Mobility Steering Synchronization System and Driving Method Thereof via Cognitive Latency Cancellation and Closed-Loop Control

Abstract

The present invention relates to a method for driving a magnetic-sync engine that is implanted into a control system of a mobility means or a mobile robot device to restructure a user's cognitive path, wherein (a) A step in which a cognitive state monitoring unit measures a real-time cognitive load based on multimodal sensor data including operation data applied to the user's eye movement and steering operation device; (b) A step in which a Cognitive Optimization module synthesizes a base frequency layer or a visual synchronization frequency with the sensory stimulus signal of the user to remove cognitive resistance on the user's cognitive processing pathway and induce a state of brain immersion; (c) A virtual force calculator that calculates a virtual force corresponding to the spatial distance between the user's operation input point and a target trajectory for driving or work trajectory control; (d) A step in which a pre-emption timing controller provides a guide corresponding to the target trajectory ahead of the expected arrival time of the moving means or the mobile robot device in order to offset the cognitive delay occurring in the user's brain; and (e) A method for driving a magnetic sink engine characterized by including a real-time responsiveness maximization step in which the immersion induction by the cognitive optimization module and the cancellation of the cognitive delay operate as a continuous feedback loop to synchronize the user's steering response by performing closed-loop control that updates the size of the virtual workforce and the advance provision interval of the guide in real time based on the measured cognitive load.

Description

Technology Field

[0001] The present invention relates to the field of Human-Computer Interaction technology, and more specifically, to an interactive cognitive guidance system and method that guides user input to a target trajectory through virtual dynamic coupling forces while monitoring the user's cognitive state in real time.

[0003] More specifically, the present invention relates to a real-time interactive cognitive guidance system and method comprising a Magnetic-Sync Engine (ESE) that applies the concept of magnetic force in physics to a cognitive interface to generate a virtual attractive force between a user input point and a target trajectory presented by the system, pre-projects a visual guide to compensate for the delay in processing visual information in the brain, and dynamically adjusts the size of the virtual attractive force by fusion of multimodal sensor data such as eye tracking and tactile input to calculate the cognitive load in real time. Background Technology

[0004] The present invention relates to a general-purpose cognitive guidance technology that can be applied to various fields, such as cognitive rehabilitation therapy, attention deficit hyperactivity disorder (ADHD) training, dementia prevention programs, precision motor control training, and educational games.

[0006] In modern society, the importance of cognitive training and rehabilitation therapy through digital interfaces is continuously increasing. In particular, with the advancement of an aging society, the demand for dementia prevention and the maintenance of cognitive function is surging, making effective cognitive-motor training systems essential in various fields, such as motor rehabilitation for stroke or traumatic brain injury patients, improving concentration in children with ADHD, and technical training for high-precision workers like surgeons and pilots.

[0008] In response to this demand, various forms of cognitive training and biofeedback systems have been developed. For example, technologies have been proposed that utilize eye-tracking technology to monitor a user's attention state (Patents No. 10-2024-0050367, No. 10-2024-0049379, etc.) or to evaluate cognitive function through electroencephalogram (EEG) measurements (Patents No. 10-2004-0056154, No. 10-2006-0033392, etc.). Additionally, biofeedback-based training systems (Patents No. 10-1998-0053878, No. 10-2002-0082197, etc.) and interactive training systems utilizing virtual reality (VR) environments (Patents No. 10-2024-0139947, No. 10-2024-0126816, etc.) have also been developed.

[0010] However, these conventional technologies are limited to passive and reactive systems that merely monitor the user's state and provide feedback afterward. Specifically, conventional eye-tracking systems stop at detecting the user's gaze position to assess attention levels and lack a mechanism to actively guide the user along the correct path. Furthermore, existing visual guidance systems remain static, merely displaying the target location at the current moment, or passive, following user input; this leads to increased cognitive burden when the user must make sudden changes in direction or follow complex paths.

[0012] Furthermore, conventional technology does not account for the inevitable delay (typically 100 to 200 milliseconds) required for the brain to process visual information, which has limitations in that a temporal discrepancy occurs between user input and target trajectory in tasks requiring rapid motor control. This reduces the accuracy of visuomotor synchronization and poses a problem that limits training effectiveness, particularly in surgical training or musical instrument training that require precise control.

[0014] Furthermore, existing difficulty adjustment methods employ a discrete and stepwise approach that presents pre-set stages sequentially, which limits their ability to flexibly respond to individual learning speeds or real-time changes in cognitive states. There is a concern that users may experience excessive cognitive load or, conversely, boredom, which leads to problems such as reduced motivation for sustained participation and decreased learning efficiency.

[0016] Furthermore, conventional sensor-based cognitive evaluation systems rely on single sensors, such as brainwaves or eye tracking, or are limited to simple data aggregation even when multiple sensors are used, making it difficult to precisely grasp a user's complex cognitive state. For example, eye tracking alone cannot capture the user's motor control stability or tension levels, which poses a limitation that restricts the accuracy of cognitive load assessment.

[0018] Furthermore, conventional systems employ discontinuous feedback methods—such as providing audible warnings, visual alerts, or prompting a restart when a user deviates from the target path—which causes frustration and disrupts the natural learning flow. The absence of intuitive feedback mechanisms, such as physical constraints or gradual return guidance, limits their ability to facilitate unconscious learning.

[0020] Due to the limitations of these conventional technologies, there is a growing technical need for a new type of interactive cognitive guidance system that can precisely evaluate the user's cognitive state in real time, compensate for cognitive processing delays in the brain, actively guide the user toward a target trajectory, and provide natural and intuitive feedback.

[0022] Furthermore, conventional cognitive training and audio feedback systems have limitations as they are confined to 'post-processing' methods that simply apply frequencies to finished audio sources or to passive auditory feedback, thereby overlooking the biomechanical effects of digital and analog signals on human brainwaves and cognitive pathways. In order to reduce user auditory fatigue and induce brainwave entrainment in the saturated digital audio market and interactive training environments, there is an urgent need for source interface optimization technology that synchronizes cognitive engineering frequencies starting from the performer (singer) and instrument stages, which are the sources of sound generation.

[0024] The problem that the present invention aims to solve may be to overcome the following technical limitations of conventional cognitive training and interactive systems.

[0026] First, conventional systems remain in a passive and reactive structure that monitors the user's current state and provides subsequent feedback, which may result in limited real-time synchronization accuracy between user input and the target trajectory. In particular, a one-way approach that simply displays the target path or provides warnings when deviating may have limitations in actively inducing the user to precisely follow the target trajectory.

[0028] Second, existing technologies do not account for the inevitable latency required for the brain's processing of visual information, which may lead to a decrease in the accuracy of visuomotor synchronization in tasks requiring rapid motor control. Due to the time required for visual information to be transmitted from the retina to the cerebral cortex and processed, reliance solely on real-time feedback based on the current viewpoint can result in the accumulation of temporal errors in the user's cognitive-motor control.

[0030] Third, conventional systems evaluate a user's cognitive state by relying on a single sensor (e.g., brainwaves or eye tracking) or simply aggregating sensor data, which may limit their ability to measure cognitive load in a multidimensional and precise manner. Consequently, it is difficult to determine the optimal level of support for each individual and situation in real time, which can lead to problems such as excessive frustration or boredom resulting from uniform difficulty adjustments.

[0032] Fourth, existing technologies may have the problem of hindering user immersion by providing discontinuous feedback, such as warning sounds, vibrations, or visual notifications, or by requiring the user to restart the task when deviating from the target trajectory, making it difficult to induce a natural return.

[0034] Fifth, conventional visual guides are provided as static or simple tracking types centered on the current location, which may lead to a problem where the user lacks the information necessary to predict future paths and prepare in advance, resulting in delayed reaction times and increased cognitive burden in sections of rapid trajectory changes. Prior art literature

[0035] Korean Patent Publication No. 10-2003-0101713 (December 31, 2003) Korean Patent Publication No. 10-1996-0702379 (August 28, 1995) The problem to be solved

[0036] Accordingly, the present invention may aim to provide an innovative real-time interactive cognitive guidance system and method that actively guides through dynamic coupling between user input and target trajectory, provides preemptive timing control that compensates for cognitive delay in the brain, provides real-time adaptive support based on precise cognitive load evaluation through multimodal sensor fusion, prevents deviation through natural physical feedback, and clearly guides the user's cognitive path through future-oriented visual guides. means of solving the problem

[0037] The first objective of the present invention is a method for driving a Magnetic-Sync Engine, which is implanted into a control system of a mobility means or a mobile robot device to restructure a user's cognitive path, comprising: (a) a step in which a cognitive state monitoring unit measures a real-time cognitive load based on multimodal sensor data including operation data applied to the user's eye movement and steering operation device; (b) a step in which a cognitive optimization module synthesizes a base frequency layer or a visual synchronization frequency with the user's sensory stimulus signal to remove cognitive resistance on the user's cognitive processing path and induce a state of brain immersion; (c) a step in which a virtual force calculator calculates a virtual force corresponding to the spatial distance between the user's operation input point and a target trajectory for driving or work trajectory control; and (d) a step in which a pre-emption timing controller provides a guide corresponding to the target trajectory ahead of the expected arrival time of the mobility means or the mobile robot device to offset cognitive delay occurring in the user's brain. and (e) a real-time responsiveness maximization step in which the immersion induction by the cognitive optimization module and the cancellation of the cognitive delay operate as a continuous feedback loop to synchronize the user's steering response by performing closed-loop control that updates the size of the virtual workforce and the advance provision interval of the guide in real time based on the measured cognitive load, can be achieved as a magnetic sink engine driving method.

[0038] The second objective of the present invention can be achieved by a method for driving a cognitive optimization module, wherein the method comprises: a step of measuring a real-time cognitive load based on multimodal sensor data including data of a user's eye movements and a steering control device for synchronizing steering responses of a means of transportation or a mobile robot device; and a cognitive optimization step of inducing a state of immersion in the brain by blocking cognitive resistance, which is neurological friction on the user's cognitive processing path, by layering and synthesizing a base frequency layer or a visual synchronization frequency on the user's sensory stimulus signal. The immersion state information induced by the cognitive optimization step is transmitted to a Magnetic-Sync Engine core that offsets cognitive delay with a target trajectory based on the user's real-time response data to form continuous closed-loop feedback.

[0039] The above step (d) may be characterized by forming a cognitive highway that allows the user to unconsciously follow a driving or work trajectory without undergoing a conscious analysis process by extracting the current velocity vector and acceleration of the means of transport or the mobile robot device to calculate a future expected position to be reached after 0.1 to 0.2 seconds, and projecting a guide in advance onto a point on the target trajectory corresponding to the expected position.

[0040] The above closed-loop control may be characterized by a step of preemptively preventing user operation errors or path deviation of the robot device by performing magnetic damping control, which increases the coupling coefficient of the virtual force and applies virtual viscosity resistance to the user's steering movement when a deviation acceleration is detected that attempts to deviate from the target trajectory due to the user's operation being sudden.

[0041] When performing the magnetic damping control, the method may further include a step of inducing the user to intuitively perceive the state of coupling with the target trajectory not only visually but also tactilely by transmitting a fine vibration or resistance sensation to the steering control device in response to the direction in which the virtual force acts through a haptic feedback module.

[0042] In claim 1, the guidance provision of step (d) may be characterized by the tail generating unit extending the length of the streamlined tail in proportion to the driving speed of the moving means or the mobile robot device and rendering it, and dynamically enhancing the visibility of the tail in the trajectory section where an increase in the user's cognitive load is detected.

[0043] In the above closed-loop control, the method may further include a step of mitigating the difficulty of control caused by abrupt trajectory changes in the moving means or the mobile robot device by deforming the curvature or allowable width of the target trajectory in real time based on the measured cognitive load.

[0044] The cognitive state monitoring unit of step (a) above may be characterized by calculating the user’s gaze point dispersion and pupil diameter change through a camera sensor, extracting gripping pressure and frequency variability of pressure through a tactile input sensor equipped in the steering device, and then fusing them with a neural network state estimator to calculate the real-time cognitive load index.

[0045] When providing the guide in step (d) above, the magnetic coupling visualizer may further include the step of displaying the dynamic coupling state by adding a visual tension effect similar to the magnetic field lines of a magnet to the user's display centered on the target trajectory.

[0046] The method may further include a step in which, if the synchronization error between the above-mentioned operation input point and the above-mentioned target trajectory is maintained below a preset threshold for a certain period of time or longer, the neurofeedback output unit simultaneously outputs a stabilization audio signal of a specific frequency and a visual animation effect to induce brainwave stabilization of the user.

[0047] The third objective of the present invention can be achieved as a cognitive assistance and control system mounted on a means of transportation or a mobile robot device, comprising: a cognitive state monitoring unit that collects multimodal sensor data including user eye movement and operation data applied to a steering operation device to measure a real-time cognitive load; a cognitive optimization module that blocks cognitive resistance by synthesizing a base frequency layer or a visual synchronization frequency to the user's sensory stimulus signal; a virtual force calculator that calculates a virtual force corresponding to the spatial distance between the user's operation input point and a target trajectory for control; a preemptive timing controller that controls a guide corresponding to the target trajectory to be provided ahead of the expected arrival time of the means of transportation or the mobile robot device in order to offset cognitive delay occurring in the user's brain; and a processor that drives a cognitive synchronization engine organically coupled with the cognitive optimization module by performing closed-loop control that updates the size of the virtual force and the interval of the guide's preemptive provision in real time based on the measured cognitive load. Effects of the invention

[0048] According to the present invention, through dynamic coupling control based on virtual attraction (Fsync) that creatively applies the concept of magnetic force in physics to a cognitive interface, it is possible to provide a significant technical effect of actively and naturally guiding the user to a target trajectory compared to conventional simple path display or post-feedback methods. In particular, due to a mechanism in which the magnitude of the virtual attraction changes dynamically in proportion to the distance between the user input point and the target trajectory, it is possible to solve a technical challenge that could not be achieved with existing unidirectional guidance systems by implementing bidirectional interaction, thereby inducing the user to naturally follow the target while feeling a sense of being 'pulled'.

[0050] In addition, the pre-emption timing control of the present invention can fundamentally improve the accuracy of visuomotor synchronization compared to conventional current-time-based real-time feedback systems by explicitly modeling and compensating for the delay time inevitably required for the brain's visual information processing. Through a prediction-based proactive approach that presents a visual guide to the point expected to be reached by the user ahead of the actual time of arrival, the user can prepare and react in advance even in sections of rapid trajectory change, resulting in the advantageous effect of shortening reaction time and reducing cognitive burden.

[0052] In addition, the multimodal sensor fusion technology of the present invention integrates heterogeneous data collected from eye-tracking sensors and tactile input detectors to precisely calculate the user's cognitive load in a multidimensional manner, thereby enabling the real-time determination of the optimal level of support for each individual and situation compared to conventional single-sensor-based evaluation methods. Through closed-loop control that varies the coupling coefficient of virtual personnel in real-time according to the calculated cognitive load, it is possible to overcome the limitations of uniform difficulty adjustment and provide adaptive support optimized for each user's current state, thereby providing a significant technical effect.

[0054] In addition, the formation of a cognitive highway through tail generation according to the present invention projects a target trajectory in advance toward a future direction to clearly guide the user's cognitive path, thereby significantly reducing cognitive uncertainty regarding 'where to go' compared to conventional static or current location-centered visual guides. Through adaptive visualization technology that dynamically adjusts the length and visibility of the tail according to the user's movement speed, level of cognitive confidence, and trajectory complexity, it is possible to provide personalized visual information, resulting in the advantageous effect of improved learning efficiency.

[0056] In addition, the magnetic damping control of the present invention applies a virtual resistance force by applying the viscous resistance principle of fluid dynamics when acceleration is detected that causes the user to deviate from the target trajectory, thereby providing a natural and intuitive induction of return compared to conventional discontinuous feedback methods such as warning sounds or restarting operations. Due to the promotion of unconscious learning through a sense of physical constraint, it can provide a significant technical effect in that the user can quickly acquire the correct behavioral pattern without explicit instructions.

[0058] In addition, the trajectory deformation matrix of the present invention can provide a continuous and personalized learning environment compared to conventional fixed content or stepwise difficulty adjustment methods by deforming the curvature or width of the target trajectory in real time in response to the user's proficiency in operation. It has the advantageous effect of preventing excessive frustration or boredom and inducing continuous participation by providing challenges optimized for each user's learning curve.

[0061] In addition, the neurofeedback output unit of the present invention can provide powerful positive reinforcement through multimodal synergy effects compared to conventional general audiovisual feedback by complexly outputting an audio signal including a solfège frequency and a blooming animation when the synchronization between the input point and the target trajectory is maintained above a threshold. This can provide significant technical effects, such as enhanced long-term learning effects, by strengthening user motivation and improving the sense of accomplishment due to the immediate reward signal.

[0062] Meanwhile, the effects obtainable from the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present invention belongs from the description below. Brief explanation of the drawing

[0063] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings. FIG. 1 is a block diagram showing the overall configuration of a real-time interactive cognitive guidance system using a magnetic sync engine according to an embodiment of the present invention, illustrating the connection relationships and data flow of a cognitive guidance system (10), a magnetic-sync engine (hereinafter MSE) (100), a cognitive state monitoring unit (110), an eye tracking sensor (111), a tactile input detector (112), a neural network state estimator (113), a virtual pull calculator (101), a preemptive timing controller (102), a trajectory deformation matrix (103), a tail generator (120), a preemptive projection engine (121), a magnetic coupling visualizer (122), a dynamic afterimage controller (123), a neurofeedback output unit (130), an audio synchronizer (131), a visual reward generator (132), and a haptic feedback module (133). FIG. 2 is a flowchart illustrating the overall operation process of a real-time interactive cognitive induction method using a magnetic sink engine according to an embodiment of the present invention, and illustrates a control flow including a multimodal cognitive state monitoring and initial trajectory generation step (S210), a magnetic sink-based virtual attraction and coupling coefficient calculation step (S220), a preemptive timing determination step for brain cognitive delay compensation (S230), a tail dynamic variable rendering and preemptive projection step (S240), a synchronization success determination and neurofeedback output step (S250), and a cognitive state feedback loop and engine parameter update step (S260). FIG. 3 is a time-position diagram for explaining the preemptive timing control, which is a key feature of the present invention, with the horizontal axis representing time and the vertical axis representing position, showing that the temporal lead interval between the user's input point curve and the tail's projection point curve is 0.1 to 0.2 seconds, and illustrating the principle of forming a cognitive highway by compensating for the delay in the brain's visual information processing. FIG. 4 is a virtual attraction vector analysis diagram for explaining the magnetic sink mechanism of the present invention, in which the direction and magnitude of the virtual attraction acting between the target trajectory tail and the user input point are represented by arrows, and dotted lines similar to magnetic field lines are placed around the trajectory to illustrate the dynamic coupling state that pulls the user to the target path. FIG. 5 illustrates an embodiment in which the tail generating unit of the present invention dynamically changes the shape of the tail according to the user's perception state, showing (a) a thin and clear tail when concentration is high, (b) a tail that becomes thicker or has an enhanced luminous effect and expanded width when hesitation or perception load is high, and (c) a tail with a long tail portion when moving at high speed. FIG. 6 is a diagram illustrating the audiovisual reward provided by the neurofeedback output unit of the present invention upon successful synchronization, illustrating a multimodal feedback mechanism that stimulates the brain reward circuit by combining blooming animation frames, a visual effect of golden particles spreading out, and a 528Hz solfège frequency waveform when the user's input point perfectly coincides with the tail. Specific details for implementing the invention

[0064] The above objects, other objects, features, and advantages of the present invention will be easily understood through the following preferred embodiments associated with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided to ensure that the disclosed content is thorough and complete and to ensure that the spirit of the invention is sufficiently conveyed to a person skilled in the art.

[0065] In this specification, when a component is described as being on another component, it means that it may be formed directly on the other component or that a third component may be interposed between them. Also, in the drawings, the thicknesses of the components are exaggerated for the effective description of the technical content.

[0066] The embodiments described herein will be explained with reference to cross-sectional and / or plan views, which are exemplary illustrations of the invention. In the drawings, the thicknesses of films and regions are exaggerated for effective explanation of the technical content. Accordingly, the shapes of the exemplary drawings may be modified by manufacturing techniques and / or tolerances, etc. Accordingly, the embodiments of the invention are not limited to the specific shapes depicted but include variations in shape produced according to the manufacturing process. For example, a region depicted as a right angle may be rounded or have a certain curvature. Accordingly, the regions illustrated in the drawings have properties, and the shapes of the regions illustrated in the drawings are intended to illustrate specific shapes of the regions of the device and are not intended to limit the scope of the invention. Although terms such as first, second, etc., have been used to describe various components in the various embodiments of this specification, these components should not be limited by such terms. These terms are used merely to distinguish one component from another. The embodiments described and illustrated herein also include their complementary embodiments.

[0067] The terms used herein are for describing the embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, 'comprises' and / or 'comprising' do not exclude the presence or addition of one or more other components to the mentioned components.

[0068] In describing the specific embodiments below, various specific details have been included to explain the invention more specifically and to aid understanding. However, a reader with sufficient knowledge in the art to understand the invention will recognize that it can be used without these various specific details. In some cases, it is noted in advance that commonly known aspects that are not significantly related to the invention have been omitted to prevent unnecessary confusion in describing the invention.

[0070] The present invention relates to a real-time interactive cognitive guidance system and method using a Magnetic-Sync Engine that monitors the user's cognitive state in real time and actively controls the dynamic coupling between the user's input motion and the target trajectory presented by the system.

[0072] Existing cognitive training or interactive systems have been limited to simply displaying target paths to the user or providing retrospective feedback by passively tracking user input. This approach fails to account for the physiological delays that occur when a user perceives visual information and converts it into motor commands, and suffers from reduced learning efficiency due to the inability to adapt in real-time to the user's cognitive load.

[0074] To solve this problem, the present invention aims to provide an innovative cognitive guidance environment that enables a user to accurately follow a target trajectory without conscious effort by combining pre-emption timing control technology, which explicitly compensates for the delay in visual information processing of the brain, and magnetic sink technology, which provides natural guidance by generating a virtual attractive force similar to the magnetic force of physics between a user input point and a target trajectory.

[0076] FIG. 1 is a block diagram illustrating the overall configuration of a real-time interactive cognitive guidance system (10) using a variable magnetic sink engine based on user cognitive state monitoring according to one embodiment of the present invention.

[0078] The cognitive guidance system (10) of the present invention may be configured around a magnetic-sync engine (hereinafter 'MSE') (100) that tracks the user's cognitive state in real time and controls the dynamic coupling between the user's input point (Puser) and the target trajectory (Ptarget) presented by the system. The MSE (100) may include a cognitive state monitoring unit (110) that monitors the user's cognitive state in real time, a virtual force calculator (101) that calculates a virtual force between the user input and the target trajectory, a preemptive timing controller (102) that compensates for the delay in processing visual information of the brain, and a trajectory deformation matrix (103) that deforms the geometric characteristics of the target trajectory in real time.

[0080] The cognitive state monitoring unit (110) can perform the role of quantifying the current cognitive concentration and load state by collecting the user's physical response and neurological response in combination. The cognitive state monitoring unit (110) may include an eye tracking sensor (111) that detects the user's eye movements and a tactile input sensor (112) that detects the user's manipulation speed and pressure. The eye tracking sensor (111) can measure the visual cognitive load by detecting the user's eye movement patterns, such as saccades and pupil dilation. Specifically, the eye tracking sensor (111) can acquire an eye image through an infrared camera and calculate a gaze vector by detecting the pupil center and corneal reflection point. The user's cognitive load level can be evaluated by analyzing the gaze movement pattern to classify fixation and saccade, and by measuring the pupil diameter to calculate the rate of change relative to a reference value. Dilation of pupil size can generally be used as an indicator of increased cognitive load, and the spatial dispersion of fixation points can reflect the user's degree of visual attention.

[0082] The tactile input sensor (112) can collect the user's contact pressure, movement speed, acceleration, etc. through a pressure sensor embedded in a touchscreen or a dedicated input device. The tactile input sensor (112) can collect contact location, pressure, and timestamp from touch events and calculate movement speed from consecutive contact points. It can calculate the pressure change rate and average pressure, and calculate a motion control stability score by extracting micro-tremor components through frequency domain analysis. Excessive pressure can be used as an indicator of the user's tension or high cognitive load, and irregularity in speed change can reflect cognitive uncertainty. Tremor patterns can be used to evaluate fatigue or neurological condition.

[0084] The cognitive state monitoring unit (110) may further include a neural network state estimator (113) that fuses multi-sensor data collected from an eye-tracking sensor (111) and a tactile input detector (112) to calculate a comprehensive cognitive load index in real time. The neural network state estimator (113) can extract feature vectors including pupil size, gaze dispersion, etc. from eye-tracking data, and extract feature vectors including pressure average, velocity dispersion, etc. from tactile data. After normalizing and combining the extracted feature vectors, a pre-trained machine learning model can be applied to predict the cognitive load index. A stable cognitive load index can be calculated by removing noise and smoothing through time series filtering. This multimodal data fusion method enables precise cognitive state evaluation that is impossible with a single sensor, and can correct for individual differences by measuring the relative load relative to an individual baseline.

[0086] The virtual force calculator (101) of the MSE (100) can measure the spatial distance between the user's current input point (Puser) and the target trajectory (Ptarget) presented by the system in real time, and perform the role of calculating the corresponding virtual force (Fsync) (synchronization). The virtual force calculator (101) can obtain the Puser coordinates from the input device and obtain the Ptarget coordinates at the current time from the target trajectory database. It can calculate the Euclidean distance between the two points and calculate the virtual force corresponding to the distance. The virtual force is based on a principle similar to the magnetic force in physics, and generates a stronger force as the distance increases, thereby naturally guiding the user toward the target trajectory. The virtual force calculator (101) can dynamically adjust the coupling coefficient according to the cognitive load index received from the cognitive state monitoring unit (110). As the cognitive load increases, the coupling coefficient is increased to provide stronger guidance, and as the cognitive load decreases, the coupling coefficient is decreased to grant autonomy to the user. The calculated virtual force can be converted into an input correction signal and reflected in the user interface, so that the user feels a stronger return force as they deviate from the target trajectory, thereby inducing natural path following.

[0088] The preemptive timing controller (102) can perform the role of controlling the output of a visual guide of a point expected to be reached by the user ahead of the actual arrival time to compensate for the delay in the brain's visual information processing. The human brain takes a certain amount of time for visual information to be transmitted from the retina to the cerebral cortex and processed, and this physiological delay can be a major cause of error in rapid motor control. The preemptive timing controller (102) can explicitly model this cognitive delay and predict a future arrival point by analyzing the user's current movement speed, acceleration, and movement pattern. It can extract the current velocity vector and acceleration from the user's input trajectory history data and calculate the expected position after the preemptive time through a prediction model. It can determine a point on the target trajectory corresponding to the expected position and transmit it to the tail generator (120) to command the output of a preemptive visual guide. The preemptive timing controller (102) can dynamically adjust the preemptive time interval according to the user's reaction speed and proficiency, and can continuously optimize the preemptive time value by feeding back user reaction data. This can improve the accuracy of visual-motor synchronization, and users can see and move in advance, which reduces reaction time and enables smooth tracking even in sections of rapid trajectory changes.

[0090] The trajectory deformation matrix (103) can perform the role of deforming the geometric characteristics of the target trajectory in real time according to the user's operation proficiency, cognitive state, and task difficulty requirements. The trajectory deformation matrix (103) can dynamically adjust the curvature or width of the target trajectory. It can adjust the difficulty by deforming sharp curves into gentle curves or applying appropriate curvature to straight sections, and can adjust the level of precision required by widening or narrowing the tolerance range. The trajectory deformation matrix (103) receives the current cognitive load level from the cognitive state monitoring unit (110) and can calculate a proficiency index based on the user's past performance history in the proficiency evaluation module. Based on the cognitive load level and the proficiency index, it can calculate curvature adjustment coefficients and width adjustment coefficients and generate a deformed trajectory by applying a deformation function to the original trajectory data. The deformed trajectory can be transmitted to the virtual force calculator (101) and the tail generator (120) and presented to the user. Through these adaptive trajectory variations, personalized difficulty adjustment becomes possible, preventing excessive frustration or boredom and enhancing immersion by providing continuous challenges.

[0092] MSE (100) can perform magnetic damping control to apply virtual viscous resistance to the user's input movement when acceleration is detected at the user's input point attempting to deviate from the target trajectory. Magnetic damping control can generate a resistance force proportional to the velocity when attempting to deviate by applying the viscous resistance principle of fluid dynamics. An acceleration vector can be calculated from the input trajectory, and a deviation vector from the target trajectory can be calculated to extract the deviation acceleration component. When the deviation acceleration is detected, the damping force can be calculated and converted into an input correction signal to be reflected in the user interface. The damping strength can be dynamically adjusted according to the degree of deviation and user proficiency, and can include a return component in the direction of the target trajectory rather than simple resistance. Through magnetic damping control, a sticky sensation can be provided when attempting to deviate to induce unconscious return, and work accuracy can be improved by preventing sudden deviation.

[0094] The tail generator (120) can perform the role of generating a visual guide under the control of the MSE (100). The tail generator (120) can form a cognitive highway by projecting a target trajectory onto the screen at a leading time interval determined by the preemptive timing controller (102). The tail generator (120) may include a leading projection engine (121), a magnetic coupling visualizer (122), and a dynamic afterimage controller (123). The leading projection engine (121) can render a streamlined trajectory that leads the actual correct path according to the instructions of the MSE (100). It can receive the leading time and predicted position from the preemptive timing controller (102) and extract the target trajectory segment from the current time point to the leading time. The length of the tail (L-Tail, hereinafter L-Tail) is calculated in proportion to the user speed, and sections requiring enhanced visibility can be identified through trajectory complexity analysis. By determining rendering parameters and displaying them on the screen, the user can clearly recognize where to go.

[0096] The magnetic coupling visualizer (122) can make the color of the trajectory vivid when the user is following the trajectory well, and provide a visual tension effect as if a magnetic force is acting when the user deviates. By visually expressing the degree of synchronization between the user's input point and the target trajectory, the user can intuitively grasp the current state. The dynamic afterimage controller (123) can ensure visual continuity by extending the length of the L-tail as the user's movement speed increases. A natural visual flow can be provided by applying a gradient effect that becomes gradually transparent towards the end of the L-tail, and a sense of motion can be conveyed through dynamic animation that makes the L-tail appear to flow along the target trajectory.

[0098] The neurofeedback output unit (130) can perform the role of providing positive reinforcement by outputting a reward signal when the synchronization between the user's input point and the target trajectory is maintained above a threshold. The neurofeedback output unit (130) may include an audio synchronizer (131), a visual reward generator (132), and a haptic feedback module (133). The audio synchronizer (131) may output an audio signal containing a specific solfège frequency. The solfège frequency is a specific frequency that can help stabilize brainwaves and improve concentration, and may be output in the form of binaural beats. The current synchronization error is received from the virtual force calculator (101), and the time during which the error is maintained below a threshold is measured. If the maintenance time is longer than a reference time, a reward trigger is generated, and the audio generation module may output a solfège frequency signal.

[0100] An audio synchronizer (131) according to one embodiment of the present invention goes beyond simple compensation sound output, Cognitive Engineering-Based Frequency Mastering and Source Real-Time Synchronization Technology This can be performed. It is characterized by combining specific frequencies from the stages of the sound source, such as the export and mastering processes, as well as the performer (singer) and instrument (physical / digital) stages.

[0101] Specifically, the audio synchronizer (131) can perform source interface optimization control as follows.

[0102] First, it can enhance the performer's brain-muscle coordination. By analyzing the singer's voice print or the instrument's resonance characteristics in real time, it calculates an 'optimal base-cosmic frequency' that does not cause interference. By feeding this back to the performer in real time to induce brainwaves into an alpha state, it reduces the performer's physical tension and maximizes brain-muscle coordination and performance efficiency.

[0103] Second, the resonance structure of the instrument can be cognitively varied. The aforementioned base frequency layer is directly inserted and controlled into the structural data of the physical instrument's soundbox or the signal generation algorithm of the digital instrument. Through this, the instrument itself functions as a cognitive medium that induces brainwave entrainment in the user.

[0104] Third is the integrity of the raw data. At the initial input stage of hardware such as microphones or audio interfaces, the vocal glottal signal and the base frequency are combined through phase synchronization. This provides the effect of fundamentally blocking cognitive resistance that may occur during the user's reception of audio information.

[0105] The visual reward generator (132) can provide a visual reward in the form of a blooming animation of flowers blooming around the target trajectory in response to the synchronization maintenance. The blooming animation may include visual elements that provide psychological stability and a sense of accomplishment, such as a golden glowing effect or a dance of butterflies. By playing the blooming animation in the graphic rendering module, immediate positive feedback can be provided to the user and motivation can be strengthened. The haptic feedback module (133) can generate fine vibrations at points where the virtual force acts strongly, allowing the user to feel magnetic coupling not only visually but also through touch. By synchronizing auditory and visual stimuli, the multimodal reward effect can be maximized, and the intensity or frequency of the reward can be increased in proportion to the synchronization maintenance time.

[0107] The system (10) of the present invention may further include additional features such as variable magnetic damping technology, a multi-layer cognitive highway, and environment-adaptive luminance control. Variable magnetic damping technology can correct cognitive errors by increasing virtual viscosity and controlling the movement more heavily when the user moves too fast and accuracy decreases. Multi-layer cognitive highway can train cognitive flexibility by presenting multiple L-tails according to the user's proficiency rather than a single path and allowing the user to select. Environment-adaptive luminance control can minimize visual fatigue by automatically adjusting the brightness of the L-tails according to the ambient light level or the user's pupil size.

[0110] *Fig. 2 illustrates a flowchart of a real-time interactive cognitive induction method using a magnetic sink engine according to one embodiment of the present invention.

[0112] The cognitive induction method of the present invention may include a multimodal cognitive state monitoring and initial trajectory generation step (S210), a magnetic sink-based virtual attraction and coupling coefficient calculation step (S220), a preemptive timing determination step for brain cognitive delay compensation (S230), a dynamic variable rendering and preemptive projection step of an L-tail (S240), a synchronization success determination and neurofeedback output step (S250), and a cognitive state feedback loop and engine parameter update step (S260).

[0114] In step S210, when the system is activated, multimodal data such as the user's gaze, touch pressure, and movement speed can be sampled in real time through the cognitive state monitoring unit (110). The MSE (100) can calculate the user's current cognitive load based on the collected data and generate a base target trajectory in response. The target trajectory can be prepared in a state where the curvature and amplitude are adjusted in real time to match the user's initial reaction speed, rather than in a fixed form. The gaze tracking sensor (111) detects the user's eye movement pattern, and the tactile input detector (112) can collect touch pressure and movement speed. The neural network state estimator (113) can fuse the collected data to calculate a cognitive load index, which can be used as a criterion for determining system control parameters in subsequent steps.

[0116] In step S220, the relative distance and vector direction between the user's real-time input point (Puser) and the target trajectory (Ptarget) can be analyzed. A virtual attraction calculator (101) within the MSE (100) can calculate a virtual magnetic force that becomes stronger as the distance between the two points increases. If the user shows a tendency to deviate from the trajectory, the coupling coefficient can be increased to induce the user's perception to return to the trajectory, which can form a dynamic control state where an invisible magnet pulls the user's hand toward the correct path. The virtual attraction can be calculated as a function corresponding to distance, and the coupling coefficient can be dynamically adjusted according to the level of cognitive load. The calculated virtual attraction can be converted into an input correction signal and reflected in the user interface, and the user can be naturally guided toward the target trajectory.

[0118] In step S230, to form the cognitive highway which is the core of the present invention, a physiological delay time can be calculated in which visual information is processed in the brain and leads to a muscle response. The preemptive timing controller (102) can advance the output timing so that a guide is projected in advance to the next point expected to be reached by the user, based on the user's current movement acceleration. The current velocity vector and acceleration are extracted from the user's input trajectory history data, and the expected position after the preemptive time can be calculated through a prediction model. A point on the target trajectory corresponding to the expected position can be determined and transmitted to the tail generator (120) to command the output of a preemptive visual guide. This can create a preemptive projection environment in which the brain moves as if it is unconsciously sliding, rather than the user seeing and following, when the system clears the way.

[0120] In step S240, an L-tail can be displayed on the screen based on the determined pre-trimming timing and the calculated force. The tail generation unit (120) can render a streamlined trajectory whose length varies in proportion to the user's speed, rather than a simple line. When the user enters the cognitive highway synchronized with the trajectory, the transparency of the trajectory can be adjusted or the thickness of the line can be changed to visually convey that the magnetic coupling has stabilized. The length of the L-tail can be extended as the user's movement speed increases, or the visibility of the L-tail can be dynamically enhanced in sections where the user lacks confidence in perception. In sections with high trajectory complexity, the color, transparency, and thickness of the L-tail can be adjusted to provide clear path guidance, and a gradient effect that becomes progressively transparent towards the end of the L-tail can be applied to provide a natural visual flow.

[0122] In step S250, it can be continuously determined whether the user's input point remains within the tolerance range set by the MSE (100). If synchronization is maintained above a set threshold, the neurofeedback output unit (130) can immediately output a combination of solfège frequency and blooming animation. The current synchronization error is received from the virtual force calculator (101), and the time during which the error is maintained below the threshold can be measured. If the maintenance time is longer than the reference time, a reward trigger is generated, the audio generation module outputs a solfège frequency signal, and the graphic rendering module plays a blooming animation. This stimulates the user's dopamine circuit to maximize cognitive achievement and can provide a strong psychological motivation for the user to maintain a state of synchronization with the system. The haptic feedback module (133) can provide tactile feedback by generating micro-vibrations in the direction in which the virtual force acts.

[0124] In step S260, the control parameters of the MSE (100) can be updated in real time based on the measured cognitive response. If the user continuously feels fatigued or an error occurs, the system can immediately reduce the cognitive load by weakening the magnetic attraction or readjusting the leading projection interval. Upon receiving the updated cognitive load index from the cognitive state monitoring unit (110), the coupling coefficient of the virtual attraction calculator (101), the leading time interval of the leading timing controller (102), and the curvature and width adjustment coefficients of the trajectory deformation matrix (103) can be adjusted. Through this closed-loop control, a customized cognitive guidance environment optimized for the user's state can be continuously maintained, and system parameters can be continuously optimized by feeding back the user's performance data.

[0126] FIG. 3 illustrates a pre-emption timing diagram for cognitive delay compensation according to one embodiment of the present invention.

[0128] Figure 3 demonstrates the temporal precedence, which is the most significant differentiating feature of the present invention, that the guide is sent out before the user responds. In a graph with the horizontal axis representing time (t) and the vertical axis representing position (P), the projection point (Ptarget) curve of the L-tail can be positioned ahead of the user's input point (Puser) curve on the time axis. The interval between the two curves can be indicated as the lead time interval, which represents the physiological delay that occurs from the time the human brain processes visual information until it outputs an actual muscle response.

[0130] The pre-precision timing controller (102) can form a cognitive highway environment in which the user unconsciously gets on the trajectory without undergoing a conscious analysis process by projecting an L-tail ahead of the user's expected movement path. By analyzing the user's current movement speed, acceleration, and movement pattern, it can predict the future destination point and determine a point on the target trajectory corresponding to the predicted point. By projecting an L-tail ahead of the determined point, the user is already presented with a guide while processing visual information, which can shorten the reaction time. The pre-precision time interval can be dynamically adjusted according to the user's reaction speed and proficiency, and can be continuously optimized by feeding back user reaction data.

[0132] Figure 4 illustrates a magnetic virtual attraction (Fsync) (synchronization) vector analysis diagram between a user input and a target trajectory according to an embodiment of the present invention.

[0134] Figure 4 is not merely a guideline, but can mathematically visualize a physical coupling that attracts like a magnet. An L-tail, which is the target trajectory, is placed in the center, and a user input point (Puser) can be marked as a dot at a point slightly off the trajectory. A thick arrow (Fsync) (synchronization) pointing from the Puser toward the L-tail is drawn, and dotted lines similar to the magnetic field lines of a magnet can be placed around the trajectory.

[0136] The virtual pull calculator (101) of the MSE (100) can calculate the distance between the user's current location and the correct path in real time. When a deviation occurs, the system can generate a virtual pull (Fsync) (synchronization) to pull the user's cognitive attention toward the trajectory. As the user gets closer to the trajectory, the coupling coefficient increases, which can serve as a physical basis for the user to feel a sense of sync as if the device and the user are connected as one. The virtual pull can be calculated as a function corresponding to distance, and a stronger pull is generated as the distance increases to guide the user toward the target trajectory. The coupling coefficient can be dynamically adjusted according to the level of cognitive load, and as the cognitive load increases, the coupling coefficient can be increased to provide stronger guidance.

[0138] FIG. 5 illustrates an embodiment of dynamic shape variation of an L-tail according to a cognitive state according to one embodiment of the present invention.

[0140] Figure 5 illustrates software flexibility that corresponds to irregular trajectories and changes the shape of the guide according to the cognitive load. When the level of concentration is high, the L-tail can be displayed as thin and sharp with an appropriate leading length. When there is hesitation or high cognitive load, the L-tail can be displayed as thicker, the luminescence effect stronger, and the width expanded. When moving at high speed, the tail portion of the L-tail can be displayed as extended to match the user's speed.

[0142] According to the user's proficiency or fatigue detected by the cognitive state monitoring unit (110), the MSE (100) can change the visual parameters of the L-tail in real time. When hesitation is detected, the visibility of the guide is increased to provide cognitive confidence, and during high-speed immersion, the afterimage effect is adjusted to maintain cognitive continuity, thereby maintaining the optimal state of entry into the cognitive high-speed highway. The trajectory deformation matrix (103) calculates curvature adjustment coefficients and width adjustment coefficients based on the cognitive load level and proficiency index, and can generate a deformed trajectory by applying a deformation function to the original trajectory data. The deformed trajectory can be transmitted to the tail generation unit (120) and presented to the user, and can provide a visual guide optimized for the user's state.

[0144] Figure 6 illustrates an example of neurofeedback and blooming effects upon successful synchronization according to one embodiment of the present invention.

[0146] Figure 6 can visualize the psychological reward mechanism resulting from successful cognitive induction. With the user's input point perfectly aligned with the L-tail at the center, blooming animation frames resembling flowers blooming can be arranged around it. An effect of golden particles spreading out is depicted, and a solfège frequency waveform can be displayed alongside a speaker icon.

[0148] When the magnetic coupling is maintained above a threshold, the neurofeedback output unit (130) can be activated. By combining and outputting a solfège frequency sound optimized for brainwave stabilization along with a visual blooming effect, the user's brain reward circuit can be stimulated and cognitive learning efficiency can be maximized. The audio synchronizer (131) outputs an audio signal containing solfège frequencies, and the visual reward generator (132) can play a blooming animation. The haptic feedback module (133) can provide tactile feedback by generating micro-vibrations, and can maximize multimodal reward effects by synchronizing auditory, visual, and tactile stimuli. The intensity or frequency of the reward can be increased in proportion to the synchronization maintenance time, and motivation can be strengthened by providing immediate positive feedback to the user.

[0150] This embodiment can focus on a method in which the system projects a guide first before the user's brain analyzes visual information, thereby inducing action without cognitive load.

[0152] The eye tracking sensor (111) and tactile input detector (112) of the cognitive state monitoring unit (110) can sample the user's gaze focus point and touch input speed in real time. The eye tracking sensor (111) can acquire an eye image through an infrared camera and calculate a gaze vector by detecting the pupil center and corneal reflection point. The tactile input detector (112) can collect contact location, pressure, and timestamp from touch events and calculate movement speed from consecutive contact points. The neural network state estimator (113) can calculate a cognitive load index by fusing the collected data.

[0154] MSE (100) can calculate the expected point to be reached after a human physiological cognitive delay time based on the user's current movement vector. The preemptive timing controller (102) can extract the current velocity vector and acceleration from the user's input trajectory history data and calculate the expected position after a preemptive time through a prediction model. It can determine a point on the target trajectory corresponding to the expected position and transmit it to the tail generator (120) to command it to output a preemptive time guide.

[0156] The tail generation unit (120) can induce the user to follow the trajectory unconsciously without conscious judgment by first projecting a guide trajectory at the calculated predicted point to form a cognitive highway. The leading projection engine (121) can render a streamlined trajectory that leads the actual correct path according to the instructions of the MSE (100), and the dynamic afterimage controller (123) can adjust the length of the L-tail according to the user's movement speed. Through this, the user can have the guide already presented while processing visual information, which can shorten the reaction time and enable smooth following even in sections of rapid trajectory change.

[0158] This embodiment can focus on a physical control method that maintains precise synchronization by applying a magnetic attraction between user input and target trajectory.

[0160] A virtual force calculator (101) can generate a virtual magnetic force (Fsync) (synchronization) corresponding to the distance between a user input point (Puser) and a target trajectory (Ptarget). It can obtain Puser coordinates from an input device and obtain Ptarget coordinates at the current time from a target trajectory database. It can calculate the Euclidean distance between two points and compute a virtual force corresponding to the distance. The virtual force can generate a stronger force as the distance increases to guide the user to the target trajectory, and the coupling coefficient can be dynamically adjusted according to the cognitive load level.

[0162] When the user attempts to deviate from the trajectory, MSE (100) can increase the coupling coefficient to force the user's input to follow the correct path, and when the user settles within the trajectory, it can optimize the coupling coefficient to provide flexibility in operation. Magnetic damping control can induce a natural return by applying a virtual viscous resistance when acceleration is detected that the user is attempting to deviate from the target trajectory. An acceleration vector can be calculated from the input trajectory, and a deviation vector from the target trajectory can be calculated to extract the deviation acceleration component. When the deviation acceleration is detected, a damping force can be calculated and converted into an input correction signal to be reflected in the user interface.

[0164] The trajectory deformation matrix (103) can provide a customized guidance environment by deforming the curvature and width of the trajectory in real time according to the user's perception speed and operation proficiency. The current perception load level is received from the perception state monitoring unit (110), and a proficiency index based on the user's past performance history can be calculated in the proficiency evaluation module. Curvature adjustment coefficients and width adjustment coefficients are calculated based on the perception load level and proficiency index, and a deformation function is applied to the original trajectory data to generate a deformed trajectory. The deformed trajectory can be transmitted to the virtual force calculator (101) and the tail generation unit (120) and presented to the user.

[0166] This embodiment may be a method that maximizes learning efficiency by stimulating the brain's reward circuit upon successful synchronization.

[0168] The audio synchronizer (131) can improve the user's concentration by outputting a solfège frequency optimized for brainwave stabilization. The solfège frequency may include pure tones or chords of specific frequencies and may be output in the form of binaural beats. The current synchronization error is received from the virtual power calculator (101), and the time during which the error is maintained below a threshold value can be measured. If the maintenance time is longer than the reference time, a compensation trigger is generated, and the audio generation module can output a solfège frequency signal.

[0170] When the user maintains the magnetic coupling state for a certain period of time, the visual reward generator (132) can provide a sense of accomplishment by outputting a blooming animation or a golden particle effect that looks like flowers blooming around the trajectory. By playing the blooming animation in the graphic rendering module, immediate positive feedback can be provided to the user and motivation can be strengthened. The blooming animation can be expressed as a shape of flowers blooming around the target trajectory and can include visual elements that provide psychological stability and a sense of accomplishment, such as a golden glowing effect or a dance of butterflies.

[0172] The system can psychologically motivate the user to maintain the cognitive highway state on their own through these rewards and can generate a cognitive development report of the user based on accumulated data. The haptic feedback module (133) can provide tactile feedback by generating micro-vibrations in the direction in which the virtual force acts, and can maximize the multimodal reward effect by synchronizing auditory, visual, and tactile stimuli. The intensity or frequency of the reward can be increased in proportion to the synchronization maintenance time, and the cognitive learning efficiency can be maximized by stimulating the user's brain reward circuit.

[0174] This embodiment may be an example of applying MSE (100) technology to fields requiring high accuracy, such as remote medical care or precision processes.

[0176] If a risk of the user's operation deviating from the target range is detected due to sudden user operation, the MSE (100) can preemptively prevent operation errors by increasing a virtual viscosity or damping coefficient to physically and heavily restrict the user's movement. An acceleration vector can be calculated from the input trajectory, and a deviation vector from the target trajectory can be calculated to extract a deviation acceleration component. If the deviation acceleration exceeds a threshold value, a damping force can be calculated and converted into an input correction signal to be reflected in the user interface. The damping strength can be dynamically adjusted according to the degree of deviation and user proficiency, and may include a return component in the direction of the target trajectory rather than simple resistance.

[0178] Through the haptic feedback module (133), micro-vibrations or resistance sensations are transmitted in the direction in which the virtual force acts, allowing the user to feel the connection with the trajectory not only visually but also tactilely. The haptic feedback can adjust the intensity and pattern of vibrations in response to the size and direction of the virtual force, and can provide intuitive feedback by providing the user with a sense of physical constraint. This allows for minimizing manipulation errors and improving work accuracy when a surgeon performs precise surgical movements in a remote medical system or when performing precision assembly work in an industrial site.

[0180] This embodiment may be a method of providing a three-dimensional guide in a virtual reality or augmented reality space beyond a two-dimensional screen.

[0182] The L-tail can be generated as a three-dimensional trajectory in three-dimensional space, and a three-axis virtual force can be calculated with respect to the user's controller or hand tracking point. The virtual force calculator (101) can calculate the distance between the user input point and the target trajectory in three-dimensional space and generate a virtual force vector in the three-axis direction. The tail generation unit (120) can render a three-dimensional streamlined trajectory in three-dimensional space and analyze the user's line of sight depth to project a guide three-dimensionally in advance to the next coordinate the user will reach in space.

[0184] By analyzing the user's gaze depth and projecting a guide three-dimensionally in advance to the next coordinate the user will reach in space, cognitive maladaptation in virtual space can be minimized and immersion maximized. The pre-attack timing controller (102) can analyze the user's movement pattern in three-dimensional space and calculate the expected position in three-dimensional space through a prediction model. It can determine a point on the three-dimensional target trajectory corresponding to the expected position and transmit it to the tail generation unit (120) to command the output of a three-dimensional pre-attack visual guide. Through this, the user can accurately follow the target trajectory while freely moving in three-dimensional space in a virtual reality environment, and cognitive maladaptation such as motion sickness can be minimized.

[0186] The present invention can provide an innovative cognitive guidance environment that enables a user to accurately follow a target trajectory without conscious effort by combining a preemptive timing control technology that monitors the user's cognitive state in real time and explicitly compensates for the brain's visual information processing delay, and a magnetic sink technology that provides natural guidance by generating a virtual attraction similar to the magnetic force of physics between a user input point and a target trajectory.

[0188] The system and method of the present invention can be applied to various fields such as medical rehabilitation, education and training, elderly care, games and entertainment, and sports science, and can dramatically improve the accuracy of cognitive-motor synchronization of users, reduce cognitive load, induce continuous participation through personalized adaptation, and promote rapid learning through intuitive feedback. The present invention presents a new paradigm of human-computer interaction and is expected to create innovative value in a wide range of industrial fields. Explanation of the symbols

[0189] 10: Cognitive Induction System 100: Magnetic Sync Engine (MSE) 101: Virtual force calculator 102: Preemptive Timing Controller 103: Trajectory Deformation Matrix 110: Cognitive status monitoring unit 111: Eye tracking sensor 112: Tactile input sensor 113: Neural network state estimator 120: Tail Generation Unit 121: Advance Projection Engine 122: Magnetic Coupling Visualizer 123: Dynamic Afterimage Controller 130: Neurofeedback output section 131: Audio Synchronizer 132: Visual Reward Generator 133: Haptic Feedback Module

Claims

Claim 1 A method for driving a Magnetic-Sync Engine, which is implanted into a control system of a means of mobility or a mobile robot device to restructure a user's cognitive path, comprising: (a) a step in which a cognitive state monitoring unit measures a real-time cognitive load based on multimodal sensor data including operation data applied to the user's eye movement and steering operation device; (b) a step in which a cognitive optimization module synthesizes a base frequency layer or a visual synchronization frequency with the user's sensory stimulus signal to remove cognitive resistance on the user's cognitive processing path and induce a state of brain immersion; (c) a step in which a virtual force calculator calculates a virtual force corresponding to the spatial distance between the user's operation input point and a target trajectory for driving or work trajectory control; and (d) a step in which a pre-emption timing controller provides a guide corresponding to the target trajectory ahead of the expected arrival time of the means of mobility or the mobile robot device to offset cognitive delay occurring in the user's brain. and (e) a real-time responsiveness maximization step in which the immersion induction by the cognitive optimization module and the cancellation of the cognitive delay operate as a continuous feedback loop to synchronize the user's steering response by performing closed-loop control that updates the size of the virtual workforce and the advance provision interval of the guide in real time based on the measured cognitive load. Claim 2 A method for driving a cognitive optimization module to eliminate cognitive resistance of a user for synchronizing steering responses of a means of transportation or a mobile robot device, comprising: a step of measuring a real-time cognitive load based on multimodal sensor data including data of the user's eye movements and a steering control device; and a cognitive optimization step of inducing a state of immersion in the brain by blocking cognitive resistance, which is neurological friction on the user's cognitive processing path, by layering and synthesizing a base frequency layer or a visual synchronization frequency on the user's sensory stimulus signal; wherein the immersion state information induced by the cognitive optimization step is transmitted to a Magnetic-Sync Engine core that offsets cognitive delay with a target trajectory based on the user's real-time response data to form continuous closed-loop feedback. Claim 3 A magnetic sink engine driving method according to claim 1, wherein step (d) is a step of forming a cognitive highway that allows the user to unconsciously follow a driving or work trajectory without undergoing a conscious analysis process by extracting the current velocity vector and acceleration of the means of transport or the mobile robot device to calculate a future expected position to be reached after 0.1 to 0.2 seconds, and projecting a guide in advance onto a point on the target trajectory corresponding to the expected position. Claim 4 A magnetic sink engine driving method according to claim 1, characterized in that the closed-loop control is a step of preemptively preventing user operation errors or path deviation of the robot device by performing magnetic damping control that increases the coupling coefficient of the virtual attraction and applies virtual viscosity resistance to the user's steering movement when a deviation acceleration is detected that attempts to deviate from the target trajectory due to the user's operation being sudden. Claim 5 A magnetic sink engine driving method according to claim 4, further comprising the step of, when performing the magnetic damping control, transmitting a fine vibration or resistance sensation to the steering operating device in correspondence with the direction in which the virtual force acts through a haptic feedback module, thereby inducing the user to intuitively perceive the state of coupling with the target trajectory not only visually but also tactilely. Claim 6 A magnetic sink engine driving method according to claim 1, wherein the guide provision in step (d) is characterized by the tail generating unit extending the length of the streamlined trajectory tail in proportion to the driving speed of the moving means or the mobile robot device and rendering, and dynamically enhancing the visibility of the tail and outputting it in the trajectory section where an increase in the user's cognitive load is detected. Claim 7 A magnetic sink engine driving method according to claim 1, further comprising the step of, during the closed-loop control, deforming the curvature or allowable width of the target trajectory in real time based on the measured cognitive load using a trajectory deformation matrix to alleviate the difficulty of control caused by a sudden change in the trajectory of the moving means or the mobile robot device. Claim 8 A magnetic sink engine driving method according to claim 1, wherein the cognitive state monitoring unit of step (a) calculates the user’s gaze point dispersion and pupil diameter change through a camera sensor, extracts gripping pressure and frequency variability of pressure through a tactile input sensor equipped in the steering device, and then fuses the results with a neural network state estimator to calculate the real-time cognitive load index. Claim 9 A magnetic sink engine driving method according to claim 1, further comprising the step of, when providing the guide in step (d), adding a visual tension effect similar to the magnetic field lines of a magnet to the user's display centered on the target trajectory to display the dynamic coupling state. Claim 10 A method for driving a magnetic sink engine according to claim 1, further comprising the step of, when the synchronization error between the operation input point and the target trajectory is maintained below a preset threshold for a certain period of time or longer, a neurofeedback output unit simultaneously outputs a stabilized audio signal of a specific frequency and a visual animation effect to induce brainwave stabilization of the user. Claim 11 A cognitive assistance and control system mounted on a means of transportation or a mobile robot device, comprising: a cognitive state monitoring unit that collects multimodal sensor data including user eye movement and operation data applied to a steering operation device to measure a real-time cognitive load; a cognitive optimization module that blocks cognitive resistance by synthesizing a base frequency layer or a visual synchronization frequency to the user's sensory stimulus signal; a virtual force calculator that calculates a virtual force corresponding to the spatial distance between the user's operation input point and a target trajectory for control; a preemptive timing controller that controls a guide corresponding to the target trajectory to be provided ahead of the expected arrival time of the means of transportation or the mobile robot device in order to offset cognitive delay occurring in the user's brain; and a processor that drives a cognitive synchronization engine organically coupled with the cognitive optimization module by performing closed-loop control to update the size of the virtual force and the interval of the guide's preemption in real time based on the measured cognitive load. Claim 12 A magnetic sink engine driving software program stored on a computer-readable recording medium to execute the magnetic sink engine driving method of claim 1 on a computer.

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