CN118304144B - Control method for hybrid drive lower limb rehabilitation training system - Google Patents

Control method for hybrid drive lower limb rehabilitation training system Download PDF

Info

Publication number
CN118304144B
CN118304144B CN202410548814.2A CN202410548814A CN118304144B CN 118304144 B CN118304144 B CN 118304144B CN 202410548814 A CN202410548814 A CN 202410548814A CN 118304144 B CN118304144 B CN 118304144B
Authority
CN
China
Prior art keywords
target
factor
interaction force
value
current
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.)
Active
Application number
CN202410548814.2A
Other languages
Chinese (zh)
Other versions
CN118304144A (en
Inventor
孟琳
董洪涛
明东
张广举
徐瑞
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
Original Assignee
Tianjin University
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 filed Critical Tianjin University
Priority to CN202410548814.2A priority Critical patent/CN118304144B/en
Publication of CN118304144A publication Critical patent/CN118304144A/en
Application granted granted Critical
Publication of CN118304144B publication Critical patent/CN118304144B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • A61H3/00—Appliances for aiding patients or disabled persons to walk about
    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/251—Means for maintaining electrode contact with the body
    • A61B5/256—Wearable electrodes, e.g. having straps or bands
    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/296—Bioelectric electrodes therefor specially adapted for particular uses for electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—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]
    • A61B5/397—Analysis of electromyograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/0404—Electrodes for external use
    • A61N1/0472—Structure-related aspects
    • A61N1/0484—Garment electrodes worn by the patient
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36003—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation of motor muscles, e.g. for walking assistance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/36014—External stimulators, e.g. with patch electrodes
    • A61N1/3603—Control systems
    • A61N1/36031—Control systems using physiological parameters for adjustment
    • 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
    • A61H2003/007—Appliances for aiding patients or disabled persons to walk about secured to the patient, e.g. with belts
    • 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

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Pathology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Physiology (AREA)
  • Epidemiology (AREA)
  • Pain & Pain Management (AREA)
  • Rehabilitation Therapy (AREA)
  • Rehabilitation Tools (AREA)

Abstract

The present disclosure provides a control method for a hybrid drive lower limb rehabilitation training system. The method comprises the steps of determining a target track error factor based on a rotation angle of a target joint in a target training position at the current moment, determining a target muscle fatigue factor based on an electromyographic signal of a target muscle in the target training position at the current moment, determining a target interaction force factor based on man-machine interaction force between an exoskeleton robot and the target training position at the current moment, inputting the target track error factor, the target muscle fatigue factor and the target interaction force factor into a control model to obtain a target electric stimulation signal of an electric stimulation module and a target output torque of a motor module, controlling the motor module to drive the exoskeleton robot to move based on the target motor output torque so as to drive the target training position to move, and controlling the electric stimulation module to send the target electric stimulation signal to the target muscle so as to drive the target training position to move.

Description

Control method for hybrid drive lower limb rehabilitation training system
Technical Field
The disclosure relates to the field of exoskeleton robot rehabilitation, in particular to a control method for a hybrid drive lower limb rehabilitation training system.
Background
Doctors and patients can utilize the lower limb rehabilitation training system to carry out lower limb rehabilitation training by using an exoskeleton robot and a functional electric stimulation hybrid driving mode. The exoskeleton robot drives the lower limbs of the patient to move, so that the functional movement of the lower limbs of the patient can be realized. Driving the movement of the patient's lower limb by functional electrical stimulation may improve or restore the function of the patient's lower limb muscle groups.
However, the related control method for the lower limb rehabilitation training system is difficult to adapt to more complicated movement conditions, and in the rehabilitation training process by using the control method, the system has the defects of low control precision and easy initiation of muscle fatigue.
Disclosure of Invention
To at least partially overcome at least one of the above-mentioned technical drawbacks or other inventions, at least one embodiment of the present disclosure provides a control method for a hybrid drive lower limb rehabilitation training system, which can improve the system control accuracy during rehabilitation training and delay the occurrence of muscle fatigue.
In view of the above, the embodiment of the disclosure provides a control method for a hybrid-driven lower limb rehabilitation training system, which comprises an exoskeleton robot, a motor module and an electric stimulation module, and is characterized in that the method comprises the steps of determining a target track error factor based on a rotation angle of a target joint in a target training position at a current moment, determining a target muscle fatigue factor based on an electromyographic signal of a target muscle in the target training position at the current moment, determining a target interaction force factor based on man-machine interaction force between the exoskeleton robot and the target training position at the current moment, inputting the target track error factor, the target muscle fatigue factor and the target interaction force factor into a control model to obtain a target electric stimulation signal of the electric stimulation module and a target output torque of the motor module, and controlling the motor module to drive the exoskeleton robot to move so as to drive the target training position to move based on the target motor output torque, and controlling the electric stimulation module to send the target electric stimulation signal to the target muscle so as to drive the target training position to move.
In some embodiments, the inputting the target trajectory error factor, the target muscle fatigue factor, and the target interaction force factor into a control model to obtain a target electrical stimulation signal of the electrical stimulation module and a target output torque of the motor module includes solving a numerical solution of the control model based on initial values of the target trajectory error factor, the target muscle fatigue factor, the target interaction force factor, and the control model, wherein the numerical solution includes a first output capability and a second output capability, determining the target output torque based on the first output capability and a first mapping relationship, and determining the target electrical stimulation signal based on the second output capability and a second mapping relationship.
In some embodiments, the determining the target trajectory error factor based on the rotation angle of the target joint at the current time includes obtaining the target trajectory error factor based on a sum of a value of the current trajectory error factor and a first preset value when an absolute value of a difference between the rotation angle at the current time and the target rotation angle is greater than a change threshold, and determining the current trajectory error factor as the target trajectory error factor when the absolute value of the difference between the rotation angle at the current time and the target rotation angle is less than or equal to the change threshold.
In some embodiments, the determining the target muscle fatigue factor based on the electromyographic signals of the target muscle at the current moment comprises obtaining an average median frequency of the electromyographic signals at the current moment, wherein the average median frequency represents an average value of a plurality of median frequencies, the plurality of median frequencies comprise the median frequency of the electromyographic signals at the current moment and the median frequency of each of the electromyographic signals at a plurality of historical moments before the current moment, the determining the current muscle fatigue factor as the target muscle fatigue factor under the condition that the average median frequency is smaller than a current first baseline value, setting the median frequency at the current moment as a target first baseline value under the condition that the average median frequency is larger than the current first baseline value, and obtaining the target muscle fatigue factor under the condition that the average median frequency is smaller than the target first baseline value, and the average value of the difference between the average median frequency and the target first baseline value is larger than a second baseline value based on the sum of the value of the median fatigue factor of the current muscle and a second preset value, and determining the average value of the average muscle fatigue factor under the condition that the average median frequency is smaller than the first baseline value and the target baseline value is smaller than the first baseline value or equal to the average value.
In some embodiments, the determining a target interaction force factor based on the human-machine interaction force between the exoskeleton robot and the patient at the current time comprises obtaining the target interaction force factor based on a sum of a value of the current interaction force factor and a third preset value in the case that the human-machine interaction force is greater than an interaction force threshold, and determining that the current interaction force factor is the target interaction force factor in the case that the human-machine interaction force is less than or equal to the interaction force threshold.
In some embodiments, the target muscle comprises an ankle tibialis anterior muscle when the patient is in the swing phase, the target muscle comprises a gastrocnemius muscle when the patient is in the stance phase, the target muscle comprises a biceps femoris muscle when the patient is in the swing phase, and the target muscle comprises a quadriceps femoris muscle when the patient is in the end of the swing phase and in the mid-stance phase.
The disclosure also provides a hybrid-drive lower limb rehabilitation training system comprising an exoskeleton robot configured to support a patient to be trained, a motor module connected with the exoskeleton robot, the motor module configured to drive the exoskeleton robot to move so as to drive a target training position of the patient to move, an electric stimulation module arranged on the exoskeleton robot and configured to electrically stimulate target muscles so as to drive the target training position to move, a detection module arranged on the exoskeleton robot and configured to detect man-machine interaction force between the exoskeleton robot and the patient, and a control unit connected with the motor module, the detection module and the electric stimulation module, wherein the control unit is configured to control the motor module and the electric stimulation module to drive the target training position to move by using the method respectively.
In some embodiments, the control unit includes a control model including a motor output sub-model characterized as a relationship between a first output capability and a target trajectory error factor and a motor inhibitor sub-model characterized as a relationship between a first inhibitor capability and a target muscle fatigue factor, and an electrical stimulation output sub-model characterized as a relationship between a second output capability and a target trajectory error factor and an electrical stimulation inhibitor sub-model characterized as a relationship between a second inhibitor capability and a human-machine interaction force.
In some embodiments, the control model is represented as follows:
Wherein S E and S I represent sigmod functions, E represents the first output capability, I represents the first suppression capability, E 1 represents the second output capability, I 1 represents the second suppression capability, P represents the target trajectory error factor, Q 1 represents the target muscle fatigue factor, Q 2 represents the human-machine interaction force factor, τ represents the calculation rate, and t represents time ,k1、k2、k3、k4、k`1、k`2、k`3、k`4、k5、k6、k7、k8、k`5、k`6、k`7 and k' 8 represent the connection coefficient.
In some embodiments, the system further comprises an acquisition device disposed on the target muscle, the acquisition device configured to acquire myoelectric signals generated by the target muscle while the electrical stimulation module electrically stimulates the target muscle.
According to the embodiment of the disclosure, based on the target track error factor, the target muscle fatigue factor and the target interaction force factor, the target electric stimulation signal of the electric stimulation module and the target output torque of the motor module can be determined by using the trained control model, so that the calculation cost is low, and the training cost can be reduced. The human-machine interaction force can be adjusted by determining a target electrical stimulation signal and a target output torque based on the target interaction force factor. And determining a target electric stimulation signal and a target output torque based on the target track error factor, and adjusting the movement posture of the lower limb of the patient so as to ensure the movement of the lower limb of the patient along the target track line. The target electric stimulation signal and the target output torque are determined based on the target muscle fatigue factors, so that the fatigue degree of target muscles can be reduced, the rehabilitation training effect is ensured, and secondary damage is avoided. Because the target track error factor, the target interaction force factor and the target muscle fatigue factor at the current time are determined, the target electric stimulation signal and the target output torque can be regulated in real time, so that the output of the electric stimulation module and the motor module can be accurately controlled, and the control efficiency is improved.
Drawings
Fig. 1 schematically illustrates an exemplary system architecture that may be applied to a control method for a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure.
Fig. 2 schematically illustrates a flowchart of a control method for a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure.
Fig. 3 schematically illustrates a steady-state graph of a control model according to an embodiment of the present disclosure.
Fig. 4 schematically illustrates a block diagram of a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure, wherein a patient is shown.
Fig. 5 schematically illustrates a schematic diagram of a control model according to an embodiment of the present disclosure.
Fig. 6 schematically illustrates a block diagram of an electronic device for a control method of a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Fig. 1 schematically illustrates an exemplary system architecture that may be applied to a control method for a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure.
It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture that may be applied to a control method for a hybrid drive lower limb rehabilitation training system may include a terminal device, but the terminal device may implement the control method for a hybrid drive lower limb rehabilitation training system provided by the embodiments of the present disclosure without interacting with a server.
As shown in fig. 1, a system architecture 100 according to this embodiment may include a hybrid drive lower limb rehabilitation training system 101, a terminal device 102, a network 103, and a server 104. The network 103 is the medium used to provide the communication link between the hybrid drive lower extremity rehabilitation training system 101, the terminal equipment 102, and the server 104. The network 103 may include various connection types, such as wired and/or wireless communication links, etc.
A user may interact with server 104 over network 103 using hybrid drive lower limb rehabilitation training system 101, terminal device 102, to receive or send messages, etc. The hybrid drive lower limb rehabilitation training system 101 may be a device or the like for driving patient movement (by way of example only). Various communication client applications may be installed on the terminal device 102, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients and/or social platform software, to name a few.
Terminal device 102 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to televisions, tablet computers, laptop and desktop computers, and the like.
The server 104 may be various types of servers that provide various services. For example, the server 104 may be a cloud server, also called a cloud computing server or a cloud host, which is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the conventional physical hosts and VPS services (Virtual PRIVATE SERVER, virtual private servers). The server 104 may also be a server of a distributed system or a server that incorporates a blockchain.
It should be noted that, the control method for the hybrid driving lower limb rehabilitation training system provided in the embodiments of the present disclosure may be generally executed by the hybrid driving lower limb rehabilitation training system 101 or the terminal device 102.
Alternatively, the control methods for a hybrid drive lower limb rehabilitation training system provided by embodiments of the present disclosure may also be generally performed by the server 104. The control method for a hybrid drive lower limb rehabilitation training system provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 104 and is capable of communicating with the hybrid drive lower limb rehabilitation training system 101, the terminal device 102, and/or the server 104. It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of image acquisition devices, terminal devices, networks, and servers, as desired for implementation.
It should be noted that the sequence numbers of the respective operations in the following methods are merely representative of the operations for the purpose of description, and should not be construed as representing the order of execution of the respective operations. The method need not be performed in the exact order shown unless explicitly stated.
Fig. 2 schematically illustrates a flowchart of a control method for a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure.
As shown in fig. 2, an embodiment of the present disclosure provides a control method for a hybrid drive lower limb rehabilitation training system. The hybrid drive lower limb rehabilitation training system comprises an exoskeleton robot, a motor module and an electric stimulation module. The method 200 includes performing operations S210-S250.
In operation S210, a target trajectory error factor is determined based on a rotation angle of a target joint in a target training site at a current time.
In operation S220, a target muscle fatigue factor is determined based on an electromyographic signal of a target muscle in the target training site at the current time.
In operation S230, a target interaction force factor is determined based on a human-computer interaction force between the exoskeleton robot and the target training part at the current time.
In operation S240, the target trajectory error factor, the target muscle fatigue factor, and the target interaction force factor are input into the control model, and the target electrical stimulation signal of the electrical stimulation module and the target output torque of the motor module are obtained.
In operation S250, the motor module is controlled to drive the exoskeleton robot to move based on the target motor output torque to drive the target training site to move, and the electrical stimulation module is controlled to send a target electrical stimulation signal to the target muscle to drive the target training site to move.
According to embodiments of the present disclosure, the target training site may be a lower limb site of the patient. For example, the target training site may include a thigh, a calf, a hip joint, a knee joint, and an ankle joint of the patient.
According to embodiments of the present disclosure, the target joint may be a lower limb joint of the patient. For example, the target joints may include a patient's hip, knee, and ankle joints.
According to embodiments of the present disclosure, the target muscle may be a lower limb muscle of the patient. For example, the target muscles may include the patient's ankle tibialis anterior, gastrocnemius, biceps femoris, and quadriceps femoris.
According to embodiments of the present disclosure, the electromyographic signal may be characterized as a physiological electrical signal generated by the target muscle.
According to the embodiment of the disclosure, the man-machine interaction force is generated under the condition that the motor module drives the exoskeleton robot to drive the target training part to move. Man-machine interaction forces may be characterized as interaction forces between the exoskeleton robot and the target training portion.
According to the embodiment of the disclosure, based on the target track error factor, the target muscle fatigue factor and the target interaction force factor, the target electric stimulation signal of the electric stimulation module and the target output torque of the motor module can be determined by using the trained control model, so that the calculation cost is low, and the training cost can be reduced. The human-machine interaction force can be adjusted by determining a target electrical stimulation signal and a target output torque based on the target interaction force factor. And determining a target electric stimulation signal and a target output torque based on the target track error factor, and adjusting the movement posture of the lower limb of the patient so as to ensure the movement of the lower limb of the patient along the target track line. The target electric stimulation signal and the target output torque are determined based on the target muscle fatigue factors, so that the fatigue degree of target muscles can be reduced, the rehabilitation training effect can be ensured, and secondary damage is avoided. Because the target track error factor, the target interaction force factor and the target muscle fatigue factor at the current time are determined, the target electric stimulation signal and the target output torque can be regulated in real time, so that the output of the electric stimulation module and the motor module can be accurately controlled, and the control efficiency is improved.
In some embodiments, inputting the target trajectory error factor, the target muscle fatigue factor, and the target interaction force factor into the control model to obtain the target electrical stimulation signal of the electrical stimulation module and the target output torque of the motor module includes solving a numerical solution of the control model at the current time based on initial values of the target trajectory error factor, the target muscle fatigue factor, the target interaction force factor, and the control model. The numerical solution may include a first output capability, a second output capability, a first suppression capability, and a second suppression capability. The initial values of the control model may include a first output capability and a second output capability of the control model at the previous time. Based on the first output capability and the first map, a target output torque may be determined. Based on the second output capability and the second mapping relationship, a target electrical stimulation signal may be determined.
Fig. 3 schematically illustrates a steady-state graph of a control model according to an embodiment of the present disclosure.
As shown in fig. 3, after the target trajectory error factor, the target muscle fatigue factor, and the target interaction force factor are input into the control model, it may be determined that the first output capacity and the second output capacity are numerical solutions of the control model at the current time when the numerical values of the first output capacity and the second output capacity are in a steady state condition, according to an embodiment of the present disclosure.
The first map may be expressed as a relationship between the first output capacity and the output torque of the motor module. The second mapping relationship may be expressed as a relationship between the second output capability and the electrical stimulation signal of the electrical stimulation module. Based on the first mapping relationship, it may be determined that an output torque corresponding to the first output capability at the current time is the target output torque. Based on the second mapping relationship, the electrical stimulation signal corresponding to the second output capability at the current time can be determined to be the target electrical stimulation signal.
The first output capability and the second output capability may be normalized values. The first output capability may be 0 or more and 1 or less. The second output capability may be 0 or more and 1 or less. For example, the amplitude of the electric stimulation signal is in the range of [10,24] mA, and in the case of the second output capacity of 0.5, the target electric stimulation signal is 10+0.5 (24-10) mA, that is, the target electric stimulation signal is 17mA. For example, the output torque may be in the range of 0,0.6 nm×w, and the target electrical stimulation signal may be 0+0.5×0.6nm×w when the first output power is 0.5, that is, the target electrical stimulation signal may be 0.3nm×w.
According to the embodiment of the disclosure, the trained control model can be obtained by inputting a plurality of second output capacities corresponding to a plurality of electric stimulation signals of the electric stimulation module, a plurality of first output capacities corresponding to a plurality of output torques of the motor module, a plurality of first inhibition capacities, a plurality of second inhibition capacities, a plurality of trajectory error factors, a plurality of muscle fatigue factors and a plurality of interaction force factors into the model to be trained to perform target training and weight adjustment. The first output capacity, the second output capacity, the first suppression capacity, the second suppression capacity, the trajectory error factor, the muscle fatigue factor, and the interaction force factor may be derived from previously recorded data. Further, by inputting the target trajectory error factor corresponding to the trajectory error factor, the target muscle fatigue factor corresponding to the muscle fatigue factor, and the target interaction force factor corresponding to the interaction force factor into the control model, the target electrical stimulation signal of the electrical stimulation module and the target output torque of the motor module can be obtained.
In some embodiments, determining the target trajectory error factor based on the rotation angle of the target joint at the current time includes obtaining the target trajectory error factor based on a sum of the value of the current trajectory error factor and a first preset value if an absolute value of a difference between the rotation angle at the current time and the target rotation angle is greater than a change threshold. And under the condition that the absolute value of the difference value between the rotation angle at the current moment and the target rotation angle is smaller than or equal to the change threshold value, determining the current track error factor as the target track error factor.
In accordance with embodiments of the present disclosure, a hybrid drive lower limb rehabilitation training system may include a target trajectory at a fixed gait speed. The target trajectory may be characterized as the angle of rotation of the target joint during the start of a heel strike event in a current gait to the end of a heel strike event in a next gait of the exoskeleton robot. During this movement, marker points can be provided which characterize different control information. The target track line can be stretched and scaled according to the set gait speed, and different pace speeds can be adapted based on the change of the mark points, so that more complex movement conditions can be adapted. The rotation angle corresponding to the current moment in the target track line is the target rotation angle of the target joint at the current moment. The rate of change threshold and the first preset value may be set by a physician or patient according to the physical condition of the patient. When the absolute value of the difference between the rotation angle at the current time and the target rotation angle is greater than the change threshold, the target track error factor may be a sum of the value of the current track error factor and a first preset value.
According to an embodiment of the present disclosure, the difference D i (n) between the rotation angle at the current time of the current trajectory error factor and the target rotation angle may be expressed as follows:
Di(n)＝Xi(n)-xi(n) (1)。
Wherein X i (n) represents a target rotation angle, X i (n) represents a rotation angle at the current time, n represents an nth gait cycle, and i represents an ith time in the gait cycle.
According to an embodiment of the present disclosure, the target trajectory error factor P i (n) may be represented as follows:
Wherein P i-1 (n) represents the current trajectory error factor, L p represents the first preset value, and D represents the change threshold.
In some embodiments, determining the target muscle fatigue factor based on the electromyographic signals of the target muscle at the current time includes obtaining an average median frequency of the electromyographic signals at the current time. The average median frequency may represent an average of a plurality of median frequencies. The plurality of median frequencies may include a median frequency of the electromyographic signal at the current time and a median frequency of each of the electromyographic signals at a plurality of historical times prior to the current time.
And determining the current muscle fatigue factor as the target muscle fatigue factor under the condition that the average median frequency is smaller than the current first baseline value. And setting the median frequency at the current moment as a target first baseline value under the condition that the average median frequency is larger than the current first baseline value. And under the condition that the average median frequency is smaller than a target first baseline value and the ratio of the absolute value of the difference value between the average median frequency and the target first baseline value to the target first baseline value is larger than a second baseline value, obtaining the target muscle fatigue factor based on the sum of the value of the current muscle fatigue factor and a second preset value. And under the condition that the average median frequency is smaller than the target first baseline value, and the ratio of the absolute value of the difference value between the average median frequency and the target first baseline value to the target first baseline value is larger than the second baseline value, the target muscle fatigue factor is the sum of the value of the current muscle fatigue factor and the second preset value. And determining that the current muscle fatigue factor is the target muscle fatigue factor under the condition that the average median frequency is smaller than the target first baseline value and the ratio of the absolute value of the difference value between the average median frequency and the target first baseline value to the target first baseline value is smaller than or equal to the second baseline value.
The current first baseline value may be a varying value. The target first baseline value at the current time may be the current first baseline value at the next time. The initial value of the current first baseline value and the initial value of the second baseline value may be set by a physician or patient according to the physical condition of the patient. The target muscle fatigue factor may be a value updated in real time. The second baseline value may be 0.05. The target muscle fatigue factor at the current time may be the current muscle fatigue factor at the next time. The initial value of the current muscle fatigue factor may be zero.
According to embodiments of the present disclosure, the median frequency may characterize the muscle fatigue of the patient, and the median frequency MF may be as shown in equation (3) below.
Where m=1024 and p j is the amplitude of the frequency spectrum. By acquiring a plurality of electromyographic signals of the patient, a frequency curve of the electromyographic signals can be obtained, and the amplitude of the frequency curve can be characterized as the amplitude of the frequency spectrum.
According to the embodiment of the disclosure, by setting the electromyographic signal sliding window, the median frequency of the preset length is obtained, and the average value of the median frequency of the preset length is calculated, where the average value is greater than the current first baseline value, the average value may be the current first baseline value, that is, the updated first baseline value. For example, setting an electromyographic signal sliding window of 1000ms, the overlap width may be 0ms, and after acquiring 5 median frequencies, the average of the 5 median frequencies is calculated. In the case where the average value is greater than the current first baseline value, the average value may be the current first baseline value.
In some embodiments, determining the target interaction force factor based on the human-machine interaction force between the exoskeleton robot and the or the patient at the current time comprises obtaining the target interaction force factor based on a sum of a value of the current interaction force factor and a third preset value if the human-machine interaction force is greater than an interaction force threshold. And under the condition that the human-computer interaction force is smaller than or equal to the interaction force threshold value, determining the current interaction force factor as the target interaction force factor.
According to embodiments of the present disclosure, the interaction force threshold and the third preset value may be set by a doctor or a patient according to the physical condition of the patient. And when the human-computer interaction force is detected to be larger than the interaction force threshold value, determining the target interaction force factor as the sum of the value of the current interaction force factor and a third preset value. I.e. the target interaction force factor may be a value updated in real time. The target interaction force factor at the current time may be the current interaction force factor at the next time. The initial value of the current interaction force factor may be zero. The third preset value may be 0.1. The interaction force threshold may be a varying value that may be updated based on the state of motion the patient is in.
In some embodiments, the target muscle may include an ankle tibial anterior muscle with the patient in the swing phase. In the case of a patient in a standing phase, the target muscle may include a gastrocnemius muscle. In the case of a patient in the swing phase, the target muscle may include the biceps femoris muscle. In cases where the patient is in the end of the swing phase and in the mid-stance phase, the target muscle may include the quadriceps. Further, only the tibial anterior muscle of the ankle joint may be electrically stimulated during control of the patient into the swing phase. The gastrocnemius muscle may be stimulated only electrically during the phase of controlling the patient into stance phase. Only the biceps femoris muscle may be electrically stimulated during control of the patient into the swing phase. The quadriceps may be electrically stimulated only at the end of the swing phase and mid-stance phase of the patient. Through the above-mentioned electric stimulation mode, can comparatively accurate target muscle of electric stimulation, avoid the muscle to be in by activated state always to can reduce muscle fatigue, and avoid unnecessary electric stimulation to produce the interference to the low limbs motion, make the deviation appear between actual motion track and the target trajectory line.
Fig. 4 schematically illustrates a block diagram of a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure, wherein a patient is shown.
As shown in fig. 4, embodiments of the present disclosure also provide a hybrid drive lower limb rehabilitation training system 400. Hybrid drive lower limb rehabilitation training system 400 may include an exoskeleton robot 410, a motor module 420, an electrical stimulation module 430, a detection module 440, and a control unit 450. Exoskeleton robot 410 can be used to support a patient to be trained. The motor module 420 is coupled to the exoskeleton robot 410. The motor module 420 may be used to drive the exoskeleton robot 410 to move the target training site of the patient. The electro-stimulation module 430 is disposed on the exoskeleton robot 410. The electrical stimulation module 430 may be used to electrically stimulate the target muscles to drive movement of the target training site. The detection module 440 may be disposed on the exoskeleton robot 410. The detection module 440 may be used to detect human-machine interaction forces between the exoskeleton robot 410 and the patient. The interaction force threshold may be determined based on the location where the detection module 440 is disposed on the patient's leg, e.g., the interaction force threshold may be valued less where the detection module 440 is near the hip joint than where it is far from the hip joint. The control unit 450 is connected with the motor module 420, the detection module 440, and the electrical stimulation module 430. The control unit 450 may be configured to control the motor module 420 and the electro-stimulation module 430 to drive the movement of the target training site, respectively, using the methods described above. Specifically, the control unit 450 may control the motor module 420 to drive the exoskeleton robot 410 to move based on the target motor output torque obtained by the above method to drive the target training part to move. The control unit 450 may control the electric stimulation module 430 to transmit the target electric stimulation signal to the target muscle to electrically stimulate the target muscle, thereby driving the target training site to move.
In some embodiments, the hybrid drive lower limb rehabilitation training system further comprises an acquisition device. The harvesting device may be placed on the target muscle. The acquisition device may be used to acquire the electromyographic signals generated by the target muscle while the electrical stimulation module 430 electrically stimulates the target muscle.
In some embodiments, the control unit 450 may include a control model. The control model may include a motor output sub-model, a motor inhibitor sub-model, an electrical stimulation output sub-model, and an electrical stimulation inhibitor sub-model. The motor output sub-model may be characterized as a relationship between the first output capability and the target trajectory error factor. The motor inhibitor sub-model may be characterized as a relationship between the first inhibition capacity and the target muscle fatigue factor. The electrical stimulation output sub-model may be characterized as a relationship between the second output capability and the target trajectory error factor. The electrostimulation inhibition sub-model may be characterized as a relationship between the second inhibition capability and the human-machine interaction force.
Fig. 5 schematically illustrates a schematic diagram of a control model according to an embodiment of the present disclosure.
As shown in fig. 5, in some embodiments, the control model is represented as follows:
Wherein S E and S I represent sigmod functions, sigmod (x) = (1+e -x)-1, sigmod) has an upper and lower limit of (0, 1) ·e represents the first output capability, I represents the first suppression capability, E 1 represents the second output capability, I 1 represents the second suppression capability, P represents the target trajectory error factor, Q 1 represents the target muscle fatigue factor, Q 2 represents the human-machine interaction force factor, τ represents the calculation rate, t represents time .k1、k2、k3、k4、k5、k6、k7、k8、k`1、k`2、k`3、k`4、k`5、k`5、k`6、k`7 and k' 8 represents the connection coefficient, equation (4) may be represented as a motor output submodel, equation (5) may be represented as a motor suppression submodel, equation (6) may be represented as an electrical stimulation output submodel, equation (7) may be represented as an electrical stimulation suppression submodel, the control model may be obtained by training the wilson-coon model optimization.
Fig. 6 schematically illustrates a block diagram of an electronic device for a control method of a hybrid drive lower limb rehabilitation training system according to an embodiment of the present disclosure. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, ROM 602ram 603 are connected to each other by a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.
According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605, including an input portion 606, including a keyboard, mouse, etc. Including an output portion 607 such as a speaker, e.g., a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc. Including a storage portion 608 of a hard disk or the like. A communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments. Or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Such as, but not limited to, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program comprising program code for performing the methods provided by the embodiments of the present disclosure, when the computer program product is run on an electronic device, for causing the electronic device to carry out the above-described methods provided by the embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may comprise program code that is transmitted using any appropriate network medium, including but not limited to wireless, wireline, etc., or any suitable combination of the preceding.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high level procedural and/or object oriented programming languages, and/or in assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims (8)

1.一种混合驱动下肢康复训练系统，包括：1. A hybrid drive lower limb rehabilitation training system, comprising: 外骨骼机器人，被配置为支撑待训练的患者；an exoskeleton robot configured to support a patient to be trained; 电机模块，与所述外骨骼机器人连接，被配置为驱动所述外骨骼机器人运动，以带动所述患者的目标训练部位运动；a motor module connected to the exoskeleton robot and configured to drive the exoskeleton robot to move so as to drive the target training part of the patient to move; 电刺激模块，设置在所述外骨骼机器人上，被配置为电刺激目标肌肉，以驱动所述目标训练部位运动；An electrical stimulation module, disposed on the exoskeleton robot, and configured to electrically stimulate target muscles to drive the target training part to move; 检测模块，设置在所述外骨骼机器人上，被配置为检测所述外骨骼机器人与患者之间的人机交互力；以及a detection module, disposed on the exoskeleton robot, configured to detect a human-machine interaction force between the exoskeleton robot and a patient; and 控制单元，与所述电机模块、所述检测模块和所述电刺激模块连接，被配置为：A control unit is connected to the motor module, the detection module and the electrical stimulation module, and is configured as follows: 基于在当前时刻下目标关节的转动角度与目标转动角度的差值，确定目标轨迹误差因子；Determine the target trajectory error factor based on the difference between the rotation angle of the target joint at the current moment and the target rotation angle; 基于在当前时刻下目标肌肉的肌电信号的平均中值频率，确定目标肌肉疲劳因子，平均中值频率表示多个中值频率的平均值，多个中值频率包括当前时刻下肌电信号的中值频率和当前时刻之前的多个历史时刻下肌电信号各自的中值频率；Determine the target muscle fatigue factor based on the average median frequency of the electromyographic signal of the target muscle at the current moment, where the average median frequency represents the average value of multiple median frequencies, and the multiple median frequencies include the median frequency of the electromyographic signal at the current moment and the median frequencies of the electromyographic signals at multiple historical moments before the current moment; 基于在当前时刻下外骨骼机器人与目标训练部位之间的人机交互力和交互力阈值，确定目标交互力因子；Determine the target interaction force factor based on the human-machine interaction force and interaction force threshold between the exoskeleton robot and the target training part at the current moment; 将所述目标轨迹误差因子、所述目标肌肉疲劳因子和所述目标交互力因子输入控制模型，得到所述电刺激模块的目标电刺激信号和所述电机模块的目标输出扭矩，控制模型包括表征第一输出能力与目标轨迹误差因子之间关系电机输出子模型、表征第一抑制能力与目标肌肉疲劳因子之间关系的电机抑制子模型、表征第二输出能力与目标轨迹误差因子之间关系的电刺激输出子模型、以及表征第二抑制能力与人机交互力之间关系的电刺激抑制子模型；The target trajectory error factor, the target muscle fatigue factor and the target interaction force factor are input into a control model to obtain a target electrical stimulation signal of the electrical stimulation module and a target output torque of the motor module, wherein the control model includes a motor output sub-model representing the relationship between a first output capability and a target trajectory error factor, a motor inhibition sub-model representing the relationship between a first inhibition capability and a target muscle fatigue factor, an electrical stimulation output sub-model representing the relationship between a second output capability and a target trajectory error factor, and an electrical stimulation inhibition sub-model representing the relationship between a second inhibition capability and a human-computer interaction force; 基于所述目标电机输出扭矩控制所述电机模块驱动所述外骨骼机器人运动以带动所述目标训练部位运动，并控制所述电刺激模块向所述目标肌肉发送所述目标电刺激信号以驱动所述目标训练部位运动。Based on the target motor output torque, the motor module is controlled to drive the exoskeleton robot to move so as to drive the target training part to move, and the electrical stimulation module is controlled to send the target electrical stimulation signal to the target muscle to drive the target training part to move. 2.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，所述将所述目标轨迹误差因子、所述目标肌肉疲劳因子和所述目标交互力因子输入控制模型，得到所述电刺激模块的目标电刺激信号和所述电机模块的目标输出扭矩包括：2. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that the target trajectory error factor, the target muscle fatigue factor and the target interaction force factor are input into a control model to obtain a target electrical stimulation signal of the electrical stimulation module and a target output torque of the motor module, comprising: 基于所述目标轨迹误差因子、所述目标肌肉疲劳因子、所述目标交互力因子和所述控制模型的初始值，解出所述控制模型的数值解，其中，所述数值解包括第一输出能力和第二输出能力；Based on the target trajectory error factor, the target muscle fatigue factor, the target interaction force factor and the initial value of the control model, solving a numerical solution of the control model, wherein the numerical solution includes a first output capacity and a second output capacity; 基于所述第一输出能力和第一映射关系，确定所述目标输出扭矩；以及determining the target output torque based on the first output capacity and a first mapping relationship; and 基于所述第二输出能力和第二映射关系，确定所述目标电刺激信号。Based on the second output capacity and the second mapping relationship, the target electrical stimulation signal is determined. 3.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，所述基于在当前时刻下目标关节的转动角度与目标转动角度的差值，确定目标轨迹误差因子包括：3. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that the determining of the target trajectory error factor based on the difference between the rotation angle of the target joint at the current moment and the target rotation angle comprises: 在所述当前时刻的转动角度与目标转动角度的差值的绝对值大于变化阈值的情况下，基于当前轨迹误差因子的数值与第一预设值的和值，得到所述目标轨迹误差因子；以及When the absolute value of the difference between the rotation angle at the current moment and the target rotation angle is greater than the change threshold, the target trajectory error factor is obtained based on the sum of the value of the current trajectory error factor and the first preset value; and 在所述当前时刻的转动角度与目标转动角度的差值的绝对值小于或等于变化阈值的情况下，确定当前轨迹误差因子为所述目标轨迹误差因子。When the absolute value of the difference between the rotation angle at the current moment and the target rotation angle is less than or equal to the change threshold, the current trajectory error factor is determined to be the target trajectory error factor. 4.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，所述基于在当前时刻下目标肌肉的肌电信号的平均中值频率，确定目标肌肉疲劳因子包括：4. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that the determination of the target muscle fatigue factor based on the average median frequency of the electromyographic signal of the target muscle at the current moment comprises: 在所述平均中值频率小于当前第一基线值的情况下，确定当前肌肉疲劳因子为所述目标肌肉疲劳因子；When the average median frequency is less than the current first baseline value, determining the current muscle fatigue factor as the target muscle fatigue factor; 在所述平均中值频率大于当前第一基线值的情况下，设置当前时刻下的中值频率为目标第一基线值；When the average median frequency is greater than the current first baseline value, the median frequency at the current moment is set as the target first baseline value; 在所述平均中值频率小于所述目标第一基线值，且所述平均中值频率和所述目标第一基线值的差值的绝对值与目标第一基线值的比值大于第二基线值的情况下，基于当前肌肉疲劳因子的数值与第二预设值的和值，得到所述目标肌肉疲劳因子；When the average median frequency is less than the target first baseline value, and the ratio of the absolute value of the difference between the average median frequency and the target first baseline value to the target first baseline value is greater than the second baseline value, the target muscle fatigue factor is obtained based on the sum of the value of the current muscle fatigue factor and the second preset value; 在所述平均中值频率小于所述目标第一基线值，且所述平均中值频率和所述目标第一基线值的差值的绝对值与目标第一基线值的比值小于或等于第二基线值的情况下，确定当前肌肉疲劳因子为所述目标肌肉疲劳因子。When the average median frequency is less than the target first baseline value, and the ratio of the absolute value of the difference between the average median frequency and the target first baseline value to the target first baseline value is less than or equal to the second baseline value, the current muscle fatigue factor is determined to be the target muscle fatigue factor. 5.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，所述基于在当前时刻下所述外骨骼机器人与或患者之间的人机交互力和交互力阈值，确定目标交互力因子包括：5. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that the determining of the target interaction force factor based on the human-machine interaction force and interaction force threshold between the exoskeleton robot and the patient at the current moment comprises: 在所述人机交互力大于交互力阈值的情况下，基于当前交互力因子的数值与第三预设值的和值，得到所述目标交互力因子；以及When the human-computer interaction force is greater than the interaction force threshold, obtaining the target interaction force factor based on the sum of the value of the current interaction force factor and the third preset value; and 在所述人机交互力小于或等于交互力阈值的情况下，确定当前交互力因子为所述目标交互力因子。When the human-computer interaction force is less than or equal to the interaction force threshold, the current interaction force factor is determined as the target interaction force factor. 6.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于：6. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that: 在患者处于摆动相的情况下，目标肌肉包括踝关节胫骨前肌；With the patient in the swing phase, target muscles include the tibialis anterior at the ankle; 在患者处于站立相的情况下，目标肌肉包括腓肠肌；With the patient in the stance phase, target muscles include the gastrocnemius; 在患者处于摆动相的情况下，目标肌肉包括股二头肌；以及With the patient in swing phase, target muscles include the biceps femoris; and 在患者处于摆动相末期和站立相中期的情况下，目标肌肉包括股四头肌。With the patient in late swing phase and mid stance phase, target muscles include the quadriceps. 7.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，所述控制模型如下表示：7. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that the control model is expressed as follows: 其中，和表示函数，表示所述第一输出能力，表示第一抑制能力，表 示第二输出能力，表示第二抑制能力，表示所述目标轨迹误差因子，表示目标肌肉疲 劳因子，表示人机交互力因子，表示计算速率，表示时间，、、、、、、 、、、、、、、、表示连接系数。 in, and express function, represents the first output capability, Indicates the first inhibition ability, Indicates the second output capability, Indicates the second inhibitory ability, represents the target trajectory error factor, Indicates the target muscle fatigue factor, represents the human-computer interaction factor, represents the calculation rate, Indicates time, , , , , , , , , , , , , , , Represents the connection coefficient. 8.根据权利要求1所述的混合驱动下肢康复训练系统，其特征在于，还包括：8. The hybrid drive lower limb rehabilitation training system according to claim 1, characterized in that it also includes: 采集装置，设置在目标肌肉上，所述采集装置被配置为在电刺激模块对所述目标肌肉进行电刺激的同时，采集所述目标肌肉产生的肌电信号。The acquisition device is arranged on the target muscle, and is configured to acquire the electromyographic signal generated by the target muscle while the electrical stimulation module electrically stimulates the target muscle.
CN202410548814.2A 2024-05-06 2024-05-06 Control method for hybrid drive lower limb rehabilitation training system Active CN118304144B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202410548814.2A CN118304144B (en) 2024-05-06 2024-05-06 Control method for hybrid drive lower limb rehabilitation training system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410548814.2A CN118304144B (en) 2024-05-06 2024-05-06 Control method for hybrid drive lower limb rehabilitation training system

Publications (2)

Publication Number Publication Date
CN118304144A CN118304144A (en) 2024-07-09
CN118304144B true CN118304144B (en) 2025-05-06

Family

ID=91722596

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202410548814.2A Active CN118304144B (en) 2024-05-06 2024-05-06 Control method for hybrid drive lower limb rehabilitation training system

Country Status (1)

Country Link
CN (1) CN118304144B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111408042A (en) * 2020-03-27 2020-07-14 浙江迈联医疗科技有限公司 Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system
CN117717754A (en) * 2023-12-25 2024-03-19 西安交通大学 An electrical stimulation-assisted exoskeleton exercise rehabilitation training system and method thereof

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101317817B1 (en) * 2011-09-29 2013-10-15 연세대학교 원주산학협력단 EMG feedback-based active training system for rehabilitation exercise
EP3372040B1 (en) * 2015-11-06 2022-06-29 Telefonaktiebolaget LM Ericsson (PUBL) Apparatus and method for listen before talk-based random access with partial subframes
CN106176134A (en) * 2016-07-12 2016-12-07 王春宝 A kind of method and system of autonomous training
WO2018068013A1 (en) * 2016-10-06 2018-04-12 The Regents Of The University Of California An implantable electrocorticogram brain-computer interface system for restoring extremity movement
CN107397649B (en) * 2017-08-10 2020-08-14 燕山大学 A method for motion intention recognition of upper limb exoskeleton based on radial basis neural network
CN108785997B (en) * 2018-05-30 2021-01-08 燕山大学 Compliance control method of lower limb rehabilitation robot based on variable admittance
CN109589247A (en) * 2018-10-24 2019-04-09 天津大学 It is a kind of based on brain-machine-flesh information loop assistant robot system
CN110141239B (en) * 2019-05-30 2020-08-04 东北大学 A kind of motion intention recognition and device method for lower extremity exoskeleton
CN111991694B (en) * 2020-07-24 2022-09-30 清华大学 Lower limb exoskeleton device driven by functional electrical stimulation and motor in hybrid mode and control method
JPWO2022190738A1 (en) * 2021-03-09 2022-09-15
KR102690570B1 (en) * 2022-05-31 2024-07-31 엘아이지넥스원 주식회사 Wearable Robot and interaction force measurement wearable system including the same
CN115192905A (en) * 2022-07-05 2022-10-18 上海格润科技有限公司 Myoelectric stimulation rehabilitation training method, myoelectric stimulation rehabilitation training device, myoelectric stimulation rehabilitation training equipment and myoelectric stimulation rehabilitation training medium
CN116492203B (en) * 2023-02-28 2025-07-25 浙江大学 Collaborative rehabilitation system and method for fusing functional electrical stimulation and lower limb exoskeleton

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111408042A (en) * 2020-03-27 2020-07-14 浙江迈联医疗科技有限公司 Functional electrical stimulation and lower limb exoskeleton intelligent distribution method, device, storage medium and system
CN117717754A (en) * 2023-12-25 2024-03-19 西安交通大学 An electrical stimulation-assisted exoskeleton exercise rehabilitation training system and method thereof

Also Published As

Publication number Publication date
CN118304144A (en) 2024-07-09

Similar Documents

Publication Publication Date Title
Freeman et al. A model of the upper extremity using FES for stroke rehabilitation
CN111956452B (en) Control method and device for upper limb rehabilitation robot
CN105963100A (en) Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method
US20150332013A1 (en) Human joint kinematics information extraction method from multi-channel surface electromyogram signals, recording medium and device for performing the method
CN109276807A (en) Functional electrical stimulation therapy device for lower limbs of hemiplegic patients based on mirror rehabilitation therapy
CN104010613A (en) Motion assisting device, and synchronous control method for motion assisting device
CN109124998B (en) Power exoskeleton control method and device, computer equipment and storage medium
CN111603136B (en) Remote consultation system for foot diagnosis and treatment
CN118305781B (en) Method, device, terminal equipment and storage medium for determining hip joint assistance value
CN113995629A (en) Upper limb double-arm rehabilitation robot admittance control method and system based on mirror force field
CN117085301A (en) Multi-feedback rehabilitation training method and system for rehabilitation robots based on virtual reality
CN109106339A (en) A kind of On-line Estimation method of elbow joint torque under functional electrostimulation
CN118304144B (en) Control method for hybrid drive lower limb rehabilitation training system
CN121030535A (en) Exoskeleton-Assisted Gait Recognition Method and System Based on Neuromuscular Signals
CN118304145B (en) Hybrid driving lower limb rehabilitation training system and control method thereof
CN112426624A (en) Multi-degree-of-freedom wrist tremor suppression system based on functional electrical stimulation
CN111375128B (en) Walking assisting method and device based on functional muscle electrical stimulation
Zhou et al. Development and validation of a finger tremor simulator
Lyu et al. Comparison of particle filter to established filtering methods in electromyography biofeedback
CN119424157A (en) An adaptive limb motion control system
CN111251276A (en) Power assisting method and device based on gesture, server and storage medium
CN111403019A (en) Ankle joint prosthesis model establishing method, model-free control method and verification method
Zhang et al. PT-ATN: Cross-Subject Lower Limb Joint Angle Estimation Under Small-Sample Data
Falkowski et al. Investigation of Delays and Connection Stability in Teleoperation via VR Capabilities for Remote Exoskeleton-Aided Physiotherapy
WO2023223379A1 (en) Electrical stimulation system, electrical stimulation control device, electrical stimulation control method, and program

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