CN117796812A - 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 46
- 210000003141 lower extremity Anatomy 0.000 title claims abstract description 31
- 239000013585 weight reducing agent Substances 0.000 title claims abstract description 9
- 210000002414 leg Anatomy 0.000 claims abstract description 70
- 230000005484 gravity Effects 0.000 claims abstract description 35
- 210000004394 hip joint Anatomy 0.000 claims abstract description 32
- 210000000629 knee joint Anatomy 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims abstract description 14
- 230000003068 static effect Effects 0.000 claims abstract description 5
- 210000000689 upper leg Anatomy 0.000 claims description 24
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- 238000012549 training Methods 0.000 description 2
- 206010008190 Cerebrovascular accident Diseases 0.000 description 1
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- 230000009286 beneficial effect Effects 0.000 description 1
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000004064 dysfunction Effects 0.000 description 1
- 210000001624 hip Anatomy 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
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- A61B5/224—Measuring muscular strength
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- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- 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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- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- 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
- A61H2201/164—Feet or leg, e.g. pedal
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Abstract
The application discloses a weight reduction auxiliary method and medium for a bedside lower limb rehabilitation robot, wherein the method comprises the following steps: 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 method and the device, the leg gravity of the patient is identified by solving the force required by leg holding, so that the detection value of the force sensor on the bedside lower limb rehabilitation robot is compensated, the force of the patient is more accurate, and the muscle force recovery condition of the patient is accurately estimated.
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 utility model 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 the leg holding, so as to compensate the detection value of a force sensor on the bedside lower limb rehabilitation robot, thereby obtaining more accurate output 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 present application provides a weight-loss assisting method for a bedside lower limb rehabilitation robot, comprising the 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;
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 position E p,lc Patient hip joint angle beta 2 Patient knee joint angle beta 3 Thigh length L of patient 1 Patient calf length L 2 Thigh center of gravity relative to hip joint position k 1 L 1 The position k of the center of gravity of the lower leg relative to the knee joint 2 L 2 The thigh and the shank are subjected to the gravity G 1 、G 2 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-x 0 ) 2 +(y-y 0 ) 2 +(z-z 0 ) 2 ＝R 2 (2)
wherein (x) 0 ,y 0 ,z 0 ) Is the spherical center coordinate P 0 (x, y, z) is the end position coordinate P, R is the sphere radius;
respectively connected with sampling points P 1 And P i Form line segment P 1 P i (a i ,b i ,c i ) Wherein P is 1 1 st robot end position information for sampling, P i The method comprises the steps of sampling the position information of the tail end of an ith 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 (x i ,y i ,z i ) The sagging equation for the line segment is as follows:
a i *(x-x i )+b i *(y-y i )+c i *(z-z i )＝0 (2)
simultaneous plane equations:
converting it into a matrix form:
solving the overdetermined linear equation system by using a least square method to obtain the spherical center coordinate P 0 After solving the coordinates of the sphere center, calculating the distance between each sampling point and the sphere center, taking the average value, and obtaining the radius length, namely the total length L of the legs total ；
Wherein,for the end position coordinate P to the sphere center coordinateP 0 Is a length of (2);
the thigh and calf length L of the patient can be obtained according to the preset thigh and calf ratio mu 1 And L is equal to 2 。
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 beta is converted according to the end position information in the patient coordinate system and the thigh and calf lengths 3 Angle beta of hip joint 2 。
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 ·sinβ 2 Not less than 0 or
In this case, the first rod acts as a support for the second rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed 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 ＝k 1 *L 1 *cosβ 2 ，k 1 the weight ratio of the center of gravity of the thigh to the thigh is preset;
d 2 ＝L 1 *cosβ 2 +k 2 *L 2 *cosβ 3 ，k 2 the weight ratio of the center of gravity of the lower leg to the lower leg is preset;
d 3 ＝E p,lc (1)-O p,lc (1)
d 4 ＝E p,lc (3)-O p,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 ·sinβ 2 <0 or
In this case, the second rod acts as a support for the first rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed along its direction, and the force balance equation is as follows:
the gravity direction expression after simplification is as follows:
the resulting force is appliedOutput 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 acquired by a force sensor when a patient uses a robot for the first time Sensor The correction coefficients are:
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 present application are:
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 utility model;
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 utility model;
FIG. 4 is a leg two-bar model in the prone position of the patient in the present application;
FIG. 5 is a schematic illustration of a two-bar model statics analysis in 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 detailed description of specific embodiments of the present application refers to the accompanying drawings.
Examples: the embodiment of the application provides a weight-reduction auxiliary method for a bedside lower limb rehabilitation robot, and referring to fig. 1, the method comprises the following steps:
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;
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 illustrated by taking a typical bedside lower limb rehabilitation robot as an example, and the working state of the bedside lower limb rehabilitation robot is shown in fig. 2.
As shown in fig. 3, the force sensor on the robot collects force F Sensor The utility model comprises the leg gravity G of the patient and the force F of the patient, and the theoretical leg gravity G is obtained by calculation of a leg two-link model cal The interference item of the leg gravity G of the patient can be counteracted, the output F of the patient is obtained, and the muscle strength recovery condition of the patient is accurately estimated.
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 position E p,lc Patient hip joint angle beta 2 (default counterclockwise positive), patient knee angle β 3 (positive counter-clockwise by default) patient thigh (between hip and knee) length L 1 Length L of patient's lower leg (between knee and ankle joint) 2 Thigh center of gravity relative to hip joint position k 1 L 1 The position k of the center of gravity of the lower leg relative to the knee joint 2 L 2 The thigh and the shank are subjected to the gravity G 1 、G 2 Vertical and horizontal forces required for 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-x 0 ) 2 +(y-y 0 ) 2 +(z-z 0 ) 2 ＝R 2 (3)
wherein (x) 0 ,y 0 ,z 0 ) Is the spherical center coordinate P 0 (x, y, z) is the end position coordinate P, R is the sphere radius;
respectively connected with sampling points P 1 And P i Form line segment P 1 P i (a i ,b i ,c i ) Wherein P is 1 1 st robot end position information for sampling, P i The method comprises the steps of sampling the position information of the tail end of an ith 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 (x i ,y i ,z i ) The sagging equation for the line segment is as follows:
a i *(x-x i )+b i *(y-y i )+c i *(z-z i )＝0 (2)
simultaneous plane equations:
converting it into a matrix form:
solving the overdetermined linear equation system by using a least square method to obtain the spherical center coordinate P 0 After solving the coordinates of the sphere center, calculating the distance between each sampling point and the sphere center, taking the average value, and obtaining the radius length, namely the total length L of the legs total ；
Wherein,for the end position coordinate P to the sphere center coordinate P 0 Is a length of (2);
the thigh and calf length L of the patient can be obtained according to the preset thigh and calf ratio mu 1 And L is equal to 2 。
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 is converted according to the end position information in the patient coordinate system and the thigh and calf lengths 3 Angle beta of hip joint 2 。
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 ·sinβ 2 Not less than 0 or
In this case, the first rod acts as a support for the second rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed 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 ＝k 1 *L 1 *cosβ 2 ，k 1 the weight ratio of the center of gravity of the thigh to the thigh is preset;
d 2 ＝L 1 *cosβ 2 +k 2 *L 2 *cosβ 3 ，k 2 the weight ratio of the center of gravity of the lower leg to the lower leg is preset;
d 3 ＝E p,lc (1)-O p,lc (1)
d 4 ＝E p,lc (3)-O p,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 ·sinβ 2 <0 or
In this case, the second rod acts as a support for the first rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed along its direction, and the force balance equation is as follows:
the gravity direction expression after simplification is as follows:
the resulting force is appliedOutput as a compensation force.
In step 6, determining a gravity correction coefficient: recording force data F acquired by a force sensor when a patient uses a robot for the first time Sensor The correction coefficients are:
the corrected compensation force is:
as shown in fig. 3, the force sensor's acquisition force F Sensor And G cal The difference in (2) may be considered the patient's force, which does not include a 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 can be made by those skilled in the art without departing from the inventive concept of the present application, which fall within the protection scope of the present application.
Claims (9)
1. The weight reduction auxiliary method for the bedside lower limb rehabilitation robot is characterized by comprising 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.
2. The weight-loss assisting method for a bedside lower limb rehabilitation robot according to claim 1, wherein the patient leg two-link model established in step 1 comprises the following parameters: patient hip joint position O p,lc Patient knee joint position K p,lc Patient ankle position E p,lc Patient hip joint angle beta 2 Patient knee joint angle beta 3 Thigh length L of patient 1 Patient calf length L 2 Thigh center of gravity relative to hip joint position k 1 L 1 The position k of the center of gravity of the lower leg relative to the knee joint 2 L 2 The thigh and the shank are subjected to the gravity G 1 、G 2 Vertical and horizontal forces required for leg retention
3. 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.
4. The weight-loss assisting method for a bedside lower limb rehabilitation robot according to claim 1, wherein in step 3, the patient's leg straightens during the identification, the hip joint position remains unchanged, and the ankle joint position overlaps with the robot end position, so that the robot end position during the movement can be regarded as the movement of the patient's ankle joint on the sphere; in the spatial coordinate system, the spherical equation is as follows:
(x-x 0 ) 2 +(y-y 0 ) 2 +(z-z 0 ) 2 ＝R 2 (1)
wherein (x) 0 ,y 0 ,z 0 ) Is the spherical center coordinate P 0 (x, y, z) is the end position coordinate P, R is the sphere radius;
respectively connected with sampling points P 1 And P i Form line segment P 1 P i (a i ,b i ,c i ) Wherein P is 1 1 st robot end position information for sampling, P i The method comprises the steps of sampling the position information of the tail end of an ith 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 (x i ,y i ,z i ) The sagging equation for the line segment is as follows:
a i *(x-x i )+b i *(y-y i )+c i *(z-z i )＝0 (2)
simultaneous plane equations:
converting it into a matrix form:
solving the overdetermined linear equation system by using a least square method to obtain the spherical center coordinate P 0 After solving the coordinates of the sphere center, calculating the distance between each sampling point and the sphere center, taking the average value, and obtaining the radius length, namely the total length L of the legs total ；
Wherein,for the end position coordinate P to the sphere center coordinate P 0 Is a length of (2);
the thigh and calf length L of the patient can be obtained according to the preset thigh and calf ratio mu 1 And L is equal to 2 。
5. The weight-reduction assisting method for 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 is converted according to the end position information in the patient coordinate system and the thigh and calf lengths 3 Angle beta of hip joint 2 。
6. The weight-reduction assisting method for a bedside lower limb rehabilitation robot according to claim 5, wherein in step 5, in the patient leg two-bar model, the thigh and the calf of the patient are respectively a first bar and a second bar; if the first bar bears part of the weight of the second bar, i.e.
N 2 ·sinβ 2 Not less than 0 or
In this case, the first rod acts as a support for the second rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed 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 ＝k 1 *L 1 *cosβ 2 ，k 1 the weight ratio of the center of gravity of the thigh to the thigh is preset;
d 2 ＝L 1 *cosβ 2 +k 2 *L 2 *cosβ 3 ，k 2 the weight ratio of the center of gravity of the lower leg to the lower leg is preset;
d 3 ＝E p,lc (1)-O p,lc (1)
d 4 ＝E p,lc (3)-O p,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 ·sinβ 2 <0 or
In this case, the second rod acts as a support for the first rod, so that the force N of the first rod against the second rod 2 The second rod should be individually force analyzed along its direction, and the force balance equation is as follows:
the gravity direction expression after simplification is as follows:
the resulting force is appliedOutput as a compensation force.
7. 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 acquired by a force sensor when a patient uses a robot for the first time Sensor The correction coefficients are:
the corrected compensation force is:
8. 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 1-7.
9. 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 1-7.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100076348A (en) * | 2008-12-26 | 2010-07-06 | 재단법인 포항지능로봇연구소 | Biped robot and method for controlling of walking biped robot |
| CN103536424A (en) * | 2013-10-26 | 2014-01-29 | 河北工业大学 | Control method of gait rehabilitation training robot |
| US20160007885A1 (en) * | 2007-10-15 | 2016-01-14 | Alterg, Inc. | Method of gait evaluation and training with differential pressure system |
| WO2016149891A1 (en) * | 2015-03-20 | 2016-09-29 | 中国科学院自动化研究所 | Multi-pose lower-limb rehabilitation training robot |
| US20180071580A1 (en) * | 2016-09-12 | 2018-03-15 | Lunghwa University Of Science And Technology | Pneumatic lower extremity gait rehabilitation training system |
| DE202017107583U1 (en) * | 2017-06-30 | 2018-04-19 | Hyundai Motor Company | Multi-limbed linkage and exoskeletal robot of the lower extremity |
| CN108056898A (en) * | 2017-12-21 | 2018-05-22 | 东南大学 | The virtual-scene interacting recovery exercising robot and its control method of information are felt based on lower limb connecting rod model and power |
| CN108297130A (en) * | 2018-01-10 | 2018-07-20 | 浙江大学 | A kind of weight losing method for robot palletizer |
| CN110801226A (en) * | 2019-11-01 | 2020-02-18 | 西安交通大学 | Human knee joint moment testing system method based on surface electromyographic signals and application |
| CN111888186A (en) * | 2020-07-21 | 2020-11-06 | 埃斯顿(南京)医疗科技有限公司 | Three-degree-of-freedom bedside exoskeleton lower limb rehabilitation robot and use method thereof |
| CN115805594A (en) * | 2023-02-06 | 2023-03-17 | 中国科学技术大学 | Compound optimization method for track and configuration of reconfigurable rope-driven lower limb rehabilitation robot |
| WO2023077768A1 (en) * | 2021-11-08 | 2023-05-11 | 武汉可德医疗器械有限公司 | Knee osteoarthritis treatment and rehabilitation equipment based on koapt |
| CN116702455A (en) * | 2023-05-25 | 2023-09-05 | 浙江工业大学 | Sliding mode observer-based lower limb rehabilitation exoskeleton robot joint angular velocity estimation method |
-
2023
- 2023-12-08 CN CN202311681585.3A patent/CN117796812B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160007885A1 (en) * | 2007-10-15 | 2016-01-14 | Alterg, Inc. | Method of gait evaluation and training with differential pressure system |
| KR20100076348A (en) * | 2008-12-26 | 2010-07-06 | 재단법인 포항지능로봇연구소 | Biped robot and method for controlling of walking biped robot |
| CN103536424A (en) * | 2013-10-26 | 2014-01-29 | 河北工业大学 | Control method of gait rehabilitation training robot |
| WO2016149891A1 (en) * | 2015-03-20 | 2016-09-29 | 中国科学院自动化研究所 | Multi-pose lower-limb rehabilitation training robot |
| US20180071580A1 (en) * | 2016-09-12 | 2018-03-15 | Lunghwa University Of Science And Technology | Pneumatic lower extremity gait rehabilitation training system |
| DE202017107583U1 (en) * | 2017-06-30 | 2018-04-19 | Hyundai Motor Company | Multi-limbed linkage and exoskeletal robot of the lower extremity |
| CN108056898A (en) * | 2017-12-21 | 2018-05-22 | 东南大学 | The virtual-scene interacting recovery exercising robot and its control method of information are felt based on lower limb connecting rod model and power |
| CN108297130A (en) * | 2018-01-10 | 2018-07-20 | 浙江大学 | A kind of weight losing method for robot palletizer |
| CN110801226A (en) * | 2019-11-01 | 2020-02-18 | 西安交通大学 | Human knee joint moment testing system method based on surface electromyographic signals and application |
| CN111888186A (en) * | 2020-07-21 | 2020-11-06 | 埃斯顿(南京)医疗科技有限公司 | Three-degree-of-freedom bedside exoskeleton lower limb rehabilitation robot and use method thereof |
| WO2023077768A1 (en) * | 2021-11-08 | 2023-05-11 | 武汉可德医疗器械有限公司 | Knee osteoarthritis treatment and rehabilitation equipment based on koapt |
| CN115805594A (en) * | 2023-02-06 | 2023-03-17 | 中国科学技术大学 | Compound optimization method for track and configuration of reconfigurable rope-driven lower limb rehabilitation robot |
| CN116702455A (en) * | 2023-05-25 | 2023-09-05 | 浙江工业大学 | Sliding mode observer-based lower limb rehabilitation exoskeleton robot joint angular velocity estimation method |
Non-Patent Citations (3)
| Title |
|---|
| BORISOV, A. ET AL: "Controlling 3-d model of two exoskeleton links with variable length", 《INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE ENVIRONMENTAL RISKS AND SAFETY IN MECHANICAL ENGINEERING (ERSME-2020) 2020 IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING 1001 》, 31 December 2020 (2020-12-31) * |
| 沈林勇;章亚男;钱晋武;徐柳伶;文忠;: "下肢步行康复训练机器人的人-机耦合力检测", 电子测量技术, no. 12, 15 December 2012 (2012-12-15) * |
| 赵富强; 杜特; 常宝玉; 牛志刚: "肢腿履带足机构抬腿工况动力学分析与实验研究", 《 工程设计学报》, 1 September 2022 (2022-09-01) * |
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