A method and system for motion vision training based on a naked-eye 3D display

Abstract

The application relates to the technical field of sports training methods, and discloses a sports visual training method and system based on a naked-eye 3D display, which comprises the following steps: determining a training item according to a training target; reading preset scene construction parameters, depth-of-field control parameters and training action types in the training item; determining a target depth-of-field presentation mode according to the depth-of-field control parameters; matching corresponding peripheral unit according to the training action types, wherein the peripheral unit comprises a feedback collection unit and / or an auxiliary stimulation unit; and calling a generation algorithm to generate a corresponding training scene based on the scene construction parameters and the target depth-of-field presentation mode, dynamically adjusting the training difficulty through an adaptive algorithm, and realizing progressive training intensity improvement. The method can make the trainer comprehensively train through an ordinary naked-eye 3D display device, can comprehensively stimulate various functional modules of a visual system, and can greatly improve the overall training effect through organic combination of various training modules.

Description

Technical Field

[0001] This invention relates to the field of sports training methods, specifically to a sports vision training method and system based on a naked-eye 3D display. Background Technology

[0002] The term "Sport Vision" emerged in the United States in the 1960s and refers to an interdisciplinary field that applies the concept of optometric vision training to the field of sports. Key visual skills involved in vision training include eye movement skills, accommodation skills, binocular vision, visual cognition, visual-spatial abilities, visual analysis skills, visual-motor integration, and visual-auditory integration.

[0003] In the field of competitive sports, sports vision can provide valuable assistance to athletes in terms of movement accuracy, visual-spatial perception, hand-eye coordination, peripheral vision awareness, visual-spatial memory, visual prediction ability, and sensory integration ability.

[0004] However, the current market for training equipment and solutions for motion vision is relatively scarce. Existing technologies suffer from the following shortcomings: traditional training equipment is often simplistic, relying heavily on devices such as stroboscopic vision training glasses, resulting in a tedious and uncomfortable training process and poor trainee compliance; simultaneously, training methods lack a systematic grading system and quantitative indicators, making it difficult to evaluate effectiveness; moreover, most are 2D displays, disconnected from real 3D motion scenes, making it difficult to transfer training effects to actual sports; furthermore, dedicated equipment is expensive, hindering its widespread adoption in home settings; and existing systems lack multimodal perception and interaction, making it difficult to achieve integrated training of vision, motion, and balance. Therefore, how to construct a scientific and systematic motion vision training solution has become a pressing technical problem to be solved in this field. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that the existing technology lacks a systematic hierarchical system and quantitative indicators, making it difficult to evaluate the effects and disconnected from real three-dimensional motion scenarios, and making it difficult to transfer the training effects to actual motion.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a motion vision training method based on a naked-eye 3D display, comprising: determining training items according to training objectives; reading preset scene construction parameters, depth control parameters, and training action types in the training items; determining a target depth-of-field presentation mode based on the depth control parameters; matching corresponding peripheral units according to the training action types, the peripheral units including a feedback acquisition unit and / or an auxiliary stimulation unit; calling a generation algorithm to generate a corresponding training scene based on the scene construction parameters and the target depth-of-field presentation mode, and outputting the corresponding training scene to a naked-eye 3D display module for naked-eye 3D presentation; wherein, during the presentation of the corresponding training scene, the feedback signal generated by the trainee in response to the corresponding training scene is acquired through the matched feedback acquisition unit, and / or an auxiliary stimulation signal synchronized with the corresponding training scene is output through the matched auxiliary stimulation unit; obtaining the trainee's performance data based on the feedback signal; and determining the next training item or the next training state of the current training item through an adaptive algorithm based on the performance data, and updating the scene construction parameters and / or depth control parameters of subsequent training scenes.

[0008] In a preferred embodiment of the motion vision training method of the present invention, the generation algorithm includes an image calculation algorithm and a display algorithm; the image calculation algorithm generates an original training image based on the scene construction parameters; the display algorithm generates a corresponding training scene adapted to the naked-eye 3D display module based on the original training image and the target depth-of-field presentation mode; wherein the output result of the image calculation algorithm is used as the input condition of the display algorithm, so that the corresponding training scene retains the spatial relationship defined by the scene construction parameters and forms a depth-of-field relationship corresponding to the target depth-of-field presentation mode.

[0009] In a preferred embodiment of the motion vision training method of the present invention, the adaptive algorithm includes an inter-level switching strategy and an internal state switching strategy. The inter-level switching strategy is used to determine the next training item after the current training item meets the preset completion conditions. The internal state switching strategy is used to determine the next training state of the current training item when the current training item does not meet the preset completion conditions. The preset completion conditions are determined based on the performance data, so that the next training item or the next training state corresponds to the feedback result of the trainee in the current training item.

[0010] As a preferred embodiment of the motion vision training method of the present invention, the training program includes a first training class, which is dynamic visual acuity training; the training action type corresponding to the first training class includes at least one of target tracking action, target following action, and trigger response action; the target depth-of-field presentation mode corresponding to the first training class is a multi-depth-of-field dynamic presentation mode; when the training action type is target tracking action or target following action, the matching peripheral unit includes an eye tracking module; when the training action type is trigger response action, the matching peripheral unit includes a touch sensing module; in the first training class, a visual stimulus target in motion is presented through a naked-eye 3D display module, and the feedback signal generated by the trainee in response to the visual stimulus target is collected through the matching peripheral unit.

[0011] As a preferred embodiment of the motion vision training method of the present invention, the training program includes a second training class, which is visual perception training; the training action type corresponding to the second training class includes at least one of touch reproduction action, memory recall action, and visual recognition action; the target depth-of-field presentation mode corresponding to the second training class is a random depth-of-field presentation mode; when the training action type is touch reproduction action, the matching peripheral unit includes a touch perception module; when the training action type is memory recall action, the matching peripheral unit includes a touch perception module; when the training action type is visual recognition action, the matching peripheral unit includes an eye tracking module; in the second training class, a visual stimulus graphic with random depth-of-field relationship is presented through a naked-eye 3D display module, and the feedback signal generated by the trainee in response to the visual stimulus graphic is collected through the matching peripheral unit.

[0012] As a preferred embodiment of the motion vision training method of the present invention, the training program includes a third training class, which is eye movement training; the training action type corresponding to the third training class includes depth-of-field switching response action; the target depth-of-field presentation mode corresponding to the third training class is an alternating switching mode between inner depth-of-field and outer depth-of-field; the peripheral unit matched with the third training class includes an eye-tracking module; in the third training class, visual stimuli with different depth-of-field attributes are presented through a naked-eye 3D display module, and the depth-of-field attributes of the visual stimuli are controlled to switch between inner depth-of-field and outer depth-of-field, and the eye-tracking module collects the trainee's eye movement feedback signals during the depth-of-field switching process.

[0013] As a preferred embodiment of the motion vision training method of the present invention, the training program includes a fourth training category, which is vision-motion training; the training action type corresponding to the fourth training category includes at least one of planar precision response action, external depth space reach action, and pure vision tracking action; the target depth of field presentation mode corresponding to the fourth training category is a random position dynamic presentation mode; when the training action type is planar precision response action, the matching peripheral unit includes a touch sensing module; when the training action type is external depth space reach action, the matching peripheral unit includes a three-dimensional vision sensing module; when the training action type is pure vision tracking action, the matching peripheral unit includes an eye tracking module; in the fourth training category, a visual stimulus target is presented at a random position and / or at a random time through a naked-eye 3D display module, and the feedback signal generated by the trainee in response to the visual stimulus target is collected through the matching peripheral unit.

[0014] As a preferred embodiment of the motion vision training method of the present invention, the training program includes a fifth training class, which is a fusion function training; the training action type corresponding to the fifth training class includes a planar and depth-of-field switching gaze action; the target depth-of-field presentation mode corresponding to the fifth training class is a switching mode between a planar position and a preset depth-of-field position; the peripheral unit matched with the fifth training class includes an eye-tracking module; in the fifth training class, a planar display object and a depth-of-field display object are presented through a naked-eye 3D display module, and the depth-of-field display object is controlled to switch between a planar position and a preset depth-of-field position, and the eye-tracking module collects the eye movement feedback signal of the trainee during the gaze switching process.

[0015] As a preferred embodiment of the motion vision training method of the present invention, wherein: the training items include a sixth training category, the sixth training category being sensory integration training; the training action type corresponding to the sixth training category includes at least one of postural balance action, spatial motion feedback action, and visual-vestibular coordination action; the target depth-of-field presentation mode corresponding to the sixth training category is a dynamic depth-of-field presentation mode that coordinates with proprioceptive feedback and / or vestibular feedback; when the training action type is a postural balance action, the matching peripheral unit includes a balance posture acquisition module; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a three-dimensional visual sensing module; when the training action type is a postural balance action, the matching peripheral unit includes a three-dimensional visual sensing module; when the training action type is a spatial motion feedback ... spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action, the matching peripheral unit includes a spatial motion feedback action; when the training action type is a spatial motion feedback action; when the When the training type is visual-vestibular coordination, the matched peripheral unit includes a balance posture acquisition module and / or a three-dimensional visual sensing module, and synchronously calls a strobe stimulation module, which switches between transparent and light-blocking states at a preset frequency. In the sixth training type, a dynamic depth-of-field training scene is presented through a naked-eye 3D display module, and the feedback signals of the trainee in visual tasks, proprioceptive tasks, and / or vestibular coordination tasks are collected through the matched peripheral unit. When the strobe stimulation module is called, the dynamic depth-of-field training scene is presented under intermittent visual input conditions, enabling the trainee to complete posture balance, spatial motion feedback, and / or visual-vestibular coordination training under limited visual stimulation.

[0016] The beneficial effects of this invention are as follows: This motion vision training method determines the target depth-of-field presentation mode based on the training category, matches corresponding peripheral units based on the training action type, and constructs a dynamic naked-eye 3D training scene by combining image calculation algorithms, display algorithms, and feedback acquisition links. This allows the spatial position relationship, front-back depth relationship, continuous motion relationship, and action feedback relationship to be preserved simultaneously during the training process, thereby improving the consistency between the training scene and the real three-dimensional motion task, enhancing the ability to transfer training results to the actual motion scene, and solving the problems of disconnection from the real three-dimensional motion scene and difficulty in transferring training effects.

[0017] Meanwhile, by using adaptive algorithms and combining inter-level switching strategies with internal state switching strategies, the training difficulty, training pace, and training state are dynamically adjusted, enabling the training process to have the ability to be progressively graded, repeatedly reinforced, and adapted in real time. It can also generate quantitative evaluation results based on parameters such as response time, hit results, trajectory following deviation, depth-of-field switching accuracy, and spatial positioning error, thereby solving the technical problems of existing training methods lacking a systematic graded system and quantitative indicators, and making it difficult to evaluate training effects.

[0018] Another objective of this invention is to provide a motion vision training system, which constructs a simulation training system through a three-dimensional vision sensing module, a naked-eye 3D display module, a touch perception module, and a strobe stimulation module. This system aims to solve the technical problems of traditional training systems, such as expensive and limited training equipment, discomfort when worn, and disconnection from real three-dimensional motion scenarios.

[0019] As a preferred embodiment of the motion vision training system of the present invention, the motion vision training method includes: a naked-eye 3D display module for presenting the corresponding training scene in naked-eye 3D mode; a touch sensing module integrated with the naked-eye 3D display module for receiving touch operations from the trainee and generating touch signals; an eye tracking module for acquiring the trainee's gaze point position and eye movement trajectory in real time; a three-dimensional vision sensing module for collecting the trainee's motion signals in three-dimensional space; a balance posture acquisition module for collecting the trainee's posture balance signals during training; a stroboscopic stimulation module for switching between transparent and light-blocking states at a preset frequency during training to enhance the trainee's reaction training and spatial perception ability; and a processing unit connected to the naked-eye 3D display module, the touch sensing module, the eye tracking module, the three-dimensional vision sensing module, and the... The balance posture acquisition module and the stroboscopic stimulation module are connected; wherein, the processing unit is used to determine training items according to the training objective, read the preset scene construction parameters, depth control parameters and training action types in the training items, determine the target depth rendering mode according to the depth control parameters, call the touch perception module, the human eye tracking module, the three-dimensional vision sensing module, the balance posture acquisition module and / or the stroboscopic stimulation module according to the training action type, and call the generation algorithm to generate the corresponding training scene based on the scene construction parameters and the target depth rendering mode; the processing unit is also used to obtain the trainee's performance data according to the feedback signals collected by the called module, and determine the next training item or the next training state of the current training item according to the performance data through an adaptive algorithm, and update the scene construction parameters and / or depth control parameters of the subsequent training scene.

[0020] The beneficial effects of the present invention are as follows: The present invention constructs a multimodal motion vision training platform integrating three-dimensional display, motion perception, posture acquisition and auxiliary stimulation by synergistic configuration of naked-eye 3D display module, touch perception module, human eye tracking module, three-dimensional vision sensing module, balance posture acquisition module, stroboscopic stimulation module and processing unit, thereby improving the problems of expensive and single equipment, discomfort when wearing, and boring training process in the existing training system.

[0021] This provides a hardware foundation for the above-mentioned motion vision training methods, enabling collaborative training of multi-channel information such as vision, movement, balance, and vestibular information. This solves the technical problems of being disconnected from real three-dimensional motion scenes and the difficulty in achieving comprehensive training, thereby improving the comprehensiveness, adaptability, and application value of the training. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This invention provides a training process diagram for a motion vision training system based on a naked-eye 3D display.

[0024] Figure 2 This invention provides a time training diagram for a motion vision training system based on a naked-eye 3D display.

[0025] Figure 3 This invention provides a rhythm training diagram for a motion vision training system based on a naked-eye 3D display.

[0026] Figure 4 This invention provides a rapid visual training diagram for a motion visual training system based on a naked-eye 3D display.

[0027] Figure 5 This invention provides a visual overlap training diagram for a motion vision training system based on a naked-eye 3D display.

[0028] Figure 6 This invention provides a memory copying training image for a motion vision training system based on a naked-eye 3D display.

[0029] Figure 7 This invention provides a sequential memory training diagram for a motion vision training system based on a naked-eye 3D display.

[0030] Figure 8 This invention provides a hand speed training diagram for a motion vision training system based on a naked-eye 3D display.

[0031] Figure 9 This invention provides a spatial memory training diagram for a motion vision training system based on a naked-eye 3D display.

[0032] Figure 10This invention provides a dynamic rotation training image for a motion vision training system based on a naked-eye 3D display, wherein... Figure 10 (a) is the first dynamic rotation training image. Figure 10 (b) is the second dynamic rotation training image. Figure 10 (c) is the third dynamic rotation training diagram.

[0033] Figure 11 This invention provides a central training diagram for a motion vision training system based on a naked-eye 3D display.

[0034] Figure 12 This invention provides a path training diagram for a motion vision training system based on a naked-eye 3D display.

[0035] Figure 13 This invention provides a peripheral vision training map for a motion vision training system based on a naked-eye 3D display.

[0036] Figure 14 This invention provides a peripheral attention training map for a motion vision training system based on a naked-eye 3D display.

[0037] Figure 15 This invention provides a static vestibular training diagram for a motion vision training system based on a naked-eye 3D display.

[0038] Figure 16 This invention provides a directional training diagram for a motion vision training system based on a naked-eye 3D display.

[0039] Figure 17 This invention provides a fusion training diagram for a motion vision training system based on a naked-eye 3D display.

[0040] Figure 18 This invention provides a spatial tactile external training diagram for a motion vision training system based on a naked-eye 3D display.

[0041] Figure 19 This invention provides a spatial tactile training diagram for a motion vision training system based on a naked-eye 3D display.

[0042] Figure 20 This invention provides a convergence and divergence training diagram for a motion vision training system based on a naked-eye 3D display.

[0043] Figure 21 This invention provides a dynamic visual acuity training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 21 (a) is the first dynamic visual acuity training image. Figure 21 (b) is the second dynamic visual acuity training image. Figure 21 (c) is the third dynamic visual acuity training diagram.

[0044] Figure 22 This invention provides a rapid vocabulary training diagram for a motion vision training system based on a naked-eye 3D display.

[0045] Figure 23 The present invention provides a circular space training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 23 (a) is the training image for the first circle space. Figure 23 (b) is the training image for the second circle space. Figure 23 (c) is the training diagram of the third circle space.

[0046] Figure 24 This invention provides a fast follow-up training image for a motion vision training system based on a naked-eye 3D display, wherein... Figure 24 (a) is the first fast-following training image. Figure 24 (b) is the second fast-following training image. Figure 24 (c) is the third fast follow training graph.

[0047] Figure 25 The present invention provides a training diagram of strobe glasses for a motion vision training system based on a naked-eye 3D display.

[0048] Figure 26 This invention provides a tracking training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 26 (a) is the first tracking training image. Figure 26 (b) is the second tracking training graph.

[0049] Figure 27 This invention provides a rotating training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 27 (a) is the first rotation training image. Figure 27 (b) is the second rotation training diagram.

[0050] Figure 28 This invention provides a spatial attention training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 28 (a) is a training diagram for first-space attention. Figure 28 (b) is a training diagram for second-space attention.

[0051] Figure 29 This invention provides a spatial positioning training diagram for a motion vision training system based on a naked-eye 3D display, wherein... Figure 29 (a) is the first-space localization training image. Figure 29(b) is the training diagram for second-space positioning.

[0052] Figure 30 This invention provides a motion memory training map for a motion vision training system based on a naked-eye 3D display, wherein... Figure 30 (a) is the first motor memory training diagram. Figure 30 (b) is the second motor memory training diagram. Figure 30 (c) is the third motor memory training diagram. Figure 30 (d) is the fourth motor memory training diagram. Figure 30 (e) is the fifth motor memory training diagram.

[0053] Figure 31 This invention provides a point distance training diagram for a motion vision training system based on a naked-eye 3D display.

[0054] Figure 32 This invention provides a blind vision training diagram for a motion vision training system based on a naked-eye 3D display.

[0055] Figure 33 This invention provides a depth vision training diagram for a motion vision training system based on a naked-eye 3D display.

[0056] Figure 34 The present invention provides a hardware structure topology diagram of a motion vision training system based on a naked-eye 3D display. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0058] Example 1, referring to Figure 1 In one embodiment of the present invention, a motion vision training method based on a naked-eye 3D display is provided, including using a naked-eye 3D display module 100 to present a three-dimensional vision training scene. The naked-eye 3D display module 100 is equipped with peripheral units for capturing the trainee's feedback in real time.

[0059] According to the training objective, training project 800 is displayed on the naked-eye 3D display module 100. Training project 800 is used to carry training content corresponding to the training objective. The training content includes preset scene construction parameters, depth control parameters, and training action types. Scene construction parameters include necessary material content such as images, letters, and motion rules for generating original training images. Depth control parameters are necessary content for constructing the target depth presentation mode, such as depth position, depth change mode, depth random rules, time control rules, and object correspondence. Training action types include the training actions to be performed by this training project 800, as well as the identification codes, interfaces, and markings of the external peripheral units required by this training project 800.

[0060] Then, the scene construction parameters preset in training project 800 are converted into original training images through image calculation algorithms, and then into target depth-of-field presentation modes through display algorithms. These are then combined with peripheral units and their SDKs and embedded into the same dynamic 3D training environment. They are combined according to predetermined data transmission relationships and execution order, so that the training scene retains the spatial position relationships, front-back depth relationships, continuous motion relationships, and action feedback relationships in real 3D motion tasks, thus providing a technical foundation for transferring training results to actual motion tasks.

[0061] The feedback acquisition unit in the peripheral unit includes at least one of the following: touch sensing module 200, human eye tracking module 300, three-dimensional vision sensing module 400, and balance posture acquisition module 500; the auxiliary stimulation unit includes a strobe stimulation module 600.

[0062] In existing visual training methods, most only retain planar recognition relationships, static depth relationships, or single action response relationships, making it difficult to simultaneously establish the coupling relationships between spatial localization, depth-of-field switching, continuous target motion, and trainee action feedback during the same training process. Due to the lack of synchronous existence of the above key constraints, the perceptual results formed by the trainee during training are inconsistent with the perceptual-action relationships in actual motion tasks, resulting in insufficient coupling between the training scene and the real 3D motion scene. Based on this, this embodiment embeds the image calculation algorithm, display algorithm, and peripheral unit SDK into the same training chain in the order of original image generation, dynamic 3D display construction, and action feedback closed-loop access, so that the output results of the previous stage become the input conditions of the next stage, and the continuity of key constraints is maintained throughout the entire execution process.

[0063] In this embodiment, the image calculation algorithm is first called to generate the original training image, the training task is determined as the corresponding training item 800, and the scene construction parameters in the training item 800 are further read.

[0064] Specifically, the processing unit 700, based on the training task, invokes an image computing algorithm to determine at least one training target object or at least one reference object, and further determines the initial coordinates, quantity parameters, size parameters, distribution area, boundary range, and positional relationship relative to the reference object for each training target object in the display interface. The reference object can be the screen center point, central gaze point, central ring, central target box, path start point, or other display objects used to form a stable spatial reference; the training target object can be a point, ring, letter, sphere, graphic block, path node, or a combination of the above objects. For training tasks requiring a center-periphery positioning relationship, the image computing algorithm can distribute multiple training target objects around the reference object; for training tasks requiring edge tracking or path movement relationships, the image computing algorithm can pre-establish trajectory lines, circular lines, spiral lines, or multiple node paths; for training tasks requiring random recognition or spatial memory relationships, the image computing algorithm can generate randomly distributed target objects according to preset rules and record their positional order or distribution pattern. After the original training image is generated, the processing unit 700 simultaneously outputs basic spatial parameters, which include at least the coordinates of the reference object, the coordinates of the training target object, the target distribution area, the target boundary range, and the relative positional relationship parameters between the objects.

[0065] Image computing algorithms generate original training images containing target objects, reference objects, and relative spatial relationships in advance. This enables the entire training chain to have a unified spatial coordinate basis under a single training device, thus providing a consistent object basis for subsequent display algorithms to construct depth of field, overlay trajectory, and update motion state, and further establishing the basic training scene.

[0066] After the image computation algorithm outputs the original training image and basic spatial parameters, the processing unit 700 further calls the display algorithm to construct depth and adapt the display of the original training image. By adding depth parameters or parallax parameters to the original training image, the target object forms an adjustable depth state; on the other hand, trajectory changes and time constraints are further introduced, so that the target object changes depth while its spatial position changes, thereby forming a dynamic naked-eye 3D training scene. The display algorithm includes a 3D synthesis algorithm and a local parallax algorithm. The 3D synthesis algorithm is used to complete the basic three-dimensional imaging of the training scene or visual stimulus object; the local parallax algorithm is used to perform differentiated depth adjustment for specific objects, specific regions, or specific stages based on the basic three-dimensional imaging. The two can be used alone or in combination; when the training scene only needs overall 3D presentation, the 3D synthesis algorithm can be used; when the training scene needs to form different depth levels, depth inversion, dynamic depth changes, or multi-target differentiated depth of field between local objects, the local parallax algorithm is used further.

[0067] Specifically, after reading the original training image and basic spatial parameters output by the image calculation algorithm, the processing unit 700 calls the display algorithm to configure depth parameters or disparity parameters for the training target object and / or reference object. Preferably, the reference object can be maintained at a zero position or a fixed depth position, and the training target object can be set to an inner depth, outer depth, or multi-layer distributed depth; alternatively, the training target object can gradually transition from a zero position to the target depth, or a switch between inner and outer depth can occur during training. For training tasks requiring dynamic depth changes, the display algorithm can also continuously update the disparity value of the training target object according to the training stage, time progression, object position changes, or state switching rules. Furthermore, the display algorithm can also superimpose continuous motion trajectories and temporal rules on the original training image. The continuous motion trajectory can be a straight line trajectory, a loop trajectory, a spiral trajectory, a semi-circular trajectory, or a multi-node sequential trajectory, and the temporal rules can be the target appearance time, the target disappearance time, the direction switching time, the depth switching time, the prompt sound output time, and the effective response time window. Preferably, the display algorithm synchronizes depth changes with trajectory changes; that is, as the position of the training target object changes, its depth parameters or parallax parameters are updated accordingly, so that the target object simultaneously exhibits depth level changes during movement. Then, the processing unit 700 calls the SDK of the glasses-free 3D display module 100 to adapt the training scene data containing depth parameters, trajectory parameters, and temporal parameters into corresponding glasses-free 3D display content, and outputs it to the glasses-free 3D display module 100 for display.

[0068] Furthermore, by combining image computing and display algorithms, depth parameters or parallax parameters are introduced on the basis of the positional relationships already established in the original training images, so that the training scene is upgraded from reflecting only two-dimensional distribution relationships to a naked-eye 3D training scene that simultaneously has positional relationships and front-back hierarchical relationships.

[0069] After the display algorithm outputs a dynamic naked-eye 3D training scene, the processing unit 700 further calls the corresponding peripheral unit according to the type of training action, and collects the trainee's feedback signals through the SDK of each peripheral unit. Since in actual motion tasks, trainees not only need to observe dynamic targets, but also need to make action responses such as eye tracking, touch clicking, spatial reach, posture control, or center of gravity adjustment based on observation, it is necessary to embed the peripheral unit and its SDK into the aforementioned dynamic naked-eye 3D training scene to ensure that the training results have a technical basis for transfer to actual motion tasks, thus forming a closed-loop correspondence between dynamic three-dimensional visual stimuli and actual action responses.

[0070] refer to Figure 1Specifically, when the training task requires the trainee to perform gaze tracking actions, the processing unit 700 calls the eye tracking module 300 and uses its SDK to collect gaze point position, eye movement direction, and eye movement trajectory information. This information is then mapped to the display trajectory, target area, or depth-of-field switching node of the training target object to determine whether the trainee is effectively following the dynamic target. When the training task requires the trainee to perform precise planar touch actions or precise depth-of-field response actions, the processing unit 700 calls the touch sensing module 200 and uses its SDK to collect touch coordinates. These touch coordinates are then mapped to the effective area of ​​the training target object on the display interface to determine whether the training... The processing unit 700 determines whether the trainee hits the target within a predetermined time window. When the training task requires the trainee to perform an out-of-space reach action, the processing unit 700 calls the 3D vision sensing module 400 and collects the spatial position information of the fingers or limbs through its SDK. This spatial position information is then matched with the corresponding convex spatial range of the training target object to determine whether an effective spatial reach has been achieved. When the training task requires the trainee to control the training target object through posture adjustment or center of gravity shift, the processing unit 700 calls the balance posture acquisition module 500 and collects the center of gravity change or posture change through its SDK. These changes are then converted into displacement control values ​​of the training target object in the display interface. For training tasks requiring the use of stroboscopic glasses, the processing unit 700 can also call the stroboscopic stimulation module 600 and its SDK to synchronize it with the dynamic naked-eye 3D scene. Through these methods, the peripheral unit and its SDK integrate the trainee's actual action feedback into the dynamic naked-eye 3D training scene and output feedback parameters. These parameters include at least response time, hit result, trajectory following deviation, depth-of-field switching accuracy, spatial positioning error, and round completion markers.

[0071] All of the above devices use commercially available mature equipment and communicate with the processing unit 700 through corresponding interfaces built with SDK. The above algorithms are all built into purchased standard components / commercial mature software modules, or can be implemented using publicly available algorithms, and are not within the scope of improvement of this invention. Therefore, their internal principles and program logic will not be described in detail.

[0072] The peripheral units and their SDKs further form an online judgment and adjustment mechanism with the processing unit 700. The processing unit 700 judges the results of each training round based on the feedback parameters output by the SDKs of each peripheral unit; the result judgment includes at least one of response time judgment, hit area judgment, trajectory following judgment, sequence correctness judgment, and spatial overlap range judgment. When a training round meets the preset completion conditions, it is recorded as valid completion; when the training round does not meet the preset completion conditions, the processing unit 700 preferably adjusts the target size, target quantity, and object distribution parameters in the original training image, or adjusts the depth of field amplitude, target speed, trajectory complexity, cue rhythm, or effective response time window in the display algorithm to change the intensity of the training stimulus and continue the training task. In this way, the SDKs of the peripheral units are not only responsible for "collecting feedback," but also use the feedback results to influence the subsequent outputs of the image calculation algorithm and the display algorithm, enabling the training scene to perform closed-loop adaptive adjustment based on the trainee's action feedback.

[0073] This training scenario combines dynamic naked-eye 3D training scenes with SDK feedback access from various peripheral units to create a closed-loop coupling of visual stimulation and motion feedback. Compared to open training with only dynamic 3D display or single-sensory training, this training scenario requires trainees to process spatial position, depth level, and continuous motion information while also completing corresponding action responses. This comprehensively improves trainees' balance, body coordination, spatial perception, and visual information processing capabilities, ensuring that they can truly react faster, more accurately, and have a wider range of control after training.

[0074] In this embodiment, the image calculation algorithm, display algorithm, peripheral unit and its SDK are not executed independently, but form a continuous data transfer and constraint superposition relationship around the same training task.

[0075] First, the original training image and basic spatial parameters output by the image computation algorithm are used as input to the display algorithm. The display algorithm uses the target object coordinates, reference object coordinates, target distribution area, and relative positional relationships already established by the image computation algorithm, and further adds depth parameters, trajectory parameters, and temporal parameters without changing the basic spatial skeleton. The resulting technical effect is that the spatial constraints in the naked-eye 3D training scene remain continuous, and the display algorithm does not need to re-establish new spatial references when constructing depth relationships and dynamic trajectory relationships, thus ensuring the consistency of positional relationships in the training scene.

[0076] Secondly, the display algorithm outputs the training target state, depth state, trajectory state, and time window state, which serve as the scene basis for feedback judgment by the peripheral unit's SDK. The peripheral unit's SDK does not independently collect actions outside the display scene, but rather performs action mapping and feedback judgment based on the depth hierarchy, dynamic trajectory, and effective area already formed in the dynamic naked-eye 3D training scene.

[0077] The resulting objective technical effect is that the trainee's recognition of the training target object is no longer limited to the static depth state, but can be extended to target processing under dynamic depth changes and continuous motion conditions. Therefore, the depth constraints and motion constraints in the training scene are closer to real three-dimensional motion tasks.

[0078] Furthermore, the feedback results acquired by the SDK of the peripheral unit in turn affect the generation and display parameters of the training images. Preferably, when the response time, depth-of-field switching accuracy, trajectory following deviation, or spatial positioning error output by the SDK of the peripheral unit meet preset conditions, the processing unit 700 triggers the image calculation algorithm or display algorithm to continue execution at a higher target speed, a larger depth of field, a more complex trajectory, or a shorter time window; when the above feedback parameters do not meet the preset conditions, execution continues at a lower speed, a smaller depth of field, a simpler trajectory, or a longer time window. The resulting objective technical effect is that the execution conditions of the next training state are determined by the action feedback results of the previous training state, and the entire training chain can maintain a continuous and adaptive stimulus intensity according to the trainee's actual performance.

[0079] As can be seen from the above combination relationship, the innovation of this embodiment does not lie in the individual use of any existing technology among image computing algorithms, display algorithms, or peripheral unit SDKs, but rather in: incorporating the original training images generated by the image computing algorithm, the dynamic naked-eye 3D scene constructed by the display algorithm, and the motion feedback results collected by the peripheral unit SDK into the same training chain for sequential combination and closed-loop transmission, so that the spatial position relationship, front-back depth relationship, continuous motion relationship, and motion feedback relationship are gradually preserved in the training scene. In other words, this embodiment is not simply "display first, then provide feedback", but through the continuous coupling of image generation, 3D display, and motion feedback, the training environment is gradually transformed from a single visual stimulus into a composite constraint scene that is closer to a real 3D motion task.

[0080] In this embodiment, the processing unit 700 can select several training items 800 from a preset training item library according to the training objective, and execute the training task through the cooperation of image calculation algorithm, display algorithm, and the SDK of the peripheral unit. The processing unit 700 collects the trainee's response results and response time in each training item in real time, and calculates the response accuracy within a judgment period based on a preset number of consecutive responses. After the training state reaches the preset completion condition, a completion flag of the training state is output, and the start of the next training state is triggered; if the training state does not reach the preset completion condition, state adjustment or re-execution is performed within the training state. Preferably, the processing unit 700 can further execute the training item level switching strategy and the internal state switching strategy based on the above feedback results, so that the training process is completed automatically. In this way, the original training items no longer exist independently as isolated subjects, but are embedded in the combined training chain driven by the image calculation algorithm, display algorithm, and the SDK of the peripheral unit, respectively undertaking the functions of original image generation, dynamic 3D display, and feedback closed-loop access.

[0081] The 3D image synthesis algorithms used include a 3D synthesis algorithm and a local parallax algorithm. The 3D synthesis algorithm calculates the basic spatial mapping relationship between the left and right eye views based on scene spatial coordinates, target positions, and motion trajectory data, completing the overall reconstruction and fusion output of the two-dimensional image into a three-dimensional stereoscopic scene. The local parallax algorithm calculates independent parallax offsets for different training target partitions in the image based on a preset depth-of-field mode. Through differentiated parallax assignment, it achieves local hierarchical separation, dynamic depth-of-field adjustment, and differentiated stereoscopic presentation of multiple targets. These algorithms are all integrated into purchased standard components / commercial mature software modules, or can be implemented using publicly available algorithms, and are not within the scope of this invention. Therefore, their internal principles and program logic will not be elaborated upon.

[0082] Example 2, the second embodiment of the present invention, provides a method for motion vision training based on a naked-eye 3D display. This training method follows a progressive order from basic monocular / binocular visual functions to visual information perception and processing, then to autonomous eye movement control, visual and limb movement coordination, binocular fusion and spatial positioning, and multi-sensory system integration. It strictly corresponds to core physiological mechanisms such as the division of labor between macrocellular and microcellular systems, regulatory-convergent linkage, eye-brain-limb reflex pathways, and vestibular-visual integration. The training content is layered according to basic visual abilities, intermediate visual processing, and advanced motion vision, ensuring that the training design fully matches the development of human visual nerves and the acquisition path of motor skills. It is further divided into six functional areas: dynamic visual acuity training area, visual perception training area, eye movement ability training area, visual-motor training area, fusion function training area, and sensory integration training area. Based on the core characteristics of visual training in each area, several training items (800 in total) are constructed, forming a complete training system.

[0083] Furthermore, based on the aforementioned training program 800, a training plan is provided. This plan follows the overall design logic of balancing the training intensity of each group, training different visual regions while avoiding excessive load. The 32 training contents can be divided into 3 training chapters, with training twice a week and at least one day between training sessions. The training duration is 2 months, totaling 16 sessions.

[0084] Table 1. Three-Section Training Curriculum System

[0085] Specifically, the numbers in Table 1 represent the corresponding training subjects. For example, the number 3 corresponds to subject 3, which uses... Figure 4 The diagram illustrates a rapid visual training system based on a naked-eye 3D display for motion visual training.

[0086] The processing unit 700 collects the trainee's response results and response time in each training item 800 in real time, and calculates the response accuracy within a judgment period based on a preset number of consecutive responses. Then, it executes the inter-level switching strategy and the internal state switching strategy of training item 800 through an adaptive algorithm. The inter-level switching strategy of training item 800 is used to switch to the next training item 800 according to the item arrangement order in the preset training chapter after the current training item 800 meets the preset completion conditions. The internal state switching strategy is used to cyclically switch between multiple training states corresponding to the current training item 800 during the execution of the current training item 800, based on preset time, preset number of times, prompt signals, the completion result of the current target, or the current training state parameters, so as to repeatedly execute the current training item 800. When the current training item 800 completes a preset number of training rounds or reaches the preset completion conditions through the internal state switching strategy, the inter-level switching strategy of training item 800 is triggered, so that the training can continue and thus automatically complete the training process.

[0087] Example 3, referring to Figure 21 and 24 This is a third embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training program 800 includes the following types: First training type: Dynamic visual acuity training. This dynamic visual acuity training presents a moving visual stimulus target in naked-eye 3D, and the trainee tracks or follows the moving target, stimulating visual acuity and binocular coordination ability. Specifically, it includes, for example... Figure 21 Subject 20 shown: Training content for dynamic visual acuity and such Figure 24 Subject 23 shown: Training content for fast following. However, due to the special nature of naked-eye 3D images, they will appear blurry in screenshot mode.

[0088] Specifically, such as Figure 21 The training content for dynamic visual acuity shown involves the processing unit 700 retrieving preset audio prompts, the number of training letters, letter size parameters, rotation time, and direction switching rules before the training begins. The training letters are preferably black letters selected from the SLOAN visual acuity chart at scales of 0.63, 0.8, 1.0, and 1.2, with a size conforming to a 1.5-meter distance standard.

[0089] At the beginning, the processing unit 700 first generates a black dot at the center of the naked-eye 3D display module 100 using a 3D synthesis algorithm and assigns a corresponding parallax value to create an external depth effect. Then, multiple black letters for naked-eye 3D display are generated around the black dot, distributed starting from the right side of the naked-eye 3D display module 100, i.e., the positive x-axis direction. The content and display position of the letters are determined by a random algorithm, and the display size of each letter is calculated proportionally according to a preset scale, so that letters of different sizes are randomly mixed and displayed.

[0090] During training, the processing unit 700 uses trigonometric functions to calculate the positional changes of each letter in real time, causing the letters to rotate continuously around the black dot. Preferably, the letters first rotate clockwise for 20 seconds, then the offset length from the initial position to the original point after three rotations is calculated. When the offset length meets a preset value, the direction is switched to counterclockwise rotation for 20 seconds. This alternation between clockwise and counterclockwise rotation constitutes a cycle, which is executed three times. Simultaneously, the processing unit 700 uses a frame-time algorithm to control the playback rhythm of audio prompts, preferably issuing a prompt sound every 3 seconds, prompting the trainee to look at the current letter and say the recognition result aloud. This stimulates visual acuity and promotes binocular coordination, further improving stereoscopic vision and fine visual perception, ultimately integrating the visual system with auditory and speech functions.

[0091] like Figure 24 The training content shown is a fast-following exercise. Before training begins, the processing unit 700 pre-retrieves parameters such as a green background image, tennis ball graphic parameters, tennis ball display size, stride length, and movement direction rules. At the start of training, the processing unit 700 first draws a light green background and then generates a 3D yellow tennis ball image with a stereoscopic display effect using a 3D synthesis algorithm. The preferred size of the yellow tennis ball image is 2 cm.

[0092] During training, the processing unit 700 determines the starting and target coordinates of the tennis ball's movement by acquiring the screen width and height of the naked-eye 3D display module 100. Subsequently, the processing unit 700 controls the tennis ball to randomly move horizontally or vertically within the naked-eye 3D display module 100. Specifically, during horizontal movement, the Y-coordinate is randomly determined: when the tennis ball moves from left to right, the starting coordinate is (0, Y), and the target coordinate is (screen width, Y); when the tennis ball moves from right to left, the starting coordinate is (screen width, Y), and the target coordinate is (0, Y). During vertical movement, the X-coordinate is randomly determined: when the tennis ball moves from top to bottom, the starting coordinate is (X, screen height), and the target coordinate is (X, 0); when the tennis ball moves from bottom to top, the starting coordinate is (X, 0), and the target coordinate is (X, screen height). Meanwhile, the processing unit 700 calculates the straight path between the starting point and the target point based on the starting point coordinates and the target coordinates, and controls the tennis ball to move at a constant speed along the path in combination with the preset step size, so as to ensure that the movement process is smooth and reduces the shaking phenomenon, and the starting position of each movement remains randomly changing.

[0093] During training, participants need to quickly follow the trajectory of a tennis ball and calculate the corresponding time. The algorithm controls the direction of the tennis ball's movement, and 3D synthesis technology generates 3D tennis ball images in real time. These 3D images stimulate and improve stereoscopic vision and fine visual perception, as well as enhance eye movement and convergence abilities.

[0094] Example 4, refer to Figure 6 , 7 Numbers 9, 11, 12, 16, 22, 26, and 32 are the fourth embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training item 800 includes the following types: Second training type: visual perception training. The images in this visual perception training present 3D graphics with random positions and random order. The trainee completes the training through touch or memory recall, improving visual memory and spatial perception. Specifically, it includes... Figure 6 Subject 5 shown: Training content for memory copying, such as... Figure 7 Subject 6 shown: Training content for sequential memory, such as... Figure 9 The training content for subject 8, spatial memory, is shown below. Figure 11 Subject 10 shown: The training content of the center, such as... Figure 12 Subject 11 shown: Training content for paths, such as... Figure 16 Subject 15 shown: Training content in the direction, such as... Figure 22 Subject 21 shown: Training content for rapid vocabulary, such as... Figure 26 Subject 25 shown: Tracking training content, such as... Figure 32 Subject 31 shown: Training content for blindness.

[0095] Specifically, such as Figure 6 The training content for memory replication shown is as follows: The processing unit 700 calls up several randomly generated original images on the naked-eye 3D display module 100. The training graphics are preferably basic geometric shapes such as circles and squares, and the size parameters and display positions of each graphic are randomly set.

[0096] Subsequently, the processing unit 700 synthesizes the original image into a corresponding 3D image using a 3D synthesis algorithm and outputs it to the naked-eye 3D display module 100 for display. After observing the 3D graphic, the trainee redraws the observed graphic with their finger through the touch perception module 200. During this process, the processing unit 700 memorizes the coordinates of the trainee's touch path on the touch perception module 200 and forms the corresponding drawing graphic on the screen based on the touch path coordinates. After the trainee completes the drawing, the processing unit 700 controls the naked-eye 3D display module 100 to re-display the previous target graphic in 2D form, so that the trainee can compare the difference between the drawn graphic and the original graphic. This forms training content involving short-term graphic presentation, memory recall, and difference comparison, thereby improving the trainee's visual perception and visual memory abilities, and enhancing their visual manipulation ability and lateral awareness.

[0097] like Figure 7 The training content for sequential memory shown involves the processing unit 700 first selecting a set of training images, preferably 5 to 7 images, and randomly arranging the display order of the images so that they are displayed sequentially from left to right. Subsequently, the processing unit 700 generates corresponding original images and uses a 3D synthesis algorithm to synthesize the original images into corresponding 3D images, which are then output to the naked-eye 3D display module 100 for display.

[0098] During training, the processing unit 700 controls each graphic to light up sequentially every second, recording its display position and content, and marking already displayed graphics to prevent duplicate display within the same batch. After all graphics in the batch have finished displaying and disappeared, the processing unit 700 re-invokes a random graphic algorithm in the central area of ​​the naked-eye 3D display module 100 to shuffle the graphic list and generate a corresponding graphic sequence for display, prompting the trainee to click in the order the graphics lit up. During training, the processing unit 700 uses a detection algorithm to obtain the trainee's click results and compares the clicked graphic order with the original display list one by one; when the comparison result is correct, the corresponding graphic is displayed in green; when the comparison result is incorrect, the corresponding graphic is displayed in red. Through the training content of memorizing graphic order and clicking to reproduce, the trainee's visual memory and visual perception abilities are improved.

[0099] like Figure 9The spatial memory training content shown involves the processing unit 700 first dividing the display interface of the naked-eye 3D display module 100 into regions. Preferably, the screen is divided into 6 large regions using thick lines, and each large region is further divided into 4 small regions using thin red lines. Subsequently, the processing unit 700 randomly selects a target large region from the 6 large regions sequentially using a random algorithm, and then randomly selects a small region from the corresponding target large region, simultaneously storing and recording the selected large and small regions. The processing unit 700 determines the display position of the black dots within the small region and synthesizes a protruding black 3D dot at the corresponding position using a 3D synthesis algorithm for display. Preferably, each black 3D dot lights up sequentially at a preset time interval and remains displayed until the last black 3D dot lights up, at which point the processing unit 700 controls all black 3D dots to turn off simultaneously.

[0100] After observing the lighting sequence of all the black 3D dots, the trainee needs to use the touch perception module 200 to touch the corresponding areas in the same order as the previous black 3D dots lit up. During training, the processing unit 700 uses a detection algorithm to judge the trainee's touch input. The detection includes: whether the trainee clicked within a predetermined area, whether only a small area was clicked within each large area, and whether the clicked large and small areas are consistent with the pre-stored display order. When the judgment result is correct, the processing unit 700 controls the corresponding position to light up a green prompt point; when the judgment result is incorrect, it controls the corresponding position to light up a red prompt point. Through area sequence memory and spatial positioning training content, the trainee's spatial visual perception and memory ability are improved, auditory and visual integration is promoted, and minimum attention duration is increased.

[0101] like Figure 11 The training content shown at the center involves the processing unit 700 first generating a mesh-like training pattern composed of multiple black rays on the naked-eye 3D display module 100 using a 3D synthesis algorithm, and setting a small central circle as the target position at the intersection of each black ray. Simultaneously, the processing unit 700, combined with a local parallax algorithm, randomly generates a 3D red sphere within the blank area between the black rays, assigning the 3D red sphere a parallax value greater than the background mesh pattern, making it present a more pronounced convex stereoscopic display effect relative to the black lines. Preferably, the depth of field is set to approximately half the size of the red sphere.

[0102] After training begins, the trainee controls the movement of a 3D red ball through the balance posture acquisition module 500. The processing unit 700 acquires the center of gravity offset value from the balance posture acquisition module 500 and controls the red ball to move accordingly on the screen based on the center of gravity offset direction and a preset movement step size. Specifically, the processing unit 700 stores the current center of gravity value and calculates the movement direction and displacement distance of the red ball based on the change in center of gravity at the next moment, causing the red ball to continuously move from its initial appearance area towards the central small circle.

[0103] During training, the processing unit 700 detects the distance between the red ball and the central circle. When the red ball enters the preset overlap range and completely covers the central circle, the current red ball disappears, and a new 3D red ball is randomly generated in other blank areas to enter the next round of training. This training is preferably repeated at least 20 times. By forming spatial positioning training content based on center-of-gravity control, this improves the trainee's spatial visual perception and memory, promotes gross motor and visual integration, and enhances coordination and lateral skills.

[0104] like Figure 12 The training content of the path shown is prepared by the processing unit 700 in advance by creating a graphical list of multiple training trajectory lines, preferably including at least 6 paths of different shapes.

[0105] At the start of training, the processing unit 700 randomly selects a trajectory line from the image list and displays it in the center of the screen of the naked-eye 3D display module 100. At the same time, it generates a corresponding 3D red ball through a 3D synthesis algorithm and displays it at the starting position of the trajectory line or at a randomly determined starting coordinate.

[0106] During training, the processing unit 700 pre-sets the movement step size of the red ball and obtains the center of gravity offset value of the balance posture acquisition module 500. Based on the center of gravity changes of the balance posture acquisition module 500 in different directions, it calculates the movement direction and displacement distance of the red ball, thereby controlling the red ball to move continuously along the trajectory line.

[0107] Preferably, the processing unit 700 stores the current center of gravity value and adjusts the position of the red ball in real time based on the change in center of gravity at the next moment, so that the trainee can control the red ball to move as smoothly as possible along the predetermined trajectory line through the balance posture acquisition module 500. The processing unit 700 can also combine a local parallax algorithm to perform real-time 3D imaging processing on the red ball, so that the red ball presents a more obvious stereoscopic display effect on the trajectory line background. By forming balance control training content that moves along the trajectory line, the trainee's visual integration and vestibular control ability are promoted, and their ability to control focus is improved.

[0108] like Figure 16The training content for the indicated directions is first preset by the processing unit 700 using a 3D synthesis algorithm, which includes multiple arrow graphics. Preferably, the arrow graphics include at least four directions: upward, downward, leftward, and rightward, and the arrows are displayed as raised 3D images with white interiors on a white background. Preferably, the processing unit 700 randomly generates an 8-row, 12-column arrow array on the naked-eye 3D display module 100 and randomly determines the orientation of each arrow.

[0109] During training, the processing unit 700 randomly selects an arrow from the arrow array and controls it to turn black, lighting it up once every second and turning it off after one second, continuously in a loop. In the first stage, the trainee needs to move their arm in the direction indicated by the black arrow. The 3D vision sensing module 400 collects information and transmits it to the processing unit 700, preferably completing this 20 times consecutively. Then, in the second stage, the processing unit 700 continues to randomly light up arrows in the same manner, and the trainee needs to move their arm in the opposite direction of the arrow, preferably also completing this 20 times consecutively. Preferably, the first and second stages constitute a training cycle, and two cycles of training are executed continuously. By forming training content involving direction recognition and reverse action response, the trainee's peripheral and central vision integration is promoted, and their lateral awareness and sense of direction are improved.

[0110] like Figure 22 The training content for the rapid vocabulary shown is as follows: The processing unit 700 retrieves a pre-established training word database. At the start of training, the processing unit 700 randomly selects target words from the training word database. These words can be a single English letter or a combination of multiple English letters, or a single Chinese character or a combination of multiple Chinese characters. The displayed words are stored and marked to avoid repetition within the same training cycle. Subsequently, the processing unit 700 arranges and displays the target words in the center of the screen of the naked-eye 3D display module 100, and generates corresponding 3D display content using a 3D synthesis algorithm. Preferably, each group of words is displayed for 3 seconds and then disappears. The trainee needs to read the words aloud during or after their display. The processing unit 700 collects sound data from the microphone device on the naked-eye 3D display module 100. The processing unit 700 displays multiple groups of words in a loop as described above, preferably performing three rounds of training. The display rhythm or number of training sessions can be adjusted according to the preset difficulty level. By developing short-term word recognition and oral output training content, trainees' visual perception speed and memory ability are improved, and the integration of vision and hearing is promoted.

[0111] like Figure 26The training content for tracking shown includes the processing unit 700 retrieving the orbital step length, movement step length, depth step length, movement direction, maximum starting coordinate point, and target point coordinates of a preset target point. The target point is preferably the center point of the screen of the naked-eye 3D display module 100.

[0112] At the start of training, the processing unit 700 synthesizes a 3D target dot in real time using a local parallax algorithm. Starting from the left edge of the naked-eye 3D display module 100, the target dot moves clockwise along a spiral trajectory towards the center of the screen. During this movement, the processing unit 700 continuously calculates the trajectory coordinates of the target dot using trigonometric functions, gradually bringing it closer to the central target point. It also dynamically adjusts the parallax value of the target dot using a depth step, ensuring the target dot initially exhibits maximum 3D depth of field. Subsequently, as it moves towards the center, the parallax value gradually decreases, transitioning to minimum 3D depth of field, thus guaranteeing a smooth, seamless, and jumpless overall motion. After the target dot reaches the inner position, the processing unit 700 controls it to move in the opposite direction along the spiral trajectory from the inside out, gradually increasing the parallax value as it moves away from the center, resulting in a gradually enlarging and closer stereoscopic display effect. During this process, the processing unit 700 collects eye movement data from the eye-tracking module 300. Preferably, the processing unit 700 controls the target dot to complete a clockwise spiral motion four times, from the outside in and then from the inside out, and further controls it to complete the same process four times counterclockwise. By forming spiral motion training content with dynamic depth-of-field changes, the trainee's stereoscopic visual perception ability and 3D perception ability during the movement are enhanced.

[0113] like Figure 32 The training content for blind vision shown is that the processing unit 700 first randomly generates one or two target coordinate points on the display plane of the naked-eye 3D display module 100, and synthesizes black protruding 3D points at the corresponding coordinate positions through a local parallax algorithm. Preferably, the size of the 3D point is about 3 cm.

[0114] At the start of training, the trainee first observes the distribution of the black 3D dots. The processing unit 700 controls the black 3D dots to be displayed continuously for a preset time, preferably 4 seconds. After the display time ends, the processing unit 700 controls the naked-eye 3D display module 100 to output an audio prompt signal to remind the trainee to close their eyes. Subsequently, without observing the position of their fingers, the trainee uses both index fingers to touch the previously displayed target position.

[0115] During training, the processing unit 700 collects data identified by the 3D vision sensing module 400 to determine whether the trainee's index finger touches the predetermined spatial range of the corresponding black 3D point. When the touch result meets the requirements, the training round is considered correct; if the target range is not touched, the training continues until the trainee can complete the positioning touch. Preferably, only one black 3D point is displayed in the initial stage of training. After the trainee can stably complete single-point positioning, the training transitions to positioning training with two black 3D points. By forming closed-eye spatial positioning training content, the trainee's spatial visual perception and memory ability are enhanced, and the visual sensation in space is stimulated.

[0116] Example 5, refer to Figure 20 and 30 This is the fifth embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training item 800 includes the following types: Third training type: eye movement training. This eye movement training presents 3D images with inner and outer depths, and the trainee performs convergence and divergence training when switching between inner and outer depths of the image, enhancing stereoscopic visual perception. Specifically, as shown... Figure 20 Subject 19 shown: Training content for convergence and divergence, such as... Figure 30 Subject 29 shown: Training content for motor memory.

[0117] Specifically, such as Figure 20 The training content shown is clustered and dispersed. The processing unit 700 retrieves a preset audio prompt file and generates a training image in the center of the screen of the naked-eye 3D display module 100 through a 3D synthesis algorithm. The training image includes a 3D rugby ball image in the center and a 3D black thick-lined ring set around the rugby ball image.

[0118] At the start of training, the processing unit 700 assigns different parallax values ​​to the rugby image and the ring, so that the rugby image presents an inner depth display effect and the ring presents an outer depth display effect, thus forming distinct front and back spatial layers within the same field of view.

[0119] During training, the processing unit 700 outputs an audio prompt signal every 5 seconds to remind the trainee to switch their gaze between the rugby image and the circle. Specifically, the trainee first moves their gaze from the rugby image to the circle, and then moves their gaze back from the circle to the rugby image. This is preferably performed 10 times consecutively.

[0120] After completing the above training, the processing unit 700 switches the parallax relationship between the rugby image and the ring, making the rugby image display an outer depth-of-field effect and the ring display an inner depth-of-field effect. This training is repeated 10 times using the same method. By assigning different parallax values ​​to the two images using a naked-eye 3D synthesis algorithm, two images with different depths of field are generated, achieving depth reversal. This improves recognition speed during depth-of-field switching, enhances stereoscopic vision perception, and stimulates adjustment flexibility.

[0121] like Figure 30 The training content for motor memory shown is first generated by the processing unit 700 using a 3D synthesis algorithm to randomly generate 16 red circles on the naked-eye 3D display module 100. Preferably, each red circle is about 3 centimeters in size and is distributed in different positions on the screen.

[0122] After training begins, the processing unit 700 randomly selects 1 to 7 target circles from 16 red circles as key points for this round of training and stores the coordinates of the selected target circles. Subsequently, the processing unit 700 uses a local parallax algorithm and calculates frame time to control the target circles to sequentially execute red-filled frame animations, gradually presenting a raised 3D display effect to prompt the trainee to memorize their positions. Preferably, after all target circles are displayed, the display remains active for a preset time, preferably 5 seconds, before turning off. Afterward, the trainee rotates clockwise once and counterclockwise once in place, then faces the naked-eye 3D display module 100 again and clicks on the previously lit target circle positions sequentially via the touch sensing module 200 to reproduce the memorized position sequence.

[0123] During training, the processing unit 700 collects data from the touch sensing module 200 and judges the trainee's click results. When the number of clicks matches the number of target circles and the click position corresponds to the pre-stored target circle position, it is judged as correct, and the corresponding position is controlled to light up a green prompt; when the click position does not correspond, it is judged as incorrect, and the corresponding position is controlled to light up a yellow prompt. By combining algorithms such as random coordinate generation, random selection of target circles, local parallax 3D display, frame animation control, and click detection, this embodiment can form rotated spatial position memory training content, thereby enhancing the trainee's visual memory ability and awareness of their own body's position in space.

[0124] Example 6, refer to Figure 4 , 8Numbers 10, 13, 14, 18, 19, 23, 27, 28, 31, and 33 are the sixth embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training item 800 includes the following types: Fourth training category: Vision-Motion Training. This vision-motion training presents motion stimulus signals through naked-eye 3D, and the trainee tracks or quickly clicks on randomly appearing targets, stimulating fine eye movements and saccadic abilities. Specifically, it includes... Figure 4 Subject 3 shown: Training content for fast vision, such as... Figure 8 Subject 7 shown: Hand speed training content, such as... Figure 10 Subject 9 shown: Training content for dynamic rotation, such as... Figure 13 Subject 12 shown: Training content for peripheral vision, such as... Figure 14 Subject 13 shown: Training content for peripheral attention, such as... Figure 18 Subject 17 shown: Training content other than spatial tactile perception, such as... Figure 19 Subject 18 shown: Training content within spatial tactile perception, such as... Figure 23 Subject 22 shown: Training content for circular space, such as... Figure 27 Subject 26 shown: Training content for rotation, such as... Figure 28 Subject 27 shown: Spatial attention training content, such as... Figure 31 Subject 30 shown: Training content for dot distance, such as... Figure 33 Subject 32 shown: Training content for depth vision.

[0125] Among them, such as Figure 4 The training content for the fast vision shown is as follows: The processing unit 700 first generates a red 3D protrusion in the center of the screen of the naked-eye 3D display module 100 through a 3D synthesis algorithm and a local parallax algorithm. Preferably, the size of the red 3D protrusion is about 1.5 cm. Then, three black 3D circles with different depths and more convex than the red protrusion are generated inside it to form a central gaze target.

[0126] After training begins, the processing unit 700 generates at least one target point of about 1 cm in size at a random position on the naked-eye 3D display module 100, and controls the target point to be displayed and changed at a preset speed. The preferred speed is divided into four progressively increasing levels so that the appearance of the target point gradually accelerates.

[0127] During training, the trainee must continuously focus on the central red 3D protruding point and quickly click on randomly appearing target points using only peripheral vision. The processing unit 700 collects data from the touch sensing module 200. The processing unit 700 then dynamically adjusts the difficulty, gradually increasing the training difficulty in two dimensions: target point size and appearance time. Specifically, it gradually reduces the target point size and shortens the display interval of random target points. Simultaneously, the processing unit 700 randomly determines the appearance position of the target point and uses a frame-time-based timing algorithm to determine if the trainee completes the click within the valid time. Then, a spatial positioning algorithm is used to determine whether the trainee's click falls within the predetermined spatial range corresponding to the target point, based on the target point's spatial position and size. By creating training content that combines central fixation with rapid peripheral response, the trainee's fine eye movement ability is improved, enhancing visual accuracy and visual prediction capabilities.

[0128] like Figure 8 The training content for hand speed shown is as follows: The processing unit 700 first generates a red button in the center of the naked-eye 3D display module 100 through a 3D synthesis algorithm, and generates a blue square button on the left and right sides of the red button.

[0129] After training begins, the trainee first presses and holds the red button. The processing unit 700 collects the press status of the corresponding position of the red button on the touch sensing module 200 and times the event within a random time interval of 2 to 6 seconds. After the corresponding random time is reached, the processing unit 700 randomly controls the blue square button on the left or right to light up, forming a visual stimulus signal. After seeing the lit blue square button, the trainee must quickly release the red button and click the corresponding lit blue square button to turn it off. During training, the processing unit 700 collects the press status of the blue square button position on the touch sensing module 200 and whether the blue square button clicked by the trainee is lit up. When the clicked square is lit up, the processing unit 700 records the time from when the trainee's hand leaves the red button to when the blue square button turns off, as the reaction time for this training session. When the clicked square is not lit up, the click is considered invalid. Preferably, the processing unit 700 controls the blue square buttons on the left and right sides to light up alternately in a random manner, and the trainee performs training by alternating between the left and right hands. By forming visual response training content with unpredictable time intervals, the trainee's motor response ability to visual stimuli is improved, and the visual reaction time is shortened.

[0130] like Figure 10 The training content shown is dynamically rotated. The processing unit 700 displays a fixed 2D red dot as the central gaze target in the center of the screen of the naked-eye 3D display module 100, and generates multiple rotatable target points around it.

[0131] Before training begins, the processing unit 700 sets pre-defined training difficulty parameters, which include at least the rotation speed, the number of target points, and the target point color rules. Subsequently, the processing unit 700 randomly generates the coordinates of multiple target points with different radii around the center of the screen, and uses a 3D synthesis algorithm to generate protruding 3D dots at the corresponding coordinates to avoid overlap between the target points.

[0132] During training, the processing unit 700 controls each target point to rotate continuously around the central 2D red dot. It preferably uses trigonometric functions to calculate the coordinates after rotation (the next coordinate point after rotation). The specific rotation angle is calculated in real time by converting the base radian, step size (rotation angle), frame time, radius length, and radian. The X coordinate is obtained by using the rotation angle and radius value, and the Y coordinate is obtained by using the rotation angle and radius value. The coordinates of each target point after rotation are calculated in real time, and a synchronous update method is used to maintain the consistency of the rotation process of all target points. Preferably, the rotation direction changes every three revolutions.

[0133] Specifically, this embodiment can set three training sequences. In the first training sequence, the rotating target points are all black dots about 1 cm in size. The trainee eliminates them by touching the corresponding target point, and an audio prompt is emitted when the target point disappears. In the second training sequence, the rotating target points are also about 1 cm in size, but the color is randomly set to red, green, or blue. The trainee needs to eliminate the target points of the corresponding color by touching them in a preset order. The color elimination order can be randomly set by the processing unit 700. In the third training sequence, the rotating target points are black dots. The trainee needs to touch them quickly to eliminate them. When the processing unit 700 detects that the central fixed red dot has entered a flashing state, the trainee must touch the central fixed red dot first to turn it off, and then continue to eliminate the surrounding rotating black dots. During the training process, the processing unit 700 determines whether the trainee's touch coordinates correspond to valid coordinates in the current target point list. When a correspondence exists, the corresponding target point is eliminated, and the corresponding audio feedback is triggered. By forming multi-sequence rotating target click training content, the trainee's predictive ability and spatial positioning ability are improved, and the coordination accuracy between vision and hand is developed.

[0134] like Figure 13 The peripheral vision training content shown is specifically operated as follows: the processing unit 700 first generates a fixed blue dot in the center of the screen of the naked-eye 3D display module 100 as the central gaze target, and controls the trainee to train at a distance of about 20 centimeters in front of the naked-eye 3D display module 100.

[0135] After training begins, the trainee needs to keep looking at the blue dot. The processing unit 700 obtains the screen display size of the naked-eye 3D display module 100 and randomly generates black dot coordinates in the surrounding area away from the center blue dot. Then, a 3D synthesis algorithm is used to generate black target points at the corresponding coordinate positions.

[0136] During training, the trainee must eliminate black target points by touching the naked-eye 3D display module 100 without removing their gaze from the central blue dot. The processing unit 700 collects data from the touch sensing module 200 to determine whether the trainee's finger touches a predetermined area corresponding to the black target point. When the determination is successful, the corresponding black target point disappears. Preferably, the training is repeated at least 20 times. By forming peripheral localization training content under central fixation, the trainee's saccade movement ability and binocular synchronization ability are improved, and peripheral visual localization ability and hand-eye coordination are enhanced.

[0137] like Figure 14 The training content for peripheral attention shown is as follows: The processing unit 700 first generates a 2D thick black cross in the center of the screen of the naked-eye 3D display module 100 as the central target, and controls the trainee to fixate on the cross at a distance of about 30 to 40 centimeters in front of the naked-eye 3D display module 100.

[0138] After training begins, the processing unit 700 generates target coordinates at random positions on the naked-eye 3D display module 100 using a random coordinate algorithm, and generates 3D target points at corresponding positions using a 3D synthesis algorithm and a local parallax algorithm. The 3D target points preferably include two shapes: solid red dots and hollow red circles, with a preferred size of about 3 centimeters and a white background.

[0139] During training, while maintaining focus on the central thick black cross, the trainee uses the 3D vision sensing module 400 to grasp and move a 3D target point closer to the cross on the screen. The 3D vision sensing module 400 acquires the spatial position information of the trainee's fingers and matches it with the display coordinates of the naked-eye 3D display module 100. The processing unit 700 calculates the movement trajectory of the 3D target point in real time based on the finger position and updates its display position synchronously, thus enabling touch, dragging, and position adjustment of the 3D target point. When the processing unit 700 detects that the 3D target point has moved to the predetermined centering range of the central thick black cross, it turns off the current target point and lights up a new 3D target point at a random location to begin the next round of training. By forming peripheral grasping and positioning training content under central fixation, the trainee's peripheral positioning ability and hand-eye integration ability are improved, and attention and visual manipulation abilities are enhanced.

[0140] like Figure 18The training content shown, excluding spatial tactile feedback, involves the processing unit 700 first generating a black 2D point on the zero plane at the center of the screen of the naked-eye 3D display module 100 as the central gaze target. Around this point, multiple red 3D protrusions are generated using a local parallax algorithm and a 3D synthesis algorithm. Preferably, the size of the red 3D protrusions is approximately 2 cm, and they are evenly arranged in a circle along the circumference. Preferably, the processing unit 700 arranges the red 3D protrusions sequentially at circumferential angular intervals, so that each red 3D protrusion presents a distinctly convex stereoscopic display effect relative to the central black 2D point.

[0141] After training begins, the processing unit 700 controls the red 3D protrusion to rotate around the central black 2D dot. The trainee uses the 3D vision sensing module 400 to touch the red 3D protrusion in space. The 3D vision sensing module 400 acquires the spatial position information of the trainee's finger in real time and matches it with the target point display coordinates on the naked-eye 3D display module 100. When the processing unit 700 detects that the trainee's finger touches the corresponding red 3D protrusion, it determines that the touch is successful and outputs a corresponding sound prompt.

[0142] During training, after touching each red 3D raised dot, the trainee must refocus their gaze on the central black 2D dot before moving on to the next target point. Preferably, the training is performed once in a clockwise direction and once in a counterclockwise direction. By creating spatial touch training content around the central fixation point, the trainee's spatial positioning ability, hand-eye coordination, and visual perception are improved, and synchronized eye movements are stimulated.

[0143] like Figure 19 The training content shown in the spatial haptic demonstration uses touch detection by the touch perception module 200 to adapt to the training scenario of the interior depth parallax map. The processing unit 700 first retrieves the audio prompt file in advance and generates a black dot with an exterior depth effect at the center of the screen of the naked-eye 3D display module 100 through a 3D synthesis algorithm, making it appear to protrude from the screen. At the same time, multiple red dots are arranged around the black dot along the circumferential direction, preferably at angular intervals of 360 / 9 degrees, and the red dots are given an interior depth effect through a 3D synthesis algorithm, making them appear to be in a stereoscopic display state that is significantly lower than the screen surface relative to the central black dot.

[0144] After training begins, the trainee touches each of the surrounding red dots in sequence, and the touch sensing module 200 detects the trainee's touch position. When the training detects that the trainee has touched a corresponding red dot, the processing unit 700 outputs a corresponding audio prompt. Preferably, the training is performed once in a clockwise direction and once in a counterclockwise direction. By forming spatial touch training content targeting interior depth targets, the trainee's spatial positioning ability, visual perception ability, and hand-eye coordination ability are improved.

[0145] like Figure 23 The training content for the illustrated circular space involves the processing unit 700 first generating a 3D ring on the left and right sides of the screen of the naked-eye 3D display module 100 using a 3D synthesis algorithm. A red dot is then generated in the center between the two rings as the gaze target. The trainee must keep their gaze fixed on the red dot throughout the training process. The training consists of two parts. In the first part, the trainee uses their left and right hands to draw circles within the left and right rings using the touch sensing module 200. The processing unit 700 determines whether the finger touch trajectory is within the inner and outer boundaries of the corresponding ring and stores and connects the touch path coordinates using a line drawing processing algorithm to form a continuous drawing trajectory. When the touch trajectory is detected to exceed the ring range or touch the edge of the ring, the processing unit 700 outputs a corresponding sound prompt. Preferably, the processing unit 700 gradually increases the required drawing speed to form a training loop. In the second part, 3D red balls are synthesized in real time within the left and right rings using a local parallax algorithm. The trainee controls the movement of these 3D red balls within the left and right rings using a balance posture acquisition module 500. The processing unit 700 acquires the center-of-gravity offset information from the balance posture acquisition module 500 and controls the direction and distance of movement of the corresponding 3D red ball accordingly. It also detects whether the 3D red ball remains within the inner and outer boundaries of the corresponding ring. An audio prompt is output when the ball touches the edge of the ring or goes beyond its range. Preferably, the ball movement training within the left and right rings constitutes another training cycle. By combining hand-drawing circles with balance control training content, the trainee's fine motor control and hand-eye coordination are improved, and balance and vestibular control abilities are stimulated.

[0146] like Figure 27 The rotation training content shown is specifically implemented by the processing unit 700 first pre-setting parameters such as the clock trajectory length and orbital step length of the target point's movement, and then generating training images on the naked-eye 3D display module 100 through a 3D synthesis algorithm and a local parallax algorithm. Preferably, the processing unit 700 generates a reference dot in the center of the screen and a movable 3D red target point around its periphery.

[0147] After training begins, the processing unit 700 uses a fixed clock trajectory motion calculation algorithm to control the 3D red target point to move continuously along a preset semi-circular trajectory. The clock trajectory is used to determine the radius distance of the moving target relative to the central reference point, and by gradually changing the position of the target point on the circumference, a smooth, fluid motion effect without noticeable jitter is achieved. Preferably, the 3D red target point moves back and forth 10 times between the 12 o'clock and 6 o'clock positions along the semi-circular trajectory according to a preset training sequence, moves back and forth 10 times in the opposite direction along the corresponding semi-circular trajectory, and moves back and forth 10 times between the 3 o'clock and 9 o'clock positions.

[0148] During training, trainees need to continuously follow the movement trajectory of a 3D red target point with their eyes. By creating eye-tracking training content that moves along a semi-circular path, the trainee's peripheral eye movement control ability is improved.

[0149] like Figure 28 The training content for spatial attention shown involves the processing unit 700 pre-setting parameters such as the number of training letters, the letter depth step size, and the balance board movement step size. At the start of training, the processing unit 700 first generates a red circle at the center of the naked-eye 3D display module 100 using a 3D synthesis algorithm. The size of the red circle is preferably set to just cover a single target letter. Subsequently, the processing unit 700 randomly generates multiple 2D bold uppercase letters in the surrounding area of ​​the naked-eye 3D display module 100 and stores the display positions of the generated letters. Then, the processing unit 700 randomly selects a letter from the stored letter list and gradually changes the depth of field of that letter using a local parallax algorithm, transforming it from a 2D display state to a 3D display state until the preset maximum depth of field value is reached, thus forming the current target letter.

[0150] During training, the trainee controls the movement of the red circle on the naked-eye 3D display module 100 via the balance posture acquisition module 500 or the 3D vision sensing module 400. Specifically, the processing unit 700 acquires the center of gravity offset value from the balance posture acquisition module 500 or the displacement information acquired by the 3D vision sensing module 400, and calculates the direction and distance of movement based on the current value and the change value at the next moment, thereby controlling the position change of the red circle on the display interface. At the same time, the processing unit 700 uses a detection algorithm to detect the distance between the center point of the red circle and the center point of the target letter in real time. When the distance is less than a preset range value, it is determined that the trainee has completed the positioning and coverage of the current 3D letter. At this time, the processing unit 700 controls the current target letter to return to its original display state, and makes the red circle return to the center position of the naked-eye 3D display module 100, and then randomly selects the next letter from the letter list to change to the 3D display state to continue the next round of training.

[0151] By creating training content that randomly highlights target letters and allows trainees to actively control the circles for positioning and coverage, trainees' peripheral visual perception, visual manipulation, and visual-motor integration abilities can be improved.

[0152] like Figure 31 The training content for the dot distance shown involves the processing unit 700 pre-generating a red dot and a blue dot using a 3D synthesis algorithm, and drawing a thick black cross in the center of each dot, while also pre-setting corresponding audio prompt files. At the start of training, the processing unit 700 randomly determines two display coordinates on the naked-eye 3D display module 100 and displays the red and blue dots at the corresponding coordinate positions. Preferably, the two dots are the same size, approximately 3 centimeters, but the processing unit 700 assigns different parallax values ​​to the two dots using a local parallax algorithm to create different depth-of-field effects.

[0153] During training, the trainee stands at a preset distance in front of the naked-eye 3D display module 100, preferably 4 meters away, and holds a rope. Based on the perception of the visual distance between two 3D points, the trainee copies the observed distance between the two points on the rope. Then, the trainee moves closer to the naked-eye 3D display module 100 and compares and adjusts the distance marked on the rope with the actual distance between the two dots until the two match.

[0154] Meanwhile, the processing unit 700 controls the playback of the audio prompt file, emitting a first prompt tone when the two dots light up, and a second prompt tone after a preset duration, preferably reminding the trainee to complete the rope comparison training within 20 seconds. By forming training content that compares the perceived distance with the actual distance, the trainee's ability to perceive distance relationships is improved, spatial association ability is stimulated, and spatial positioning ability is enhanced.

[0155] like Figure 33The training content for depth vision shown involves the processing unit 700 generating multiple rings of training letters on the naked-eye 3D display module 100. The letters in the central area have a smaller protrusion, while the protrusion gradually increases from the inside out. Specifically, the processing unit 700 uses coordinate positioning to determine the display position of each letter. Preferably, the three angles of an isosceles right triangle are used as the positioning basis, with the right-angle vertex corresponding to the target letter coordinates and the two acute angles corresponding to the screen center point and the vertical or horizontal center positioning point, respectively. Then, using the screen center point as the rotation reference, a set of letter coordinates is determined every 45° rotation until one full rotation is completed, thus forming a multi-ringed array of letters on the naked-eye 3D display module 100. Subsequently, the processing unit 700 performs stereoscopic display processing on each letter using a 3D synthesis algorithm, assigning different depth values ​​based on the distance between each letter and the screen center point. The farther the letter is from the screen center point, the larger the corresponding depth value, creating a depth-of-field effect that gradually increases from the inside out.

[0156] During training, the trainee fixates on the central letter and reads aloud the contents of the surrounding rings of letters without moving their gaze. The processing unit 700 detects the correctness of the pronunciation. By combining central fixation with peripheral recognition in the training content, the trainee's peripheral visual perception and eye movement abilities are stimulated, thereby improving peripheral visual span.

[0157] Example 7, referring to Figure 5 and 29 This is the seventh embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training item 800 includes the following types: Fifth training category: fusion function training, presenting a mixed 2D and 3D image, where the trainee switches between the plane and depth of field of the image to stimulate fusion ability and adjustment flexibility, specifically including... Figure 5 Subject 4 shown: Training content for visual overlap, such as... Figure 29 Subject 28 shown: Training content for spatial positioning.

[0158] like Figure 5The training content shown involves visual overlap. The processing unit 700 generates a red 3D dot and its corresponding ring on the naked-eye 3D display module 100. Specifically, the processing unit 700 performs stereoscopic display processing on the red 3D dot and the ring using a 3D synthesis algorithm and a local parallax algorithm, and uses a dynamic depth-of-field adjustment algorithm to control the depth of field of the red 3D dot in a regular, real-time manner. Preferably, the processing unit 700 adjusts the depth of field of the red 3D dot at preset time intervals, preferably every 80 milliseconds, with each depth-of-field change preferably being 0.1, thus gradually bringing the red 3D dot closer to the size of the ring. When the red 3D dot grows to overlap with the ring, the processing unit 700 provides the trainee with a preset operation time, preferably about 300 milliseconds, to trigger a button operation; if the trainee does not complete the operation within this time, the red 3D dot continues to grow and exceeds the size of the ring.

[0159] By creating training content that involves judging the overlap of a red 3D dot with a circular ring, the trainee's fine static stereoscopic vision and coarse dynamic stereoscopic vision are stimulated, and the correspondence between "what is seen" and "where it is" is improved.

[0160] like Figure 29 The training content for spatial positioning shown is prepared by the processing unit 700, which pre-sets multiple lists of colored dots, the number of dots, and corresponding audio prompt files, and displays multiple colored dots in 2D state on the naked-eye 3D display module 100.

[0161] During training, the processing unit 700 first randomly determines and caches the display positions of multiple colored dots to form a data list, and then randomly selects a colored dot from the data list as the current target dot. Subsequently, the processing unit 700 times and, in conjunction with 3D synthesis algorithms and local parallax algorithms, gradually changes the depth of field of the current target dot, slowly transforming it from a 2D state to a 3D state with the maximum external depth of field. When the target dot stops changing, the trainee touches the target dot in space with their finger through the 3D vision sensing module 400. At this time, the processing unit 700 uses a detection algorithm to determine the trainee's touch position to detect whether the spatial coordinates of the trainee's finger fall within the convex space range corresponding to the target dot; when the detection result is correct, the processing unit 700 controls the current target dot to return to the 2D state and plays a prompt sound, and then randomly selects another colored dot to conduct the next round of depth-of-field change training from 2D to 3D. Preferably, the above process is executed for 20 cycles. By creating aerial touch training content that gradually transitions from 2D to 3D depth of field, the trainee's fusion ability is enhanced, peripheral stereoscopic vision is stimulated, and natural 3D visual perception is improved through the integration of movement and balance.

[0162] Example 8, referring to Figure 2 , 315, 17, and 25 are the eighth embodiment of the present invention, providing a motion vision training method based on a naked-eye 3D display. Further, the training item 800 includes the following types: Sixth training category: sensory integration training, which simultaneously collects motion sensor signals or balance board posture signals. The trainee completes the training under the coordination of visual stimulation and proprioceptive feedback, integrating the visual system and the vestibular system, specifically including... Figure 2 Subject 1 shown: Time-related training content, such as... Figure 3 Subject 2 shown: Rhythm training content, such as... Figure 15 Subject 14 shown: Training content for the static vestibular system, such as... Figure 17 Subject 16 shown: Integrated training content, such as... Figure 25 Subject 24 shown: Training content for stroboscopic glasses.

[0163] like Figure 2 The training content shown is as follows: The processing unit 700 generates a 3D circular button on the naked-eye 3D display module 100 through a 3D synthesis algorithm and a local parallax algorithm.

[0164] At the start of training, the processing unit 700 plays an audio prompt file. Upon hearing the sound, the trainee must press the 3D circular button as quickly as possible. Simultaneously, the processing unit 700 accumulates the time between hearing the prompt and pressing the 3D circular button, preferably using frame time to improve timing accuracy and reduce latency. When the trainee completes the pressing operation within 500 milliseconds, the processing unit 700 displays a green prompt; when the trainee completes the pressing operation after 500 milliseconds, the processing unit 700 displays a red prompt and simultaneously shows the corresponding specific reaction time value. By creating sound-triggered stereoscopic button response training content, the integration ability between the trainee's binocular stereoscopic vision and auditory perception is improved, and the synchronicity of the action response is enhanced.

[0165] like Figure 3The training content for the rhythm shown is as follows: the processing unit 700 generates two 3D circular buttons on the naked-eye 3D display module 100 using a 3D synthesis algorithm. The spacing between the two 3D circular buttons is preferably about 40 centimeters. During training, the processing unit 700 controls the audio prompt file to output a sound signal with a uniform rhythm, using a slower rhythm in the initial stage of training. Subsequently, the processing unit 700 gradually decreases the time interval between adjacent sounds using a dynamic adjustment time algorithm, preferably increasing the rhythm once after every three sounds, thereby achieving a gradual increase in training intensity. After hearing the sound rhythm, the trainee needs to press the two 3D circular buttons according to the sound rhythm, preferably using an alternating left and right hand method for continuous pressing. At the same time, the processing unit 700 uses a timing algorithm to accumulate the application frame time of the trainee's pressing moments and compares the actual pressing rhythm with the preset sound rhythm. When the trainee continuously matches the sound rhythm, the processing unit 700 controls the corresponding position to light up a green indicator light; if the rhythm is not maintained, no indicator light is lit. By creating rhythmic double-button pressing exercises, trainees can improve the integration of their visual, hand, and auditory systems and stimulate coordination.

[0166] like Figure 15 The training content shown is for static vestibular training. The training system is equipped with a support for placing the naked-eye 3D display module 100 face down on the ground so that the trainee can perform the training while lying supine with their face facing the center of the naked-eye 3D display module 100.

[0167] At the start of training, the processing unit 700 generates a white background on the naked-eye 3D display module 100 using a 3D synthesis algorithm, and draws a black dot with a diameter of approximately 2 cm in the center of the naked-eye 3D display module 100. Subsequently, the processing unit 700 obtains the screen size parameters of the naked-eye 3D display module 100, and calculates eight target movement directions based on the screen size. The eight target movement directions preferably include the upper left corner, left side, lower left corner, lower side, lower right corner, right side, upper right corner, and upper side.

[0168] During training, the processing unit 700 uses the center of the screen as the starting point, selects the endpoint coordinates corresponding to each target direction according to a preset order or rule, and calculates the movement path of the black dot using the starting and endpoint coordinates. This causes the black dot to move along a straight path from the center of the screen towards the corresponding corner or edge. Preferably, the black dot moves twice towards each corner and each edge. The trainee continuously follows the movement trajectory of the black dot during training. By forming a straight-line tracking training content extending from the center in each direction, vestibular interference is suppressed as the trainee follows the movement of the black dot, achieving separation of visual and vestibular sensations and improving binocular visual ability.

[0169] like Figure 17 The training content shown involves the processing unit 700 generating a white background on the naked-eye 3D display module 100 and pre-drawing a thick red ring in the center of the module, while displaying a rugby ball image within the red ring. The training process can include two modes. In the first mode, the rugby ball is displayed in 3D, while the red ring remains in a 2D plane. The processing unit 700 generates a stereoscopic image of the rugby ball using a 3D synthesis algorithm and controls the rugby ball to gradually shift inward in a four-stage progressive manner, combining this with 3D parallax simulation to create a gradually increasing inner depth effect. Then, the rugby ball is controlled to gradually shift outward in a four-stage progressive manner, creating a gradually increasing outer depth effect, while the red ring remains stationary on the display plane. In the second mode, the rugby ball remains in a 2D display, while the red ring shifts inward and outward in a four-stage progressive manner as described above, combining this with 3D parallax simulation to achieve dynamic changes in inner and outer depth. Meanwhile, the processing unit 700 controls the step time of depth-of-field changes at each stage and the playback time of audio prompts, preferably using frame time to precisely time each step of change to ensure a stable training rhythm. By creating depth-of-field separation training content between the central target and peripheral references, the trainee's ability to fuse the center and periphery is improved, and the integration between convergence and accommodation is stimulated.

[0170] like Figure 25 The training content shown is for the stroboscopic glasses, and the exercises performed using the stroboscopic stimulation module 600 are as follows: Figure 4 The training content shown is for fast vision, such as Figure 10 The training content shown is dynamic rotation, such as Figure 12 The path training content shown is as follows: Figure 24 The training content shown is a rapid follow-along exercise. Spatial perception is trained by wearing the stroboscopic stimulation module 600, visual information processing is stimulated to enhance vision, and perception speed is improved by integrating all areas (motor, visual, and auditory).

[0171] Example 9, the ninth embodiment of the present invention, provides a motion vision training method based on a naked-eye 3D display. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0172] This embodiment selects training participants from a provincial team and university sports competitions as subjects, establishing a closed-loop process of "training-data acquisition-evaluation-adaptive parameter tuning" to verify the effectiveness and transferability of the naked-eye 3D motion vision training method. A total of 144 trainees (corrected visual acuity ≥1.0, no significant abnormalities in binocular vision, and no history of dizziness or migraines in the past month) were randomly divided into 6 groups of 24 participants each, stratified by baseline dynamic visual acuity and reaction time, corresponding to 6 training conditions: Control A: 2D planar visual training; Control B: 2D training with stroboscopic stimulation module 600 superimposed; Implementation C: Basic naked-eye 3D training; Implementation D: Naked-eye 3D training with eye-tracking module 300 superimposed and adaptive difficulty; Implementation E: Naked-eye 3D training with 3D motion acquisition using 3D visual sensing module 400 superimposed; Implementation F: Naked-eye 3D training with 3D visual sensing module 400 and posture signals from balance posture acquisition module 500 superimposed and adaptive parameter tuning enabled. The hardware side employs a naked-eye 3D display module 100 and a touch sensing module 200. An eye-tracking module 300 is used above the screen to capture gaze points and eye trajectories in real time. A 3D vision sensing module 400, such as a KINECT motion sensor, is used to collect upper limb arrival, trunk rotation, and gait displacement. A balance posture acquisition module 500 is used to collect center of gravity sway and posture stability. The system is centered around a processing unit 700, which performs training instruction generation, naked-eye 3D synthesis and local parallax control, error judgment and real-time feedback, and adaptive parameter tuning.

[0173] The training cycle is 4 weeks, with 3 sessions per week, each lasting 40 minutes (totaling 120 minutes / week). Each training session begins with a 3-minute individualized calibration: this includes eye-tracking fixation mapping, confirmation of the optimal visual area for naked-eye 3D display, touch coordinate deviation correction, and recording of daily fatigue self-assessment to rule out abnormal fluctuations. This is followed by the modular training process: (1) Dynamic visual acuity training: In a naked-eye 3D scene, moving targets of different sizes and depth levels (straight lines, spirals or rotational trajectories are randomly switched) are presented. The trainee follows by gazing and confirms by touching or "locking" the gaze when prompted; the system records the gaze trajectory, target trajectory and response time simultaneously.

[0174] (2) Visual perception training: Randomly generate 3D graphics (position, order, and depth of field are random), and trainees perform memory recall and spatial reproduction; the system calculates the spatial deviation between the touch point and the target point, and in the next round, the direction with the larger deviation is used as the reinforcement training area.

[0175] (3) Eye movement training: The inner and outer depth images are switched alternately at preset intervals, and the trainee completes the synchronous response of eye convergence and divergence and hand arrival action; eye tracking is used to determine whether the convergence and divergence switching is completed and to record the convergence and divergence response time.

[0176] (4) Visual-motor training: Targets pop up at random times and locations, with the target speed gradually increasing in levels; trainees need to quickly scan and locate the target and confirm it by touching or looking at it; the system counts the scanning latency and response delay.

[0177] (5) Fusion function training: 2D and 3D mixed images switch between plane and depth state, and the trainee completes the switching gaze; the system records the time required for fusion to stabilize and provides real-time prompts.

[0178] (6) Sensory integration training (implementation of E / F activation): At the same time as the visual stimulus appears, the motion signal of the three-dimensional visual sensing module 400 and / or the posture signal of the balance posture acquisition module 500 are collected. The trainee is required to complete the visual task and the arrival action while maintaining a stable posture, so as to achieve the coordination of vision-motor-vestibular / proprioception.

[0179] Adaptive strategies are implemented in D / E / F (Disease / Earning / Further Rendering): When the response accuracy reaches a high threshold for three consecutive times, the rendering speed is automatically increased, the spatial range is expanded, and depth levels and randomness are added; when the accuracy falls below a low threshold for three consecutive times, the speed is automatically reduced, the spatial range is narrowed, and the prompt time is extended; when the response time stabilizes within a preset range, the next level is unlocked. Naked-eye 3D image generation uses a "naked-eye 3D synthesis + local parallax" approach: based on preset parallax levels, the left / right eye display content is regionalized into depth layers (e.g., near-field, mid-field, far-field layers), and higher-attention areas (determined by windows near the fixation point) are prioritized for more refined parallax and anti-crosstalk processing, thereby improving stereoscopic effect and recognizability without increasing wearing burden. The entire training process is automatically recorded: completion rate, key indicators for each module, real-time feedback results, and difficulty change trajectories form a traceable, quantitative training archive. Four weeks later, the same set of post-test evaluations were conducted, including dynamic visual acuity, random target reaction time, saccade latency, fusion stabilization time, posture swing area, and practical transfer task score (standardized scoring).

[0180] Table 2 Experimental Data

[0181] As shown in Table 2, different training conditions exhibit clear gradient differences in "compliance, comfort, improvement of core visual capabilities, cross-modal integration and practical transfer", and these differences are highly consistent with the key technical features of the solution, forming a convincing chain of technical effects.

[0182] Firstly, in terms of compliance and discomfort, the naked-eye 3D implementation group significantly outperformed the traditional method. Control B (flicker glasses + 2D) had the highest discomfort score of 4.9 / 10, with a completion rate of only 68%, reflecting that the discomfort and fatigue caused by wearing glasses and flicker directly inhibited the willingness to continue training; this aligns with the pain point of "poor compliance due to wearing discomfort" mentioned in the background technology. In contrast, the discomfort scores of implementations C / D / E / F were all ≤1.9, and the completion rates were all ≥88%, with implementation F achieving a completion rate of 94%. This indicates that naked-eye 3D presentation without wearing glasses can significantly reduce physiological burden under the premise of comparable training intensity, thereby improving training sustainability; this advantage is not simply due to interface optimization, but rather a result of the change in hardware form and interaction paradigm brought about by "naked-eye 3D display replacing wearable flicker devices."

[0183] Secondly, in terms of dynamic visual acuity, reaction time, and saccadic ability, the implementation group showed a systematic advantage. The improvement in dynamic visual acuity (incremental over 4 weeks) was only 1.1 lines in control A, 1.4 lines in control B, while implementation C improved to 2.3 lines; with the introduction of eye tracking and adaptation (implementation D), it further improved to 2.8 lines, and implementation F reached 3.0 lines. This trend indicates that 2D training alone, even with the addition of stroboscopic stimulation, is insufficient to provide depth cues and fixation stability consistent with real 3D motion; the depth layering and local parallax control of naked-eye 3D make moving targets "followable, distinguishable, and capable of judging distance changes" in space, thus more effectively stimulating binocular coordination and visual acuity. Improvements in reaction time also reflect this chain: control A reduced by 48 ms, control B by 55 ms, while implementation F reduced by 118 ms, approximately 2.46 times that of control A. The improvement in saccade latency from 14ms in control A to 41ms in implementation F indicates that "random spatiotemporal target + three-dimensional depth of field" has a stronger training effect on rapid localization and saccade initiation.

[0184] Again, in terms of fusion functionality and depth-of-field switching capabilities, the advantages of naked-eye 3D are more direct. The fusion stabilization time is reduced to only 0.21s in control A, while it reaches 0.44s in implementation C, 0.55s in implementation D, and 0.63s in implementation F. Since the core of fusion function training lies in the stability and adjustment flexibility of the planar-depth-field state switching, 2D planar displays naturally lack real information on depth-of-field jumps / gradual changes in the stimulus dimension. Therefore, even when designing the same task, its stimulus intensity and transferability are still limited; the depth-of-field attribute switching of naked-eye 3D provides the necessary visual conditions for this capability. Furthermore, implementation D improves upon implementation C, showing that eye tracking can be used to correct gaze position in real time and reduce invalid gazes, thereby shortening the time to achieve stable fusion; this reflects the quantitative closed-loop value of "real-time capture of gaze points + instant feedback + adaptive algorithm parameter tuning".

[0185] More importantly, it improves sensory integration and practical application transfer. Compared to A / B, the reduction in posture sway area was only 6% / 8%, while implementation E increased it to 18%, and implementation F reached 24%; the practical application transfer score improved by 5% for A and 20% for F. This data directly supports the "transfer advantage brought by multimodal sensory interaction": the 3D visual sensing module 400 and the balance posture acquisition module 500 make training no longer a purely visual task, but require visual judgment, action control, and posture stability to be completed simultaneously, thus more closely resembling the multi-channel information processing needs of a real competitive environment. In other words, implementation E / F not only improves "seeing clearly and seeing quickly," but also improves the linkage quality of "seeing-movement-stability," which is precisely the weak link that existing 2D or single strobe solutions cannot cover.

[0186] Finally, the "Quantitative Coverage" in Table 2 further explains why the implementation group was able to achieve a stable advantage: Control A could only automatically collect 2 types of indicators (e.g., reaction time, completion rate), Control B collected 3 types, while Implementation F covered 9 types of indicators. This means that the implementation plan not only improved training effectiveness but also provided a traceable, tiered, and evaluable quantitative system, making adjustments to training difficulty data-driven and solving the problem of existing technologies "lacking systematic tiered and quantitative indicators, and making it difficult to evaluate effectiveness."

[0187] In summary, the above comparisons show that: naked-eye 3D presentation and local parallax depth control solve the problem of missing 3D scenes; eye tracking and adaptive algorithms solve the problems of mismatched training intensity and ineffective training; and the 3D vision sensing module 400 and the balance posture acquisition module 500 solve the problems of insufficient multimodal collaboration and transfer. These effects form a consistent chain of evidence in terms of compliance, discomfort, improved visual ability, and practical transfer, supporting the invention's significant innovation and beneficial effects in the implemented scenarios.

[0188] Example 10, the tenth embodiment of the present invention, refers to Figure 34A motion vision training system is provided, including a motion vision training method, comprising a naked-eye 3D display module 100 for presenting the corresponding training scene in naked-eye 3D mode; a touch sensing module 200, integrated with the naked-eye 3D display module 100, for receiving touch operations from the trainee and generating touch signals; an eye tracking module 300 for acquiring the trainee's gaze point position and eye movement trajectory in real time; a three-dimensional vision sensing module 400 for acquiring the trainee's motion signals in three-dimensional space; a balance posture acquisition module 500 for acquiring the trainee's posture balance signals during training; a stroboscopic stimulation module 600 for switching between transparent and light-blocking states at a preset frequency during training to enhance the trainee's reaction training and spatial perception ability; and a processing unit 700 connected to the naked-eye 3D display module 100, the touch sensing module 200, the eye tracking module 300, the three-dimensional vision sensing module 400, the balance posture acquisition module 500, and the stroboscopic stimulation module 600.

[0189] The human eye tracking module 300 can be a human eye tracking camera, which is fixed above or below the naked-eye 3D display module 100 and connected to the processing unit 700 via USB or a dedicated interface; during initialization, it completes the calibration of the pupil / corneal reflection point and outputs the coordinates of the gaze point and the eye movement trajectory, such as scanning, following, convergence / divergence trend, etc.

[0190] The 3D vision sensing module 400 can use a KINECT motion sensing device or a depth camera. The 3D vision sensing module 400 is connected to the host computer where the processing unit 700 is located via USB. When the training content is started, the SDK of the corresponding device is invoked simultaneously to collect key points and depth maps of the trainee's upper body, so as to realize "air touch / grab / move" and other interactions in 3D space.

[0191] The balance attitude acquisition module 500 can use a balance board device and connect to the host via an external Bluetooth sensor. After connection, it can simulate mouse / joystick type input. The processing unit 700 collects sensor data such as center of gravity shift or pitch / roll angle on the balance attitude acquisition module 500 to realize the direction control of the target on the screen, such as a ball.

[0192] The stroboscopic stimulation module 600 can be implemented using stroboscopic glasses. The stroboscopic stimulation module 600 communicates with the processing unit 700 via Bluetooth or wired connection. The processing unit 700 outputs frequency and duty cycle commands, causing the module to switch between transparent and light-blocking states at a preset frequency. The stroboscopic stimulation module 600 is not activated in all training sessions, but rather in certain dynamic tests to enhance training effectiveness, and the number of exercises using it is limited to avoid overloading the training load.

[0193] The processing unit 700 has a built-in training project library and can create personal visual parameter profiles for each trainee, such as interpupillary distance (IPD), dominant eye, basic visual acuity level, comfortable convergence / divergence range, and fixation stability threshold, to generate and present 3D training content with depth-of-field variations.

[0194] In one specific implementation, the system is used by performing the following steps: Step S1: Trainer login and calibration.

[0195] The trainee stands or sits at a distance of 30-60cm in front of the naked-eye 3D display module 100, which is adjusted according to the requirements of the training program. Whether or not the trainee wears the strobe stimulation module 600 is determined by the training program. The system acquires the trainee's visual parameters, including interpupillary distance, basic visual acuity level, comfortable convergence range and / or dominant eye information. Then, it sequentially completes: coordinate calibration of the touch perception module 200, nine-point calibration of human eye tracking, skeleton recognition and spatial boundary calibration by the three-dimensional visual sensing module 400, and zero-point calibration of the balance posture acquisition module 500.

[0196] Step S2: Processing unit 700 selects the type of training item 800 and issues training instructions.

[0197] The processing unit 700 selects the current training item set from the training item library according to the training cycle, for example, extracting several items from each of the first, second, and third sections, and generates training instructions, including the training item number 800, target depth-of-field configuration, image element generation rules, touch parameters of the touch perception module 200, recognition of the 3D vision sensing module 400, parameters of the balance posture acquisition module 500, timing rules, pass / fail criteria, whether to enable the strobe stimulation module 600 and strobe parameters, etc. The training cycle and item organization method can be implemented in the manner of "32 training contents divided into 3 training chapters, training twice a week, with an interval of at least one day between the two training sessions, for a total of 16 sessions".

[0198] Step S3: The naked-eye 3D display module 100 presents the training scene.

[0199] The processing unit 700 executes the generation algorithm and presents it on the naked-eye 3D display module 100; at the same time, the interface displays necessary prompt elements such as countdown, score / light feedback, and prompt sounds.

[0200] Step S4: Multimodal interactive acquisition and fusion.

[0201] The touch sensing module 200 collects touch coordinates for interactions such as "tap to eliminate", "trajectory drawing", and "button response"; the human eye tracking module 300 outputs the gaze point and eye movement trajectory in real time to determine whether "the gaze center point is maintained", "whether there is any unwanted saccade", and "whether there is any follow-up lag"; the three-dimensional vision sensing module 400 collects three-dimensional motion signals of finger touch / grasping / displacement in space for "air-based touch control of 3D targets"; the balance posture acquisition module 500 collects posture balance signals for "center of gravity driving ball movement"; if the strobe stimulation module 600 is enabled, the processing unit 700 controls the switching between transparency and light blocking at a preset frequency to enhance reaction training and spatial perception.

[0202] Step S5: Training determination and adaptive adjustment.

[0203] The processing unit 700 calculates training metrics for each training item in real time, such as reaction time (RT), hit rate, gaze deviation rate, follow lag, balance offset amplitude, and hand-to-target spatial error. It also adapts the training difficulty based on thresholds, such as increasing the target movement speed, increasing / decreasing the depth difference, shortening the cue interval, and increasing randomness.

[0204] Step S6: Output and record training results.

[0205] After each training item, the processing unit 700 executes an adaptive algorithm. Following the strategy of the current training item 800, if the switching strategy between levels of the current training item 800 is to switch to the next training subject, then the next training item 800 is switched for training. If the current training item 800 requires multiple training sessions, then the training for that training item 800 is repeated. Throughout the training process, the system monitors the trainee's gaze point position in real time through the eye-tracking module 300. When gaze deviation or gaze drift is detected, the system can automatically dynamically adjust the display area of ​​the 3D image to ensure that the training stimulus always falls within the trainee's effective gaze range, avoiding ineffective training due to deviation.

[0206] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device, such as a personal computer, server, or network device, to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0207] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processing unit, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device. For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0208] A more specific, non-exhaustive list of examples of computer-readable media includes the following: electronic devices with electrical connections having one or more wires, portable computer disk drives, magnetic devices, random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM) or flash memory, fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0209] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0210] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A motion vision training method based on a naked-eye 3D display, characterized in that: include Based on the training objectives, determine the training items (800); Read the preset scene construction parameters, depth control parameters and training action types in the training project (800); The target depth-of-field rendering mode is determined based on the aforementioned depth-of-field control parameters; The corresponding peripheral unit is matched according to the type of training action, and the peripheral unit includes a feedback acquisition unit and / or an auxiliary stimulation unit; The generation algorithm is invoked to generate a corresponding training scene based on the scene construction parameters and the target depth of field presentation mode, and the corresponding training scene is output to the naked-eye 3D display module (100) for naked-eye 3D presentation; During the presentation of the corresponding training scenario, the feedback signals generated by the trainee in response to the corresponding training scenario are collected by the matched feedback acquisition unit, and / or the auxiliary stimulation signals synchronized with the corresponding training scenario are output by the matched auxiliary stimulation unit. The trainee's performance data is obtained based on the feedback signal; Based on the performance data, the next training state of the next training item (800) or the current training item (800) is determined by an adaptive algorithm, and the scene construction parameters and / or depth control parameters of the subsequent training scene are updated.

2. The motion vision training method based on a naked-eye 3D display as described in claim 1, characterized in that: The generation algorithm includes an image calculation algorithm and a display algorithm; The image calculation algorithm generates the original training image based on the scene construction parameters; The display algorithm generates a corresponding training scene adapted to the naked-eye 3D display module (100) based on the original training image and the target depth-of-field presentation mode; The output of the image calculation algorithm is used as the input condition of the display algorithm, so that the corresponding training scene retains the spatial relationship defined by the scene construction parameters and forms a depth relationship corresponding to the target depth rendering mode.

3. The motion vision training method based on a naked-eye 3D display as described in claim 1 or 2, characterized in that: The adaptive algorithm includes inter-level switching strategy and intra-state switching strategy; The inter-level switching strategy is used to determine the next training item (800) after the current training item (800) meets the preset completion conditions. The internal state switching strategy is used to determine the next training state of the current training item (800) when the current training item (800) does not meet the preset completion condition; The preset completion conditions are determined based on the performance data, so that the next training item (800) or the next training state corresponds to the trainee's feedback results in the current training item (800).

4. The motion vision training method based on a naked-eye 3D display as described in claim 3, characterized in that: The training program (800) includes a first training class, which is dynamic visual acuity training; The training action types corresponding to the first training class include at least one of target tracking action, target following action, and trigger response action; The target depth-of-field rendering mode corresponding to the first training class is a multi-depth-of-field dynamic rendering mode; When the training action type is a target tracking action or a target following action, the matching peripheral unit includes an eye tracking module (300). When the training action type is a trigger response action, the matched peripheral unit includes a touch sensing module (200). In the first training class, a visual stimulus target in motion is presented through a naked-eye 3D display module (100), and feedback signals generated by the trainee in response to the visual stimulus target are collected through a matched peripheral unit.

5. The motion vision training method based on a naked-eye 3D display as described in any one of claims 1, 2, and 4, characterized in that: The training program (800) includes a second training class, which is visual perception training; The training action types corresponding to the second training category include at least one of touch reproduction action, memory recall action, and visual recognition action; The target depth-of-field rendering mode corresponding to the second training class is the random depth-of-field rendering mode; When the training action type is a touch reproduction action, the matching peripheral unit includes a touch sensing module (200). When the training action type is a memory recall action, the matching peripheral unit includes a touch sensing module (200). When the training action type is a visual recognition action, the matching peripheral unit includes an eye tracking module (300). In the second training class, visual stimulus graphics with random depth relationships are presented through a naked-eye 3D display module (100), and feedback signals generated by the trainee in response to the visual stimulus graphics are collected through a matched peripheral unit.

6. The motion vision training method based on a naked-eye 3D display as described in claim 5, characterized in that: The training program (800) includes a third training category, which is eye movement training; The training action types corresponding to the third training category include depth-of-field switching response actions; The target depth-of-field presentation mode corresponding to the third training class is an alternating mode of inner depth-of-field and outer depth-of-field; The peripheral unit for the third training class matching includes an eye-tracking module (300). In the third training class, visual stimulus objects with different depth attributes are presented through the naked-eye 3D display module (100), and the depth attributes of the visual stimulus objects are controlled to switch between inner depth and outer depth. The eye-tracking module (300) collects the eye movement feedback signals of the trainee during the depth switching process.

7. The motion vision training method based on a naked-eye 3D display as described in claim 4 or 6, characterized in that: The training program (800) includes a fourth training category, which is visual-motor training; The training action types corresponding to the fourth training category include at least one of planar precise response actions, external depth space reach actions, and pure vision tracking actions. The target depth-of-field rendering mode corresponding to the fourth training class is a random position dynamic rendering mode; When the training action type is a planar precise response action, the matching peripheral unit includes a touch sensing module (200). When the training action type is an external depth space reach action, the matching peripheral unit includes a three-dimensional vision sensing module (400). When the training action type is a pure visual tracking action, the matching peripheral unit includes a human eye tracking module (300). In the fourth training class, visual stimulus targets are presented at random locations and / or at random times through a naked-eye 3D display module (100), and feedback signals generated by the trainee in response to the visual stimulus targets are collected through a matched peripheral unit.

8. The motion vision training method based on a naked-eye 3D display as described in claim 7, characterized in that: The training item (800) includes a fifth training class, which is a fusion function training; The training action types corresponding to the fifth training category include plane and depth-of-field switching gaze actions; The target depth-of-field presentation mode corresponding to the fifth training class is a switching mode between a planar position and a preset depth-of-field position; The fifth training class matching peripheral unit includes an eye tracking module (300). In the fifth training class, a planar display object and a depth display object are presented through a naked-eye 3D display module (100), and the depth display object is controlled to switch between a planar position and a preset depth position. The eye-tracking module (300) collects the eye movement feedback signal of the trainee during the switching gaze process.

9. The motion vision training method based on a naked-eye 3D display as described in any one of claims 1, 2, 4, 6, and 8, characterized in that: The training program (800) includes a sixth training category, which is sensory integration training; The training action types corresponding to the sixth training category include at least one of postural balance actions, spatial motion feedback actions, and visual-vestibular coordination actions. The target depth-of-field presentation mode corresponding to the sixth training class is a dynamic depth-of-field presentation mode that is coordinated with proprioceptive feedback and / or vestibular feedback. When the training action type is a posture balance action, the matching peripheral unit includes a balance posture acquisition module (500). When the training action type is a spatial motion feedback action, the matching peripheral unit includes a three-dimensional vision sensing module (400). When the training action type is a visual-vestibular coordinated action, the matching peripheral unit includes a balance posture acquisition module (500) and / or a three-dimensional visual sensing module (400), and synchronously calls a strobe stimulation module (600) so that the strobe stimulation module (600) switches between transparent and light-blocking states according to a preset frequency. In the sixth training class, a dynamic depth-of-field training scene is presented through a naked-eye 3D display module (100), and feedback signals of the trainee in visual tasks, proprioceptive tasks and / or vestibular coordination tasks are collected through the matched peripheral unit. When the strobe stimulation module (600) is invoked, the dynamic depth-of-field training scene is presented under intermittent visual input conditions, enabling trainees to complete posture balance, spatial motion feedback and / or visual-vestibular coordination training under limited visual stimulation.

10. A motion vision training system, employing the motion vision training method as described in any one of claims 1 to 9, characterized in that: include, A naked-eye 3D display module (100) is used to present the corresponding training scene in naked-eye 3D mode; The touch sensing module (200), integrated with the naked-eye 3D display module (100), is used to receive the touch operation of the trainee and generate touch signals; The human eye tracking module (300) is used to acquire the trainee's gaze point position and eye movement trajectory in real time; A three-dimensional vision sensing module (400) is used to acquire motion signals of the trainee in three-dimensional space; The balance posture acquisition module (500) is used to acquire the posture balance signal of the trainee during the training process; The strobe stimulation module (600) is used to switch between transparent and light-blocking states at a preset frequency during training to enhance the trainee's reaction training and spatial perception ability. The processing unit (700) is connected to the naked-eye 3D display module (100), the touch sensing module (200), the human eye tracking module (300), the three-dimensional vision sensing module (400), the balance posture acquisition module (500), and the strobe stimulation module (600), respectively. The processing unit (700) is used to determine training items (800) according to the training objective, read the preset scene construction parameters, depth control parameters and training action types in the training items (800), determine the target depth presentation mode according to the depth control parameters, call the touch perception module (200), the human eye tracking module (300), the three-dimensional vision sensing module (400), the balance posture acquisition module (500) and / or the strobe stimulation module (600) according to the training action type, and call the generation algorithm to generate the corresponding training scene based on the scene construction parameters and the target depth presentation mode; The processing unit (700) is also used to obtain the trainee's performance data based on the feedback signal collected by the called module, and determine the next training state of the next training item (800) or the current training item (800) based on the performance data through an adaptive algorithm, and update the scene construction parameters and / or depth control parameters of the subsequent training scene.

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