CN115192041A

CN115192041A - Exoskeleton personalized auxiliary exercise rehabilitation method and device and electronic equipment

Info

Publication number: CN115192041A
Application number: CN202210850375.1A
Authority: CN
Inventors: 张心觉; 高军晖; 何熲
Original assignee: Shanghai Nuanhenao Science And Technology Co ltd
Current assignee: Shanghai Nuanhenao Science And Technology Co ltd
Priority date: 2022-07-20
Filing date: 2022-07-20
Publication date: 2022-10-18

Abstract

The invention provides an exoskeleton personalized auxiliary exercise rehabilitation method, device and electronic equipment, relating to the technical field of limb exercise rehabilitation training and comprising the steps of acquiring electroencephalogram data of a user, electromyogram data of the user and exoskeleton pose information; combining the user electroencephalogram data, the user electromyogram data and the exoskeleton pose information to obtain user multi-mode data; inputting user multi-mode data into a depth certainty strategy gradient model to obtain the moment of the exoskeleton equipment in the motion direction; and completing the rehabilitation movement of the user based on the moment of the exoskeleton device in the movement direction. The application provides real-time assistance for the movement of the patient through the auxiliary control of the upper limb exoskeleton, can provide individualized moment assistance for the patient to move, and enables the patient to meet appropriate resistance when moving at every time, thereby improving the rehabilitation efficiency of the patient.

Description

Exoskeleton personalized auxiliary exercise rehabilitation method and device and electronic equipment

Technical Field

The invention relates to the technical field of limb movement rehabilitation training, in particular to an exoskeleton personalized auxiliary movement rehabilitation method, device and electronic equipment.

Background

Nearly 4 billion occupational accidents occur to workers engaged in physical labor in industries such as construction, manufacturing, storage and the like every year in the world; meanwhile, the brain function damage caused by stroke, cerebral palsy, brain trauma and the like can also cause limb movement dysfunction, and heavy burden is brought to patients and families and society thereof. And the auxiliary system of the exoskeleton can help the patient to prevent and recover part of motor dysfunction caused by the occupation and the diseases.

Rehabilitation training is an important means for recovering the motor function of a patient, but the training needs to be guided by nursing staff or trained by using a rehabilitation robot, personalized rehabilitation training schemes are made according to various conditions of the patient, and the motor function of the patient is usually evaluated only for a few times regularly, so that the updating of the training schemes is extremely impossible to keep up with the rehabilitation process of the patient, and the rehabilitation efficiency is difficult to improve.

Therefore, an exoskeleton personalized auxiliary exercise rehabilitation method, device and electronic equipment are provided.

Disclosure of Invention

The specification provides an exoskeleton personalized auxiliary exercise rehabilitation method, an exoskeleton personalized auxiliary exercise rehabilitation device and electronic equipment, real-time assistance is provided for the movement of a patient through the auxiliary control of an upper limb exoskeleton, personalized moment assistance exercise can be provided for the patient, the patient can meet appropriate resistance during each exercise, and therefore the rehabilitation efficiency of the patient is improved.

The present specification provides a variety of exoskeleton personalized assisted exercise rehabilitation methods, comprising:

acquiring electroencephalogram data of a user, electromyogram data of the user and exoskeleton pose information;

combining the user electroencephalogram data, the user electromyogram data and the exoskeleton pose information to obtain user multi-mode data;

inputting the multi-modal user data into a depth certainty strategy gradient model to obtain the moment of the exoskeleton equipment in the motion direction;

and completing the rehabilitation movement of the user based on the moment of the exoskeleton device movement direction.

Optionally, the depth-deterministic policy gradient model includes:

the system comprises an Actor module, an experience playback pool and a criticic module which are connected in sequence;

the Actor module comprises an Actor current network and an Actor target network which are sequentially connected;

the Critic module comprises a Critic current network and a Critic target network which are sequentially connected.

Optionally, the inputting the multi-modal user data into the depth certainty strategy gradient model to obtain the moment of the exoskeleton device in the motion direction includes:

the Actor current network is according to the current state S _i Generating action A _i And combining the actions A _i To the exoskeleton device;

the exoskeleton device performs the action A _i To obtain the next state S _i+1 And a reward R;

integrating the empirical data (S) of the exoskeleton device _i ，A _i ，R，S _i+1 ) Storing the experience in an experience playback pool;

judging whether the experience data storage amount of the experience playback pool reaches a threshold value or not;

and when the empirical data storage capacity of the empirical playback pool reaches a threshold value, randomly extracting a target number of empirical data as training data of the Actor module and the Critic module.

Optionally, when the experience data storage amount of the experience playback pool reaches a threshold, randomly extracting a target amount of experience data as training data of the Actor module and the Critic module includes:

the current state S _i The above-mentioned action A _i Inputting the moment to the current network of the Actor to obtain the score Q (a) of the moment of the current motion direction of the exoskeleton device;

the next state S _i+1 Inputting the predicted action A 'into the Actor target network to obtain a predicted action A';

the next state S _i+1 Inputting the prediction action A 'into the Critic target network to obtain a prediction score Q (a');

updating the weight of the Critic current network based on the reward R, the score Q (a) of the moment of the exoskeleton device motion direction, and a prediction score Q (a');

updating the weight of the Actor's current network based on the score Q (a) of the moment of the exoskeleton device motion direction.

Optionally, the exoskeleton device performs action a _i To obtain the next state S _i+1 And a reward R comprising:

wherein, alpha, beta and gamma are proportionality coefficients, R _G And R _E Respectively representing the calculation results of the user electroencephalogram data and the user electromyogram data in the current state, R _C A change value R representing the electromyogram data of the user in the current state and the electromyogram data of the user in the previous state _th Representing a penalty value for when the joint torque exceeds or falls below a threshold.

This specification provides an individualized supplementary motion rehabilitation device of ectoskeleton, includes:

the acquisition module is used for acquiring electroencephalogram data of a user, electromyogram data of the user and exoskeleton pose information;

the combination module is used for combining the user electroencephalogram data, the user electromyogram data and the exoskeleton pose information to obtain user multi-mode data;

the input module is used for inputting the multi-mode user data into a depth certainty strategy gradient model to obtain the moment of the exoskeleton equipment in the motion direction;

and the rehabilitation module is used for completing rehabilitation movement of the user based on the moment of the exoskeleton device in the movement direction.

Optionally, the depth-deterministic policy gradient model includes:

Optionally, the input module includes:

the Actor current network is according to the current state S _i Generating action A _i And combining the action A _i To the exoskeleton device;

the exoskeleton device performs the action A _i Obtaining the next state S _i+1 And a reward R;

applying said exoskeleton device experience data (S) _i ，A _i ，R，S _i+1 ) Storing the experience into an experience playback pool;

wherein, alpha, beta and gamma are proportionality coefficients, R _G And R _E Respectively representing the calculation results of the user electroencephalogram data and the user electromyogram data in the current state, R _C A change value R representing electromyogram data of the user in the current state and the electromyogram data of the user in the previous state _th Representing a penalty value for when the joint torque exceeds or falls below a threshold.

The present specification also provides an electronic device, wherein the electronic device includes:

a processor; and the number of the first and second groups,

a memory storing computer executable instructions that, when executed, cause the processor to perform any of the methods described above.

The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the above methods.

The invention has certain anti-interference capability and stronger robustness, and can correct muscle fatigue and electrode positioning inaccuracy in time; long-time off-line training and data labeling are not needed; the system can provide personalized assistance for users, such as providing different optimal assistance torques according to different users; meanwhile, real-time assistance is provided for the movement of the patient through the auxiliary control of the upper limb exoskeleton, individualized moment assistance movement can be provided for the patient, the patient can meet appropriate resistance during movement every time, and therefore the rehabilitation efficiency of the patient is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram illustrating a principle of an exoskeleton personalized assisted exercise rehabilitation method provided in an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of an exoskeleton personalized assisted exercise rehabilitation device provided in an embodiment of the present specification;

fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification;

fig. 4 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.

Detailed Description

The following description is provided to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments described below are by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.

Exemplary embodiments of the present invention are described more fully below with reference to the accompanying figures 1-4. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.

Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.

The described features, structures, characteristics, or other details of the present invention are provided to enable those skilled in the art to fully understand the embodiments in the present specification. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.

Fig. 1 is a schematic diagram of a principle of an exoskeleton customized assisted exercise rehabilitation method provided in an embodiment of the present specification, including:

s110: acquiring electroencephalogram data of a user, electromyogram data of the user and exoskeleton pose information;

in the specific implementation manner of the present specification, electroencephalogram data of a user is acquired through equipment such as a brain ring; and acquiring an electric signal through an electrode plate of the electromyograph, and amplifying the electric signal through an amplifier to obtain the electromyogram data of the user. Brain wave data is a method for recording brain activity by using electrophysiological indexes, in which the postsynaptic potentials generated synchronously by a large number of neurons are summed up during brain activity, and the recorded brain wave data records the electric wave change during brain activity, which is the overall reflection of the electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp. Electromyogram data refers to bioelectrical patterns of muscles recorded by an electromyograph. An exoskeleton is a rigid external structure that can provide the configuration, construction and protection of biologically soft internal organs.

S120: combining the user brain wave data, the user electromyogram data and the exoskeleton pose information to obtain user multi-modal data;

s130: inputting the multi-modal user data into a depth certainty strategy gradient model to obtain the moment of the exoskeleton equipment in the motion direction;

in a specific embodiment of the present specification, the depth-deterministic policy gradient model includes:

In the specific implementation mode of the description, a reinforcement learning algorithm Actor-Critic is combined with an incentive mechanism so as to explore global optimality and ensure the accuracy and robustness of the motion direction of the exoskeleton device.

In a specific embodiment of the present specification, the step S130 includes:

integrating the empirical data (S) of the exoskeleton device _i ，A _i ，R，S _i+1 ) Storing the experience into an experience playback pool;

In the specific embodiment of the present specification, in order to avoid the over-fitting (over fit) problem during training, it is necessary to break the correlation between samples, so that data is often selected for training the network by using a random sampling method during training the network.

In a specific embodiment of the present specification, when the experience data storage amount of the experience playback pool reaches a threshold, randomly extracting a target amount of experience data as training data of the Actor module and the Critic module includes:

the current state S is compared _i The above-mentioned action A _i Inputting the input to a current network of the Actor to obtain a score Q (a) of the moment of the current exoskeleton equipment in the motion direction;

In the specific implementation manner of the specification, the score Q (a) of the moment of the reward R and the exoskeleton device motion direction and the prediction score Q (a') are transmitted to the loss function to realize gradient reduction, so that the weight of the Critic current network is updated, and the score of the action a by learning the Critic current network with existing data is more and more accurate. The score Q (a) of the moment in the exoskeleton equipment moving direction is transmitted to the loss function, the weight of the Actor current network is updated in the direction of increasing the score Q (a) of the moment in the exoskeleton equipment moving direction, and the Q (a) corresponding to the action output by the Actor current network can be higher and higher through updating.

In a specific embodiment of the present specification, the exoskeleton device performs action A _i Obtaining the next state S _i+1 And a reward R comprising:

wherein, alpha, beta and gamma are proportionality coefficients, R _G And R _E Respectively representing the calculation results of the user electroencephalogram data and the user electromyogram data in the current state, R _C A change value R representing the electromyogram data of the user in the current state and the electromyogram data of the user in the previous state _th Representing a penalty value when the joint torque exceeds or falls below a threshold.

In the embodiment of the present specification, the state S is a certain step of the current environment, and each step in the reinforcement learning, namely, each step of executing an action, will obtain a reward R according to the change of the environment. In order to identify the movement intention of the user more quickly and more swiftly, the reward R is a negative value, and after convergence, the user can obtain the lowest auxiliary gain torque by using weaker electroencephalogram data and electromyogram data.

S140: and completing the rehabilitation movement of the user based on the moment of the exoskeleton device movement direction.

In the specific implementation manner of the present specification, after learning through the above procedure for a certain time, the body state of each person can be gradually adapted, so as to provide a suitable moment in the exoskeleton device motion direction for the user to perform the motion.

Fig. 2 is a schematic diagram of an exoskeleton personalized assisted exercise rehabilitation device provided in an embodiment of the present specification, including:

the acquisition module 10 is used for acquiring electroencephalogram data of a user, electromyogram data of the user and exoskeleton pose information;

the combination module 20 is configured to combine the user electroencephalogram data, the user electromyogram data, and the exoskeleton pose information to obtain user multi-modal data;

the input module 30 is used for inputting the user multi-modal data into the depth certainty strategy gradient model to obtain the moment of the exoskeleton device in the motion direction;

and the rehabilitation module 40 is used for completing rehabilitation movement of the user based on the moment of the exoskeleton device in the movement direction.

Optionally, the depth deterministic policy gradient model includes:

the Actor module, the experience playback pool and the Critic module are connected in sequence;

Optionally, the input module 30 includes:

Optionally, when the experience data storage amount of the experience playback pool reaches a threshold, randomly extracting a target number of experience data as training data of the Actor module and the Critic module includes:

updating the weight of the Critic current network based on the reward R, the score Q (a) of the moment of the exoskeleton device motion direction, and the predicted score Q (a');

and updating the weight of the current network of the Actor based on the score Q (a) of the moment of the exoskeleton equipment motion direction.

The functions of the apparatus in the embodiment of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details that are not described in the present embodiment, and further details are not described herein.

Based on the same inventive concept, the embodiment of the specification further provides the electronic equipment.

In the following, embodiments of the electronic device of the present invention are described, which may be seen as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. The details described in the embodiments of the electronic device of the invention are to be regarded as supplementary for the embodiments of the method or the apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.

Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. An electronic device 300 according to this embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one memory unit 320, a bus 330 connecting the various system components (including the memory unit 320 and the processing unit 310), a display unit 340, and the like.

Wherein the storage unit stores program code executable by the processing unit 310 to cause the processing unit 310 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 310 may perform the steps as shown in fig. 1.

The storage unit 320 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 3201 and/or a cache memory unit 3202, and may further include a read-only memory unit (ROM) 3203.

The storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 330 may be any bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. Network adapter 360 may communicate with other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1.

A computer program implementing the method shown in fig. 1 may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).

In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. An exoskeleton personalized assisted locomotion rehabilitation method, comprising:

combining the user brain wave data, the user electromyogram data and the exoskeleton pose information to obtain user multi-modal data;

and completing the rehabilitation movement of the user based on the moment of the exoskeleton equipment movement direction.

2. The exoskeleton personalized assisted locomotor rehabilitation method of claim 1, wherein the depth deterministic strategy gradient model comprises:

3. The exoskeleton personalized assistive locomotion rehabilitation method of claim 2, wherein the inputting the user multimodal data into the depth certainty strategy gradient model to obtain the moment of the exoskeleton device locomotion direction comprises:

and when the experience data storage capacity of the experience playback pool reaches a threshold value, randomly extracting a target number of experience data as training data of the Actor module and the Critic module.

4. The exoskeleton personalized assistive motor rehabilitation method according to claim 3, wherein randomly extracting a target amount of experience data as training data of the Actor module and the Critic module when the experience data storage amount of the experience playback pool reaches a threshold value comprises:

5. The exoskeleton personalized assistive locomotion rehabilitation method of claim 3, wherein the exoskeleton device performs action a _i Obtaining the next state S _i+1 And a reward R comprising:

wherein, alpha, beta and gamma are proportionality coefficients, R _G And R _E Respectively representing the calculation results of the user electroencephalogram data and the user electromyogram data in the current state, R _C A change value R representing electromyogram data of the user in the current state and the electromyogram data of the user in the previous state _th Representing a penalty value when the joint torque exceeds or falls below a threshold.

6. An exoskeleton personalized assisted locomotion rehabilitation device, comprising:

and the rehabilitation module is used for completing rehabilitation movement of the user based on the moment of the exoskeleton equipment in the movement direction.

7. The exoskeleton personalized assistive locomotion rehabilitation method of claim 6, wherein the depth-determinative tactical gradient model comprises:

8. The exoskeleton personalized assistive locomotion rehabilitation device of claim 7, wherein the input module comprises:

the Actor current network is according to the current stateS _i Generating action A _i And combining the action A _i To the exoskeleton device;

applying said exoskeleton device experience data (S) _i ，A _i ，R，S _i+1 ) Storing the experience in an experience playback pool;

9. An electronic device, wherein the electronic device comprises:

a processor; and (c) a second step of,

a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-5.

10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-5.