Upper limb rehabilitation training system and method based on virtual reality

Abstract

The embodiment of the invention discloses an upper limb rehabilitation training system and method based on virtual reality, and relates to the technical field of medical rehabilitation, and the system comprises a virtual reality display module which is used for displaying a virtual training scene to a target user performing upper limb rehabilitation training; the first data acquisition module is used for acquiring first motion data; the data processing module is connected with the virtual reality display module and the first data acquisition module, and the data processing module is configured to determine second motion data of the virtual hand under the condition that the motion distance corresponding to the first position is equal to a target distance determined by a motion distance upper limit value, and the virtual hand is controlled to move towards the target object based on the second motion data to guide the real hand to approach the target object, so that the compliance of the target user in the rehabilitation training process is improved, and the rehabilitation training process is accelerated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical rehabilitation, and in particular to an upper limb rehabilitation training system and method based on virtual reality. BACKGROUND

[0002] Upper limb motor dysfunction is a typical clinical manifestation of sequelae of cerebral stroke, and its rehabilitation treatment relies on long-term, high-intensity repetitive training to reconstruct the neural motor pathway. In this process, the patient stimulates the function recovery of the affected limb by completing a designated action task (such as grasping an object).

[0003] In the related art, if the upper limb rehabilitation training is to be performed, the patient needs to receive long-term, high-intensity continuous rehabilitation training in the hospital, and the patient is difficult to see the progress of the upper limb motor ability in a short time. Such a situation often makes the patient lose confidence and gradually no longer comply with the rehabilitation process, thereby worsening the rehabilitation effect.

[0004] It can be seen that the upper limb rehabilitation training scheme in the related art has the problem of affecting the rehabilitation training compliance of the patient, thereby delaying the rehabilitation training process.

[0005] SUMMARY

[0006] Therefore, one of the purposes of the present application is to provide an upper limb rehabilitation training system and method based on virtual reality, which can improve the compliance of the target user, i.e., the patient, in the rehabilitation training process, and thereby speed up the rehabilitation training process.

[0007] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows:

[0008] In a first aspect, the embodiments of the present application provide an upper limb rehabilitation training system based on virtual reality, which comprises:

[0009] a virtual reality display module configured to display a virtual training scene to a target user performing upper limb rehabilitation training;

[0010] a first data acquisition module configured to acquire first motion data, the first motion data being data of a real hand in the process of acquiring a target object, the real hand being a hand of the target user, and the target object being a virtual object in the virtual training scene for guiding the real hand to acquire;

[0011] a data processing module connected with the virtual reality display module and the first data acquisition module, the data processing module being configured to:

[0012] acquire the first motion data and an upper limit value of the motion distance of the real hand in the target direction;

[0013] determine a first position of the real hand and a motion distance corresponding to the first position according to the first motion data; and

[0014] determining whether the movement distance corresponding to the first position is equal to a target distance according to the movement distance corresponding to the first position and a movement distance upper limit value, the target distance being determined by the movement distance upper limit value;

[0015] in a case where the movement distance corresponding to the first position is equal to the target distance, determining second movement data of a virtual hand, the virtual hand being used to guide movement of a real hand in a virtual training scene;

[0016] controlling the virtual hand to move to a position where the target object is located according to the second movement data.

[0017] In a possible implementation, the first data acquisition module comprises:

[0018] a first data acquisition submodule connected with the data processing module and configured to acquire a movement image of the real hand in a process of acquiring the target object;

[0019] a second data acquisition submodule connected with the data processing module and configured to acquire a first initial position of the real hand in the process of acquiring the target object, the first movement data comprising the movement image and the first initial position;

[0020] The data processing module is further configured to:

[0021] acquire the first initial position and the movement image;

[0022] determine a second initial position according to the movement image;

[0023] determine the first position according to the first initial position and the second initial position.

[0024] In a possible implementation, the system further comprises:

[0025] a second data acquisition module connected with the data processing module and configured to acquire physiological characteristic information of the real hand in the process of acquiring the target object;

[0026] The data processing module is further configured to:

[0027] determine a speed correction parameter according to the physiological characteristic information;

[0028] in a case where the movement distance corresponding to the first position is equal to the target distance, determine an average movement speed of the virtual hand according to the movement distance corresponding to the first position and a movement time corresponding to the movement distance;

[0029] determine a target movement speed according to the speed correction parameter and the average movement speed, the second movement data comprising the target movement speed.

[0030] In a possible implementation, the second data acquisition module includes a plurality of electromyography sensors, the plurality of electromyography sensors are uniformly attached to the surface of the upper limb corresponding to the real hand, and the physiological characteristic information includes electromyography signals detected by the plurality of electromyography sensors.

[0031] In a possible implementation, the system further includes a co-training module connected to the data processing module, and the co-training module includes:

[0032] a gravity compensation submodule configured to provide a target auxiliary force for the upper limb of the target user during the rehabilitation training of the upper limb of the target user;

[0033] The data processing module is configured to:

[0034] determine a target muscle strength grade corresponding to the electromyography signal, and different muscle strength grades are preconfigured with corresponding resistance intervals;

[0035] determine the target auxiliary force according to the electromyography signal, so that an equivalent training resistance that needs to be overcome by the upper limb of the target user is located in the resistance interval corresponding to the target muscle strength grade;

[0036] send the target auxiliary force to the gravity compensation submodule.

[0037] In some embodiments, the gravity compensation submodule is connected to the data processing module.

[0038] In a possible implementation, the system further includes:

[0039] a safety detection module connected to the data processing module, configured to detect a motion angle of a target joint, the target joint being a joint of the upper limb where the real hand is located, and the target joint including at least one of a shoulder joint, an elbow joint, and a wrist joint;

[0040] a warning module connected to the data processing module, configured to issue a warning in a case where the data processing module issues a warning instruction;

[0041] The data processing module is further configured to:

[0042] obtain the motion angle of the target joint and a joint identifier of the target joint;

[0043] determine a target angle threshold corresponding to the joint identifier from a plurality of preconfigured angle thresholds;

[0044] generate the warning instruction in a case where the motion angle of the target joint is greater than or equal to the target angle threshold.

[0045] In a possible implementation, the safety detection module includes a plurality of inertial sensors, and the plurality of inertial sensors are uniformly attached to the surface of the target joint.

[0046] In a possible implementation, the data processing module is further configured to:

[0047] In a case where the virtual hand is controlled to move to the position where the target object is located according to the second motion data, the target object is controlled to change color to indicate that the target user has obtained the target object.

[0048] In a possible implementation, the system further includes:

[0049] The scoring module is connected to the data processing module, and is configured to record a training score of the target user in the upper limb rehabilitation training process.

[0050] The data processing module is configured to:

[0051] obtain the training score;

[0052] determine a target adjustment parameter according to the training score;

[0053] determine the target distance and the second motion data according to the target adjustment parameter and the upper limit of the motion distance.

[0054] In a second aspect, an embodiment of the present application provides a virtual reality-based upper limb rehabilitation training method, which includes:

[0055] obtaining first motion data and an upper limit of a motion distance of a real hand in a target direction, the first motion data being data of the real hand in a process of obtaining a target object, and the real hand being a hand of a target user who performs the upper limb rehabilitation training;

[0056] determining a first position of the real hand and a motion distance corresponding to the first position according to the first motion data;

[0057] determining second motion data of a virtual hand according to the motion distance corresponding to the first position and the upper limit of the motion distance, the virtual hand being used to guide the real hand to move in a virtual training scene;

[0058] controlling the virtual hand to move to a position where the target object is located according to the second motion data.

[0059] In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, and the memory stores a computer program, and the computer program is executed by the processor to implement the virtual reality-based upper limb rehabilitation training method provided in the second aspect.

[0060] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by one or more processors to implement the virtual reality-based upper limb rehabilitation training method provided in the second aspect.

[0061] In a fifth aspect, the embodiments of the present application provide a computer program product, which stores computer instructions. The computer instructions are executed by a processor to implement the method provided in the second aspect.

[0062] The virtual reality-based upper limb rehabilitation training system provided by the embodiments of the present application comprises a virtual reality display module, a first data acquisition module and a data processing module. The data processing module acquires first motion data of a real hand of a target user in the process of acquiring a target object through the first data acquisition module, and determines a first position of the real hand and a corresponding motion distance. Then, the data processing module can determine whether the motion distance corresponding to the first position is equal to a target distance based on a previously acquired upper limit value of the motion distance of the real hand in a target direction and the motion distance corresponding to the first position of the real hand. In the case where the motion distance corresponding to the first position is equal to the target distance, second motion data of a virtual hand is determined, and the virtual hand is controlled to move towards the target object based on the second motion data, so as to guide the real hand to move towards the target object, improve the compliance of the target user in the rehabilitation training process, and further speed up the rehabilitation training process. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. It should be understood that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0064] Figure 1 A functional module schematic diagram of the virtual reality-based upper limb rehabilitation training system provided by the embodiments of the present application;

[0065] Figure 2 A hand motion schematic diagram related to the virtual reality-based upper limb rehabilitation training system provided by the embodiments of the present application;

[0066] Figure 3 Another functional module schematic diagram of the virtual reality-based upper limb rehabilitation training system provided by the embodiments of the present application;

[0067] Figure 4 A flowchart of the virtual reality-based upper limb rehabilitation method provided by the embodiments of the present application;

[0068] Figure 5 A hardware structure schematic diagram of the electronic device provided by the embodiments of the present application.

[0069] Explanation of reference signs:

[0070] 100. A virtual reality-based upper limb rehabilitation training system;

[0071] 110. A virtual reality display module;

[0072] 120. A first data acquisition module;

[0073] 1210. A first data acquisition sub-module;

[0074] 1220. A second data acquisition sub-module;

[0075] 130. A data processing module;

[0076] 140. A second data acquisition module;

[0077] 150. A collaborative training module;

[0078] 1510. A gravity compensation sub-module;

[0079] 160. A safety detection module;

[0080] 170. An early warning module;

[0081] 180. A scoring module;

[0082] 501. A processor;

[0083] 502. A memory;

[0084] 503. A communication interface;

[0085] 510. A bus. DETAILED DESCRIPTION

[0086] So that the objects, technical solutions and advantages of the embodiments of the present application are more apparent, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0087] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0088] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0089] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any combination of the listed terms or all combinations thereof. For example, the expression "A or B" or "at least one of A or / and B" can include A, can include B, or can include both A and B.

[0090] In the description of the present application, it should be noted that if the terms "upper", "lower", "inner", "outer" and the like indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0091] In addition, if the terms "first", "second", and the like appear, they are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0092] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict.

[0093] Also, in the embodiments of the present application, the term "connection" can mean "electrical connection", and can also mean "direct connection". "Electrical connection" can mean that two components are directly electrically connected, or can mean that two components are electrically connected via one or more other components such as a normally open tube.

[0094] To solve the technical problems in the background art, the embodiments of the present application provide a virtual reality-based upper limb rehabilitation training system, method, electronic device, computer readable storage medium and computer program product. First, the virtual reality-based upper limb rehabilitation training system 100 provided by the embodiments of the present application is introduced.

[0095] Please refer to Figure 1 , Figure 1 A functional module schematic diagram of a virtual reality-based upper limb rehabilitation training system provided by the embodiments of the present application, the virtual reality-based upper limb rehabilitation training system 100 includes:

[0096] A virtual reality display module 110 is configured to display a virtual training scene to a target user performing upper limb rehabilitation training;

[0097] A first data acquisition module 120 is configured to acquire first motion data, the first motion data being data of a real hand in the process of acquiring a target object, the real hand being a hand of the target user, and the target object being a virtual object in the virtual training scene for guiding the real hand to acquire;

[0098] Data processing module 130 is connected to virtual reality display module 110 and first data acquisition module 120. Data processing module 130 is configured as follows:

[0099] Acquire the first motion data, and the upper limit of the actual hand's movement distance in the target direction;

[0100] Based on the first motion data, determine the first position of the real hand and the corresponding motion distance of the first position;

[0101] Based on the movement distance corresponding to the first position and the upper limit of the movement distance, determine whether the movement distance corresponding to the first position is equal to the target distance. The target distance is determined by the upper limit of the movement distance.

[0102] When the movement distance corresponding to the first position is equal to the target distance, the second motion data of the virtual hand is determined. The virtual hand is used to guide the movement of the real hand in the virtual training scenario.

[0103] Control the virtual hand to move to the location of the target object based on the second motion data.

[0104] The virtual reality-based upper limb rehabilitation training system provided in this application includes a virtual reality display module, a first data acquisition module, and a data processing module. The data processing module acquires the first motion data of the target user's real hand during the process of acquiring a target object, collected by the first data acquisition module, to determine the first position of the real hand and its corresponding movement distance. Then, based on the previously acquired upper limit of the real hand's movement distance in the target direction and the movement distance corresponding to the first position of the real hand, the data processing module determines whether the movement distance corresponding to the first position is equal to the target distance. If the movement distance corresponding to the first position is equal to the target distance, the system determines the second motion data of the virtual hand and controls the virtual hand to move towards the target object based on the second motion data, thereby guiding the real hand to approach the target object, improving the target user's compliance during rehabilitation training, and thus accelerating the rehabilitation training process.

[0105] The following will address, for example Figure 1 The various components of the virtual reality-based upper limb rehabilitation training system 100 will be introduced.

[0106] The virtual reality display module 110 described above can be used to create virtual training scenarios. Specifically, the virtual reality display module 110 and the data processing module 130 can define the type of virtual training scenario created by the virtual reality display module 110.

[0107] Exemplarily, the virtual training scene can include, but is not limited to, a daily life type scene and a task-oriented game type scene. The daily life type scene can include a home scene and an offline shopping scene, and can reproduce a home or shopping environment for the target user. In the daily life type scene, the target user can be guided to simulate daily life actions, for example, the target user is guided to use the target upper limb to obtain an object, so as to perform upper limb rehabilitation training on the target upper limb of the target user.

[0108] The target user is a user who needs to use the virtual reality-based upper limb rehabilitation training system 100 to perform upper limb rehabilitation training. The target upper limb is the limb of the target user that needs to be trained.

[0109] In some embodiments, the virtual reality display module 110 can include a head-mounted display, which can be connected to the data processing module 130. The head-mounted display can be used to present the virtual training scene to the target user. The connection between the head-mounted display and the data processing module 130 can include wired connection and wireless connection, which are not limited herein.

[0110] In some embodiments, the virtual reality display module 110 can further include a spatial audio sub-module, which can be connected to the data processing module 130. The spatial audio sub-module can be used to play voice to instruct the target user to perform an operation. For example, the voice content "touch the spherical object in front of you" can instruct the target user to touch the spherical object in front of the target user by using the target limb.

[0111] The first data acquisition module 120 can be used to acquire motion data of the real hand in the process of obtaining the target object. The first data acquisition module 120 is connected to the data processing module 130. The data processing module 130 can acquire the motion data corresponding to the real hand acquired by the first data acquisition module 120, or the first data acquisition module actively sends the motion data corresponding to the real hand to the data processing module 130 after acquiring the motion data. The connection between the first data acquisition module 120 and the data processing module 130 can include wired connection and wireless connection, which are not limited herein.

[0112] The real hand can refer to the hand of the target upper limb of the target user. Correspondingly, the virtual hand is a hand in the digital image in the virtual training scene. In some embodiments, the data processing module 130 can acquire image information of the real hand, and then generate a virtual hand with an appearance close to the real hand based on the image information of the real hand.

[0113] The target object is a virtual object in a virtual training scene, and can be used to guide the real hand to perform the acquisition. In some embodiments, the "can be used to guide the real hand to perform the acquisition" includes guiding the real hand to perform the grabbing, or guiding the real hand to perform the touching. The shape of the target object can be spherical, square, or other irregular shapes, which can be selected according to the actual needs of the target user, and is not limited herein.

[0114] If the real hand of the target user can normally grab the object, the part of the upper limb that needs to be rehabilitated is the shoulder joint, elbow joint, or wrist joint of the upper limb where the real hand of the target user is located, and the virtual hand can be used to guide the real hand to touch the target object. In this way, the time for the real hand to grab the target object can be saved, and more rehabilitation training time can be obtained for the shoulder joint, elbow joint, or wrist joint of the upper limb where the real hand is located.

[0115] If the real hand of the target user cannot normally grab the object, that is, the upper limb rehabilitation training needs to include rehabilitation training of the finger joints of the real hand, the virtual hand can be used to guide the real hand to grab the target object, so as to achieve full-range rehabilitation training of the upper limb where the real hand of the target user is located (that is, the target upper limb in the foregoing embodiment).

[0116] For unified description, the "acquisition" of the target object in the following embodiments can be collectively referred to as "touching".

[0117] Corresponding to the target object, a real object with the same shape and direction as the target object can be placed in the real scene where the target user is located. During the process of the virtual hand touching the target object, the real hand also touches the real object.

[0118] The data processing module 130 can be a mobile device, a computer, or a cloud server. The specific data processing module 130 can be selected according to actual needs, and is not limited herein.

[0119] The data processing module 130 can be used to determine the second motion data of the virtual hand when it is detected that the triggering condition is met, and control the motion of the virtual hand based on the second motion data.

[0120] The triggering condition is that the motion distance corresponding to the first position is equal to the target distance.

[0121] In some embodiments, the target distance is determined by the upper limit value of the motion distance, including:

[0122] The target distance is determined according to the upper limit value of the motion distance and the preset parameter.

[0123] Specifically, the data processing module 130 can determine the product of the upper limit value of the motion distance and the preset parameter as the target distance.

[0124] The target directions include a first direction, a second direction and a third direction based on the face direction of the target user. The first direction is a forward direction, the second direction is an upward direction, and the third direction is a leftward or rightward direction.

[0125] If the upper limb rehabilitation training is performed on the left upper limb of the target user, the target directions include the first direction, the second direction and the third direction.

[0126] If the upper limb rehabilitation training is performed on the right upper limb of the target user, the target directions include the first direction, the second direction and the third direction.

[0127] In some embodiments, the upper limit of the movement distance of the real hand in the target direction can be measured in advance and stored in the virtual reality-based upper limb rehabilitation training system 100, and specifically can be stored in a storage module of the virtual reality-based upper limb rehabilitation training system 100. The storage module is connected with the first data acquisition module 120 and the data processing module 130, and the specific connection mode is not limited herein. The data acquired by the first data acquisition module 120 can be directly sent to the data processing module 130, or can be directly stored in the storage module and obtained by the data processing module 130 from the storage module.

[0128] For example, if the upper limb rehabilitation training is performed on the left upper limb of the target user, before the rehabilitation training, the left upper limb of the target user can be guided to stretch forward, upward and rightward by the virtual reality display module 110, so as to obtain the maximum distance of stretching in the three directions, that is, the upper limit of the movement distance of the real hand in the target direction.

[0129] It should be noted that, still taking the example of performing the upper limb rehabilitation training on the left upper limb of the target user, the upper limit of the movement distance of the real hand in the target direction can be measured before each upper limb rehabilitation training cycle starts, that is, the upper limit of the movement distance of the real hand in the target direction is measured and updated before each upper limb rehabilitation training cycle starts. Alternatively, the upper limit of the movement distance of the real hand in the target direction can be measured at a preset time interval, such as every 3 days, during the complete cycle of the upper limb rehabilitation training, that is, the upper limit of the movement distance of the real hand in the target direction is measured and updated every 3 days during the complete cycle of the upper limb rehabilitation training. In this way, during the upper limb rehabilitation training, the training parameters involved in the upper limb rehabilitation training can be dynamically updated in combination with the rehabilitation training process of the target user, so as to improve the effect of the upper limb rehabilitation training.

[0130] The upper limit of the movement distance of the real hand in the target direction can be measured by the first data acquisition module 120.

[0131] Although the specific directions of the target directions, such as the directions corresponding to the forward, upward, leftward and rightward directions based on the face of the target user as the reference, are shown for the purpose of illustration, more different directions can be set as the target directions according to actual rehabilitation training needs, which are all within the protection scope of the embodiments of the present application.

[0132] The following will be described in combination with Figure 2 The data processing process involved in the data processing module 130 is introduced in the process of upper limb rehabilitation training of the left upper limb of the target user, Figure 2 A hand movement schematic diagram involved in an upper limb rehabilitation training system based on virtual reality provided by the embodiments of the present application.

[0133] If the target direction is the direction corresponding to the forward direction, in Figure 2 In the above embodiment, the target direction is the direction corresponding to the upward direction.

[0134] H1 side represents the movement process of the real hand;

[0135] H2 side represents the movement process of the virtual hand;

[0136] P0 represents the initial movement position of the real hand and the virtual hand (specifically, the position of the top end of the middle finger of the real hand or the virtual hand, or the position of the palm of the real hand or the virtual hand can be used) ;

[0137] P1 represents the target distance;

[0138] P2 represents the position corresponding to the upper limit value of the movement distance of the real hand in the target direction, or the position of the real object;

[0139] P3 represents the position of the target object.

[0140] The relationship between P1 and P2 can be represented as follows (corresponding to the "multiplication of the upper limit value of the movement distance and the preset parameter to determine the target distance" in the foregoing embodiment) :

[0141] (P1-P0) = k (P2-P0) ;

[0142] Wherein:

[0143] k can represent the preset parameter in the foregoing embodiment, and generally k is less than 1;

[0144] (P1-P0) can represent the target distance;

[0145] (P2-P0) can represent the upper limit value of the movement distance of the real hand in the target direction.

[0146] In a case where the data processing module 130 detects that the motion distance corresponding to the first position of the real hand is equal to the target distance, i.e., (P1-P0), the second motion data of the virtual hand can be further determined. The motion processes of the real hand and the virtual hand from the position P0 to the position P1 are consistent, and the distance between the position P1 and the position P0, i.e., the target distance, can be understood as the deviation triggering distance corresponding to the virtual hand.

[0147] In other words, from the position P1, the virtual hand moves to the position P3 according to the second motion data determined by the data processing module 130, and starts to deviate from the motion distance of the real hand. While the virtual hand moves to the position P3 according to the second motion data, the real hand can move to the position P2, i.e., the position corresponding to the upper limit of the motion distance of the real hand in the target direction.

[0148] In this way, when the real hand of the target user moves from the initial motion position in the process of upper limb rehabilitation training and the corresponding motion distance is equal to the deviation triggering distance, the virtual hand deviates from the real hand, and when the virtual hand moves to the position P3 where the target object is located according to the second motion data, the real hand can just move to the position P2 where the real object is located.

[0149] The above can be understood as follows: when the real hand of the target user is about to approach the real object (i.e., when the real hand moves from the position P0 to the position P1, at this time, it can be understood that the target user has made enough efforts to make the real hand approach the position P2 where the real object is located), the virtual hand in the virtual training scene starts to move according to the second motion data, and the distance between the virtual hand and the real hand deviates. When the target user does not make enough efforts to make the real hand approach the position P2 where the real object is located, the deviation triggering distance is not triggered. The deviation triggering distance set in the embodiments of the present application can make the target user give feedback after making enough efforts, i.e., trigger the virtual hand to move to the position P3 which is farther away, so that the target user can really feel that the real object which is originally difficult to touch can be touched by making efforts, and the target user can directly observe the progress in the process of upper limb rehabilitation training, thereby improving the compliance of the target user to the upper limb rehabilitation training, and further accelerating the process of upper limb rehabilitation training.

[0150] The distance between the position P3 and the position P2 is a preset length L, and after the upper limit of the motion distance of the real hand in the target direction (P2-P0) is determined, the position P3 can be further determined based on the preset length L.

[0151] In some embodiments, the preset length L=20 cm.

[0152] Please refer to Figure 3 , Figure 3Another functional module schematic diagram of a virtual reality-based upper limb rehabilitation training system provided by an embodiment of the present application.

[0153] In a possible implementation, the first data acquisition module 120 comprises:

[0154] The first data acquisition submodule 1210 is connected with the data processing module 130 and is configured to acquire a motion image of the real hand in the process of acquiring the target object.

[0155] The second data acquisition submodule 1220 is connected with the data processing module 130 and is configured to acquire a first initial position of the real hand in the process of acquiring the target object.

[0156] The data processing module 130 is further configured to:

[0157] acquire the first initial position and the motion image;

[0158] determine a second initial position according to the motion image;

[0159] determine the first position according to the first initial position and the second initial position.

[0160] The embodiment of the present application can combine the information acquired by the first data acquisition submodule 1210 and the second data acquisition submodule 1220, i.e., the motion image and the position information of the real hand, so as to reduce the data error caused by using a single data acquisition submodule, and further improve the accuracy and reliability of the determined first position.

[0161] In some embodiments, the first data acquisition submodule 1210 comprises a plurality of cameras, which can be uniformly arranged on the virtual reality display module 110, specifically on the head-mounted display included in the virtual reality display module 110, and the motion image of the real hand in the process of acquiring the target object can be acquired through the plurality of cameras. The data processing module 130 can determine the second initial position of the real hand in the process of acquiring the target object through the motion image.

[0162] In some embodiments, the second data acquisition submodule 1220 can comprise a plurality of position sensors, which can be uniformly attached to the real hand, and the first initial position of the real hand in the process of acquiring the target object can be monitored through the plurality of position sensors.

[0163] In some embodiments, the above determining the first position according to the first initial position and the second initial position comprises:

[0164] converting the first initial position into a target coordinate system to obtain a first target position;

[0165] convert the second initial position into the target coordinate system to obtain a second target position;

[0166] determine the average value between the coordinate value of the first target position and the coordinate value of the second target position as the coordinate value of the first position.

[0167] The target coordinate system is a three-dimensional Cartesian coordinate system.

[0168] For example, if the first target position is (x1, y1, z1) and the second target position is (x2, y2, z2), then the first position is (x3, y3, z3), where:

[0169] x3=(x1+x2) / 2;

[0170] y3=(y1+y2) / 2;

[0171] z3=(z1+z2) / 2.

[0172] In a possible implementation, the system further comprises:

[0173] The second data acquisition module 140 is connected with the data processing module 130 and is configured to acquire physiological characteristic information of the real hand in the process of acquiring the target object.

[0174] The data processing module 130 is further configured to:

[0175] determine a speed correction parameter according to the physiological characteristic information;

[0176] In the case that the movement distance corresponding to the first position is equal to the target distance, determine an average movement speed of the virtual hand according to the movement distance corresponding to the first position and the corresponding movement time consumption;

[0177] determine a target movement speed according to the speed correction parameter and the average movement speed, and the second movement data comprises the target movement speed.

[0178] The embodiment of the present application acquires the physiological characteristic information of the target user in the process of acquiring the target object through the second data acquisition module 140, and then determines a speed correction parameter based on the physiological characteristic information, and then determines a target movement speed based on the speed correction parameter and the target distance (at this time, the movement distance corresponding to the first position is equal to the target distance). When the virtual hand moves to the position P3 where the target object is located according to the target movement speed, the real hand can move to the position P2 where the real object is located.

[0179] In some embodiments, the virtual reality-based upper limb rehabilitation training system 100 further comprises a timer, and the movement time consumption corresponding to the movement distance corresponding to the first position can be determined by the timer.

[0180] Exemplarily, the timer can be connected with the data processing module 130, and the data processing module 130 can determine whether the real hand starts to move from the position P0 according to the first movement data collected by the first data collection module 120, and generate a first timing instruction in the case of determination, the first timing instruction can be used to instruct the timer to start timing and the time is t0 (t0 = 0).

[0181] For another example, when the real hand moves to the position P1, the data processing module 130 can generate a segmented timing instruction, which can be used to instruct the timer to record the time tl corresponding to the movement of the real hand to the position P1. If the first position processing is at the position P1, the movement time corresponding to the movement distance corresponding to the first position is (t1-t0), and the average movement speed is (P1-P0) / (t1-t0).

[0182] In the case that the real hand moves to the position P2 or the virtual hand moves to the position P3, the data processing module 130 can generate a second timing instruction, which can be used to instruct the timer to stop timing.

[0183] In some embodiments, the data processing module 130 can divide the target distance by the movement time to obtain the average movement speed of the virtual hand (the average movement speed of the real hand and the virtual hand from the position P0 to the position P1 is the same).

[0184] In a possible implementation, the second data collection module 140 includes a plurality of electromyographic sensors, and the plurality of electromyographic sensors are uniformly attached to the surface of the upper limb corresponding to the real hand. The physiological characteristic information includes the electromyographic signals detected by the plurality of electromyographic sensors.

[0185] The physiological characteristic information is the electromyographic signal.

[0186] In some embodiments, the determining the speed correction parameter according to the physiological characteristic information includes:

[0187] The difference between the first electromyographic signal and the second electromyographic signal is determined as a target difference, and the physiological characteristic information includes the first electromyographic signal and the second electromyographic signal.

[0188] The speed correction parameter is determined according to the target difference.

[0189] The determining the speed correction parameter according to the target difference includes:

[0190] The speed correction parameter is determined according to the target difference and a preconfigured difference interval, and different difference intervals correspond to different speed correction parameters.

[0191] Exemplarily, if the target difference is large, for example, in the first difference interval, it can be indicated that the motion state of the real hand is normal, and the real hand can still move to the position P2 according to the average motion speed after reaching the position P1. The speed correction parameter corresponding to the first difference interval is 1. If the average motion speed is represented as V1, the target motion speed V2 included in the second motion data can be determined by the following relationship:

[0192] (P2-P1) / V1=L / V2;

[0193] wherein L=P3-P1, P1, P2, P3 and V1 are known, and V2 can be represented as follows:

[0194] V2=L*V1*1 / (P2-P1).

[0195] Exemplarily, if the target difference is small, for example, in the second difference interval, it can be indicated that the motion state of the real hand is abnormal, and the motion is relatively difficult. The real hand will move from the position P1 to the position P2 according to a speed lower than the average motion speed. Therefore, the speed correction parameter corresponding to the second difference interval is 0.7. Correspondingly, V2 can be represented as follows:

[0196] V2=L*V1*0.9 / (P2-P1).

[0197] Exemplarily, if the target difference is small, for example, in the third difference interval, it can be indicated that the motion state of the real hand is abnormal, and the motion is very difficult. The real hand will move from the position P1 to the position P2 according to a speed lower than the average motion speed. Therefore, the speed correction parameter corresponding to the third difference interval is 0.7. Correspondingly, V2 can be represented as follows:

[0198] V2=L*V1*0.7 / (P2-P1).

[0199] Although the first difference interval, the second difference interval and the third difference interval, and the speed correction parameters corresponding to the respective difference intervals are shown for illustrative purposes, more difference intervals and corresponding speed correction parameters can be set as needed, which are all within the protection scope of the embodiments of the present application.

[0200] In some embodiments, the electromyographic signal is a surface electromyographic signal, which is a bioelectric signal generated on the surface of the skin when the muscle contracts. The motion state of the real hand can be reflected through the surface electromyographic signal.

[0201] In a possible implementation, the system further comprises a cooperative training module 150 connected with the data processing module 130, and the cooperative training module 150 comprises:

[0202] The gravity compensation submodule 1510 is configured to provide a target assisting force for the upper limb of the target user during the upper limb rehabilitation training of the target user.

[0203] The data processing module 130 is configured to:

[0204] determine a target muscle strength level corresponding to the electromyographic signal, and different muscle strength levels are preconfigured with corresponding resistance intervals;

[0205] determine the target assisting force according to the electromyographic signal, so that the equivalent training resistance that needs to be overcome by the upper limb of the target user is located in the resistance interval corresponding to the target muscle strength level;

[0206] send the target assisting force to the gravity compensation submodule 1510.

[0207] The embodiment of the present application can provide a target assisting force for a user with poor muscle strength who cannot overcome the gravity of the hand, so as to assist the user's upper limb to perform upper limb rehabilitation training by setting the gravity compensation submodule 1510.

[0208] In some embodiments, the above-mentioned gravity compensation submodule 1510 is a mechanical arm, which can be used to assist the user's upper limb movement.

[0209] The above-mentioned muscle strength level is a standard system for evaluating muscle contraction ability in medicine, which can be generally divided into 0-5 levels, and the corresponding states are complete paralysis, slight contraction, horizontal movement, anti-gravity movement, anti-partial resistance and normal muscle strength.

[0210] In some embodiments, the electromyographic signal is an average value of the signals collected by the plurality of electromyographic sensors in the foregoing embodiments, and the determination of the target assisting force according to the electromyographic signal comprises:

[0211] determining an average value of all resistances in the resistance interval corresponding to the target muscle strength level as the target assisting force;

[0212] Alternatively,

[0213] randomly selecting one resistance from all resistances in the resistance interval corresponding to the target muscle strength level as the target assisting force.

[0214] In a possible implementation, the system further comprises:

[0215] The safety detection module 160 is connected with the data processing module 130 and is configured to detect the movement angle of the target joint, the target joint being a joint of the upper limb on which the real hand is located, and the target joint comprising at least one of a shoulder joint, an elbow joint and a wrist joint;

[0216] The warning module 170 is connected with the data processing module 130 and is configured to issue a warning in the case that the data processing module 130 issues a warning instruction;

[0217] The data processing module 130 is further configured to:

[0218] obtain a motion angle of the target joint, and a joint identifier of the target joint;

[0219] determine a target angle threshold corresponding to the joint identifier from a plurality of preconfigured angle thresholds;

[0220] generate a warning instruction in a case where the motion angle of the target joint is greater than or equal to the target angle threshold.

[0221] The embodiments of the present application can generate a warning when an abnormal situation occurs in the upper limb rehabilitation training process by configuring the safety detection module 160, so as to protect the safety of the upper limbs of the target user and avoid the situation that the activity angle of the upper limb joint exceeds the limit activity angle of the joint.

[0222] The joint identifier can be used to mark the joint of the user, and different joint identifiers can correspond to different target angle thresholds, in other words, different joints can correspond to different target angle thresholds. The target angle threshold in the embodiments of the present application can be understood as a safety angle threshold, and the activity angle of the joint of the target user needs to be lower than the safety angle threshold.

[0223] The target angle threshold can be modified according to actual needs.

[0224] The form of the warning instruction includes but is not limited to voice and image.

[0225] In a possible implementation, the safety detection module 160 includes a plurality of inertial sensors, and the plurality of inertial sensors are uniformly attached to the surface of the target joint.

[0226] The plurality of inertial sensors can be used to monitor the activity angle of the joint at the corresponding position.

[0227] In a possible implementation, the data processing module 130 is further configured to:

[0228] In a case where the virtual hand is controlled to move to the position where the target object is located according to the second motion data, the target object is controlled to change color to indicate that the target user obtains the target object.

[0229] The embodiments of the present application can adjust the color change of the target object in a case where the virtual hand touches the target object, so as to prompt the target user to complete a touch task.

[0230] In some embodiments, before the virtual hand touches the target object (i.e. before the real hand touches the real object), the data processing module 130 can control the color of the target object to be red, and when the virtual hand touches the target object (i.e. when the real hand touches the real object), the data processing module 130 can control the color of the target object to be black.

[0231] In some embodiments, when the target object changes color and the real hand and the virtual hand are retracted, the data processing module 130 can control the position of the target object in the virtual training scene to be refreshed again.

[0232] Exemplarily, taking the left upper limb of the target user as an example, the position of the target object in the virtual training scene can be refreshed 18 times in one upper limb rehabilitation training cycle, including 6 times in the corresponding area in the forward direction, 6 times in the corresponding area in the upward direction, and 6 times in the corresponding area in the right direction.

[0233] If the position is refreshed 6 times in the corresponding area in the forward direction, 2 times of the refreshed positions can be located at the position corresponding to the upper limit value of the movement distance of the real hand in the forward direction, 2 times of the refreshed positions can be located at the position corresponding to 1.25 times the distance of the upper limit value of the movement distance of the real hand in the forward direction, and the remaining 2 times of the refreshed positions can be located at the position corresponding to 1.5 times the distance of the upper limit value of the movement distance of the real hand in the forward direction. In this way, the left upper limb of the target user can be gradually rehabilitated, and the target user can directly observe his progress in the upper limb rehabilitation training process.

[0234] The way of refreshing the position 6 times in the corresponding area in the upward direction and 6 times in the corresponding area in the right direction can refer to the process of refreshing the position 6 times in the corresponding area in the forward direction in the above example, which will not be described here.

[0235] Although the position of the target object in the virtual training scene is shown to be refreshed 18 times for illustrative purposes, the number of refreshes can be increased or decreased according to actual needs, which is within the protection scope of the embodiments of the present application.

[0236] In one possible implementation, the system further comprises:

[0237] The scoring module 180 is connected with the data processing module 130, and is used to record the training score of the target user in the upper limb rehabilitation training process.

[0238] The data processing module 130 is configured to:

[0239] obtain the training score;

[0240] determine the target adjustment parameter according to the training score;

[0241] determine the target distance and the second movement data according to the target adjustment parameter and the upper limit value of the movement distance.

[0242] The embodiment of the present application can obtain the training score of the target user for the rehabilitation training process after each training is completed by configuring the scoring module 180, and determine the target adjustment parameter according to the training score to adjust the target distance and the second motion data.

[0243] In some embodiments, different training scores can be pre-configured with different adjustment parameters.

[0244] As described above, the target distance can be understood as the deviation triggering distance corresponding to the virtual hand. In some embodiments, the higher the training score is, the smaller the corresponding target adjustment parameter value is.

[0245] For example, the initial adjustment parameter set before training is 0.8, and the target distance is the upper limit value of the motion distance multiplied by 0.8.

[0246] For another example, the corresponding training score after training is 50 points, the corresponding target adjustment parameter is 0.5, and the target distance is the upper limit value of the motion distance multiplied by 0.5.

[0247] For another example, the corresponding training score after training is 90 points, the corresponding target adjustment parameter is 0.1, and the target distance is the upper limit value of the motion distance multiplied by 0.1.

[0248] For another example, the corresponding training score after training is 100 points, the corresponding target adjustment parameter is 0, and the target distance is equal to the upper limit value of the motion distance.

[0249] After the target distance changes, the data processing module 130 can re-determine the second motion data based on the manner in the foregoing embodiments.

[0250] In some embodiments, the data processing module 130 can also be configured to:

[0251] obtain the training score;

[0252] determine the distance adjustment parameter according to the training score;

[0253] adjust the distance between the real object and the virtual object according to the distance adjustment parameter.

[0254] In some embodiments, the higher the training score is, the smaller the distance adjustment parameter is.

[0255] Still taking the example of the virtual hand and the virtual ball, the distance adjustment parameter is 0.5, and the target distance is the upper limit value of the motion distance multiplied by 0.5. Figure 2For example, if the initial distance between the real object and the virtual object is (P3-P2), the corresponding training score after training is 10, and the corresponding distance adjustment parameter is 2, the distance between the real object and the virtual object is 2*(P3-P2); the corresponding training score after training is 50, and the distance between the real object and the virtual object is 1*(P3-P2), that is, the initial distance; the corresponding training score after training is 100, and the distance between the real object and the virtual object is 0*(P3-P2), that is, P2 and P3 are located at the same position.

[0256] Please refer to Figure 4 , Figure 4 The virtual reality-based upper limb rehabilitation training method can be applied to the virtual reality-based upper limb rehabilitation training system 100 or the electronic device in the embodiments of the present application, and the electronic device includes a personal computer, a server, a mobile device, a cloud computing platform, a supercomputer, and the like.

[0257] As shown in the virtual reality-based upper limb rehabilitation training method Figure 4 specifically includes the following steps:

[0258] Step 410, acquiring first motion data and an upper limit value of a motion distance of a real hand in a target direction, the first motion data being data of the real hand in a process of acquiring a target object, and the real hand being a hand of a target user for upper limb rehabilitation training.

[0259] Step 420, determining a first position of the real hand and a motion distance corresponding to the first position according to the first motion data.

[0260] Step 430, determining second motion data of a virtual hand according to the motion distance corresponding to the first position and the upper limit value of the motion distance, the virtual hand being used to guide the real hand to move in a virtual training scene.

[0261] Step 440, controlling the virtual hand to move to a position where the target object is located according to the second motion data.

[0262] The virtual reality-based upper limb rehabilitation training method provided in the embodiments of the present application can realize each process realized by the system embodiments in the Figure 1 , and achieve similar or identical technical effects. To avoid repetition, details are not described here.

[0263] Figure 5 A hardware structure schematic diagram of an electronic device provided in the embodiments of the present application is shown.

[0264] The electronic device can include a processor 501 and a memory 502 having computer program instructions stored therein.

[0265] In particular, the processor 501 can include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that embody the embodiments of the present application.

[0266] The memory 502 can include mass storage for data or instructions. By way of example, and not limitation, the memory 502 can include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc (e.g., a CD or DVD), a tape drive, a USB drive, or a combination of two or more of these. The memory 502 can be removable and / or non-removable (or fixed) as appropriate. The memory 502 can be internal or external as appropriate. In certain embodiments, the memory 502 is non-volatile solid-state memory.

[0267] In some embodiments, the memory 502 can include read-only memory (ROM), random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software that, when executed (e.g., by one or more processors), is operable to perform operations described with reference to the methods provided according to embodiments of the present application.

[0268] The processor 501 implements the methods provided by the above-described embodiments by reading and executing computer program instructions stored in the memory 502.

[0269] In one example, the electronic device can further include a communication interface 503 and a bus 510. The processor 501, the memory 502, and the communication interface 503 are connected through the bus 510 and complete communication therebetween.

[0270] The communication interface 503 is mainly used to realize the communication between the modules, devices, units, and / or equipment in the embodiments of the present application.

[0271] Bus 510 includes hardware, software, or both, to couple components of electronic device to each other and to couple components of electronic device to other elements. While Figure 1 shows bus 510 as a single component, bus 510 can be composed of multiple buses or separate communication lines, which are viewed collectively for the sake of convenience. Bus 510 can include an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front-side bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (ISA) bus, an InfiniBand (IB) interconnect, a low pin count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standards board (VLB) bus, or another suitable bus or a combination of two or more of these busses. Where appropriate, bus 510 can include one or more buses. Although this application describes and shows a particular bus, this application contemplates any suitable bus or interconnect.

[0272] In addition, the embodiments of the present application can provide a computer readable storage medium to implement, in combination with the method provided in the above embodiments. The computer readable storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement any one of the methods in the above embodiments.

[0273] In addition, the embodiments of the present application can provide a computer program product to implement, in combination with the method provided in the above embodiments. The program product is stored in a storage medium, and the program product is executed by at least one processor to implement each process of the embodiments of the method provided in the above embodiments, and can achieve similar or the same technical effects. To avoid repetition, details are not described here.

[0274] It needs to be clear that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the present application.

[0275] The functions noted in the description of the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, functional cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0276] It is also important to note that the examples mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the steps mentioned above, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0277] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing device to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0278] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A virtual reality-based upper limb rehabilitation training system, characterized in that, The system comprises: a virtual reality display module, configured to display a virtual training scene to a target user for upper limb rehabilitation training; a first data acquisition module, configured to acquire first motion data, the first motion data being data of a real hand in a process of acquiring a target object, the real hand being a hand of the target user, and the target object being a virtual object in the virtual training scene for guiding the real hand to acquire; a data processing module, connected with the virtual reality display module and the first data acquisition module, and configured to: acquire the first motion data and an upper limit value of a motion distance of the real hand in a target direction; determine a first position of the real hand and a motion distance corresponding to the first position according to the first motion data; determine whether the motion distance corresponding to the first position is equal to a target distance according to the motion distance corresponding to the first position and the upper limit value of the motion distance, the target distance being determined by the upper limit value of the motion distance; in a case where the motion distance corresponding to the first position is equal to the target distance, determine second motion data of a virtual hand used for guiding the real hand to move in the virtual training scene; control the virtual hand to move to a position where the target object is located according to the second motion data.

2. The system of claim 1, wherein, The first data acquisition module comprises: a first data acquisition submodule, connected with the data processing module, and configured to acquire a motion image of the real hand in the process of acquiring the target object; a second data acquisition submodule, connected with the data processing module, and configured to acquire a first initial position of the real hand in the process of acquiring the target object, the first motion data comprising the motion image and the first initial position. The data processing module is further configured to: acquire the first initial position and the motion image; determine a second initial position according to the motion image; determine the first position according to the first initial position and the second initial position.

3. The system of claim 1, wherein, The system further comprises: a second data acquisition module, connected with the data processing module, and configured to acquire physiological characteristic information of the real hand in the process of acquiring the target object; The data processing module is further configured to: determine a speed correction parameter according to the physiological characteristic information; in a case where the motion distance corresponding to the first position is equal to the target distance, determine an average motion speed of the virtual hand according to the motion distance corresponding to the first position and a motion time consumption corresponding thereto; determine a target motion speed according to the speed correction parameter and the average motion speed, the second motion data comprising the target motion speed.

4. The system of claim 3, wherein, The second data acquisition module comprises a plurality of electromyography sensors, the plurality of electromyography sensors being evenly attached to surfaces of upper limbs corresponding to the real hand, and the physiological characteristic information comprising electromyography signals detected by the plurality of electromyography sensors.

5. The system of claim 4, wherein, The system further comprises a cooperative training module, connected with the data processing module, the cooperative training module comprising: A gravity compensation submodule is configured to provide a target assisting force for an upper limb of the target user during the upper limb rehabilitation training of the target user; The data processing module is configured to: determine a target muscle strength level corresponding to the electromyography signal, different muscle strength levels being preconfigured with corresponding resistance intervals; determine the target assisting force according to the electromyography signal, so that an equivalent training resistance that needs to be overcome by the upper limb of the target user is located in the resistance interval corresponding to the target muscle strength level; and send the target assisting force to the gravity compensation submodule.

6. The system of claim 1, wherein, The system further comprises: a safety detection module connected to the data processing module, configured to detect a motion angle of a target joint, the target joint being a joint of an upper limb on which the real hand is located, the target joint including at least one of a shoulder joint, an elbow joint, and a wrist joint; a pre-warning module connected to the data processing module, configured to pre-warn in the case where the data processing module issues a pre-warning instruction; The data processing module is further configured to: obtain the motion angle of the target joint and a joint identifier of the target joint; determine a target angle threshold corresponding to the joint identifier from a plurality of preconfigured angle thresholds; generate the pre-warning instruction in the case where the motion angle of the target joint is greater than or equal to the target angle threshold.

7. The system of claim 6, wherein, The safety detection module comprises a plurality of inertial sensors, which are uniformly attached to the surface of the target joint.

8. The system of claim 1, wherein, The data processing module is further configured to: in the case where the virtual hand is controlled to move to the position of the target object according to the second motion data, control the target object to change color to indicate that the target object is obtained by the target user.

9. The system of claim 1, wherein, The system further comprises: a scoring module connected to the data processing module, configured to record a training score of the target user in the upper limb rehabilitation training; The data processing module is configured to: obtain the training score; determine a target adjustment parameter according to the training score; determine the target distance and the second motion data according to the target adjustment parameter and the upper limit of the motion distance.

10. A virtual reality-based upper limb rehabilitation training method, characterized in that, The method comprises: obtaining first motion data and an upper limit of a motion distance of a real hand in a target direction, the first motion data being data of the real hand in the process of obtaining a target object, the real hand being a hand of a target user who is undergoing upper limb rehabilitation training; determining a first position of the real hand and a motion distance corresponding to the first position according to the first motion data; determining second motion data of a virtual hand according to the motion distance corresponding to the first position and the upper limit of the motion distance, the virtual hand being used to guide the real hand to move in the virtual training scene; controlling the virtual hand to move to a position of the target object according to the second motion data.

Images