A precision grasping intermuscular coordination test and evaluation system based on mixed reality

Abstract

The present invention discloses a test and evaluation system for accurate grasping muscle coordination based on mixed reality, including: a real scene acquisition device for acquiring real grasping information during grasping; an electromyographic sensing device for acquiring multi-channel electromyographic signals during grasping; a mixed reality platform including a processor and a mixed reality device, the processor performs three-dimensional reconstruction of the real scene according to the real grasping information and then feeds it back to the mixed reality device to construct a virtual scene, and introduces a time delay in the virtual scene; a multi-layer new weighted complex network with electromyographic sampling points as nodes and directed connections between nodes as edges is constructed according to the multi-channel electromyographic signals, and a network structure index for evaluating muscle coordination is extracted according to the phase synchronization relationship between any two layers of the multi-layer new weighted complex network. Time delay is introduced to achieve desynchronization of visual feedback information and somatosensory information; a multi-layer new weighted complex network is proposed to quantitatively evaluate muscle coordination.

Description

Technical Field

[0001] The present invention relates to the technical field of grasping test, and in particular to a precise grasping intermuscular coordination test and evaluation system based on mixed reality. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Precision grasping is a very common and important hand activity in daily life. When the human hand grasps, multiple muscles are involved in the activity and there is a muscle group synergy effect controlled by the central nervous system. As the power source of the motor control system, the coordinated activity of multiple muscles plays a key role in the process of grasping movement. It is generally believed that skilled object manipulation requires the joint action of feedforward and feedback control in the neural control mechanism. Multiple modal perceptual information, including vision, touch, proprioception, and hearing, all play an important role in perceptual feedback and are closely coupled with motor intentions to form a perceptual-motor fusion mechanism, which has an important impact on fine operations such as precision grasping. How to achieve the test and evaluation of multi-muscle coordination under perceptual-motor fusion can not only reflect the function of the neuromuscular system, but also has important significance for the integrated development of perceptual movement, the learning and cognitive mechanism of operational skills, and the diagnosis, evaluation and rehabilitation of various clinical neuromuscular diseases.

[0004] Virtual Reality (VR) uses computers to create a virtual environment that can provide users with simulated visual, tactile and auditory information, allowing users to interact with the environment as if they were actually there. However, VR technology completely isolates users from the real environment around them. When users complete actions in a virtual environment, they lack the real sense of touch of actual objects and cannot obtain effective contact perception feedback when grasping. They cannot achieve the desynchronization of vision and touch during precise grasping, and cannot achieve accurate measurement and quantitative evaluation during multi-muscle coordination. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a precise grasping intermuscular coordination test and evaluation system based on mixed reality, which introduces time delay between real scenes and virtual scenes to achieve desynchronization of visual feedback information and somatosensory information. At the same time, a new multi-layer weighted complex network is proposed to calculate the interaction relationship between multi-channel electromyographic signals and quantitatively evaluate intermuscular coordination.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a mixed reality-based precision grasping intermuscular coordination test and evaluation system, comprising: a real scene acquisition device, an electromyographic sensing device, and a mixed reality platform;

[0008] The real scene acquisition device is used to obtain real grasping information during the grasping process;

[0009] The electromyographic sensing device is used to obtain multi-channel electromyographic signals during the grasping process;

[0010] The mixed reality platform includes a processor and a mixed reality device. The processor receives real grasping information, performs three-dimensional reconstruction of the real scene according to the real grasping information, and then feeds back to the mixed reality device to construct a virtual scene, and introduces time delay in the virtual scene to achieve intermuscular coordination evaluation under different time delays;

[0011] The processor receives multi-channel electromyographic signals and evaluates intermuscular coordination based on the multi-channel electromyographic signals. Specifically, a multi-layer new weighted complex network is constructed based on the multi-channel electromyographic signals, with electromyographic sampling points as nodes and directed connections between nodes as edges. Network structure indicators for evaluating intermuscular coordination are extracted based on the phase synchronization relationship between any two layers of the network in the multi-layer new weighted complex network.

[0012] As an optional implementation, a time delay is introduced into the virtual scene to achieve desynchronization of the somatosensory feedback information and the visual feedback information, so that the somatosensory feedback information is inconsistent with the visual feedback information, so as to quantitatively evaluate the impact of visual-somatosensory perception conflict on intermuscular coordination.

[0013] As an optional implementation, the construction process of the multi-layer new weighted complex network includes: mapping the electromyographic signal of each channel into a layer of a new weighted complex network, and finally constructing a multi-layer new weighted complex network based on the natural correspondence between each electromyographic sampling point of the synchronously collected multi-channel electromyographic signal.

[0014] As an optional implementation, the phase synchronization relationship between any two layers of networks in the multi-layer novel weighted complex network is characterized by a phase-locked value. When the phase-locked value is zero, there is no phase synchronization, and when the phase-locked value is 1, there is phase synchronization.

[0015] As an optional implementation, the phase-locking value is: extracting the average weighted degree feature for each layer of the novel weighted complex network, and obtaining the phase-locking value according to the phase difference between the average weighted degree sequences of any two layers of the network.

[0016] As an optional implementation, the average weighted degree feature is the average weight of all edges of each node, and the average weighted degree sequence is obtained by the average weighted degree features of all nodes in each layer of the network.

[0017] As an optional embodiment, the network structure indicators for evaluating inter-muscle coordination include clustering coefficient and characteristic path length.

[0018] As an optional implementation, the clustering coefficient is:

[0019]

[0020] Among them, CC is the clustering coefficient, M is the number of network layers, and k p is the degree of the p-th layer network; PLV pq ,PLV rp ,PLV rq are the elements at the corresponding positions of (p,q), (r,p), and (r,q) in the matrix PLV.

[0021] As an optional implementation, the characteristic path length is:

[0022]

[0023] Among them, L p is the average characteristic path length of the p-th layer network, l pq is the shortest path length between the p-th layer and the q-th layer of the network; M is the number of network layers, and CPL is the characteristic path length.

[0024] In a second aspect, the present invention provides a method for testing and evaluating the coordination between precise grasping muscles based on mixed reality, comprising:

[0025] Acquire the real grasping information and multi-channel electromyographic signals during the grasping process;

[0026] The real scene is reconstructed in three dimensions based on the real grasping information and then fed back to the mixed reality device to construct a virtual scene. Time delay is introduced into the virtual scene to achieve intermuscular coordination evaluation under different time delays.

[0027] The process of evaluating intermuscular coordination includes: constructing a multi-layer new weighted complex network with electromyographic sampling points as nodes and directed connections between nodes as edges based on multi-channel electromyographic signals, and extracting network structure indicators for evaluating intermuscular coordination based on the phase synchronization relationship between any two layers of the multi-layer new weighted complex network.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention proposes a precision grip intermuscular coordination test and evaluation system based on mixed reality. The mixed reality platform provides visual feedback, and introduces time delay in real scenes and virtual scenes to achieve desynchronization of visual feedback information and somatosensory information, so that the visual feedback information is inconsistent with the somatosensory feedback information. The muscle activity parameters under different time delays are compared with the no-delay condition to quantify the impact of visual-somatosensory perception conflict on the coordination of multiple muscles in precision grip.

[0030] The present invention proposes a precise grasping intermuscular coordination test and evaluation system based on mixed reality, which records the electromyographic signals of multiple muscles in the hand and forearm during the grasping activity, and proposes a multi-layer novel weighted complex network for constructing a complex network in multi-channel electromyographic signals, calculating the interaction relationship between multi-channel electromyographic signals, and quantitatively evaluating intermuscular coordination, so as to realize the functional evaluation of precise grasping and the precise quantitative evaluation of the degree of functional damage and rehabilitation of grasping-related muscles, which has important application value for the perceptual-motor function test of the nervous system, the evaluation of the multi-muscle coordination function of precise grasping, the early diagnosis and rehabilitation evaluation of various neuromuscular diseases, etc.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A schematic diagram of the structure of a gripping device provided in Example 1 of the present invention;

[0034] Figure 2 A schematic diagram of a mixed reality gripping platform provided in Example 1 of the present invention;

[0035] Figure 3 A schematic diagram of a mixed reality interface provided in Example 1 of the present invention;

[0036] Figure 4 A test flow chart provided for Example 1 of the present invention;

[0037] Figure 5 A schematic diagram of a multi-layer novel weighted complex network provided in Example 1 of the present invention;

[0038] Among them, 1. column, 2. force / torque sensor, 3. force / torque sensor, 4. force / torque sensor, 5. force / torque sensor, 6. force / torque sensor, 7. base, 8. grasping device, 9. electromyographic sensing device, 10. mixed reality helmet, 11. depth camera, 12. host computer platform, 13. test platform table, 14. hand, 15. virtual frustum. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0043] Example 1

[0044] Mixed Reality (MR) is an important branch of VR. The key difference between MR and VR is that MR integrates real images with virtual scenes instead of completely isolating the user from the real environment. MR technology is used to desynchronize vision and touch during precise grasping, and visual time delay is used to generate visual interference. The motor behavior representation under the condition of inconsistent visual information and tactile and other somatosensory information is obtained, and an accurate quantitative evaluation of sensory motor function is formed under the premise of ensuring the participation and presence of participants.

[0045] Therefore, this embodiment proposes a mixed reality-based precision grasping muscle coordination test and evaluation system, including: a real scene acquisition device, an electromyographic sensing device and a mixed reality platform;

[0046] The real scene acquisition device is used to obtain real grasping information during the grasping process;

[0047] The electromyographic sensing device is used to obtain multi-channel electromyographic signals during the grasping process;

[0048] The mixed reality platform includes a processor and a mixed reality device. The processor receives real grasping information, performs three-dimensional reconstruction of the real scene according to the real grasping information, and then feeds back to the mixed reality device to construct a virtual scene, and introduces time delay in the virtual scene to achieve intermuscular coordination evaluation under different time delays;

[0049] The processor receives multi-channel electromyographic signals and evaluates intermuscular coordination based on the multi-channel electromyographic signals. Specifically, a multi-layer new weighted complex network is constructed based on the multi-channel electromyographic signals, with electromyographic sampling points as nodes and directed connections between nodes as edges. Network structure indicators for evaluating intermuscular coordination are extracted based on the phase synchronization relationship between any two layers of the multi-layer new weighted complex network.

[0050] In this embodiment, if Figure 1 The gripping device shown includes a main body 1 , force / torque sensors corresponding to respective fingers, and a base 7 ; the column 1 is arranged on the base 7 , and the force / torque sensors are arranged on the column 1 .

[0051] Specifically, the position of the force / torque sensor is determined by the natural arrangement of the human hand when grasping, and the arrangement of the thumb and the four opposite fingers force / torque sensors is based on the physiological structure of the human body and the natural grasping posture of the five fingers; wherein, the force / torque sensor 2 corresponds to the radial side (thumb) position, and the force / torque sensors 3-6 are located on the opposite side of the force / torque sensor 2, corresponding to the four ulnar fingers (index finger, middle finger, ring finger and little finger), respectively, and are used to record the real-time signal of the fingertip force / torque.

[0052] The column is a cylindrical column, and its diameter is determined according to the gripping hole diameter.

[0053] The base is a disc base, the diameter of which is slightly larger than the column part. The base increases the load and improves the stability of the gripping device to prevent the gripping device from tipping over due to improper operation during the test.

[0054] In this embodiment, the electromyographic sensing device is arranged at the corresponding position of the hand and forearm muscles, and the electromyographic sensing device is used to synchronously record the surface electromyographic signals of multiple muscles in the hand and the front of the arm during the grasping process. For example, the brachioradialis (BR), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), abductor pollicis brevis (APB) and first dorsal interosseous (FDI) and other muscles that play an important role in the grasping process can be selected to record electromyographic signals.

[0055] In this embodiment, the real scene acquisition device uses a depth camera to capture images of the human hand grasping process in real time to reconstruct the human hand grasping process in three dimensions and construct a mixed reality virtual scene. Visual time delay is introduced into the virtual scene, thereby designing a highly immersive visual mixed reality virtual scene that interacts in real time with the grasping device in the real world.

[0056] like Figure 2 The mixed reality grasping platform provided in this embodiment is shown, and the mixed reality grasping platform includes a test platform table 13, two depth cameras 11 and a host computer platform 12, the grasping device 8 and the two depth cameras 11 are placed on the test platform table 12, and the host computer platform 12 is arranged on one side of the test platform table 13;

[0057] This embodiment uses two depth cameras 11 based on the parallax principle and located at different positions to collect color images and depth images of the human hand grasping process from different positions in real time. The upper computer platform 12 receives the grasping information in the real scene collected in real time by the processor, and after pre-processing the color image and the depth image, the three-dimensional geometric information, position, posture and other data of the object are obtained by calculating the position deviation between the corresponding points of the image, so as to perform three-dimensional reconstruction of the real scene, and at the same time feed back the reconstructed three-dimensional stereoscopic scene to the mixed reality device to present it to the subject.

[0058] At the same time, starting from the dynamic time dimension of grasping activity, this embodiment introduces two levels of time delay in the virtual scene, realizes the desynchronization of somatosensory information and visual information, makes the visual feedback information inconsistent with the somatosensory feedback information, and compares the muscle activity parameters under different time delays with the condition of no delay to quantify the impact of visual-somatosensory perception conflict on the coordination of multiple muscles for precise grasping.

[0059] This embodiment introduces three different levels of visual time delay, namely, no time delay (ie, events observed in the MR scene are synchronized with actual events), visually observed events delayed by 100ms and 200ms relative to actual events.

[0060] Figure 3 It is a mixed reality interface observed by the subject, wherein the test platform table 13 is a mapping of the real scene in the mixed reality scene, the virtual frustum 15 provides a reference for the target height for the grasping activity, and the vertical height of the surface of the virtual frustum 15 relative to the test platform table 13 is 15 cm.

[0061] In such Figure 4 In the test process shown, the grasping device 8 is first placed 30 cm away from the edge of the test platform table 13, and the subject is given a mixed reality helmet 10, and an electromyographic sensor device 9 is set at the corresponding position of the hand and forearm muscles. The grasping device 8 is aligned with the subject's right shoulder. Before the formal test, each subject must practice once to familiarize himself with the MR environment and the test process.

[0062] The specific execution process of the test is as follows: the subject is ready, puts his right arm on the table, hears the first command "stop rest", and with the help of the researcher, places five fingers on the five protrusions of the grasping device 8; hears the second command "ready", and all fingers briefly leave the grasping device 8; hears the third prompt sound "start", the five fingers re-contact the grasping device 8, lifts the grasping device 8 to the height of the virtual truncated table 15 in the MR scene, and keeps it stable for 30 seconds until the fourth prompt sound "end" is heard, and the grasping device 8 is placed back on the test platform table 13; the whole process is repeated 9 times, and 3 repeated tests are performed at each time delay level. In this task, the subject is required to try to lift the grasping device 8 vertically to the same height as the platform in the field of view, and keep it as stable as possible during the holding stage.

[0063] During the test, the multi-channel electromyographic signal during the grasping process was obtained through the electromyographic sensor device. The sampling rate of the electromyographic sensor device was set to 2000 Hz. The obtained multi-channel electromyographic signal was defined as: m (t)}, m = 1, ... M, M = 7, M is the number of channels;

[0064] For any myoelectric time series with K sampling points, it can be expressed as:

[0065] X m (t k ), k=1,...K (1)

[0066] This embodiment analyzes the phase coupling relationship between multiple muscles based on a multi-layer complex network, and maps each EMG channel into a layer of complex network. To avoid losing useful EMG information during the mapping process, this method retains all sampling points of the EMG signal, and each EMG sampling point is a node of a layer of complex network composed of an EMG time series. Therefore, the multi-layer complex network composed of multi-channel EMG signals is expressed as:

[0067] {G m},m=1,...M (2)

[0068] G m =(K m ,E m ) (3)

[0069] Among them, G m Represents a complex network with K nodes and E edges, where node K m and edge E m Respectively expressed as:

[0070] K m ={k k}, k = 1, 2, ... K (4)

[0071] E m =e k ,k=1,2,...K (5)

[0072] To determine the edges between nodes, consider the directed connections between all nodes, that is, if there are K nodes, each node is connected to K-1 other nodes except itself. For example, a time series X with 10 nodes 1 (t k ), k = 1, 2, ... 10, and its complex network is represented by G 1 =(10,45).

[0073] In order to obtain more reliable and robust information, this embodiment calculates the weight coefficient of each edge by the following formula:

[0074]

[0075] Among them, k i ,k j are any two nodes of a complex network, t i ,t j is the time point corresponding to the two nodes, w ij It is k i ,k j The weight coefficient of the edge between two nodes. Only the absolute value of the weight coefficient is considered in this algorithm.

[0076] After adding the weight coefficient of each edge in each layer of complex network, each EMG channel is mapped to a layer of new weighted complex network, thus obtaining a multi-layer weighted complex network. At the same time, since each sampling point of the synchronously collected multi-channel EMG signal has a natural correspondence across multiple layers of the network between different layers, a multi-layer new weighted complex network (MNWCN) is finally obtained.

[0077] like Figure 5 The figure shows a multi-layer novel weighted complex network with M = 2 layers and K = 4 nodes, and two multi-channel electromyographic signal time series X with 4 nodes. 1 (t k ),X 2 (t k ), k = 1, 2, 3, 4, forming a network structure with two layers of complex networks {G 1 ,G 2}.

[0078] In order to retain most of the information of the complex network at the cost of less information loss, this embodiment extracts the average weighted degree (AWD) feature for each layer of the new weighted complex network to characterize the average weight of all edges of each node:

[0079]

[0080]

[0081] Among them, B(i) is the neighborhood of node i, w ij is the weight of the edge between nodes i and j, WD i is the total weight of all edges connected to node i, AWD i is the average weight of all edges connected to node i.

[0082] In each layer of the network, the average weighted degree sequence is formed by the arrangement of the average weighted degree features of all nodes in each layer of the network. For different average weighted degree sequences of two layers of networks {AWD m} and {AWD n}, the phase locking value (PLV) is used to represent the phase synchronization relationship between the two:

[0083]

[0084]

[0085] in, is the degree sequence {AWDm} and {AWD n} phase difference, and is the degree sequence {AWD m} and {AWD n} phase; when PLV mn When PLV is 0, the two degree sequences are not phase synchronized; mn When it is 1, there is a stable phase difference, that is, phase synchronization.

[0086] For the M-channel EMG signal time series, we will eventually get an M*M adjacency matrix P consisting of phase-locked values:

[0087] P=(PLV mn ) M*M (11)

[0088] Element Value PLV mn , m∈[1,M],n∈[1,M], represents the phase-locked value between the average weighted degree sequences between the corresponding two layers of networks; P is a symmetric matrix, namely PLV mn =PLV nm , m≠n,m∈[1,M],n∈[1,M].

[0089] In this embodiment, network structure indicators for evaluating intermuscular coordination are extracted, including clustering coefficient (CC) and characteristic path length (CPL), to measure the muscle network's ability to separate and integrate information, respectively;

[0090] The clustering coefficient is:

[0091]

[0092] Where M is the number of layers in the MNWCN network, CC p is the clustering coefficient of the p-th layer network, k p is the degree of the p-th layer network;

[0093]

[0094] Among them, PLV pq ,PLV rp ,PLV rq are the elements at the corresponding positions of (p,q), (r,p), and (r,q) in the matrix PLV.

[0095] The characteristic path length is:

[0096]

[0097] Among them, L p is the average characteristic path length of the p-th layer network, defined as:

[0098]

[0099] Among them, l pq is the shortest path length between the p-th layer and the q-th layer of the network.

[0100] By analyzing the changes in CC and CPL, the interactive ability of information between muscle networks is reflected, thereby accurately and quantitatively evaluating the intermuscular coordination during precise grasping.

[0101] In more embodiments, an evaluation method of the above-mentioned mixed reality-based precision grasping intermuscular coordination test and evaluation system is also provided, specifically including:

[0102] Acquire the real grasping information and multi-channel electromyographic signals during the grasping process;

[0103] The real scene is reconstructed in three dimensions based on the real grasping information and then fed back to the mixed reality device to construct a virtual scene. Time delay is introduced into the virtual scene to achieve intermuscular coordination evaluation under different time delays.

[0104] The process of evaluating intermuscular coordination includes: constructing a multi-layer new weighted complex network with electromyographic sampling points as nodes and directed connections between nodes as edges based on multi-channel electromyographic signals, and extracting network structure indicators for evaluating intermuscular coordination based on the phase synchronization relationship between any two layers of the multi-layer new weighted complex network.

[0105] It is understandable that if an invention simply modifies the geometric structure of the gripping device, it should be considered the same invention. If an invention replaces the small round table that provides the target height in the mixed reality with other objects, it should be considered the same invention. If an invention sets other types of sensors to obtain electromyographic signals, or uses the same type of electromyographic sensor device to measure the surface electromyographic signals of different muscles, it should be considered the same invention.

[0106] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A precision grasping muscle coordination test and evaluation system based on mixed reality, characterized in that: include: Real scene acquisition device, electromyography sensing device and mixed reality platform; The real scene acquisition device is used to obtain real grasping information during the grasping process; The electromyographic sensing device is used to obtain multi-channel electromyographic signals during the grasping process; The mixed reality platform includes a processor and a mixed reality device. The processor receives real grasping information, performs three-dimensional reconstruction of the real scene according to the real grasping information, and then feeds back to the mixed reality device to construct a virtual scene, and introduces time delay in the virtual scene to achieve intermuscular coordination evaluation under different time delays; The processor receives multi-channel electromyographic signals, and evaluates intermuscular coordination according to the multi-channel electromyographic signals. Specifically, a multi-layer novel weighted complex network is constructed according to the multi-channel electromyographic signals, with electromyographic sampling points as nodes and directed connections between nodes as edges, and a network structure index for evaluating intermuscular coordination is extracted according to a phase synchronization relationship between any two layers of the multi-layer novel weighted complex network; The multi-layer complex network composed of multi-channel electromyographic signals is expressed as: {G m },m=1,...M G m =(K m ,E m ) Among them, G m Represents a complex network with K nodes and E edges, where node K m and edge E m Respectively expressed as: K m ={k k },k=1,2,...K E m =e k ,k=1,2,...K In order to obtain more reliable and robust information, the weight coefficient of each edge is calculated by the following formula: Among them, k i ,k j are any two nodes of a complex network, t i ,t j is the time point corresponding to the two nodes, w ij It is k i ,k j The weight coefficient of the edge between two nodes. Only the absolute value of the weight coefficient is considered in this algorithm; After adding the weight coefficient of each edge in each layer of complex network, each EMG channel is mapped into a layer of new weighted complex network, and a multi-layer weighted complex network is obtained; The construction process of the multi-layer novel weighted complex network includes: mapping the electromyographic signal of each channel into a layer of novel weighted complex network, and finally constructing the multi-layer novel weighted complex network according to the natural correspondence between each electromyographic sampling point of the synchronously collected multi-channel electromyographic signal; The phase synchronization relationship between any two layers of the multi-layer novel weighted complex network is characterized by a phase-locked value. When the phase-locked value is zero, there is no phase synchronization, and when the phase-locked value is 1, there is phase synchronization. The phase-locked value is: extracting the average weighted degree feature of each layer of the novel weighted complex network, and obtaining the phase-locked value according to the phase difference of the average weighted degree sequence of any two layers of the network; Among them, the phase-locked value is used to characterize the phase synchronization relationship between any two layers of networks in the multi-layer new weighted complex network: in, is the degree sequence {AWD m } and {AWD n } phase difference, and is the degree sequence {AWD m } and {AWD n } phase; when PLV mn When PLV is 0, the two degree sequences are not phase synchronized; mn When it is 1, there is a stable phase difference, that is, phase synchronization; for the EMG signal time series of M channels, an M*M adjacency matrix P consisting of phase-locked values ​​will eventually be obtained: P=(PLV mn ) M*M ; Element Value PLV mn , m∈[1,M],n∈[1,M], represents the phase-locked value between the average weighted degree sequences between the corresponding two layers of networks; P is a symmetric matrix, namely PLV mn =PLV nm , m≠n,m∈[1,M],n∈[1,M].

2. A mixed reality-based precision grasping muscle coordination test and evaluation system as claimed in claim 1, characterized in that: A time delay is introduced into the virtual scene to achieve the desynchronization of the somatosensory feedback information and the visual feedback information, making the somatosensory feedback information inconsistent with the visual feedback information, so as to quantitatively evaluate the impact of visual-somatosensory perception conflict on intermuscular coordination.

3. The mixed reality-based precision gripping muscle coordination test and evaluation system according to claim 1, characterized in that: The average weighted degree feature is the average weight of all edges of each node, and the average weighted degree sequence is obtained by the average weighted degree features of all nodes in each layer of the network.

4. The mixed reality-based precision grasping muscle coordination test and evaluation system according to claim 1, characterized in that: The network structure indicators used to evaluate intermuscular coordination include clustering coefficient and characteristic path length.

5. A mixed reality-based precision gripping muscle coordination test and evaluation system as claimed in claim 4, characterized in that: The clustering coefficient is: Among them, CC is the clustering coefficient, M is the number of network layers, and k p is the degree of the p-th layer network; PLV pq ,PLV rp ,PLV rq are the elements at the corresponding positions of (p,q), (r,p), and (r,q) in the matrix PLV.

6. The mixed reality-based precision gripping muscle coordination test and evaluation system according to claim 4, characterized in that: The characteristic path length is: Among them, L p is the average characteristic path length of the p-th layer network, l pq is the shortest path length between the p-th layer and the q-th layer of the network; M is the number of network layers, and CPL is the characteristic path length.

7. A method for testing and evaluating the coordination between precise grasping muscles based on mixed reality, characterized in that: include: Acquire the real grasping information and multi-channel electromyographic signals during the grasping process; The real scene is reconstructed in three dimensions based on the real grasping information and then fed back to the mixed reality device to construct a virtual scene. Time delay is introduced into the virtual scene to achieve intermuscular coordination evaluation under different time delays. The process of evaluating intermuscular coordination includes: constructing a multi-layer new weighted complex network with myoelectric sampling points as nodes and directed connections between nodes as edges according to multi-channel electromyographic signals, and extracting network structure indicators for evaluating intermuscular coordination according to the phase synchronization relationship between any two layers of the multi-layer new weighted complex network; The multi-layer complex network composed of multi-channel electromyographic signals is expressed as: {G m },m=1,...M G m =(K m ,E m ) Among them, G m Represents a complex network with K nodes and E edges, where node K m and edge E m Respectively expressed as: K m ={k k },k=1,2,...K E m =e k ,k=1,2,...K In order to obtain more reliable and robust information, the weight coefficient of each edge is calculated by the following formula: Among them, k i ,k j are any two nodes of a complex network, t i ,t j is the time point corresponding to the two nodes, w ij It is k i ,k j The weight coefficient of the edge between two nodes. Only the absolute value of the weight coefficient is considered in this algorithm; After adding the weight coefficient of each edge in each layer of complex network, each EMG channel is mapped into a layer of new weighted complex network, and a multi-layer weighted complex network is obtained; The construction process of the multi-layer novel weighted complex network includes: mapping the electromyographic signal of each channel into a layer of novel weighted complex network, and finally constructing the multi-layer novel weighted complex network according to the natural correspondence between each electromyographic sampling point of the synchronously collected multi-channel electromyographic signal; The construction process of the multi-layer novel weighted complex network includes: mapping the electromyographic signal of each channel into a layer of the novel weighted complex network, and finally constructing the multi-layer novel weighted complex network according to the natural correspondence between each electromyographic sampling point of the synchronously collected multi-channel electromyographic signal; the phase synchronization relationship between any two layers of the network in the multi-layer novel weighted complex network is characterized by a phase-locked value, when the phase-locked value is zero, there is no phase synchronization, and when the phase-locked value is 1, there is phase synchronization; the phase-locked value is: extracting the average weighted degree feature of each layer of the novel weighted complex network, and obtaining the phase-locked value according to the phase difference of the average weighted degree sequence of any two layers of the network; Among them, the phase-locked value is used to characterize the phase synchronization relationship between any two layers of networks in the multi-layer new weighted complex network: in, is the degree sequence {AWD m } and {AWD n } phase difference, and is the degree sequence {AWD m } and {AWD n } phase; when PLV mn When PLV is 0, the two degree sequences are not phase synchronized; mn When it is 1, there is a stable phase difference, that is, phase synchronization; for the EMG signal time series of M channels, an M*M adjacency matrix P consisting of phase-locked values ​​will eventually be obtained: P=(PLV mn ) M*M ; Element Value PLV mn , m∈[1,M],n∈[1,M], represents the phase-locked value between the average weighted degree sequences between the corresponding two layers of networks; P is a symmetric matrix, namely PLV mn =PLV nm , m≠n,m∈[1,M],n∈[1,M].

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