CN111557828A - Active stroke lower limb rehabilitation robot control method based on healthy side coupling - Google Patents

Active stroke lower limb rehabilitation robot control method based on healthy side coupling Download PDF

Info

Publication number
CN111557828A
CN111557828A CN202010355333.1A CN202010355333A CN111557828A CN 111557828 A CN111557828 A CN 111557828A CN 202010355333 A CN202010355333 A CN 202010355333A CN 111557828 A CN111557828 A CN 111557828A
Authority
CN
China
Prior art keywords
patient
rehabilitation robot
lower limb
gait
healthy
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010355333.1A
Other languages
Chinese (zh)
Other versions
CN111557828B (en
Inventor
张峻霞
张鹏
张遵浩
曹琳
王新亭
苏海龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University of Science and Technology
Original Assignee
Tianjin University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University of Science and Technology filed Critical Tianjin University of Science and Technology
Priority to CN202010355333.1A priority Critical patent/CN111557828B/en
Publication of CN111557828A publication Critical patent/CN111557828A/en
Application granted granted Critical
Publication of CN111557828B publication Critical patent/CN111557828B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • A61H1/0255—Both knee and hip of a patient, e.g. in supine or sitting position, the feet being moved together in a plane substantially parallel to the body-symmetrical plane
    • A61H1/0262—Walking movement; Appliances for aiding disabled persons to walk
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/1036—Measuring load distribution, e.g. podologic studies
    • A61B5/1038—Measuring plantar pressure during gait
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • 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
    • A61H2201/12—Driving means
    • A61H2201/1207—Driving means with electric or magnetic drive
    • 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
    • A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
    • A61H2201/165—Wearable interfaces
    • 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
    • A61H2201/50—Control means thereof
    • A61H2201/5007—Control means thereof computer controlled
    • 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
    • A61H2201/50—Control means thereof
    • A61H2201/5058—Sensors or detectors
    • 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
    • A61H2201/50—Control means thereof
    • A61H2201/5058—Sensors or detectors
    • A61H2201/5071—Pressure sensors
    • 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
    • A61H2205/00—Devices for specific parts of the body
    • A61H2205/10—Leg
    • 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
    • A61H2230/00—Measuring physical parameters of the user
    • A61H2230/08—Other bio-electrical signals
    • A61H2230/085—Other bio-electrical signals used as a control parameter for the apparatus

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Pathology (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Dentistry (AREA)
  • Evolutionary Computation (AREA)
  • Epidemiology (AREA)
  • Physiology (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Pain & Pain Management (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Rehabilitation Therapy (AREA)
  • Geometry (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Primary Health Care (AREA)

Abstract

The invention relates to an active stroke lower limb rehabilitation robot control method based on healthy side coupling, which comprises the following steps: formulating a characteristic pathological gait correction strategy based on a characteristic pathological gait model; after the limb of the patient starts to move, acquiring multi-sensor signals of the healthy lower limb of the patient, and processing the multi-sensor signals; constructing a multi-sensor information fusion vector; acquiring the movement intention of a patient; the movement intention is coupled with a characteristic pathological gait correction strategy to obtain healthy-affected side coupling information, and the healthy-affected side coupling information is transmitted to the human body-rehabilitation robot closed-loop controller, so that the system function of the rehabilitation robot is controlled, and the human-machine coordination and transportation are guaranteed. The invention carries out feedback type movement re-planning and control integration through accurate identification of the movement intention of the side-healthy leg, achieves the coupling regulation of a man-machine system, and finally realizes the aim of side-healthy coupling training.

Description

Active stroke lower limb rehabilitation robot control method based on healthy side coupling
Technical Field
The invention belongs to the technical field of rehabilitation robot control methods, and particularly relates to an active stroke lower limb rehabilitation robot control method based on healthy side coupling.
Background
Stroke, also known as stroke, is an acute cerebrovascular disease. The world health organization data shows that the incidence rate of stroke in China is the first in the world, more than 700 million stroke patients exist, the disability rate is as high as 80%, and the method has great relation with the failure of effective early rehabilitation intervention. The early rehabilitation training has good promotion effect on the recovery of the motor function and the daily living activity of the patient with the hemiplegia caused by the stroke. But at present, the proportion of rehabilitation doctors to basic population in China is only 1.7 per 10 thousands of people, and a huge gap exists in rehabilitation professionals. The rehabilitation robot can effectively and economically assist the patients with limb disorders to carry out rehabilitation treatment. Therefore, the stroke rehabilitation robot has a huge market demand.
For gait rehabilitation after cerebral apoplexy, the key is to complete walking action and maintain body balance through coordinated exercise training of the two lower limbs. Therefore, the lower limb rehabilitation robot is difficult to design, and the rehabilitation robot based on idea control seems to be a more ideal solution, but for the robot to understand the unique activity mode of individual brain waves, the learning and operation capabilities of the robot need to be greatly improved, and although idea control can control limbs to complete specific actions, research for a longer time and a wider range is needed to determine whether rehabilitation training can be performed by combining with the limb functions of stroke patients so as to recover the motor functions lost by the patients. Furthermore, although no information is given about the exact cost price, such equipment is certainly too popular for the average person to reach and therefore difficult to enter the consumer market.
Therefore, an efficient and low-cost technical method is needed to enable the lower limb rehabilitation robot to have better use experience, so that the active participation degree of the rehabilitation training of the patient can be improved, and the rehabilitation training effect can be greatly improved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an active stroke lower limb rehabilitation robot control method based on healthy side coupling, so that the requirement of training the motion coordination of the two lower limbs of a stroke patient with single-side motion resistance is met, the pathological gait characteristics of the stroke patient are researched, and a gait compensation model is formulated according to the motion coordination principle of the two lower limbs of a human body; through accurate identification of the exercise intention of the side-healthy leg, feedback type exercise re-planning and control integration are performed, coupling regulation and control of a man-machine system are achieved, and the aim of side-healthy coupling training is finally achieved.
The technical problem to be solved by the invention is realized by the following technical scheme:
a control method of an active stroke lower limb rehabilitation robot based on healthy side coupling comprises the following steps:
1) establishing pathological gait characteristic parameters of a patient with lower limb movement ability loss after stroke through a gait experiment, and establishing a characteristic pathological gait correction strategy based on a characteristic pathological gait model;
2) after the limb of the patient starts to move, acquiring multi-sensor signals of the healthy lower limb of the patient, and processing the multi-sensor signals;
3) extracting the characteristics of the multi-sensor signals acquired in the step 2), including the average value, the standard deviation and the variance, and constructing a multi-sensor information fusion vector;
4) acquiring the movement intention of the patient based on deep learning, machine learning and multi-sensor information fusion methods;
5) coupling the movement intention obtained in the step 4) with the characteristic pathological gait correction strategy in the step 1) to obtain health-affected side coupling information;
6) transmitting the key-affected side coupling information obtained in the step 5) to a human body-rehabilitation robot closed-loop controller through a human body-rehabilitation robot information interaction interface, thereby controlling the system function of the rehabilitation robot and ensuring the coordination and transportation of the human machine.
Further, the step 1) specifically includes:
A. the method comprises the following steps of (1) carrying out physical basic evaluation and examination on a patient with lower limb motor disability after stroke so as to select a proper control strategy and set a motor threshold protection mechanism;
B. the gait test is carried out on a cerebral apoplexy patient, pathological gait characteristic parameters of a tested population are collected to obtain a cerebral apoplexy patient gait database, characteristic marking and data preprocessing are carried out on the cerebral apoplexy patient gait data, cerebral apoplexy gait characteristic extraction and cerebral apoplexy patient gait abnormity analysis are carried out to construct a cerebral apoplexy patient characteristic pathological gait model, and a characteristic pathological gait correction strategy is formed through comparison analysis with a normal person walking model.
In addition, the method for carrying out the physical basic evaluation and examination on the patient with lower limb movement ability loss after stroke in the step 1) is as follows: the evaluation is carried out through the built experimental platform, and the evaluation comprises a hardware platform and a software platform, wherein the hardware platform comprises a Vicon system, a Novel plantar pressure measuring system, a Noraxon muscle strength testing system, an encoder and a plantar pressure sensor, and the software platform comprises a NEXUS data acquisition application analyzer, and is used for evaluating the form and posture of a patient, evaluating the muscle strength, evaluating the joint activity, the muscle tension, balancing and coordinating capacity, carrying out neuroelectrophysiological examination and acquiring gait data.
Furthermore, the multi-sensor signal of the lower limb with healthy side collected in the step 2) comprises: surface electromyographic signals, joint angle, and plantar pressure signals.
And the specific process of the step 3) is to perform feature extraction transformation on the output data of the sensors, extract feature vectors representing observation data, perform pattern recognition on the feature vectors, complete the description of the motion of the limbs of each sensor, and synthesize the data of each sensor by using a fusion algorithm to obtain the consistency explanation and description of the motion of the limbs:
the original signals are set as a (1), a (2), a (3), … a (n), and the average value is:
Figure BDA0002473240910000031
the standard deviation is:
Figure BDA0002473240910000032
the variance is:
Figure BDA0002473240910000033
by constructing a multi-sensor information fusion vector, as follows:
Figure BDA0002473240910000034
where s is a response variable of the characteristic variable, the value of which corresponds to the five limb movement patterns and is set here as level ground walking (GND), stair climbing action (UPS), stair descending action (DWS), uphill action (UPH) and downhill action (DWH).
Further, the step 4) includes the steps of:
A. performing regression analysis on the multi-sensor information fusion vector obtained in the step 3) and a limb movement mode by a supervised machine learning method, establishing a mapping function, and constructing a limb movement prediction model;
B. inputting the multi-sensor information fusion vector obtained in the step 3) into a deep learning network model for training, and constructing a human motion intention recognition model;
C. when the human motion intention recognition model and the limb motion prediction model generate the same response variable s, the patient is considered to perform the following motion of the affected side, and the response variable s of the characteristic variable is sent to the human body-rehabilitation robot closed-loop controller.
Further, the step 6) includes the steps of:
A. aiming at the lower limb of the patient at the affected side, a motion control channel from a surface electromyographic signal, a joint angle, a man-machine contact signal and foot sole pressure information to a robot controller and a feedback channel from motion information such as a joint angle and joint torque of a rehabilitation robot to the human body are established, so that the design of an information interaction interface between the human body at the affected side and the rehabilitation robot is realized;
B. through D-H modeling and Jacobian matrix theory research, forward/backward solving is carried out on the kinematics of the robot, a kinematics and dynamics model of the rehabilitation robot based on a man-machine system is established, and after the kinematics and dynamics model is coupled with movement threshold setting for regulation and control, movement intention is transmitted to a human body-rehabilitation robot closed-loop controller, and rehabilitation training of a stroke patient is carried out.
Moreover, the healthy side of the rehabilitation robot is not driven by a motor, the exoskeleton is driven to follow up by the motion of a human body, and the hip joint and the knee joint on the affected side are provided with direct current driving motors. .
The invention has the advantages and beneficial effects that:
1. the active stroke lower limb rehabilitation robot control method based on the coupling of the healthy side drives the rehabilitation movement mode based on the sensing and detection of the patient healthy side limb movement information, avoids the biggest problem of mind control, namely decoding of electroencephalogram signals, and can realize active rehabilitation movement according to the movement intention of the patient, so that the actual training effect is even better than the mind control. Starting from the research of characteristic pathological gaits of stroke patients, the gaits compensation model is used as the basis for adjusting control parameters of the lower limb rehabilitation robot, the state of the patients can be comprehensively reflected, the control is more accurate, and the man-machine coupling performance of the lower limb rehabilitation robot is fundamentally improved on the basis of realizing the coupling of the affected side.
2. Compared with the existing rehabilitation robot control strategy, the active stroke lower limb rehabilitation robot control method based on the coupling of the healthy side and the diseased side fully utilizes the self functional characteristics of the patient, drives the diseased side to carry out coordinated motion by the healthy side motion, and simultaneously adopts the biological information and the motion information as the basis, so that the motion intention of the patient can be reliably judged, the parameter adjustment is more accurate and reliable through an inter-human-machine closed-loop control system, and the aim of flexible control is fulfilled.
Drawings
FIG. 1 is a general technical route diagram of the method of the present invention;
fig. 2 is a model diagram of a relationship between a motor rotation angle and a screw displacement according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a control strategy of the method of the present invention;
fig. 4 is a hardware block diagram of a rehabilitation robot according to an embodiment of the present invention;
FIG. 5 is a hierarchical diagram of a control system for the method of the present invention.
Detailed Description
The present invention is further illustrated by the following specific examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
A method for controlling an active stroke lower limb rehabilitation robot based on patient-side coupling, as shown in fig. 1, the method includes the following steps:
1) the method comprises the following steps of constructing pathological gait characteristic parameters of a patient with lower limb movement ability deficiency after stroke through a gait experiment, formulating a characteristic pathological gait correction strategy based on a characteristic pathological gait model to guide the formulation of a control strategy, and specifically comprising the following steps:
A. the lower limb movement ability of the patient after stroke is subjected to basic physical assessment and examination, the course of the patient is known, meanwhile, a proper control strategy is selected and a movement threshold protection mechanism is set through acquiring the lower limb joint movement threshold of the patient, pre-protection presetting for rehabilitation training is carried out, and secondary injury of the patient is prevented.
The mode for evaluating and checking the physical basis of the patient with lower limb movement disability after stroke is as follows: the evaluation is carried out through the built experimental platform, and the evaluation comprises a hardware platform and a software platform, wherein the hardware platform comprises a Vicon system, a Novel plantar pressure measuring system, a Noraxon muscle strength testing system, an encoder and a plantar pressure sensor, and the software platform comprises a NEXUS data acquisition application analyzer, and is used for evaluating the form and posture of a patient, evaluating the muscle strength, evaluating the joint activity, the muscle tension, balancing and coordinating capacity, carrying out neuroelectrophysiological examination and acquiring gait data.
B. Performing gait experiments on patients with stroke, collecting pathological gait characteristic parameters of tested people, including space-time parameters, kinematic parameters and kinetic parameters, obtaining a gait database of the patients with stroke, performing characteristic labeling (hip-lift type, knee hyperextension type, limp crutch type and circle drawing type) and data preprocessing on the gait data of the patients with stroke, extracting the gait characteristics of the stroke, analyzing the gait abnormity of the patients with stroke, including important time parameters (such as pace decline) of hemiplegic gait, important space parameters (such as the difference of the stride of hemiplegic side limbs and the non-hemiplegic side limbs) of hemiplegic gait, abnormal supporting phase kinematics of the patients with stroke, abnormal swing phase kinematics of the patients with stroke, abnormal kinetic parameters of the patients with stroke, and the like, constructing a characteristic pathological gait model of the patients with stroke, and forming a characteristic pathological gait correction strategy by comparing and a normal walking model, so as to guide the rehabilitation training of the cerebral apoplexy patient.
C. The rehabilitation training of the cerebral apoplexy patient is guided through the correction strategy. In the rehabilitation training process, the realization of the free movement of joints is the key, and the difference of the joint movement of lower limbs at two sides of a stroke patient is considered, so the correction strategy provided by the invention mainly compensates the joint position and establishes a gait compensation model. According to the characteristic of angle change, fitting the angle change curve of the right hip joint in one gait cycle by using a nonlinear fitting tool in origin software to obtain:
Figure BDA0002473240910000061
wherein x is the joint position; y is0＝14.35、x0＝27.18、w＝51.26、A＝20.27
In the same way, a fitting expression of the right knee joint can be obtained:
Figure BDA0002473240910000062
wherein y is0＝6.72、x0＝20.58、w＝8.21、A＝50.68
2) After the limb of the patient starts to move, acquiring multi-sensor signals of the healthy lower limb of the patient, and processing the multi-sensor signals;
the multisensor signal of healthy side lower limbs of gathering includes: surface electromyographic signals, joint angles, and plantar pressure signals; the data preprocessing comprises filtering and normalization, wherein a third-order Butterworth filter is selected, and a low-pass cut-off frequency f is selectedcAnd (5) carrying out low-pass filtering processing on the collected limb movement information and carrying out normalization processing to ensure data normalization.
3) Extracting the characteristics of the multi-sensor signals acquired in the step 2), including the average value, the standard deviation and the variance, and constructing a multi-sensor information fusion vector; and performing feature extraction transformation on output data of the sensors, extracting feature vectors representing observation data, performing pattern recognition on the feature vectors, completing description of each sensor about the motion of the limbs, and synthesizing data of each sensor by using a fusion algorithm to obtain the consistency explanation and description of the motion of the limbs.
The specific process is as follows:
the original signals are set as a (1), a (2), a (3), … a (n), and the average value is:
Figure BDA0002473240910000071
the standard deviation is:
Figure BDA0002473240910000072
the variance is:
Figure BDA0002473240910000073
by constructing a multi-sensor information fusion vector, as follows:
Figure BDA0002473240910000074
where s is a response variable of the characteristic variable, the value of which corresponds to the five limb movement patterns and is set here as level ground walking (GND), stair climbing action (UPS), stair descending action (DWS), uphill action (UPH) and downhill action (DWH).
4) Acquiring the movement intention of the patient based on deep learning, machine learning and multi-sensor information fusion methods; the method comprises the following steps:
A. performing regression analysis on the multi-sensor information fusion vector obtained in the step 3) and the limb movement mode through a supervised machine learning method, establishing a mapping function, and constructing a limb movement prediction model.
B. Inputting the multi-sensor information fusion vector obtained in the step 3) into a deep learning network model for training, and constructing a human motion intention recognition model; and generating a feature file of the sensor value at each moment and a tag file for marking the user posture by the multi-sensor information. The label file is used as a standard reference baseline of a training data set, and data are mixed and fused, wherein data collected by an angle encoder, a pressure sensor and a plantar pressure sensor are fused for the first time, then are fused with an electromyographic signal sensor for the second time, then two layers of statistical models of a Convolutional Neural Network (CNN) and a long-short memory neural network (LSTM) are cascaded, a new two-layer cascade self-adjusting statistical model is created to train the data set, and finally the accuracy of the calculation model is adjusted by comparing a real value with a predicted value by using a test data set, so that a human movement intention identification model is constructed.
C. When the human motion intention recognition model and the limb motion prediction model generate the same response variable s, the patient is considered to perform the following motion of the affected side, and the response variable s of the characteristic variable is sent to the affected side controller. In the human motion intention recognition used in the present embodiment, it is considered that the patient is about to perform the affected lower limb motion only if the patient intention results recognized by the limb motion prediction model and the human motion intention recognition model are the same.
5) Coupling the movement intention obtained in the step 4) with the characteristic pathological gait correction strategy in the step 1) to obtain health-affected side coupling information;
6) transmitting the key-affected side coupling information obtained in the step 5) to a human body-rehabilitation robot closed-loop controller through a human body-rehabilitation robot information interaction interface, thereby controlling the system function of the rehabilitation robot and ensuring the coordination and transportation of the human machine.
A. Aiming at the lower limb of the patient at the affected side, a motion control channel from a surface electromyographic signal, a joint angle, a man-machine contact signal and foot sole pressure information to a robot controller and a feedback channel from motion information such as a joint angle and joint torque of a rehabilitation robot to the human body are established, so that the design of an information interaction interface between the human body at the affected side and the rehabilitation robot is realized;
B. through D-H modeling and Jacobian matrix theory research, forward/backward solving is carried out on the robot kinematics, a kinematics and dynamics model of the rehabilitation robot based on a man-machine system is established, the current state of each joint on the affected side is obtained, a theoretical basis is provided for robot movement control, and after the robot kinematics and dynamics model is coupled and regulated with movement threshold value setting, rehabilitation training of a stroke patient is carried out by coupling information human body-rehabilitation robot closed-loop controller on the healthy-affected side;
in order to realize the control of the robot, a Lagrangian kinetic model equation of the lower limb at one side of the lower limb rehabilitation robot is deduced through the relation between joint moment and generalized variable:
Figure BDA0002473240910000081
where M (q) is a 2 × 2 dimensional matrix of inertial mass,
Figure BDA0002473240910000082
a friction matrix of 2 × 2 dimensions, G (q) a gravity vector of 2 × 2 dimensions,
Figure BDA0002473240910000083
the joint angle, the angular velocity and the angular acceleration of 2 × 1 dimensions respectively have the following parameter expressions:
Figure BDA0002473240910000091
wherein l1、l2Respectively representing the lengths of thighs and calves; m is1、m2Respectively representing the mass of the thigh and the shank; q. q.s1Representing the angle of the thigh to the vertical axis; q. q.s2Representing the angle between the lower leg and the upper leg.
The system takes a servo motor as a joint driving source, the rotation angle of the motor is closely connected with the displacement of a ball screw slide block, the slide block is connected with a joint push rod to drive a joint to rotate, and the mechanism principle model is shown in figure 2. According to the structural schematic diagram of the relation model between the motor rotation angle and the screw displacement, a structural geometric relation can be obtained:
Figure BDA0002473240910000092
the model is used as a hip joint and knee joint driving power model and forms a driving system together with a servo driver.
The controller of the active stroke lower limb rehabilitation robot control method based on the coupling of the patient side is composed of a three-layer controller, as shown in fig. 3 and 4, and comprises a high-layer controller, a central controller and a bottom-layer controller. The high-level controller is used for processing data and identifying intentions, the central controller is responsible for determining a control strategy according to data transmitted by the high level, and the bottom layer is responsible for executing transferred instructions. The high-rise controller adopts a data processor raspberry pi, the central controller and the bottom controller adopt a programmable logic device FPGA, the raspberry pi is used for processing data collected by the side-care sensor and is communicated with the FPGA through a parallel port, the FPGA controls the motor through a Controller Area Network (CAN) field bus, and communication is completed through a serial port. On the basis of a multi-sensor information fusion algorithm, human motion intention recognition, inverse kinematics solution and motion control of the robot and the like, the interaction force of the human body and the robot is taken as feedback information to obtain a motor motion control signal, and the smooth motion control consistent with the healthy side is completed on the affected side of the robot through a closed-loop controller.
The rehabilitation robot drives the affected side to perform rehabilitation training by recognizing the movement of the healthy side, and a hardware block diagram of a control system of the rehabilitation robot is shown in fig. 5. The lower limb rehabilitation robot is divided into a healthy side and an affected side, the healthy side is in unpowered driving, each joint moves along with the human body, each joint of the affected side drives a direct current servo motor to drive the joint to move or generate certain resistance by a driver, myoelectric signals of the lower limb of the human body are collected by a myoelectric sheet, joint positions are detected by an encoder, a plantar pressure value is measured by a plantar pressure sensor, human-computer contact force is obtained by a binding belt, and the intention of a patient is judged by fusing data of various sensors and combining machine learning and deep learning technologies. The central controller is connected with the rehabilitation robot through two CAN buses, the CAN1 bus is responsible for the communication between each joint driver on the healthy side and the computer, and the CAN2 bus is responsible for the communication between each joint driver on the affected side and the computer.
Although the embodiments of the present invention and the accompanying drawings have been disclosed for illustrative purposes, those skilled in the art will appreciate that various substitutions, alterations, and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and thus the scope of the invention is not limited to the embodiments and drawings disclosed.

Claims (8)

1. A control method of an active stroke lower limb rehabilitation robot based on healthy side coupling is characterized by comprising the following steps: the control method comprises the following steps:
1) establishing pathological gait characteristic parameters of a patient with lower limb movement ability loss after stroke through a gait experiment, and establishing a characteristic pathological gait correction strategy based on a characteristic pathological gait model;
2) after the limb of the patient starts to move, acquiring multi-sensor signals of the healthy lower limb of the patient, and processing the multi-sensor signals;
3) extracting the characteristics of the multi-sensor signals acquired in the step 2), including the average value, the standard deviation and the variance, and constructing a multi-sensor information fusion vector;
4) acquiring the movement intention of the patient based on deep learning, machine learning and multi-sensor information fusion methods;
5) coupling the movement intention obtained in the step 4) with the characteristic pathological gait correction strategy in the step 1) to obtain health-affected side coupling information;
6) transmitting the key-affected side coupling information obtained in the step 5) to a human body-rehabilitation robot closed-loop controller through a human body-rehabilitation robot information interaction interface, thereby controlling the system function of the rehabilitation robot and ensuring the coordination and transportation of the human machine.
2. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the step 1) specifically comprises the following steps:
A. the method comprises the following steps of (1) carrying out physical basic evaluation and examination on a patient with lower limb motor disability after stroke so as to select a proper control strategy and set a motor threshold protection mechanism;
B. the gait test is carried out on a cerebral apoplexy patient, pathological gait characteristic parameters of a tested population are collected to obtain a cerebral apoplexy patient gait database, characteristic marking and data preprocessing are carried out on the cerebral apoplexy patient gait data, cerebral apoplexy gait characteristic extraction and cerebral apoplexy patient gait abnormity analysis are carried out to construct a cerebral apoplexy patient characteristic pathological gait model, and a characteristic pathological gait correction strategy is formed through comparison analysis with a normal person walking model.
3. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 2, wherein: the method for evaluating and checking the physical basis of the patient with lower limb movement ability loss after stroke in the step 1) comprises the following steps: the evaluation is carried out through the built experimental platform, and the evaluation comprises a hardware platform and a software platform, wherein the hardware platform comprises a Vicon system, a Novel plantar pressure measuring system, a Noraxon muscle strength testing system, an encoder and a plantar pressure sensor, and the software platform comprises a NEXUS data acquisition application analyzer, and is used for evaluating the form and posture of a patient, evaluating the muscle strength, evaluating the joint activity, the muscle tension, balancing and coordinating capacity, carrying out neuroelectrophysiological examination and acquiring gait data.
4. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the multi-sensor signal of healthy side lower limbs of step 2) collection includes: surface electromyographic signals, joint angle, and plantar pressure signals.
5. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the specific process of the step 3) is that the output data of the sensors is subjected to feature extraction transformation, feature vectors representing observation data are extracted, the feature vectors are subjected to pattern recognition, the description of the sensors about the motion of the limbs is completed, the data of the sensors are synthesized by using a fusion algorithm, and the consistency explanation and description of the motion of the limbs are obtained:
the original signals are set as a (1), a (2), a (3), … a (n), and the average value is:
Figure RE-FDA0002580662400000021
the standard deviation is:
Figure RE-FDA0002580662400000022
the variance is:
Figure RE-FDA0002580662400000023
by constructing a multi-sensor information fusion vector, as follows:
Figure RE-FDA0002580662400000024
where s is a response variable of the characteristic variable, the value of which corresponds to the five limb movement patterns and is set here as level ground walking (GND), stair climbing action (UPS), stair descending action (DWS), uphill action (UPH) and downhill action (DWH).
6. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the step 4) comprises the following steps:
A. performing regression analysis on the multi-sensor information fusion vector obtained in the step 3) and a limb movement mode by a supervised machine learning method, establishing a mapping function, and constructing a limb movement prediction model;
B. inputting the multi-sensor information fusion vector obtained in the step 3) into a deep learning network model for training, and constructing a human motion intention recognition model;
C. when the human motion intention recognition model and the limb motion prediction model generate the same response variable s, the patient is considered to perform the following motion of the affected side, and the response variable s of the characteristic variable is sent to the human body-rehabilitation robot closed-loop controller.
7. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the step 6) comprises the following steps:
A. aiming at the lower limb of the patient at the affected side, a motion control channel from a surface electromyographic signal, a joint angle, a man-machine contact signal and foot sole pressure information to a robot controller and a feedback channel from motion information such as a joint angle and joint torque of a rehabilitation robot to the human body are established, so that the design of an information interaction interface between the human body at the affected side and the rehabilitation robot is realized;
B. through D-H modeling and Jacobian matrix theory research, forward/backward solving is carried out on the kinematics of the robot, a kinematics and dynamics model of the rehabilitation robot based on a man-machine system is established, coupling regulation and control are carried out on the kinematics and dynamics model and the movement threshold value setting, then the coupling information of the healthy side and the sick side is transmitted to a human body-rehabilitation robot closed-loop controller, and rehabilitation training of a stroke patient is carried out.
8. The active stroke lower limb rehabilitation robot control method based on the patient side coupling as claimed in claim 1, wherein: the healthy side of the rehabilitation robot is not driven by a motor, the exoskeleton is driven to follow up by the motion of a human body, and the hip joint and the knee joint on the affected side are provided with direct current driving motors.
CN202010355333.1A 2020-04-29 2020-04-29 Active stroke lower limb rehabilitation robot control method based on healthy side coupling Active CN111557828B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010355333.1A CN111557828B (en) 2020-04-29 2020-04-29 Active stroke lower limb rehabilitation robot control method based on healthy side coupling

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010355333.1A CN111557828B (en) 2020-04-29 2020-04-29 Active stroke lower limb rehabilitation robot control method based on healthy side coupling

Publications (2)

Publication Number Publication Date
CN111557828A true CN111557828A (en) 2020-08-21
CN111557828B CN111557828B (en) 2021-12-07

Family

ID=72074440

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010355333.1A Active CN111557828B (en) 2020-04-29 2020-04-29 Active stroke lower limb rehabilitation robot control method based on healthy side coupling

Country Status (1)

Country Link
CN (1) CN111557828B (en)

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112089577A (en) * 2020-09-23 2020-12-18 同济大学 Interactive training exoskeleton robot based on surface myoelectricity and functional electrical stimulation
CN112107397A (en) * 2020-10-19 2020-12-22 中国科学技术大学 Myoelectric signal driven lower limb artificial limb continuous control system
CN112220650A (en) * 2020-12-09 2021-01-15 南京伟思医疗科技股份有限公司 Online step generation control system for exoskeleton robot contralateral training
CN112515657A (en) * 2020-12-02 2021-03-19 吉林大学 Plantar pressure analysis method based on lower limb exoskeleton neural network control
CN112515923A (en) * 2020-12-07 2021-03-19 深圳市丞辉威世智能科技有限公司 Lower limb exoskeleton gait planning method, computer-readable storage medium and device
CN113110438A (en) * 2021-04-08 2021-07-13 北京机械设备研究所 Automatic following control system, method and device for tractor
CN113223666A (en) * 2021-05-18 2021-08-06 深圳大学 Advanced prediction control method for limb exercise rehabilitation based on bilateral cooperation
CN113730053A (en) * 2021-09-26 2021-12-03 上海交通大学 Lower limb prosthesis layered control system and method based on side-leg-strengthening multi-source information
CN113768760A (en) * 2021-09-08 2021-12-10 中国科学院深圳先进技术研究院 Control method and system of walking aid and driving device
CN113995956A (en) * 2021-11-30 2022-02-01 天津大学 Stroke electrical stimulation training intention recognition method based on myoelectric expected posture adjustment
CN114028775A (en) * 2021-12-08 2022-02-11 福州大学 Ankle joint movement intention identification method and system based on sole pressure
CN114366559A (en) * 2021-12-31 2022-04-19 华南理工大学 Multi-mode sensing system for lower limb rehabilitation robot
CN114569411A (en) * 2022-02-21 2022-06-03 长沙优龙机器人有限公司 Gait self-adaptive control method and system for hemiplegic patient
CN114596935A (en) * 2020-12-03 2022-06-07 深圳先进技术研究院 Rehabilitation training method and system based on human factors engineering
CN114601451A (en) * 2021-04-09 2022-06-10 长春工业大学 Surface electromyogram signal-based upper limb exoskeleton robot control method
CN115024735A (en) * 2022-06-30 2022-09-09 北京工业大学 Cerebral apoplexy patient rehabilitation method and system based on movement intention recognition model
CN115337003A (en) * 2022-07-13 2022-11-15 天津理工大学 Multi-dimensional lower limb rehabilitation evaluation device and method for stroke patient
CN115857595A (en) * 2023-03-02 2023-03-28 安徽星辰智跃科技有限责任公司 Functional environment adjusting method, system and device based on user mood

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102058464A (en) * 2010-11-27 2011-05-18 上海大学 Motion control method of lower limb rehabilitative robot
CN105213153A (en) * 2015-09-14 2016-01-06 西安交通大学 Based on the lower limb rehabilitation robot control method of brain flesh information impedance
KR20160066083A (en) * 2014-12-01 2016-06-10 한국과학기술원 Method to control rehabilitation robot for stroke patients
CN105963100A (en) * 2016-04-19 2016-09-28 西安交通大学 Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method
CN106821681A (en) * 2017-02-27 2017-06-13 浙江工业大学 A kind of upper limbs ectoskeleton control method and system based on Mental imagery
KR20170110827A (en) * 2016-03-24 2017-10-12 국방과학연구소 A method for controlling walking of wearable robot and an apparatus for the same
CN107998609A (en) * 2018-01-04 2018-05-08 浙江大学城市学院 A kind of auxiliary walking exoskeleton robot system based on Multi-sensor Fusion
US20180160946A1 (en) * 2015-06-22 2018-06-14 University Of Maryland, Baltimore Method and Apparatus for Providing Economical, Portable Deficit-Adjusted Adaptive Assistance During Movement Phases of an Impaired Ankle
CN109199786A (en) * 2018-07-26 2019-01-15 北京机械设备研究所 A kind of lower limb rehabilitation robot based on two-way neural interface
CN109394476A (en) * 2018-12-06 2019-03-01 上海神添实业有限公司 The automatic intention assessment of brain flesh information and upper limb intelligent control method and system
CN109549821A (en) * 2018-12-30 2019-04-02 南京航空航天大学 The exoskeleton robot assisted control system and method merged based on electromyography signal and inertial navigation signal
KR20190042256A (en) * 2017-10-16 2019-04-24 삼성전자주식회사 Actuator and motion assist apparatus including the same
CN109953761A (en) * 2017-12-22 2019-07-02 浙江大学 A kind of lower limb rehabilitation robot sensory perceptual system and motion intention inference method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102058464A (en) * 2010-11-27 2011-05-18 上海大学 Motion control method of lower limb rehabilitative robot
KR20160066083A (en) * 2014-12-01 2016-06-10 한국과학기술원 Method to control rehabilitation robot for stroke patients
US20180160946A1 (en) * 2015-06-22 2018-06-14 University Of Maryland, Baltimore Method and Apparatus for Providing Economical, Portable Deficit-Adjusted Adaptive Assistance During Movement Phases of an Impaired Ankle
CN105213153A (en) * 2015-09-14 2016-01-06 西安交通大学 Based on the lower limb rehabilitation robot control method of brain flesh information impedance
KR20170110827A (en) * 2016-03-24 2017-10-12 국방과학연구소 A method for controlling walking of wearable robot and an apparatus for the same
CN105963100A (en) * 2016-04-19 2016-09-28 西安交通大学 Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method
CN106821681A (en) * 2017-02-27 2017-06-13 浙江工业大学 A kind of upper limbs ectoskeleton control method and system based on Mental imagery
KR20190042256A (en) * 2017-10-16 2019-04-24 삼성전자주식회사 Actuator and motion assist apparatus including the same
CN109953761A (en) * 2017-12-22 2019-07-02 浙江大学 A kind of lower limb rehabilitation robot sensory perceptual system and motion intention inference method
CN107998609A (en) * 2018-01-04 2018-05-08 浙江大学城市学院 A kind of auxiliary walking exoskeleton robot system based on Multi-sensor Fusion
CN109199786A (en) * 2018-07-26 2019-01-15 北京机械设备研究所 A kind of lower limb rehabilitation robot based on two-way neural interface
CN109394476A (en) * 2018-12-06 2019-03-01 上海神添实业有限公司 The automatic intention assessment of brain flesh information and upper limb intelligent control method and system
CN109549821A (en) * 2018-12-30 2019-04-02 南京航空航天大学 The exoskeleton robot assisted control system and method merged based on electromyography signal and inertial navigation signal

Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112089577A (en) * 2020-09-23 2020-12-18 同济大学 Interactive training exoskeleton robot based on surface myoelectricity and functional electrical stimulation
CN112107397B (en) * 2020-10-19 2021-08-24 中国科学技术大学 Myoelectric signal driven lower limb artificial limb continuous control system
CN112107397A (en) * 2020-10-19 2020-12-22 中国科学技术大学 Myoelectric signal driven lower limb artificial limb continuous control system
CN112515657A (en) * 2020-12-02 2021-03-19 吉林大学 Plantar pressure analysis method based on lower limb exoskeleton neural network control
CN114596935A (en) * 2020-12-03 2022-06-07 深圳先进技术研究院 Rehabilitation training method and system based on human factors engineering
CN112515923A (en) * 2020-12-07 2021-03-19 深圳市丞辉威世智能科技有限公司 Lower limb exoskeleton gait planning method, computer-readable storage medium and device
CN112515923B (en) * 2020-12-07 2022-11-15 深圳市丞辉威世智能科技有限公司 Lower limb exoskeleton gait planning method, computer-readable storage medium and device
CN112220650B (en) * 2020-12-09 2021-04-16 南京伟思医疗科技股份有限公司 Online step generation control system for exoskeleton robot contralateral training
CN112220650A (en) * 2020-12-09 2021-01-15 南京伟思医疗科技股份有限公司 Online step generation control system for exoskeleton robot contralateral training
CN113110438A (en) * 2021-04-08 2021-07-13 北京机械设备研究所 Automatic following control system, method and device for tractor
CN114601451A (en) * 2021-04-09 2022-06-10 长春工业大学 Surface electromyogram signal-based upper limb exoskeleton robot control method
CN113223666A (en) * 2021-05-18 2021-08-06 深圳大学 Advanced prediction control method for limb exercise rehabilitation based on bilateral cooperation
CN113223666B (en) * 2021-05-18 2023-04-28 深圳大学 Advanced prediction control method for limb movement rehabilitation based on bilateral coordination
CN113768760A (en) * 2021-09-08 2021-12-10 中国科学院深圳先进技术研究院 Control method and system of walking aid and driving device
CN113768760B (en) * 2021-09-08 2022-12-20 中国科学院深圳先进技术研究院 Control method and system of walking aid and driving device
CN113730053A (en) * 2021-09-26 2021-12-03 上海交通大学 Lower limb prosthesis layered control system and method based on side-leg-strengthening multi-source information
CN113995956B (en) * 2021-11-30 2022-09-13 天津大学 Stroke electrical stimulation training intention recognition device based on myoelectricity expected posture adjustment
CN113995956A (en) * 2021-11-30 2022-02-01 天津大学 Stroke electrical stimulation training intention recognition method based on myoelectric expected posture adjustment
CN114028775B (en) * 2021-12-08 2022-10-14 福州大学 Ankle joint movement intention identification method and system based on sole pressure
CN114028775A (en) * 2021-12-08 2022-02-11 福州大学 Ankle joint movement intention identification method and system based on sole pressure
CN114366559A (en) * 2021-12-31 2022-04-19 华南理工大学 Multi-mode sensing system for lower limb rehabilitation robot
CN114569411A (en) * 2022-02-21 2022-06-03 长沙优龙机器人有限公司 Gait self-adaptive control method and system for hemiplegic patient
CN115024735A (en) * 2022-06-30 2022-09-09 北京工业大学 Cerebral apoplexy patient rehabilitation method and system based on movement intention recognition model
CN115024735B (en) * 2022-06-30 2024-04-09 北京工业大学 Cerebral apoplexy patient rehabilitation method and system based on movement intention recognition model
CN115337003A (en) * 2022-07-13 2022-11-15 天津理工大学 Multi-dimensional lower limb rehabilitation evaluation device and method for stroke patient
CN115337003B (en) * 2022-07-13 2024-05-24 天津理工大学 Multi-dimensional lower limb rehabilitation assessment device and assessment method for cerebral apoplexy patient
CN115857595A (en) * 2023-03-02 2023-03-28 安徽星辰智跃科技有限责任公司 Functional environment adjusting method, system and device based on user mood

Also Published As

Publication number Publication date
CN111557828B (en) 2021-12-07

Similar Documents

Publication Publication Date Title
CN111557828B (en) Active stroke lower limb rehabilitation robot control method based on healthy side coupling
CN110916679B (en) Human body lower limb pose gait detection device and method
CN114366556A (en) Multi-mode training control system and method for lower limb rehabilitation
CN105213153A (en) Based on the lower limb rehabilitation robot control method of brain flesh information impedance
Wang et al. Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network
CN109288651A (en) Personalized upper-limbs rehabilitation training robot system and its recovery training method
CN105662789B (en) A kind of exoskeleton system based on sound limb motion monitoring, control deformed limb motion
CN111506189B (en) Motion mode prediction and switching control method for complex motion of human body
CN112842824B (en) Training method for rehabilitation and recovery of lower limbs
CN114366559A (en) Multi-mode sensing system for lower limb rehabilitation robot
CN113043248A (en) Transportation and assembly whole-body exoskeleton system based on multi-source sensor and control method
CN114392137A (en) Wearable flexible lower limb assistance exoskeleton control system
CN116766197A (en) Hip joint exoskeleton power-assisted control method
Gambon et al. Effects of user intent changes on onboard sensor measurements during exoskeleton-assisted walking
Rabe et al. Use of sonomyographic sensing to estimate knee angular velocity during varying modes of ambulation
CN114795831A (en) Exercise training equipment based on multi-source information fusion, and evaluation system and method
CN114366557A (en) Man-machine interaction system and method for lower limb rehabilitation robot
Xu et al. Multi-sensor based human motion intention recognition algorithm for walking-aid robot
Zhang et al. Interactive Control of Lower-Limb Exoskeleton Robots: A Review
Kapti et al. Wearable acceleration sensor application in unilateral trans-tibial amputation prostheses
Wang et al. Bin Zhou
Lingyan et al. Lower limb-assisted exoskeleton to support walking up and down stairs
Ling Energy optimization of a wearable lower limb rehabilitation robot based on deep learning
Zhang et al. Gait Prediction and Assist Control of Lower Limb Exoskeleton Based on Inertia Measurement Unit
Narváez et al. Identification of gait patterns in walking with crutches through the selection of significant spatio-temporal parameters

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant