CN116580810A - Personalized lower limb rehabilitation gait generating device and control method - Google Patents
Personalized lower limb rehabilitation gait generating device and control method Download PDFInfo
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- 210000003141 lower extremity Anatomy 0.000 title claims abstract description 157
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000011156 evaluation Methods 0.000 claims abstract description 124
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- 238000010276 construction Methods 0.000 claims abstract description 31
- 238000012360 testing method Methods 0.000 claims abstract description 14
- 210000003414 extremity Anatomy 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 20
- 210000004394 hip joint Anatomy 0.000 claims description 13
- 210000000629 knee joint Anatomy 0.000 claims description 13
- 230000002776 aggregation Effects 0.000 claims description 8
- 238000004220 aggregation Methods 0.000 claims description 8
- 210000003127 knee Anatomy 0.000 claims description 6
- 210000002414 leg Anatomy 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 210000001624 hip Anatomy 0.000 claims description 4
- 238000012549 training Methods 0.000 abstract description 7
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- 238000011084 recovery Methods 0.000 abstract 2
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- 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
- A61H3/00—Appliances for aiding patients or disabled persons to walk about
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- 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
- A61H2201/1657—Movement of interface, i.e. force application means
- A61H2201/1659—Free spatial automatic movement of interface within a working area, e.g. Robot
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a personalized lower limb rehabilitation gait generating device and a control method. The device comprises a lower limb rehabilitation exoskeleton and a singlechip provided with a module; the singlechip is arranged on the lower limb rehabilitation exoskeleton and is worn on the leg of a user. The method comprises the following steps: obtaining a test gait characteristic parameter; obtaining an initial motion trail by using a ZMP method; inputting into the lower limb rehabilitation exoskeleton; inputting natural language evaluation into a preference cloud construction module; and after the recovery gait parameter preference cloud is obtained, the gait parameters are input into the gait generation module, the personalized gait parameters are output to the joint movement track generation module, and the final movement track is output to the lower limb recovery exoskeleton to run, so that the control of the device is realized. The method solves the problem that the subjective evaluation of the user cannot evaluate gait performance quantitatively and accurately in the lower limb rehabilitation gait evaluation, generates the lower limb rehabilitation personalized gait meeting the preference of the user, carries out safe and effective rehabilitation training on the user, and improves and restores limb movement functions.
Description
Technical Field
The invention relates to a rehabilitation gait generating device, in particular to a personalized lower limb rehabilitation gait generating device and a control method.
Background
In recent years, lower extremity exoskeletons have been widely used to assist in human locomotion. The motion reference track of the lower limb exoskeleton is a preset value, however, due to individual differences of dynamics characteristics and training contents, the preset gait type cannot completely meet the requirements of each user. Therefore, it is necessary to optimize the method for generating the gait of assisting walking by the exoskeleton of the lower limb to meet the user preference and seek the optimal gait suitable for each user.
The lower limb muscle strength of the patient with lower limb dysfunction is insufficient, and the lower limb movement function is weaker than that of a normal person, so that the lower limb rehabilitation exoskeleton cannot be used for rehabilitation training by adopting the gait of the normal person, and the individual gait parameters such as pace speed, step length and the like are required to be designed according to the physical condition and the rehabilitation degree of the patient, so that the human body is promoted to be closer to the real kinematic characteristics and the dynamic characteristics of each joint under the walking gait when wearing the lower limb rehabilitation exoskeleton, and the lower limb rehabilitation exoskeleton is matched with the wearer to complete the self-adaptive active rehabilitation training. Because of the cognitive difference, the user can hardly quantitatively evaluate the gait performance by using clear numerical values, so that the lower limb rehabilitation gait meeting the personalized preference of the user can not be established, and the popularization and the use of the lower limb bones in the actual rehabilitation training are greatly limited.
Disclosure of Invention
In order to solve the problems in the background art, the personalized lower limb rehabilitation gait generating device and the control method provided by the invention realize accurate quantitative evaluation of lower limb rehabilitation gait and prediction of the personalized lower limb rehabilitation gait meeting user preference, help patients to perform safe and effective rehabilitation training, and improve and restore limb movement functions.
The technical scheme adopted by the invention is as follows:
1. personalized lower limb rehabilitation gait generating device:
the device comprises a lower limb rehabilitation exoskeleton, and an STM32f103RCT6 type singlechip provided with an acquisition module, a rehabilitation gait parameter preference cloud construction module, a personalized lower limb rehabilitation gait generation module, an articulation track generation module and an output module; the singlechip is used as a controller to be arranged on the lower limb rehabilitation exoskeleton and is electrically connected with the lower limb rehabilitation exoskeleton, and the lower limb rehabilitation exoskeleton is worn on the legs of a user.
The acquisition module acquires natural language evaluation data of a user and then inputs the natural language evaluation data to the rehabilitation gait parameter preference cloud construction module, the rehabilitation gait parameter preference cloud construction module outputs rehabilitation gait parameter preference clouds to the personalized lower limb rehabilitation gait generation module after processing, the personalized lower limb rehabilitation gait generation module outputs personalized gait parameters to the joint movement track generation module after processing, and the joint movement track generation module outputs a final lower limb rehabilitation exoskeleton joint movement track after processing and outputs the final lower limb rehabilitation exoskeleton joint movement track to the lower limb rehabilitation exoskeleton through the output module for control.
2. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device comprises the following steps:
s1, putting the lower limb rehabilitation exoskeleton on the leg of a user, and obtaining a test gait characteristic parameter according to the reference gait characteristic parameter.
The reference gait characteristic parameters are specifically obtained according to the human body characteristic parameters of the user, wherein the human body characteristic parameters of the user comprise the height, weight, lower limb length, knee width, ankle width, foot length and foot width of the user.
S2, obtaining an initial lower limb rehabilitation exoskeleton joint movement track by using a zero moment point preview control ZMP method according to the characteristic parameters of the test gait.
S3, inputting the initial lower limb rehabilitation exoskeleton joint movement track into the lower limb rehabilitation exoskeleton, and enabling a user to walk for two preset gait cycles according to the initial lower limb rehabilitation exoskeleton joint movement track through the lower limb rehabilitation exoskeleton.
S4, the user inputs the natural language evaluation data of the gait performance index into an acquisition module of the controller and then inputs the natural language evaluation data into a rehabilitation gait parameter preference cloud construction module.
S5, the rehabilitation gait parameter preference cloud construction module generates an evaluation matrix according to natural language evaluation data, obtains rehabilitation gait parameter preference clouds according to the evaluation matrix, inputs the rehabilitation gait parameter preference clouds into the personalized lower limb rehabilitation gait generation module, and outputs personalized gait parameters to the joint movement track generation module, and the joint movement track generation module outputs a final lower limb rehabilitation exoskeleton joint movement track and outputs the final lower limb rehabilitation exoskeleton joint movement track to the lower limb rehabilitation exoskeleton through the output module.
S6, the lower limb rehabilitation exoskeleton moves according to the final lower limb rehabilitation exoskeleton joint movement track, and control of the personalized lower limb rehabilitation gait generating device is achieved.
In the step S1, the preset reference gait feature parameters include a reference step length, a reference step width and a reference step frequency, and the test gait feature parameters are obtained by adjusting the reference gait feature parameters, specifically, the reference step length is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%; the reference step width is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%; the reference step frequency is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%.
In the step S2, the initial lower limb rehabilitation exoskeleton movement track includes an initial left knee movement track, an initial right knee movement track, an initial left hip movement track and an initial right hip movement track.
In the step S4, the gait performance index includes a trajectory control assistance degree AF1 in the support phase, an initial trajectory control assistance degree AF2 in the swing phase, and a trajectory control assistance degree AF3 in the swing phase; the support phase is specifically a phase between an initial standing period and a termination standing period of a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the triggering swing stage is specifically a stage from a final standing period to an initial swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the swing phase is specifically a phase between an initial swing period and a final swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton.
In the step S4, the natural language evaluation data of the track control assistance degree AF1 in the supporting stage, the initial track control assistance degree AF2 in the triggering swing stage, and the track control assistance degree AF3 in the swing stage specifically include 5 evaluation levels: g5, very high; g4, high; g3, medium; g2, low; g1, very low.
The user performs reliability scoring on each evaluation grade in natural language evaluation data of the track control assistance degree AF1 of the support stage, each evaluation grade in natural language evaluation data of the initial track control assistance degree AF2 of the trigger swing stage and each evaluation grade in natural language evaluation data of the track control assistance degree AF3 of the swing stage within a preset score range according to the use experience, inputs the scored scores into an acquisition module of the controller, further inputs a rehabilitation gait parameter preference cloud construction module and constructs an evaluation matrix, wherein the evaluation matrix comprises scores of each evaluation grade in the natural language evaluation data of the track control assistance degree AF1 of the support stage, the initial track control assistance degree AF2 of the trigger swing stage and the track control assistance degree AF3 of the swing stage.
The sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF1 in the support stage, the sum of the scores of the respective evaluation levels in the natural language evaluation data of the initial trajectory control assistance degree AF2 in the trigger swing stage, and the sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF3 in the swing stage are equal to 1.
In the step S5, a rehabilitation gait parameter preference cloud is obtained according to the evaluation matrix, which specifically comprises the following steps:
s5.1, the rehabilitation gait parameter preference cloud construction module extracts scores of all evaluation grades in natural language evaluation data of the track control assistance degree AF1 of the supporting stage, the initial track control assistance degree AF2 of the triggering swing stage and the track control assistance degree AF3 of the swing stage in the evaluation matrix.
S5.2, the rehabilitation gait parameter preference cloud construction module inputs the scores of all evaluation grades in the natural language evaluation data of the track control assistance degree AF1 of the support stage, the scores of all evaluation grades in the natural language evaluation data of the initial track control assistance degree AF2 of the trigger swing stage and the scores of all evaluation grades in the natural language evaluation data of the track control assistance degree AF3 of the swing stage into the reverse cloud generator respectively, and the reverse cloud generator outputs cloud models of the support stage, the trigger swing stage and the swing stage.
S5.3, the rehabilitation gait parameter preference cloud construction module invokes a cloud aggregation operator, and generates a rehabilitation gait parameter preference cloud according to each score in the step S5.1 and each cloud model aggregation in the step S5.2.
In step S5, the rehabilitation gait parameter preference cloud is input to the personalized lower limb rehabilitation gait generating module, the personalized lower limb rehabilitation gait generating module outputs personalized gait parameters, specifically, the personalized lower limb rehabilitation gait generating module outputs the personalized gait parameters including the step length, the step width and the step frequency values of the personalized lower limb rehabilitation gait to the forward cloud generator according to the rehabilitation gait parameter preference Yun Shuru.
In the step S5, the personalized gait parameters are output to the joint movement track generation module, the joint movement track generation module outputs the final lower limb rehabilitation exoskeleton joint movement track, specifically, the joint movement track generation module obtains the final lower limb rehabilitation exoskeleton joint movement track by using a zero moment point method according to the personalized gait parameters, including the final left knee joint movement track, the final right knee joint movement track, the final left hip joint movement track and the final right hip joint movement track.
The beneficial effects of the invention are as follows:
1. the invention introduces the user preference information into the gait evaluation system in the form of qualitative language variables, and compared with digital scoring, the invention has more reliable determination of the optimal gait and accords with the thinking habit of the user.
2. According to the method, the cloud model is adopted to process the natural language information, and the cloud model has good uncertainty knowledge expression capability, so that the ambiguity and randomness of discrete information can be better reflected, and the information of the reaction is more comprehensive.
3. The invention greatly reduces the test process of searching the optimal gait of the user, reduces the test time and has more practicability for users with physical defects.
In a word, the method solves the problem that the subjective evaluation of a user cannot evaluate gait performance quantitatively and accurately in the lower limb rehabilitation gait evaluation, generates the lower limb rehabilitation personalized gait meeting the preference of the user, carries out safe and effective rehabilitation training on the user, and improves and restores limb movement functions.
Drawings
FIG. 1 is a flow chart of a personalized lower limb rehabilitation gait generating method of the invention;
FIG. 2 is a personalized lower limb rehabilitation gait parameter preference cloud schematic diagram of the invention;
fig. 3 is a controller block diagram of the personalized lower limb rehabilitation gait generating device of the invention.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
The personalized lower limb rehabilitation gait generating device comprises a lower limb rehabilitation exoskeleton, an acquisition module, a rehabilitation gait parameter preference cloud construction module, a personalized lower limb rehabilitation gait generating module, an articulation locus generating module and an STM32f103RCT6 type singlechip, wherein the acquisition module, the rehabilitation gait parameter preference cloud construction module, the personalized lower limb rehabilitation gait generating module, the articulation locus generating module and the output module are arranged on the singlechip; the singlechip is used as a controller to be arranged on the lower limb rehabilitation exoskeleton and is electrically connected with the lower limb rehabilitation exoskeleton, and the lower limb rehabilitation exoskeleton is worn on the legs of a user. The lower limb rehabilitation exoskeleton comprises 4 Maxon EC90F1 motors which are respectively used for controlling the motions of the bilateral hip joint and the knee joint; 2 springs for bilateral ankle passive control; and the Maxon EPOS4 type position controller is used for controlling the motor.
The acquisition module acquires natural language evaluation data of a user and inputs the natural language evaluation data to the rehabilitation gait parameter preference cloud construction module, the rehabilitation gait parameter preference cloud construction module processes the natural language evaluation data and outputs the rehabilitation gait parameter preference cloud to the personalized lower limb rehabilitation gait generation module, the personalized lower limb rehabilitation gait generation module processes the natural language evaluation data and outputs the personalized gait parameter to the joint movement track generation module, and the joint movement track generation module processes the joint movement track and outputs the final lower limb rehabilitation exoskeleton joint movement track to the lower limb rehabilitation exoskeleton for control through the output module.
The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device comprises the following steps:
s1, putting the lower limb rehabilitation exoskeleton on the leg of a user, and obtaining a test gait characteristic parameter according to the reference gait characteristic parameter. The reference gait characteristic parameters are specifically obtained according to the human body characteristic parameters of the user, wherein the human body characteristic parameters of the user comprise the height, weight, lower limb length, knee width, ankle width, foot length and foot width of the user.
In step S1, the preset reference gait feature parameters include a reference step length, a reference step width and a reference step frequency, and the test gait feature parameters are obtained by adjusting the reference gait feature parameters, specifically, the reference step length is reduced by 10%, the reference step width is reduced by 5%, and the reference step width is increased by 5% or 10%; the reference step width is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%; the reference step frequency is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%.
S2, obtaining an initial lower limb rehabilitation exoskeleton joint movement track by using a zero moment point preview control ZMP method according to the characteristic parameters of the test gait.
In step S2, the initial lower limb rehabilitation exoskeleton joint motion trajectories include an initial left knee joint motion trajectory, an initial right knee joint motion trajectory, an initial left hip joint motion trajectory, and an initial right hip joint motion trajectory.
S3, inputting the initial lower limb rehabilitation exoskeleton joint movement track into the lower limb rehabilitation exoskeleton, and enabling a user to walk for two preset gait cycles according to the initial lower limb rehabilitation exoskeleton joint movement track through the lower limb rehabilitation exoskeleton.
S4, the user inputs the natural language evaluation data of the gait performance index into an acquisition module of the controller and then inputs the natural language evaluation data into a rehabilitation gait parameter preference cloud construction module.
In step S4, the gait performance index includes a trajectory control assistance degree AF1 in the support phase, an initial trajectory control assistance degree AF2 in the swing phase, and a trajectory control assistance degree AF3 in the swing phase; the support phase is specifically a phase between an initial standing period and a termination standing period of a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the triggering swing stage is specifically a stage from a final standing period to an initial swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the swing phase is specifically a phase between an initial swing period and a final swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton.
In step S4, the natural language evaluation data of the track control assistance degree AF1 of the support phase, the initial track control assistance degree AF2 of the trigger swing phase, and the track control assistance degree AF3 of the swing phase specifically include 5 evaluation levels: g5, very high; g4, high; g3, medium; g2, low; and G1, the very low user performs reliability scoring on each evaluation grade in the natural language evaluation data of the track control assistance degree AF1 of the support stage, each evaluation grade in the natural language evaluation data of the initial track control assistance degree AF2 of the trigger swing stage and each evaluation grade in the natural language evaluation data of the track control assistance degree AF3 of the swing stage within a preset score range, the scored scores are input into an acquisition module of the controller and then are input into a rehabilitation gait parameter preference cloud construction module to be constructed into an evaluation matrix, and the evaluation matrix comprises the scores of each evaluation grade in the natural language evaluation data of the track control assistance degree AF1 of the support stage, the initial track control assistance degree AF2 of the trigger swing stage and the track control assistance degree AF3 of the swing stage.
The sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF1 in the support stage, the sum of the scores of the respective evaluation levels in the natural language evaluation data of the initial trajectory control assistance degree AF2 in the trigger swing stage, and the sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF3 in the swing stage are equal to 1.
In step S4, the natural language evaluation data is specifically shown in table 1 (a) and table 1 (b):
table 1 (a) three gait performance indicators
Table 1 (b) evaluation rating
The "value" in table 1 is used to help the user understand the semantic scale of the rating levels G1 to G5.
Table 2 (a), table 2 (b), table 2 (c), table 2 (d) and table 2 (e) show user reliability scoring diagrams including the scores of the first AF1 evaluation level, the scores of the second AF2 evaluation level and the scores of the third AF3 evaluation level in the step test of the reference gait feature parameter and the test gait feature parameter. For example, in the reference gait characteristic parameter step size experimental mode, the user has higher reliability for the G2 level and a certain reliability for the G3 level in the process of scoring the gait performance index AF2, and gives a score of 0.6 to the G2 level, 0.4 to the G3 level, and a score of 0 to the other evaluation levels, and the sum of the total scores is equal to 1.
Table 2 (a) reference pattern
Table 2 (b) step size reduction 10% mode
Table 2 (c) step size increase 10% mode
Table 2 (d) step size reduction 5% mode
Table 2 (e) step size increase 5% mode
S5, the rehabilitation gait parameter preference cloud construction module generates an evaluation matrix according to natural language evaluation data, obtains rehabilitation gait parameter preference clouds according to the evaluation matrix, inputs the rehabilitation gait parameter preference clouds into the personalized lower limb rehabilitation gait generation module, and outputs personalized gait parameters to the joint movement track generation module, and the joint movement track generation module outputs a final lower limb rehabilitation exoskeleton joint movement track and outputs the final lower limb rehabilitation exoskeleton joint movement track to the lower limb rehabilitation exoskeleton through the output module.
In step S5, a rehabilitation gait parameter preference cloud is obtained according to the evaluation matrix, specifically as follows:
s5.1, the rehabilitation gait parameter preference cloud construction module extracts scores of all evaluation grades in natural language evaluation data of the track control assistance degree AF1 of the supporting stage, the initial track control assistance degree AF2 of the triggering swing stage and the track control assistance degree AF3 of the swing stage in the evaluation matrix.
S5.2, the rehabilitation gait parameter preference cloud construction module inputs the scores of all evaluation grades in the natural language evaluation data of the track control assistance degree AF1 of the support stage, the scores of all evaluation grades in the natural language evaluation data of the initial track control assistance degree AF2 of the trigger swing stage and the scores of all evaluation grades in the natural language evaluation data of the track control assistance degree AF3 of the swing stage into the reverse cloud generator respectively, and the reverse cloud generator outputs cloud models of the support stage, the trigger swing stage and the swing stage.
S5.3, the rehabilitation gait parameter preference cloud construction module invokes a cloud aggregation operator, and generates a rehabilitation gait parameter preference cloud according to each score in the step S5.1 and each cloud model aggregation in the step S5.2, wherein weights of AF1, AF2 and AF3 are drawn by an expert.
In step S5, the rehabilitation gait parameter preference cloud is input to the personalized lower limb rehabilitation gait generating module, the personalized lower limb rehabilitation gait generating module outputs personalized gait parameters, specifically, the personalized lower limb rehabilitation gait generating module outputs the rehabilitation gait parameters to the forward cloud generator according to the rehabilitation gait parameter preference Yun Shuru, and the forward cloud generator outputs the personalized gait parameters including the step length, the step width and the step frequency value of the personalized lower limb rehabilitation gait.
In step S5, the personalized gait parameters are output to the joint motion track generation module, and the joint motion track generation module outputs a final lower limb rehabilitation exoskeleton joint motion track, specifically, the joint motion track generation module obtains a final lower limb rehabilitation exoskeleton joint motion track including a final left knee joint motion track, a final right knee joint motion track, a final left hip joint motion track and a final right hip joint motion track by using a zero moment point method according to the personalized gait parameters.
In step S5, the generated evaluation matrix is specifically as follows:
wherein V is K An evaluation matrix representing the kth user;is carried out in the ith walking experiment p i Middling gait performance index c j Is set according to the evaluation information of the (a); p is p m Represents the mth walking experiment; c n Represents the nth gait performance index.
In step S5, an aggregate preference cloud model is setTCM i Representing an ith cloud model; />Representing a desired interval value; en is provided with i Representing entropy; he (He) i Representing super entropy; the corresponding weight of each gait performance index is { omega } 1 ,ω 2 ,…,ω n |i＝1,…,n}。
And aggregating the cloud model according to the cloud aggregation operator. Cloud aggregation operator WWA is shown below:
the final generated preference cloud parameters table is shown in table 3:
table 3 preference cloud parameter table
The aggregate preference cloud model is shown in fig. 2, the abscissa represents the step value, the unit is mm, the ordinate represents the membership value, and the cloud drops in the figure represent the step values under different membership degrees.
S6, the lower limb rehabilitation exoskeleton moves according to the final lower limb rehabilitation exoskeleton joint movement track, and control of the personalized lower limb rehabilitation gait generating device is achieved.
As shown in fig. 3, an embodiment of the present invention provides a controller of a personalized lower limb rehabilitation gait generating device, configured to implement a personalized lower limb rehabilitation gait generating method, where the device includes: the acquisition module is used for acquiring natural language evaluation data of gait performance indexes of the user. And the rehabilitation gait parameter preference cloud construction module is used for determining a rehabilitation gait parameter preference cloud according to the natural language evaluation data of the gait performance indexes of the user and a reverse cloud generator algorithm. The personalized lower limb rehabilitation gait generating module is used for determining the step length, the step width and the step frequency value of the personalized lower limb rehabilitation gait according to the expected preference of the user and the forward cloud generator algorithm. The joint movement track generation module is used for determining a left knee joint movement track, a right knee joint movement track, a left hip joint movement track and a right hip joint movement track according to the personalized lower limb rehabilitation gait parameters and the zero moment point algorithm. The output module is used for outputting data of a left knee joint movement track, a right knee joint movement track, a left hip joint movement track and a right hip joint movement track.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The utility model provides a individualized recovered gait generating device of low limbs which characterized in that: the system comprises a lower limb rehabilitation exoskeleton, a singlechip, a lower limb rehabilitation gait parameter preference cloud construction module, a personalized lower limb rehabilitation gait generation module, an articulation track generation module and an output module, wherein the singlechip is provided with an acquisition module, a rehabilitation gait parameter preference cloud construction module and a joint movement track generation module; the singlechip is used as a controller to be arranged on the lower limb rehabilitation exoskeleton and is electrically connected with the lower limb rehabilitation exoskeleton, and the lower limb rehabilitation exoskeleton is worn on the legs of a user.
2. A personalized lower limb rehabilitation gait generating device according to claim 1, wherein: the acquisition module acquires natural language evaluation data of a user and then inputs the natural language evaluation data to the rehabilitation gait parameter preference cloud construction module, the rehabilitation gait parameter preference cloud construction module outputs rehabilitation gait parameter preference clouds to the personalized lower limb rehabilitation gait generation module after processing, the personalized lower limb rehabilitation gait generation module outputs personalized gait parameters to the joint movement track generation module after processing, and the joint movement track generation module outputs a final lower limb rehabilitation exoskeleton joint movement track after processing and outputs the final lower limb rehabilitation exoskeleton joint movement track to the lower limb rehabilitation exoskeleton through the output module for control.
3. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to any one of claims 1 or 2, wherein: the method comprises the following steps:
s1, putting the lower limb rehabilitation exoskeleton on the leg of a user, and obtaining a test gait characteristic parameter according to a reference gait characteristic parameter;
s2, obtaining an initial lower limb rehabilitation exoskeleton joint movement track by using a zero moment point preview control ZMP method according to the characteristic parameters of the test gait;
s3, inputting the initial lower limb rehabilitation exoskeleton joint movement track into a lower limb rehabilitation exoskeleton, and enabling a user to walk for two preset gait cycles according to the initial lower limb rehabilitation exoskeleton joint movement track through the lower limb rehabilitation exoskeleton;
s4, the user inputs the natural language evaluation data of the gait performance index into an acquisition module of the controller and then inputs the natural language evaluation data into a rehabilitation gait parameter preference cloud construction module;
s5, the rehabilitation gait parameter preference cloud construction module generates an evaluation matrix according to natural language evaluation data, acquires rehabilitation gait parameter preference clouds according to the evaluation matrix, inputs the rehabilitation gait parameter preference clouds into the personalized lower limb rehabilitation gait generation module, and outputs personalized gait parameters to the joint movement track generation module;
s6, the lower limb rehabilitation exoskeleton moves according to the final lower limb rehabilitation exoskeleton joint movement track, and control of the personalized lower limb rehabilitation gait generating device is achieved.
4. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 3, wherein: in the step S1, the preset reference gait feature parameters include a reference step length, a reference step width and a reference step frequency, and the test gait feature parameters are obtained by adjusting the reference gait feature parameters, specifically, the reference step length is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%; the reference step width is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%; the reference step frequency is reduced by 10%, reduced by 5%, increased by 5% or increased by 10%.
5. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 3, wherein: in the step S2, the initial lower limb rehabilitation exoskeleton movement track includes an initial left knee movement track, an initial right knee movement track, an initial left hip movement track and an initial right hip movement track.
6. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 3, wherein: in the step S4, the gait performance index includes a trajectory control assistance degree AF1 in the support phase, an initial trajectory control assistance degree AF2 in the swing phase, and a trajectory control assistance degree AF3 in the swing phase; the support phase is specifically a phase between an initial standing period and a termination standing period of a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the triggering swing stage is specifically a stage from a final standing period to an initial swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton; the swing phase is specifically a phase between an initial swing period and a final swing period in a preset gait cycle when a user uses the lower limb rehabilitation exoskeleton.
7. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 6, wherein: in the step S4, the natural language evaluation data of the track control assistance degree AF1 in the supporting stage, the initial track control assistance degree AF2 in the triggering swing stage, and the track control assistance degree AF3 in the swing stage specifically include 5 evaluation levels: g5, very high; g4, high; g3, medium; g2, low; g1, very low;
the user performs scoring on each evaluation grade in natural language evaluation data of the track control assistance degree AF1 of the supporting stage, each evaluation grade in natural language evaluation data of the initial track control assistance degree AF2 of the triggering swinging stage and each evaluation grade in natural language evaluation data of the track control assistance degree AF3 of the swinging stage in a preset score range, the scored scores are input into an acquisition module of the controller and then are input into a rehabilitation gait parameter preference cloud construction module to be constructed into an evaluation matrix, and the evaluation matrix comprises scores of each evaluation grade in the track control assistance degree AF1 of the supporting stage, the initial track control assistance degree AF2 of the triggering swinging stage and the natural language evaluation data of the track control assistance degree AF3 of the swinging stage;
the sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF1 in the support stage, the sum of the scores of the respective evaluation levels in the natural language evaluation data of the initial trajectory control assistance degree AF2 in the trigger swing stage, and the sum of the scores of the respective evaluation levels in the natural language evaluation data of the trajectory control assistance degree AF3 in the swing stage are equal to 1.
8. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 7, wherein: in the step S5, a rehabilitation gait parameter preference cloud is obtained according to the evaluation matrix, which specifically comprises the following steps:
s5.1, extracting scores of all evaluation grades in natural language evaluation data of a track control auxiliary degree AF1 of a supporting stage, an initial track control auxiliary degree AF2 of a triggering swinging stage and a track control auxiliary degree AF3 of the swinging stage in an evaluation matrix by a rehabilitation gait parameter preference cloud construction module;
s5.2, the rehabilitation gait parameter preference cloud construction module inputs the scores of all evaluation grades in natural language evaluation data of the track control assistance degree AF1 of the support stage, the scores of all evaluation grades in natural language evaluation data of the initial track control assistance degree AF2 of the trigger swing stage and the scores of all evaluation grades in natural language evaluation data of the track control assistance degree AF3 of the swing stage into a reverse cloud generator respectively, and the reverse cloud generator outputs cloud models of the support stage, the trigger swing stage and the swing stage;
s5.3, the rehabilitation gait parameter preference cloud construction module invokes a cloud aggregation operator, and generates a rehabilitation gait parameter preference cloud according to each score in the step S5.1 and each cloud model aggregation in the step S5.2.
9. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 3, wherein: in step S5, the rehabilitation gait parameter preference cloud is input to the personalized lower limb rehabilitation gait generating module, the personalized lower limb rehabilitation gait generating module outputs personalized gait parameters, specifically, the personalized lower limb rehabilitation gait generating module outputs the personalized gait parameters including the step length, the step width and the step frequency values of the personalized lower limb rehabilitation gait to the forward cloud generator according to the rehabilitation gait parameter preference Yun Shuru.
10. The lower limb rehabilitation gait generation control method of the personalized lower limb rehabilitation gait generation device according to claim 3, wherein: in the step S5, the personalized gait parameters are output to the joint movement track generation module, the joint movement track generation module outputs the final lower limb rehabilitation exoskeleton joint movement track, specifically, the joint movement track generation module obtains the final lower limb rehabilitation exoskeleton joint movement track by using a zero moment point method according to the personalized gait parameters, including the final left knee joint movement track, the final right knee joint movement track, the final left hip joint movement track and the final right hip joint movement track.
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