a weight reduction auxiliary method and medium for a bedside lower limb rehabilitation robot.
Background
With the acceleration of the aging trend of society, patients suffering from lower limb dysfunction caused by cerebral apoplexy and other diseases are increasing.
The utility model patent with the publication number of CN217828331U discloses a multi-degree-of-freedom full-range lower limb rehabilitation robot, and the rehabilitation training of the joints of the lower limbs of a patient can be realized in a 3D space range by relatively fixing the legs of the patient and the tail end of equipment. However, the patient's muscle strength recovery condition cannot be accurately estimated due to the great change of the end load caused by the individual variability of the patient and the angle change of the hip and knee joints in the prone position.
The invention patent with the publication number of CN108056898A discloses a virtual scene interactive rehabilitation training robot based on a lower limb connecting rod model and force sense information and a control method thereof, which can realize the acquisition of compensation models corresponding to patients with different body types, however, the following defects exist in the patents: additional Kinect equipment is required to detect patient leg position.
Disclosure of Invention
The application provides a weight reduction auxiliary method and medium for a bedside lower limb rehabilitation robot, which have the advantages that the weight of the leg of a patient is identified by solving the force required by leg holding, so as to compensate the detection value of a force sensor on the bedside lower limb rehabilitation robot, thereby obtaining more accurate force of the patient and realizing accurate assessment of the muscle strength recovery condition of the patient.
The technical scheme of the application is as follows:
In one aspect, the application provides a weight-loss assisting method for a bedside lower limb rehabilitation robot, comprising the following steps of:
step1, establishing a patient leg two-connecting-rod model;
Step 2, under the condition that the legs of the patient straighten, guiding the legs of the patient to move by any section of track, and recording the position information of the tail ends of the legs of the patient in the process;
step 3, identifying the leg length and hip joint coordinates of the patient based on the leg two-connecting-rod model;
Step 4, converting the position information of the tail end of the leg of the patient into knee joint and hip joint angles of the patient through an inverse kinematics model based on the leg length and hip joint coordinates of the patient identified in the step 3;
step 5, calculating the gravity of the leg at the current position based on a connecting rod statics model;
Further, the patient leg two-link model established in the step 1 comprises the following parameters: patient hip joint position O p,lc, patient knee joint position K p,lc, patient ankle joint position E p,lc, patient hip joint angle beta 2, patient knee joint angle beta 3, patient thigh length L 1, patient calf length L 2, thigh center of gravity relative to hip joint position K 1L1, calf center of gravity relative to knee joint position K 2L2, thigh and calf weight G 1、G2, vertical and horizontal forces required for leg retention
Further, in step 2, position information of the leg end of the patient is acquired through a plurality of acquisition points provided at the end of the robot.
The robot tail end and the patient limb tail end have better position overlap ratio, the position coordinates of the patient limb tail end can be obtained by the acquisition points arranged at the robot tail end, the acquisition points are not required to be arranged on the body of the patient, and the acquisition accuracy is favorably provided by the plurality of acquisition points.
Further, in step 3, the leg of the patient is straightened during the identification process, the hip joint position remains unchanged, and the ankle joint position overlaps with the end position of the robot, so that the end position of the robot during the movement process can be regarded as the movement of the ankle joint of the patient on the spherical surface; in the spatial coordinate system, the spherical equation is as follows:
(x-x0)2+(y-y0)2+(z-z0)2＝R2 (2)
wherein (x 0,y0,z0) is the sphere center coordinate P 0, (x, y, z) is the end position coordinate P, R is the sphere radius;
Connecting sampling points P 1 and P i respectively to form a line segment P 1Pi(ai,bi,ci), wherein P 1 is the sampled end position information of the 1 st robot, P i is the sampled end position information of the i-th robot, wherein the range of i is 2-n, and n is the number of sampling points;
Let the midpoint of the line segment be P m,1i(xi,yi,zi), the midpoint of the line segment is given by the following equation:
ai*(x-xi)+bi*(y-yi)+ci*(z-zi)＝0 (2)
Simultaneous plane equations:
converting it into a matrix form:
solving the overdetermined linear equation set by using a least square method to obtain a spherical center coordinate P 0, calculating the distance from each sampling point to the spherical center after solving the spherical center coordinate, and obtaining the radius length, namely the total leg length L total by taking an average value;
Wherein, The length from the end position coordinate P to the sphere center coordinate P 0;
The thigh and calf lengths L 1 and L 2 of the patient can be obtained according to the preset thigh and calf ratio mu.
The legs straighten in the identification process, so that the accuracy of the identification result is better.
Further, in step 4, the end position information in the robot world coordinate system obtained by the acquisition point is converted into the patient coordinate system, and then the knee joint angle β 3 and the hip joint angle β 2 are converted according to the end position information in the patient coordinate system and the thigh and calf lengths.
In the step 5, in the leg two-link model of the patient, the thigh and the calf of the patient are respectively a first rod and a second rod; if the first bar bears part of the weight of the second bar, i.e.
N 2·sinβ2 is greater than or equal to 0 or
In this case, the first rod supports the second rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the first rod, and the force balance equation is as follows:
and (3) carrying out stress analysis on the whole: the moment balance equation is as follows:
Wherein:
d 1＝k1*L1*cosβ2,k1 is the ratio of the center of gravity of the thigh to the thigh;
d 2＝L1*cosβ2+k2*L2*cosβ3,k2 is the ratio of the center of gravity of the preset shank to the shank;
d3＝Ep,lc(1)-Op,lc(1)
d4＝Ep,lc(3)-Op,lc(3)
The gravity direction expression after simplification is as follows:
the second bar taking part of the weight of the first bar, i.e
N 2·sinβ2 <0 or
In this case, the second rod supports the first rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the second rod, and the force balance equation is as follows:
The gravity direction expression after simplification is as follows:
the resulting force is applied Output as a compensation force.
The resulting force is appliedAs compensation force output, the leg gravity of the patient contained in the detection data of the robot sensor is corrected, so that more accurate patient force data is obtained, and accurate assessment of the muscle force recovery condition of the patient is facilitated.
Further, the method further comprises the steps of:
determining a gravity correction coefficient: recording force data F Sensor acquired by a force sensor when a patient uses a robot for the first time, wherein the correction coefficient is as follows:
the corrected compensation force is:
The force sensor acquisition force of the robot is recorded when the patient uses the robot for the first time, and the correction coefficient is designed, so that the robot has unique correction coefficient aiming at different patients, and the influence caused by different parameters such as height and weight of different patients is overcome.
In another aspect, a robot controller includes a processor and a memory storing a computer program that when invoked by the processor performs a method as described above.
In another aspect, a computer readable medium stores a computer program which, when invoked by a computer, performs a method as described above.
In summary, the beneficial effects of the application are as follows:
1. The leg gravity of the patient is identified by solving the force required by leg holding, so as to compensate the detection value of the force sensor on the bedside lower limb rehabilitation robot, thereby obtaining more accurate patient output and realizing accurate assessment of the patient muscle strength recovery condition;
2. The condition that the muscle strength recovery condition of a patient cannot be accurately estimated due to the large change of the end load caused by the angle change of the hip and knee joints under the individual variability and the prone position of the patient is avoided;
3. the two-link model is used for mapping the tail end position with the knee and hip joint angle in the process of the rehabilitation exercise of the lower limb of the patient, so that the transparency of the assessment process of the tail end traction rehabilitation robot is provided.
Drawings
FIG. 1 is a schematic flow chart of a weight-reducing auxiliary method of a bedside lower limb rehabilitation robot provided by the invention;
FIG. 2 is a schematic diagram of the operating state of a typical bedside lower limb rehabilitation robot;
FIG. 3 is a schematic diagram of the present invention;
FIG. 4 is a leg two-bar model of the present application in a prone position of a patient;
FIG. 5 is a schematic illustration of a two-bar model statics analysis in accordance with the present application;
FIG. 6 is a graph of end position versus knee and hip joint angle during rehabilitation of lower extremities;
Figure 7 is a graph of the position of the extremities versus force applied during rehabilitation of the lower extremities.
Detailed Description
The following describes in detail the embodiments of the present application with reference to the drawings.
Examples: the embodiment of the application provides a weight reduction auxiliary method for a bedside lower limb rehabilitation robot, which comprises the following steps with reference to fig. 1:
step1, establishing a patient leg two-connecting-rod model;
Step 2, under the condition that the legs of the patient straighten, guiding the legs of the patient to move by any section of track, and recording the position information of the tail ends of the legs of the patient in the process;
step 3, identifying the leg length and hip joint coordinates of the patient based on the leg two-connecting-rod model;
Step 4, converting the position information of the tail end of the leg of the patient into knee joint and hip joint angles of the patient through an inverse kinematics model based on the leg length and hip joint coordinates of the patient identified in the step 3;
step 5, calculating the gravity of the leg at the current position based on a connecting rod statics model;
and 6, correcting the gravity coefficient of the leg based on the reading of the force sensor.
The embodiment of the application is exemplified by a typical bedside lower limb rehabilitation robot, and the working state of the bedside lower limb rehabilitation robot is shown in fig. 2.
As shown in FIG. 3, the acquisition force F Sensor of the upper force sensor of the robot comprises the leg gravity G of the patient and the output force F of the patient, and the theoretical leg gravity G cal is calculated through a leg two-link model, so that the interference item of the leg gravity G of the patient can be counteracted, the output force F of the patient is obtained, and the accurate assessment of the muscle strength recovery condition of the patient is realized.
As shown in fig. 4, the patient leg two-bar model established in step 1 includes the following parameters: patient hip joint position O p,lc, patient knee joint position K p,lc, patient ankle joint position E p,lc, patient hip joint angle beta 2 (positive by default), patient knee joint angle beta 3 (positive by default), patient thigh (between hip and knee joints) length L 1, patient calf (between knee and ankle joints) length L 2, thigh center of gravity relative to hip joint position K 1L1, calf center of gravity relative to knee joint position K 2L2, thigh and calf weight G 1、G2, vertical and horizontal forces required during leg retention
In step 2, position information of the leg end of the patient is acquired through a plurality of acquisition points provided at the robot end.
In the step 3, the legs of the patient straighten in the identification process, the positions of the hip joints are kept unchanged, and the positions of the ankle joints are overlapped with the positions of the tail ends of the robots, so that the positions of the tail ends of the robots in the movement process can be regarded as the movement of the ankle joints of the patient on the spherical surface; in the spatial coordinate system, the spherical equation is as follows:
(x-x0)2+(y-y0)2+(z-z0)2＝R2 (3)
wherein (x 0,y0,z0) is the sphere center coordinate P 0, (x, y, z) is the end position coordinate P, R is the sphere radius;
Connecting sampling points P 1 and P i respectively to form a line segment P 1Pi(ai,bi,ci), wherein P 1 is the sampled end position information of the 1 st robot, P i is the sampled end position information of the i-th robot, wherein the range of i is 2-n, and n is the number of sampling points;
Let the midpoint of the line segment be P m,1i(xi,yi,zi), the midpoint of the line segment is given by the following equation:
ai*(x-xi)+bi*(y-yi)+ci*(z-zi)＝0 (2)
Simultaneous plane equations:
converting it into a matrix form:
solving the overdetermined linear equation set by using a least square method to obtain a spherical center coordinate P 0, calculating the distance from each sampling point to the spherical center after solving the spherical center coordinate, and obtaining the radius length, namely the total leg length L total by taking an average value;
Wherein, The length from the end position coordinate P to the sphere center coordinate P 0;
The thigh and calf lengths L 1 and L 2 of the patient can be obtained according to the preset thigh and calf ratio mu.
In step 4, the end position information in the robot world coordinate system obtained by the acquisition point is converted into a patient coordinate system, and then the knee joint angle beta 3 and the hip joint angle beta 2 are converted according to the end position information in the patient coordinate system and the thigh and calf lengths.
In step 5, as shown in fig. 5, in the patient leg two-bar model, the thigh and the calf of the patient are respectively a first bar (link 1 in the figure) and a second bar (link 2 in the figure); if the first bar bears part of the weight of the second bar, i.e.
N 2·sinβ2 is greater than or equal to 0 or
In this case, the first rod supports the second rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the first rod, and the force balance equation is as follows:
and (3) carrying out stress analysis on the whole: the moment balance equation is as follows:
Wherein:
d 1＝k1*L1*cosβ2,k1 is the ratio of the center of gravity of the thigh to the thigh;
d 2＝L1*cosβ2+k2*L2*cosβ3,k2 is the ratio of the center of gravity of the preset shank to the shank;
d3＝Ep,lc(1)-Op,lc(1)
d4＝Ep,lc(3)-Op,lc(3)
The gravity direction expression after simplification is as follows:
the second bar taking part of the weight of the first bar, i.e
N 2·sinβ2 <0 or
In this case, the second rod supports the first rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the second rod, and the force balance equation is as follows:
The gravity direction expression after simplification is as follows:
the resulting force is applied Output as a compensation force.
In step 6, determining a gravity correction coefficient: recording force data F Sensor acquired by a force sensor when a patient uses a robot for the first time, wherein the correction coefficient is as follows:
the corrected compensation force is:
As shown in fig. 3, the difference between the force sensors 'acquisition forces F Sensor and G cal can be considered the patient's force, which does not include the gravitational term, and the result is more accurate.
For a typical lower limb rehabilitation process, the change curve of the end position and the knee and hip joint angles in the lower limb rehabilitation process is shown in fig. 6, and the change curve of the end position and the stress in the lower limb rehabilitation process is shown in fig. 7.
The embodiment of the application also provides a robot controller, which comprises a processor and a memory, wherein the memory stores a computer program, and the computer program executes the method when being called by the processor.
Embodiments of the present application also provide a computer readable medium storing a computer program which, when invoked by a computer, performs a method as described above.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and improvements could be made by those skilled in the art without departing from the inventive concept, which falls within the scope of the present application.
Claims (6)
1. The weight reduction auxiliary method for the bedside lower limb rehabilitation robot is characterized by comprising the following steps of:
step1, establishing a patient leg two-connecting-rod model;
Step 2, under the condition that the legs of the patient straighten, guiding the legs of the patient to move by any section of track, and recording the position information of the tail ends of the legs of the patient in the process;
step 3, identifying the leg length and hip joint coordinates of the patient based on the leg two-connecting-rod model;
Step 4, converting the position information of the tail end of the leg of the patient into knee joint and hip joint angles of the patient through an inverse kinematics model based on the leg length and hip joint coordinates of the patient identified in the step 3;
step 5, calculating the gravity of the leg at the current position based on a connecting rod statics model;
The leg two-link model of the patient established in the step 1 comprises the following parameters: patient hip joint position O p,lc, patient knee joint position K p,lc, patient ankle joint position E p,lc, patient hip joint angle beta 2, patient knee joint angle beta 3, patient thigh length L 1, patient calf length L 2, thigh center of gravity relative to hip joint position K 1L1, calf center of gravity relative to knee joint position K 2L2, thigh and calf weight G 1、G2, vertical and horizontal forces required for leg retention
In the step 3, the legs of the patient straighten in the identification process, the positions of the hip joints are kept unchanged, and the positions of the ankle joints are overlapped with the positions of the tail ends of the robots, so that the positions of the tail ends of the robots in the movement process can be regarded as the movement of the ankle joints of the patient on the spherical surface; in the spatial coordinate system, the spherical equation is as follows:
(x-x0)2+(y-y0)2+(z-z0)2＝R2 (1)
wherein (x 0,y0,z0) is the sphere center coordinate P 0, (x, y, z) is the end position coordinate P, R is the sphere radius;
Connecting sampling points P 1 and P i respectively to form a line segment P 1Pi(ai,bi,ci), wherein P 1 is the sampled end position information of the 1 st robot, P i is the sampled end position information of the i-th robot, wherein the range of i is 2-n, and n is the number of sampling points;
Let the midpoint of the line segment be P m,1i(xi,yi,zi), the midpoint of the line segment is given by the following equation:
ai*(x-xi)+bi*(y-yi)+ci*(z-zi)＝0 (2)
Simultaneous plane equations:
converting it into a matrix form:
Solving an overdetermined linear equation set by using a least square method to obtain a spherical center coordinate P 0, calculating the distance from each sampling point to the spherical center after solving the spherical center coordinate, and obtaining the radius length, namely the total leg length L total by taking an average value;
Wherein, The length from the end position coordinate P to the sphere center coordinate P 0;
The thigh and calf lengths L 1 and L 2 of the patient can be obtained according to the preset thigh and calf ratio mu;
in the step 5, in the leg two-connecting-rod model of the patient, the thigh and the shank of the patient are respectively a first rod and a second rod; if the first bar bears part of the weight of the second bar, i.e.
N 2·sinβ2 is greater than or equal to 0 or
In this case, the first rod supports the second rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the first rod, and the force balance equation is as follows:
and (3) carrying out stress analysis on the whole: the moment balance equation is as follows:
Wherein:
d 1＝k1*L12*cosβ2,k1 is the ratio of the center of gravity of the thigh to the thigh;
d 2＝L1*cosβ2+k2*L2*cosβ3,k2 is the ratio of the center of gravity of the preset shank to the shank;
d3＝Ep,lc(1)-Op,lc(1)
d4＝Ep,lc(3)-Op,lc(3)
The gravity direction expression after simplification is as follows:
the second bar taking part of the weight of the first bar, i.e
N 2·sinβ2 <0 or
In this case, the second rod supports the first rod, so the force N 2 of the first rod against the second rod should be analyzed by force on the second rod alone along the direction of the second rod, and the force balance equation is as follows:
The gravity direction expression after simplification is as follows:
the resulting force is applied Output as a compensation force.
2. The weight-saving support method for a bedside lower limb rehabilitation robot according to claim 1, wherein in step 2, the position information of the leg end of the patient is acquired through a plurality of acquisition points provided at the robot end.
3. The weight-reduction assisting method for a bedside lower limb rehabilitation robot according to claim 1, wherein in step 4, the end position information in the robot world coordinate system obtained by the acquisition point is converted into the patient coordinate system, and then the knee joint angle beta 3 and the hip joint angle beta 2 are converted according to the end position information in the patient coordinate system and the thigh and calf lengths.
4. The weight-loss assisting method for a bedside lower limb rehabilitation robot according to claim 1, further comprising the steps of:
determining a gravity correction coefficient: recording force data F Sensor acquired by a force sensor when a patient uses a robot for the first time, wherein the correction coefficient is as follows:
the corrected compensation force is:
5. A robot controller comprising a processor and a memory, the memory storing a computer program which, when invoked by the processor, performs the method according to any one of claims 1-4.
6. A computer readable medium, characterized in that the computer readable medium stores a computer program, which when called by a computer performs the method according to any of claims 1-4.
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