CN112587242A

CN112587242A - Surgical robot master hand simulation method, master hand and application

Info

Publication number: CN112587242A
Application number: CN202011464511.0A
Authority: CN
Inventors: 潘立志; 刘凯; 王树新
Original assignee: Institute Of Medical Robot And Intelligent System Tianjin University
Current assignee: Shandong Weigao Surgical Robot Co Ltd
Priority date: 2020-12-11
Filing date: 2020-12-11
Publication date: 2021-04-02
Anticipated expiration: 2040-12-11
Also published as: CN112587242B

Abstract

The present disclosure provides a surgical robot master hand simulation method, a master hand and an application, wherein the surgical robot master hand simulation method comprises: collecting myoelectric signals generated by target muscles of the upper limbs in a time period; preprocessing the electromyographic signals to obtain corresponding amplitudes of the electromyographic signals at different moments; acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signal at different moments; driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs; acquiring the motion angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the upper limb target muscle; and driving the slave hand end to move according to the movement angles of the metacarpophalangeal joints and the wrist joints.

Description

Surgical robot master hand simulation method, master hand and application

Technical Field

The disclosure relates to the field of medical equipment and mechanical control, in particular to a muscle-skeleton-model-based surgical robot master hand simulation method, a master hand and application.

Background

In the process of modern society, the robot gradually changes the production and living modes of human beings, improves the production efficiency and the product quality of various industries, can perform operations by remotely operating a medical system as an operation robot belonging to a high-end and intelligent medical instrument product, has unique advantages in the fields of prevention, diagnosis, treatment, rehabilitation and the like of auxiliary surgical operations, and has huge development potential. The surgical robot combines a teleoperational medical system with a traditional minimally invasive surgery, which allows a surgeon to indirectly complete the surgical operation by controlling the master hand of the mechanical surgical robot, increasing the accuracy of the surgical operation.

However, the main hand of the mechanical surgical robot in the prior art has some disadvantages, for example, the ZEUS surgical robot system developed by Computer Motion company cannot eliminate the shaking signal generated by the hand, and is easy to have an operation error phenomenon, and the system has a large volume, a small working space and insufficient flexibility, which brings great limitation to the development of the system itself; the Da Vinci Surgical robot developed by intutive Surgical company is high in price, large in system and complex in structure, long-time training needs to be conducted on an operator in the process of operating the master hand of the Surgical robot, and fatigue of the operator is easily caused when the master hand operating lever of the Surgical robot is operated for a long time.

Therefore, the existing mechanical master has the problems of large system, complex structure, long training time, easy fatigue of operators and the like, and a person skilled in the art is urgently needed to overcome the problems.

Disclosure of Invention

Technical problem to be solved

The present disclosure provides a surgical robot master hand simulation method, master hand and application to solve the technical problems set forth above.

(II) technical scheme

According to an aspect of the present disclosure, there is provided a surgical robot master hand simulation method, including:

collecting myoelectric signals generated by target muscles of the upper limbs in a time period;

preprocessing the electromyographic signals to obtain corresponding amplitudes of the electromyographic signals at different moments;

acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signal at different moments;

driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs; and

and acquiring the movement angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the target muscles of the upper limbs so as to drive the movement from the hand end.

In some embodiments of the present disclosure, the performing signal preprocessing on the electromyographic signal, and acquiring corresponding amplitudes of the electromyographic signal at different times includes:

amplifying the electromyographic signals to obtain amplified electromyographic signals;

filtering the amplified electromyographic signals to obtain filtered electromyographic signals;

rectifying the electromyographic signals after the filtering processing to obtain the electromyographic signals after the rectifying processing; and

and detecting the action time interval of the electromyographic signals after rectification processing, and determining the action effective time interval of the electromyographic signals.

In some embodiments of the present disclosure, the obtaining the activation state of the upper limb target muscle according to the corresponding amplitudes of the electromyographic signal at different times includes:

the neural activation state at time t is:

u(t)＝k×A(t)(t-d)-l₁×u(t-1)-l₂×u(t-2)

wherein A (t) is electromyographic signal at tCorresponding amplitudes, k, l₁、l₂Respectively are nerve activation coefficients, d is time delay; wherein the neural activation coefficient satisfies:

l₁＝α₁+α₂(|α₁|＜1；|α₂|＜1)

l₂＝α₁·α₂(|α₁|＜1；|α₂|＜1)

k-l₁-l₂＝1

wherein alpha is₁And alpha₂Is the electromyographic signal delay coefficient;

the muscle activation state a (t) is:

wherein c, d, m and b are muscle activation coefficients; u (t) is the state of neural activation at time t; and

when the muscle activation state a (t) is 0, the upper limb target muscle is not activated; in the muscle activation state 0 < a (t) < 1, the upper limb target muscle portion is activated; in the muscle activation state a (t) 1, the upper limb target muscle is fully activated.

In some embodiments of the present disclosure, the driving the joints of the hilt-type muscle model to generate motion according to the upper limb target muscle activation state, and acquiring the upper limb target muscle contraction force includes:

constructing a Hill-type muscle model comprising a contracting element, a parallel elastic element and a series elastic element;

calculating the tension generated by the contracting element; wherein the tension F generated by the contracting element_ceComprises the following steps:

F_ce＝f(l)f(v)a

wherein f (l) is the instantaneous muscle contraction length coefficient of the contracting element; (v) is the instantaneous muscle contraction velocity coefficient of the contractile elements, a is the upper limb target muscle activation state;

the instantaneous muscle contraction length coefficient f (l) of the contraction element and the instantaneous muscle contraction velocity coefficient f (v) of the contraction element satisfy:

f(l)＝F_max(1-(l_ce-l_ce0)²/w²(l_ce0)²)

f(v)＝(v_ce0-v_ce)/(v_ce0+(v_ce/c))

wherein, F_maxIs the maximum equidistant retraction tension of the retraction element; l_ce0Is the optimal length of the contracting elements; l_ceThe length of the contracting element after tension change; w is the range of forces generated by the contracting element; v. of_ce0Is the optimal retraction speed of the retraction element; v. of_ceC is a hyperbolic form factor for the contraction speed of the contracting element after being subjected to a change in tension;

calculating the tension generated by the parallel elastic elements; wherein the tension F generated by the parallel elastic elements_peeComprises the following steps:

F_pee＝K_pee(l_ce-l_ce0)²

wherein, K_peeIs the modulus of elasticity of the parallel elastic elements;

calculating a damping force generated by the contracting element; wherein the damping force f generated by the contracting element_cComprises the following steps:

f_c＝Cv_ce

wherein C is a damping coefficient of the contracting element; and

calculating the muscle force generated by the upper limb target muscle according to the tension generated by the contraction element, the tension generated by the parallel elastic element and the damping force generated by the contraction element; wherein the muscular force F generated by the ith target muscle_iComprises the following steps:

F_i＝F_ce+F_pee+f_c。

in some embodiments of the present disclosure, the acquiring the movement angles of the metacarpophalangeal joints and the wrist joints according to the upper limb target muscle contraction force includes:

applying a moment to a metacarpophalangeal joint and/or a wrist joint, wherein the metacarpophalangeal joint and/or the wrist joint is configured as a connecting portion connecting any two members; the sum moment Mi of the rotation of the component around the metacarpophalangeal joint and/or the wrist joint is as follows:

M_i＝∑F_id_i+∑M_f

wherein, F_iA muscle force generated for the ith said target muscle; d_iDistance of muscle force from joint point, M_fA resisting moment generated for the damping force f;

the damping force f is:

wherein, c_iThe damping coefficient for the rotation of the member about the metacarpophalangeal joint and/or the wrist joint,

the angular velocity at which the member rotates about the metacarpophalangeal joint and/or the wrist joint;

according to the momentum moment model, the sum moment of the rotation of the component around the metacarpophalangeal joint and/or the wrist joint is expressed as:

wherein J is the moment of inertia of each member,

angular acceleration of the member about the metacarpophalangeal joint and/or the wrist joint; and

and obtaining the motion angles of the metacarpophalangeal joints and the wrist joints.

In some embodiments of the present disclosure, the target muscle comprises: flexor digitorum superficialis, extensor digitorum, flexor carpi radialis, extensor carpi longus, extensor carpi radialis brevis, extensor carpi ulnaris, flexor carpi pronator, and flexor digitorum cruris.

According to an aspect of the present disclosure, there is provided a surgical robot master hand, comprising:

the electromyographic signal acquisition module is used for acquiring electromyographic signals generated by target muscles of the upper limbs in a time period;

the signal preprocessing module is used for preprocessing the electromyographic signals to acquire corresponding amplitudes of the electromyographic signals at different moments; and

the muscle skeleton model module is used for acquiring the activation state of the upper limb target muscle according to the corresponding amplitude values of the electromyographic signals at different moments; driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs; and acquiring the movement angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the target muscles of the upper limbs so as to drive the movement from the hand end.

In some embodiments of the present disclosure, the musculoskeletal model module comprises:

the muscle activation submodule is used for acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signals at different moments;

the Hill-type muscle model analysis module is used for driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs so as to obtain the muscle force of the target muscles of the upper limbs; and

the forward dynamics analysis module is used for acquiring the motion angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the upper limb target muscle;

wherein the signal preprocessing module comprises:

the signal amplification sub-module is used for amplifying the electromyographic signals to obtain amplified electromyographic signals;

the digital filtering submodule is used for carrying out filtering processing on the amplified electromyographic signals to obtain filtered electromyographic signals;

the signal rectification submodule is used for carrying out rectification processing on the electromyographic signals after the filtering processing to obtain the electromyographic signals after the rectification processing; and

and the action time interval detection submodule is used for carrying out action time interval detection on the electromyographic signals after rectification processing and determining action effective time intervals of the electromyographic signals.

According to an aspect of the present disclosure, there is provided an electronic device including:

one or more processors;

a memory to store one or more instructions;

wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to implement the above-described method.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the above-described method.

(III) advantageous effects

According to the technical scheme, the main hand simulation method, the main hand and the application of the surgical robot have at least one or part of the following beneficial effects:

(1) the remote synchronous operation is adopted in the method, and in the processes of signal collection, data processing and control from the hand end, because the whole process does not contact with the patient, the operator is prevented from contacting the patient in the process of minimally invasive surgery, and the possibility of infection of the patient is greatly reduced.

(2) The myoelectric joint control system is simple in structure and low in production cost, the myoelectric signal is mapped to the joint movement angle by using the musculoskeletal model only by attaching the myoelectric signal acquisition system to the arm of an operator, the movement of the slave hand end surgical instrument is controlled, the master hand of the existing mechanical surgical robot is replaced, the floor area of the system is reduced, and the manufacturing cost of the surgical instrument is greatly reduced.

(3) The operation of the method is simple, the method is easy to operate, an operator can control the slave hand end to realize the movement of four degrees of freedom by utilizing the actions of four different degrees of freedom, the step that the operator learns and controls the master hand of the mechanical surgical robot is omitted, and the learning burden and the financial expenditure of training of the operator are reduced.

(4) The surgical robot master hand based on the musculoskeletal model can be used as a special surgical robot master hand, can control gestures with four degrees of freedom from a hand end, including flexion and extension of fingers, flexion and extension of wrists, up-and-down cutting of wrists and inward and outward turning of forearms, belongs to a novel surgical robot master hand, can reduce labor intensity of operators, shortens treatment time, and has immeasurable wide market prospect as a novel industry in development.

Drawings

FIG. 1 schematically illustrates an exemplary system architecture to which a surgical robot master hand simulation method and master hand may be applied, according to an embodiment of the disclosure;

fig. 2 schematically illustrates a flow of a surgical robot master hand simulation method provided by an embodiment of the present disclosure;

FIG. 3a is a schematic diagram illustrating the relationship between muscles and joints provided by an embodiment of the present disclosure;

FIG. 3b is a schematic diagram illustrating the relationship between muscles and joints provided by an embodiment of the present disclosure;

FIG. 3c is a schematic diagram illustrating the relationship between muscles and joints provided by an embodiment of the present disclosure;

FIG. 4 is a flow chart schematically illustrating an electromyographic signal preprocessing method provided by an embodiment of the present disclosure;

fig. 5 schematically shows a flowchart of a method for acquiring an activation state of an upper limb target muscle provided by the embodiment of the disclosure;

fig. 6 schematically illustrates a flowchart of a method for obtaining muscle force of an upper limb target muscle provided by an embodiment of the present disclosure;

FIG. 7 is a schematic diagram illustrating a Hill-type muscle model structure provided by an embodiment of the disclosure;

FIG. 8 is a flow chart schematically illustrating a method for acquiring the motion angles of the metacarpophalangeal joints and the wrist joints according to the embodiment of the disclosure;

FIG. 9a schematically illustrates a forward dynamics analysis diagram of a musculoskeletal model provided by an embodiment of the present disclosure;

FIG. 9b schematically illustrates a forward dynamics analysis diagram of a musculoskeletal model provided by an embodiment of the present disclosure;

FIG. 9c schematically illustrates a forward dynamics analysis diagram of a musculoskeletal model provided by an embodiment of the present disclosure;

fig. 10 schematically illustrates a block diagram of a surgical robot master hand, in accordance with an embodiment of the present disclosure;

fig. 11 schematically illustrates a block diagram of a computer system suitable for implementing a surgical robot master hand simulation method 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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 convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have 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.).

The present disclosure provides a surgical robot master hand simulation method, including: collecting myoelectric signals generated by target muscles of the upper limbs in a time period; preprocessing the electromyographic signals to obtain corresponding amplitudes of the electromyographic signals at different moments; acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signal at different moments; driving the joints of the Hill-type muscle model to move according to the activation state of the target muscle of the upper limb, and acquiring the muscle force of the target muscle of the upper limb; acquiring the motion angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the target muscles of the upper limbs; the slave hand end is driven to move according to the movement angles of the metacarpophalangeal joints and the wrist joints.

Fig. 1 schematically illustrates an example system architecture 100 to which a surgical robot master hand simulation method and master hand may be applied, according to an embodiment of the disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the system architecture 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may be installed with various information systems, such as a visual feedback system, a database system, and other business systems.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the master hand simulation method of the surgical robot provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the surgical robot master hand provided by embodiments of the present disclosure may generally be located in the server 105. The master hand simulation method for the surgical robot provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the master surgical robot hand provided by the embodiments of the present disclosure may also be disposed in a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Alternatively, the surgical robot master hand simulation method provided by the embodiment of the present disclosure may also be executed by the

terminal device

101, 102, or 103, or may also be executed by another terminal device different from the

terminal device

101, 102, or 103. Accordingly, the master hand of the surgical robot provided by the embodiment of the present disclosure may also be disposed in the

terminal device

101, 102, or 103, or in another terminal device different from the

terminal device

101, 102, or 103.

For example, the signal preprocessing data related to the electromyographic signal may be originally stored in any one of the

terminal apparatuses

101, 102, or 103 (for example, the terminal apparatus 101, but not limited thereto), or stored on an external storage apparatus and may be imported into the terminal apparatus 101. Then, the terminal device 101 may locally perform the surgical robot master simulation method provided by the embodiment of the present disclosure, or transmit the signal preprocessing data related to the electromyographic signal to another terminal device, a server, or a server cluster, and perform the surgical robot master simulation method provided by the embodiment of the present disclosure by another terminal device, a server, or a server cluster that receives the signal preprocessing data related to the electromyographic signal.

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 terminal devices, networks, and servers, as desired for implementation.

The master hand of the surgical robot needs to have extremely high operation accuracy, but when the master hand of the mechanical surgical robot is used for performing surgery, the method for performing the surgery operation by using the master hand operating lever is greatly different from the surgery operation method of the traditional minimally invasive surgery, so that an operator needs to be skilled in the ability of controlling the master hand operating lever of the surgical robot. In addition, when an operator controls the main hand of the mechanical surgical robot, the operator needs to use a large force to operate the main hand of the mechanical surgical robot, so that the labor intensity of the operator is increased, and further some misoperation (such as hand vibration) is caused, and a generated vibration signal causes serious consequences. The intelligent and precise master hand operation system is used for further intelligentizing and accurately performing master hand operation of an operator, so that the problems that an existing mechanical master hand system is large, complex in structure, long in training time, easy to cause fatigue of the operator and the like are effectively solved.

The invention provides a master hand simulation method of a surgical robot and a master hand, wherein eight upper limb target muscles related to four-degree-of-freedom motions of finger flexion and extension, wrist up-down incision and forearm eversion of an operator are selected, and the motion of four degrees of freedom from a hand end can be controlled by utilizing the actions of four different degrees of freedom through establishing mapping between myoelectric signals and joint motion angles, so that the motion of a slave hand end surgical instrument is controlled, the master hand of the existing mechanical surgical robot is replaced, the manufacturing and learning cost of the surgical instrument is greatly reduced, and the accuracy and precision of the operation from the hand end are effectively improved.

Fig. 2 schematically shows a musculoskeletal model diagram of the relationship between muscles and joint structures provided by the embodiment of the disclosure.

As shown in fig. 2, the method includes operations S201 to S205.

In operation S201, in a time period, a myoelectric signal generated by an upper limb target muscle is collected, that is, a myoelectric signal diagram.

According to an embodiment of the present disclosure, as shown in fig. 3a, 3b, and 3c, the upper limb target muscle is selected from eight muscles related to four degrees of freedom motions of the operator such as flexion and extension of fingers, flexion and extension of wrists, incision of upper and lower wrists, and eversion of forearm, which are superficial flexor fingers 1-5, extensor digitorum 1-6, flexor carpi radialis 1-7, extensor carpi radialis longus 1-8, extensor carpi radialis shortus 1-10, extensor carpi ulnaris 1-11, circular muscle before pronation 1-12, and supinator muscle 1-13.

According to one embodiment of the present disclosure, as shown in fig. 3a, one end of the metacarpophalangeal joint 1-1 is connected with the tail end of the thumb member 1-3, the other end is connected with the head end of the metacarpophalangeal member 1-4, and the thumb member 1-3 rotates around the metacarpophalangeal joint 1-1 in the axial direction to perform flexion and extension movement of the fingers by means of traction of the superficial flexor 1-5 and the extensor digitorum 1-6. Two ends of the superficial flexors 1-5 and the extensors 1-6 are respectively positioned at the position close to the tail end of the thumb component 1-3 and the head end of the ulnar component 1-9, the superficial flexors 1-5 are positioned at the lower side of the connecting line of the thumb component 1-3, the metacarpophalangeal joint 1-1, the metacarpophalangeal component 1-4, the wrist joint 1-2 and the ulnar component 1-9, and the extensors 1-6 are positioned at the upper side of the connecting line of the thumb component 1-3, the metacarpophalangeal joint 1-1, the metacarpophalangeal component 1-4, the wrist joint 1-2 and the ulnar component 1-9. When the right hand of the operator makes the finger flexion, the muscle activation state is obtained through analysis and calculation, the target muscle superficial flexor 1-5 is driven, the thumb component 1-3 rotates around the axial direction of the metacarpophalangeal joint 1-1 to make the flexion of the finger, the rotation angle of the thumb component 1-3 which bends around the metacarpophalangeal joint 1-1 is determined, and the specific method refers to the following operations S202-S205. When the right hand of the operator makes a finger stretching action, the muscle activation state is obtained through analysis and calculation, the target muscle extensor muscles 1-6 are driven, the thumb member 1-3 rotates around the axial direction of the metacarpophalangeal joint 1-1 to make the stretching movement of the fingers, the rotation angle of the thumb member 1-3 stretching around the metacarpophalangeal joint 1-1 is determined, and the specific method refers to the following operations S202-S205.

According to an embodiment of the present disclosure, as shown in fig. 3a, one end of the wrist joint 1-2 is connected to the tail end of the metacarpophalangeal member 1-4, the other end is connected to the head end of the ulna member 1-9, and the metacarpophalangeal member 1-4 rotates axially around the wrist joint 1-2 to perform wrist flexion and extension movements by means of the flexor carpi radialis 1-7 and the extensor carpi radialis 1-8. Two ends of flexors 1-7 and extensor carpi radialis longus 1-8 are respectively positioned at positions close to the tail end of a metacarpal-phalangeal member 1-4, the head end of an ulnar member 1-9 of radius and between a superficial flexor digitalis 1-5 and an extensor digitalis 1-6, the flexors 1-7 of the wrist radialis are positioned at the lower side of the connecting line of the metacarpal-phalangeal member 1-4, a wrist joint 1-2 and an ulnar member 1-9 of radius, and the extensor digitalis 1-6 is positioned at the upper side of the connecting line of the metacarpal-phalangeal member 1-4, the wrist joint 1-2 and the ulnar member 1-9 of radius. When the right hand of the operator does the wrist buckling movement, the muscle activation state is obtained through analysis and calculation, the target muscle flexor carpi radialis 1-7 is driven, the palm and finger component 1-4 rotates around the wrist joint 1-2 axially to do the wrist buckling movement, the rotation angle of the palm and finger component 1-4 buckling around the wrist joint 1-2 is determined, and the specific method refers to the following operations S202-S205. When the right hand of the operator does the wrist stretching movement, the muscle activation state is obtained through analysis and calculation, the target muscle extensor radialis longus 1-8 is driven, the finger component 1-4 rotates around the wrist joint 1-2 axially to do the wrist stretching movement, the rotation angle of the palm finger component 1-4 stretching around the wrist joint 1-2 is determined, and the specific method refers to the following operations S202-S205.

According to an embodiment of the present disclosure, as shown in fig. 3b, one end of the metacarpophalangeal joint 1-1 is connected with the tail end of the thumb member 1-3, the other end is connected with the head end of the metacarpophalangeal member 1-4, one end of the wrist joint 1-2 is connected with the tail end of the metacarpophalangeal member 1-4, the other end is connected with the head end of the ulnar member 1-9, and the metacarpophalangeal member 1-4 is drawn by the extensor carpi radialis 1-10 and the extensor carpi ulnaris 1-11 to swing transversely around the wrist joint 1-2 to perform the upper and lower cutting movements of the wrist. Two ends of extensor carpi radialis 1-10 and extensor carpi ulnaris 1-11 are respectively positioned at a position close to the tail end of the metacarpophalangeal member 1-4 and a position close to the head end of the extensor carpi radialis member 1-9, the extensor carpi radialis 1-10 is positioned at the left side of the connecting line of the metacarpophalangeal member 1-4, the wrist joint 1-2 and the extensor carpi radialis member 1-9, and the extensor carpi ulnaris 1-11 is positioned at the right side of the connecting line of the metacarpophalangeal member 1-4, the wrist joint 1-2 and the extensor carpi radialis member 1-9. When the right hand of the operator makes a transverse upcut motion around the wrist joint 1-2, the muscle activation state is obtained through analysis and calculation, the target muscle extensor radialis brevis 1-10 is driven, the metacarpophalangeal member 1-4 swings transversely around the wrist joint 1-2 to make an upcut motion of the wrist, the rotation angle of the metacarpophalangeal member 1-4 transversely upcut around the wrist joint 1-2 is determined, and the specific method refers to the following operations S202-S205. When the right hand of the operator performs the action of cutting down around the wrist joint 1-2, the muscle activation state is obtained through analysis and calculation, the target muscle extensor carpi ulnaris 1-11 is driven, the metacarpophalangeal member 1-4 swings transversely around the wrist joint 1-2 to perform the cutting down movement of the wrist, the rotation angle of the metacarpophalangeal member 1-4 cutting down transversely around the wrist joint 1-2 is determined, and the specific method refers to the following operations S202-S205.

According to an embodiment of the present disclosure, as shown in fig. 3c, both ends of the circular preformer 1-12 and the circular supinator 1-13 are on the ulnar member 1-9, the circular preformer 1-12 is located on the left side of the ulnar member 1-9, the circular supinator 1-13 is located on the right side of the ulnar member 1-9, and the musculoskeletal model rotates along the axis of the ulnar member 1-9 by the circular preformer 1-12 and the circular supinator 1-13 to complete the varus and valgus movement of the arm. When the right hand of the operator makes an arm inversion movement, the muscle activation state is obtained through analysis and calculation, the target muscle is driven to rotate the anterior circular muscle 1-12, the ulna radius component 1-9 rotates anticlockwise along the axis to make the arm inversion movement, and the rotation angle of the ulna radius component 1-9 which inverts along the axis is determined. When the right hand of the operator makes an arm eversion action, a muscle activation state is obtained through analysis and calculation, a target muscle supinator muscle 1-13 is driven, the radius ulna component 1-9 rotates clockwise along the axis to make arm eversion movement, the rotation angle of the radius ulna component 1-9 along the axis eversion is determined, and the specific method refers to the following operations S202-S205.

In operation S202, the electromyographic signal is preprocessed to obtain corresponding amplitudes of the electromyographic signal at different times.

According to an embodiment of the present disclosure, as shown in fig. 4, operation S202 further includes the following steps:

in operation S2021, the electromyographic signal is amplified to obtain an amplified electromyographic signal.

In operation S2022, the amplified electromyographic signal is filtered to obtain a filtered electromyographic signal.

In operation S2023, the filtered electromyographic signal is rectified to obtain a rectified electromyographic signal. Specifically, all the electromyographic signals below the resting baseline in the electromyogram are folded over the baseline.

In operation S2024, the rectified electromyographic signal is subjected to motion period detection to determine a motion valid period of the electromyographic signal.

And operation S203, acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signal at different moments.

According to an embodiment of the present disclosure, as shown in fig. 5, operation S203 further includes the steps of:

in operation S2031, the neural activation state at time t is:

u(t)＝k×A(t)(t-d)-l₁×u(t-1)-l₂×u(t-2)

wherein A (t) is the corresponding amplitude of the electromyographic signal at t moment, k, l₁、l₂Respectively are nerve activation coefficients, d is time delay; wherein the neural activation coefficient satisfies:

l₁＝α₁+α₂(|α₁|＜1；|α₂|＜1)

l₂＝α₁·α₂(|α₁|＜1；|α₂|＜1)

k-l₁-l₂＝1

in operation S2032, the muscle activation state a (t) is:

wherein c, d, m and b are muscle activation coefficients; u (t) is the state of neural activation at time t;

in operation S2033, when the muscle activation state a (t) is 0, the upper limb target muscle is not activated.

In operation S2034, the upper limb target muscle portion is activated when the muscle activation state 0 < a (t) < 1.

In operation S2035, the upper limb target muscle is fully activated when the muscle activation state a (t) is 1.

And operation S204, driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs by combining the kinematic parameters and the muscle anatomical parameters.

According to an embodiment of the present disclosure, as shown in fig. 6, operation S204 further includes the following steps:

operation S2041, a hill-type muscle model is constructed, which includes a contraction element 710, a parallel elastic element 720 and a series elastic element 730, as shown in fig. 7.

Operation S2042, calculating a tension generated by the contracting element 710; wherein the tension force F generated by the contracting element 710_ceComprises the following steps:

F_ce＝f(l)f(v)a

wherein f (l) is the instantaneous muscle contraction length coefficient of the contracting element 710; (v) is the instantaneous muscle contraction rate coefficient of the contracting element 710, and α is the upper limb target muscle activation state;

the instantaneous muscle contraction length coefficient f (l) of the contraction element 710 and the instantaneous muscle contraction velocity coefficient f (v) of the contraction element 710 satisfy:

f(l)＝F_max(1-(l_ce-l_ce0)²/w²(l_ce0)²)

f(v)＝(v_ce0-v_ce)/(v_ce0+(v_ce/c))

wherein, F_maxMaximum isometric contraction tension and maximum muscle contraction (maximum isometric volume contraction) for the contracting element 710Station, MVC); l_ce0Is the optimal length of the contracting elements 710; l_ceThe length of the contracting element 710 after being subjected to tension; w is the range of force generated by the contracting element 710; v. of_ce0Is the optimal retraction speed of the retraction element 710; v. of_ceC is a hyperbolic form factor for the rate of contraction of the contracting element 710 after being subjected to a change in tension.

Operation S2043, calculating the tension generated by the parallel elastic element 720; wherein the tension F generated by the parallel elastic element 720_peeComprises the following steps:

F_pee＝K_pee(l_ce-l_ce0)²

wherein, K_peeIs the modulus of elasticity of the shunt elastic element 720;

operation S2044, calculating a damping force generated by the contracting element 710; wherein the damping force f generated by the contracting element 710_cComprises the following steps:

f_c＝Cv_ce

where C is the damping coefficient of the contracting elements 710.

Operation S2045, calculating a muscle force generated by the upper limb target muscle according to the tension generated by the contraction element 710, the tension generated by the parallel elastic element 720, and the damping force generated by the contraction element 710; wherein the muscular force F generated by the ith target muscle_iComprises the following steps:

F_i＝F_ce+F_pee+f_c。

since the series elastic element 730 has a large stiffness and does not greatly affect the muscle force, the tension generated by the series elastic element 730 is equal to the muscle force.

In operation S205, the movement angles of the metacarpophalangeal joints and the wrist joints are obtained according to the muscle force of the target muscle of the upper limb, so as to drive the movement from the hand end.

According to an embodiment of the present disclosure, as shown in fig. 8, operation S205 further includes the following steps:

an operation S2051 of applying a moment to a metacarpophalangeal joint and/or a wrist joint configured as a connecting portion connecting any two members; the members being wound around the metacarpophalangeal joints and/orSum moment M of wrist joint rotation_iComprises the following steps:

M_i＝∑F_id_i+∑M_f

wherein, F_iThe muscle force generated for the ith target muscle; d_iDistance of muscle force from joint point, M_fThe resistive torque generated for the damping force f.

The damping force f is:

wherein, c_iIs the damping coefficient for the rotation of the member about the metacarpophalangeal joint and/or the wrist joint,

is the angular velocity at which the member rotates about the metacarpophalangeal and/or wrist joints.

Operation S2052, the sum moment of rotation of the member about the metacarpophalangeal joint and/or the wrist joint is expressed as:

wherein J is the moment of inertia of each member,

is the angular acceleration of the rotation of the member about the metacarpophalangeal and/or wrist joints.

Operation S2053, solving the motion angle theta of the component around the metacarpophalangeal joint and/or the wrist joint_i

Fig. 9a is a force analysis diagram of the process of finger flexion and extension and wrist flexion with two degrees of freedom according to an embodiment of the present disclosure. Referring also to FIGS. 3a to 3c, F₁Muscle force produced by the superficial flexors 1-5, F₂Muscular force of extensor digitorum 1-6, F₃The muscular force produced by the flexor carpi radialis 1-7, F₄The muscle force generated by extensor carpi radialis longus 1-8, f₁Damping force, f, generated for rotation of the thumb member 1-3 about the metacarpophalangeal joint 1-4₂The angle θ of the rotation of the thumb member 1-3 about the point A (metacarpophalangeal joint 1-1) can be solved for the damping force generated by the rotation of the metacarpophalangeal member 1-4 about the wrist joint 1-2 based on the analysis process of the forward dynamics provided in the steps S2051-S2053₁The angle theta of the palm and finger component 1-4 rotating around the point B (wrist joint 1-2)₂。

FIG. 9b is a force analysis diagram of a wrist up-down cut motion process with one degree of freedom, according to one embodiment of the present disclosure. Referring also to FIGS. 3a to 3c, F₅The muscle force generated by the extensor carpi radialis muscles 1-10, F₆The muscle force generated by extensor carpi ulnaris 1-11, f₃For the damping force generated by the swinging of the thumb component 1-3 and the palm and finger component 1-4 around the wrist joint 1-2 in the wrist bending and stretching process, the angle theta of the swinging of the thumb component 1-3 and the palm and finger component 1-4 around the point B (the wrist joint 1-2) can be solved based on the analysis process which is provided by the step S2051 to the step S2053 and utilizes the forward dynamics₃。

Fig. 9c is a force analysis diagram of the forearm varus and valgus movement process with one degree of freedom, according to an embodiment of the present disclosure. Referring also to FIGS. 3a to 3c, F₇Muscular force produced by the circumflex muscles 1-12, F₈Muscle force produced by the supinator muscles 1-13, f₄For the damping force generated by the rotation of the ulnar component 1-9 around the axis of the ulnar component 1-9 during the process of forearm eversion, the angle theta of the ulnar component 1-9 around the o point (the axis of the ulnar component 1-9) can be solved based on the analysis process of the forward dynamics provided by the step S2051-operation S2053₄。

In operation S2054, the movement angles of the metacarpophalangeal joints and the wrist joints are determined to drive the movement from the hand ends.

In an application scenario of the present disclosure, an operator may configure parameters and set functions in a visual feedback system, the visual feedback system may display a picture of an operation site to the operator in real time through a display, and the operator performs the master-hand simulation method of the surgical robot provided by the embodiment of the present disclosure in combination with the displayed picture to drive a surgical instrument at a slave hand end, thereby completing a surgical treatment process.

Fig. 10 schematically illustrates a block diagram of a surgical robot master hand, in accordance with an embodiment of the present disclosure. As shown in fig. 10, the surgical robot master hand includes: an electromyographic signal acquisition module 1010, a signal preprocessing module 1020, and a musculoskeletal model module 1030.

The electromyographic signal acquisition module 1010 is used for acquiring electromyographic signals generated by target muscles of the upper limbs in a time period.

And a signal preprocessing module 1020, configured to perform signal preprocessing on the electromyographic signals to obtain corresponding amplitudes of the electromyographic signals at different times.

According to an embodiment of the present disclosure, the signal preprocessing module 1020 includes:

and the signal amplification sub-module is used for amplifying the electromyographic signals to obtain the amplified electromyographic signals.

And the digital filtering submodule is used for carrying out filtering processing on the amplified electromyographic signals to obtain the electromyographic signals after the filtering processing.

And the signal rectification submodule is used for rectifying the electromyographic signals after the filtering processing to obtain the electromyographic signals after the rectifying processing.

The muscle skeleton model module 1030 is used for acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude values of the electromyographic signal at different moments; driving the joints of the Hill-type muscle model to move according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs by combining the kinematic parameters and the muscle anatomical parameters; according to the muscle force of the target muscle of the upper limb, the movement angles of the metacarpophalangeal joints and the wrist joints are obtained, and the movement from the hand end is driven.

According to an embodiment of the present disclosure, the musculoskeletal model module 1030 comprises:

and the muscle activation submodule is used for acquiring the activation state of the target muscle of the upper limb according to the corresponding amplitude of the electromyographic signal at different moments.

And the Hill-type muscle model analysis module is used for driving the joints of the Hill-type muscle model to generate movement according to the activation state of the target muscles of the upper limbs, and acquiring the muscle force of the target muscles of the upper limbs by combining the kinematic parameters and the muscle anatomical parameters.

And the forward dynamics analysis module is used for acquiring the motion angles of the metacarpophalangeal joints and the wrist joints according to the muscle force of the target muscles of the upper limbs.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any plurality of the electromyographic signal acquisition module 1010, the signal preprocessing module 1020, and the musculoskeletal model module 1030 may be combined and implemented in one module/unit/sub-unit, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the present disclosure, at least one of the electromyographic signal acquisition module 1010, the signal preprocessing module 1020, and the musculoskeletal model module 1030 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the electromyographic signal acquisition module 1010, the signal pre-processing module 1020, and the musculoskeletal model module 1030 may be implemented at least in part as a computer program module that, when executed, may perform a corresponding function.

It should be noted that, the data processing system part in the embodiment of the present disclosure corresponds to the data processing method part in the embodiment of the present disclosure, and the description of the data processing system part specifically refers to the data processing method part, which is not described herein again.

Fig. 11 schematically illustrates a block diagram of a computer system suitable for implementing a surgical robot master hand simulation method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 11 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.

As shown in fig. 11, a computer system 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1102 or a program loaded from a storage section 1108 into a Random Access Memory (RAM) 1103. The processor 1101 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 1101 may also include on-board memory for caching purposes. The processor 1101 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to the embodiments of the present disclosure.

In the RAM 1103, various programs and data necessary for the operation of the system 1100 are stored. The processor 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. The processor 1101 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1102 and/or the RAM 1103. It is noted that the programs may also be stored in one or more memories other than the ROM 1102 and RAM 1103. The processor 1101 may also perform various operations of the method flows according to the embodiments of the present disclosure by executing programs stored in the one or more memories.

System 1100 may also include an input/output (I/O) interface 1105, which input/output (I/O) interface 1105 is also connected to bus 1104, according to an embodiment of the present disclosure. The system 1100 may also include one or more of the following components connected to the I/O interface 1105: an input portion 1106 including a keyboard, mouse, and the like; an output portion 1107 including a signal output unit such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1108 including a hard disk and the like; and a communication section 1109 including a network interface card such as a LAN card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. A driver 1110 is also connected to the I/O interface 1105 as necessary. A removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1110 as necessary, so that a computer program read out therefrom is mounted into the storage section 1108 as necessary.

According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. 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 containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 1109 and/or installed from the removable medium 1111. The computer program, when executed by the processor 1101, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are 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 present 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, a computer-readable storage medium may include the ROM 1102 and/or the RAM 1103 and/or one or more memories other than the ROM 1102 and the RAM 1103 described above.

The flowchart 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A surgical robot master hand simulation method includes:

2. The master hand simulation method of a surgical robot according to claim 1, wherein the pre-processing the electromyographic signals to obtain corresponding amplitudes of the electromyographic signals at different times comprises:

3. The surgical robot master hand simulation method according to claim 1, wherein the obtaining of the activation state of the upper limb target muscle according to the corresponding amplitude of the electromyographic signal at different time comprises:

the neural activation state at time t is:

u(t)＝k×A(t)(t-d)-l₁×u(t-1)-l₂×u(t-2)

l₁＝α₁+α₂(|α₁|＜1；|α₂|＜1)

l₂＝α₁·α₂(|α₁|＜1；|α₂|＜1)

k-l₁-l₂＝1

the muscle activation state a (t) is:

4. The surgical robot master hand simulation method according to claim 1, wherein the driving the joints of the hilt-type muscle model to generate motions according to the activation state of the upper limb target muscles, and acquiring the upper limb target muscle contraction force comprises:

F_ce＝f(lf)(v)a

f(l)＝F_max(1-(l_ce-l_ce0)²/w²(l_ce0)²)

f(v)＝(v_ce0-v_ce)/(v_ce0+(v_ce/c))

F_pee＝K_pee(l_ce-l_ce0)²

wherein, K_peeIs the modulus of elasticity of the parallel elastic elements;

f_c＝C_vce

wherein C is a damping coefficient of the contracting element; and

F_i＝F_ce+F_pee+f_c。

5. the surgical robot master hand simulation method according to claim 1, wherein the acquiring of the movement angles of the metacarpophalangeal joints and the wrist joints according to the upper limb target muscle contraction force comprises:

M_i＝∑F_id_i+∑M_f

the damping force f is:

wherein J is the moment of inertia of each member,

6. The surgical robot master hand simulation method according to any one of claims 1 to 5, wherein the target muscle comprises: flexor digitorum superficialis, extensor digitorum, flexor carpi radialis, extensor carpi longus, extensor carpi radialis brevis, extensor carpi ulnaris, flexor carpi pronator, and flexor digitorum cruris.

7. A surgical robotic master hand, comprising:

8. The surgical robotic master hand of claim 7, wherein the musculoskeletal model module comprises:

wherein the signal preprocessing module comprises:

9. An electronic device, comprising:

one or more processors;

a memory to store one or more instructions;

wherein the one or more instructions, when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.