A method and system for virtual device-aware guided gesture prompting for motion-based games

Abstract

The present application relates to a kind of virtual device perception guiding somatosensory game posture prompt method and system, belong to virtual reality interaction and somatosensory control technical field.Therein, the method includes: somatosensory device space motion feature is solved, obtains device space time sequence state vector, according to the indirect constraint posture of virtual character motion state, obtains virtual barycenter coupling migration parameter set;Barycenter guiding migration model is constructed, device space trajectory signal is regarded as the observation of barycenter projection change, the direction sensitivity of barycenter shift weight is carried out energy level reorganization, generates device perception convergence prompt information;Through barycenter driven response function, mobile barycenter distribution is abstracted as calculable control quantity, synchronizes the device perception convergence prompt information;Establish device virtual motion control mechanism, the weight coefficient in the virtual barycenter coupling migration parameter set is iteratively corrected, controls mobile perception terminal game prompt rhythm.

Description

Technical Field

[0001] This invention belongs to the field of virtual reality interaction and motion control technology, specifically relating to a method and system for motion-sensing game posture prompts guided by virtual device perception. Background Technology

[0002] With the rapid development of virtual reality (VR), augmented reality (AR), and motion-sensing interaction technologies, immersive games and training systems place higher demands on the real-time perception and guidance of player movement postures. Traditional motion-sensing interaction methods mainly rely on motion signals such as acceleration and angular velocity collected by sensors, controlling the movement of virtual characters through simple mapping or preset rules. However, these methods have several limitations: First, motion-sensing devices are prone to integral drift and pseudo-static phenomena when stationary or in a low-amplitude motion state, leading to inaccurate device state calculations and affecting the naturalness of virtual character movement and player interaction experience; second, traditional methods rely heavily on explicit motion mapping, lacking implicit correlation analysis between player posture and virtual character movement, making it difficult to achieve personalized and flexible posture guidance; furthermore, for complex multi-degree-of-freedom movements, existing systems have limited capabilities in center of gravity control, motion stability maintenance, and feedback prompts, failing to effectively balance action accuracy and interactive comfort, especially in fast or continuous action scenarios, where players are prone to posture deviation, operational incoordination, or motion fatigue.

[0003] To address these issues, academia and industry have proposed various motion compensation, center-of-gravity guidance, and feedback optimization methods in recent years. For example, stagnation constraint compensation algorithms are used to determine the temporal stability of pseudo-stationary signals, enabling dynamic correction of the motion state; dominant motion pattern extraction and multi-dimensional motion feature mapping are employed to correlate player actions with the virtual character's motion space, improving the response accuracy of the virtual character's motion; and a mass-guided control framework abstracts the virtual character's motion into a controlled mass model, combined with deviation-driven feedback, to achieve smooth constraint and convergence optimization of the motion path. However, these methods often have shortcomings in areas such as center-of-gravity offset weight adjustment, multi-dimensional modulation of posture cue signals, and synchronization of device-sensed convergence information, making it difficult to form a continuous, flexible, and adaptive posture guidance mechanism.

[0004] Based on this, this invention proposes a method and system for providing posture prompts in motion-sensing games using virtual device perception guidance. By performing stagnation constraint compensation and temporal state calculation on the spatial motion characteristics of the motion-sensing device, an implicit motion feature association window is constructed to indirectly constrain the player's posture. A center-of-gravity guided migration model is built based on a virtual center-of-gravity coupled migration parameter set, and a quality-guided control framework is used to map changes in center-of-gravity projection into perceptible transfer prompts. Simultaneously, center-of-gravity offset is abstracted as the main driving variable of the virtual character's movement, and iterative optimization is performed through a virtual motion control mechanism of the device. Combined with prompt modulation in time, space, and intensity dimensions, adaptive control of the game's prompt rhythm is achieved. This invention can significantly improve the accuracy and naturalness of virtual character movement, while optimizing the player's posture feedback experience, providing an efficient and flexible motion guidance solution for immersive games and motion-sensing interactions. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method for providing gesture prompts in motion-sensing games using virtual device perception guidance. The objective of this invention can be achieved through the following technical solutions: S1: Obtain the spatial motion features of the motion sensing device, perform state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtain the device spatial temporal state vector, map the device spatial temporal state vector to the virtual character motion space, construct an implicit motion feature association window, and indirectly constrain the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set. S2: Construct a center-of-gravity guided migration model based on the virtual center-of-gravity coupled migration parameter set, regard the equipment spatial trajectory signal as an observation of the center-of-gravity projection change, and map the observation of the center-of-gravity projection change into a transfer prompt instruction based on the quality-guided control framework. Perform energy level reshaping on the directional sensitivity of the center-of-gravity offset weight to generate equipment perception convergence prompt information. S3: Couple and map the observed changes in the center of gravity projection with the movement behavior of the virtual character in the game, and abstract the movement center of gravity allocation into a computable controllable quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronize the device perception convergence prompt information. S4: Establish a virtual motion control mechanism for the device, iteratively correct the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhance the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the rhythm of game prompts on the mobile sensing terminal.

[0006] Specifically, the solution process of the stagnation constraint compensation algorithm is as follows: the abnormal stationary signal in the spatial motion characteristics of the haptic device is judged in the time domain, the variance of the device acceleration vector in the continuous time window is calculated, and the variance is used as the motion activity criterion. If the variance is lower than the preset stagnation threshold, the spatial motion characteristics of the haptic device in the current sampling interval are determined to be in a pseudo-static constraint state. Based on the pseudo-static constraint state, the integral drift term of the device state at the current moment is subjected to reverse constraint convergence based on the historical motion trajectory, and the device spatial temporal state vector is generated.

[0007] Specifically, the method for constructing the implicit motion feature association window is as follows: the motion state quantities of multiple frames of virtual characters within the window coverage area are stacked in chronological order, the original multidimensional motion trajectory obtained by stacking is projected onto the feature subspace spanned by the dominant feature vector, the dominant motion mode vector group that maps the player's motion is extracted, and based on the dominant motion mode vector group, the device motion trajectory is mapped to the virtual character motion space to construct the implicit motion feature association window.

[0008] Specifically, the method for indirectly constraining the player's posture is as follows: the virtual character's motion state is regarded as a feedback signal, the device position state feedback is calculated by introducing the virtual character's centroid motion parameters, and based on the coupling relationship between the device position state feedback and the device's spatial temporal state vector, the posture deviation projection amount of the current device in the implicit motion feature subspace is calculated, the player's current posture is softly constrained, and the game interaction prompt rules are adaptively adjusted based on the posture deviation projection amount to generate game posture prompt information.

[0009] Specifically, the construction method of the center of gravity guided migration model is as follows: based on the virtual center of gravity coupled migration parameters, the evolution mode of the target center in the motion space of the virtual character is reconstructed, the dominant evolutionary direction and local inertial features of the trajectory are extracted, based on the mass-guided control framework, the deviation gradient between the current center of gravity projection change and the target stable domain is calculated, a migration correction vector is generated, and according to the adaptive guided offset of the virtual center of gravity in the multi-dimensional motion space, the motion state of the virtual character is abstracted into controlled mass dynamic parameters, and the migration weights of different offset directions of the virtual character are nonlinearly adjusted to construct the center of gravity guided migration model.

[0010] Specifically, the quality-guided control framework includes an equivalent controlled evolution structure and a deviation-driven feedback structure; The equivalent controlled evolution structure is used to abstract the motion of the virtual character into a multi-dimensional controlled mass point. By introducing equivalent inertial parameters into the motion space of the virtual character, the position state of the virtual center of gravity is modeled. Combined with the historical motion trajectory and local inertial trend, smoothing constraints and convergence constraints are applied to the center of gravity migration path. The deviation-driven feedback structure is used to apply an adjustable gain to the migration correction vectors in different offset directions, calculate the deviation gradient of the virtual center of gravity projection change relative to the target stable domain, adjust the convergence performance of the center of gravity migration trajectory, and cooperate with the equivalent controlled evolution structure as an intermediate station for the adaptive guided migration closed loop of the virtual center of gravity in the multidimensional motion space.

[0011] Specifically, the device-sensing convergence prompt information includes information in the time modulation dimension, spatial pointing dimension, and intensity modulation dimension. The time modulation dimension involves setting the prompt trigger interval and duration based on the rate of change of the center of gravity projection and the posture convergence trend, and rapidly responding to posture changes through a short-time response channel while gradually attenuating the prompt dependence through a long-time trend channel. The spatial pointing dimension involves vectorizing the prompt signal along the player's degrees of freedom of movement based on the virtual center of gravity deviation gradient vector and the adjustment results of the direction-sensitive weights, forming a direction guidance effect and outputting a non-single forced signal that allows the player to perceive the direction of motion correction. The intensity modulation dimension involves non-linearly scaling the visual brightness, auditory loudness, or tactile amplitude of the prompt signal based on the amplitude and rate of change of the posture deviation energy index, enhancing the prompts for unstable movements and gradually weakening the prompts for stable actions.

[0012] Specifically, the execution process of the center of gravity driven response function is as follows: the center of gravity offset component is mapped to the virtual character's motion degree of freedom subspace to generate a control basis vector; the amplitude of the control basis vector is recalibrated according to the center of gravity offset magnitude; response sensitivity is assigned to the offset state far from the stable region; saturation interval restrictions and time continuity constraints are applied to the control basis vector; the processed control basis vector is output as the main driving variable for the virtual character's movement; and based on the deviation collaborative response algorithm, the center of gravity offset magnitude and the control basis vector are mapped to the modulation parameters of the device perception convergence prompt information, and the device perception convergence prompt information is synchronized.

[0013] Specifically, the method for iteratively correcting the weight coefficients in the virtual motion control mechanism of the device is as follows: a multi-source state fusion layer is constructed, the device spatial temporal state vector and the virtual center of gravity migration trend are aligned to obtain the control observation state vector, when the control observation state vector approaches the preset approaching stability boundary threshold, the feedback weight of the device perception convergence prompt information is increased, and the weight coefficients in the virtual center of gravity coupling migration parameter set are iteratively corrected based on the degree of dependence of the main driving variables in the vicinity of the stability domain.

[0014] Specifically, the method for controlling the rhythm of game prompts on the mobile sensing terminal is as follows: the frequency of prompt stimulation is regarded as a measure of the degree of occupancy of the player's perception channel, and the prompt rhythm parameter is adjusted in two channels: the short-time channel is used to quickly respond to sudden posture instability, and the long-time channel is used to gradually reduce the dependence on prompts. The rhythm of the prompt autonomous transition guidance is adjusted according to the feedback weight of the device's perception convergence prompt information.

[0015] Specifically, the control basis vector is based on the degree-of-freedom mapping matrix, which projects the virtual center of gravity offset vector onto the virtual character's motion degree-of-freedom subspace. In the degree-of-freedom mapping matrix, the vector dimension is consistent with the number of controllable motion degrees of freedom of the virtual character, and each component corresponds to the basic driving contribution ratio of the virtual character in different motion degree-of-freedom directions, forming the basic vector skeleton of the virtual character's main driving variable for movement.

[0016] Specifically, a motion-sensing game posture prompting system guided by virtual device perception includes: Motion sensing calculation module: acquires the spatial motion features of the motion sensing device, performs state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtains the device spatial temporal state vector, maps the device spatial temporal state vector to the virtual character motion space, constructs an implicit motion feature association window, and indirectly constrains the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set; Perception convergence prompt module: Based on the virtual center of gravity coupling migration parameter set, a center of gravity guided migration model is constructed. The device spatial trajectory signal is regarded as an observation of the center of gravity projection change. Based on the mass guidance control framework, the observation of the center of gravity projection change is mapped into a transfer prompt instruction. The energy level is re-normalized for the directional sensitivity of the center of gravity offset weight to generate device perception convergence prompt information. Center of gravity driven control module: Couples and maps the observed changes in center of gravity projection with the movement behavior of the virtual character in the game, and abstracts the movement center of gravity allocation into a computable control quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronizes the device perception convergence prompt information; The prompt rhythm control module establishes a virtual motion control mechanism for the device, iteratively corrects the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhances the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the game prompt rhythm on the mobile sensing terminal.

[0017] The beneficial effects of this invention are as follows: This invention achieves high-precision measurement and dynamic correction of device states by performing stagnation constraint compensation and temporal state calculation on the spatial motion characteristics of motion-sensing devices, effectively solving the motion deviation problems caused by pseudo-staticity and integral drift in traditional motion-sensing interactions. By constructing an implicit motion feature association window and extracting the dominant motion pattern, the invention flexibly maps player movement to the virtual character's motion space, achieving indirect constraint and adaptive prompts on player posture, thus improving the naturalness and accuracy of virtual character movement.

[0018] Furthermore, this invention constructs a center-of-gravity guided migration model based on a virtual center-of-gravity coupled migration parameter set, and combines it with a quality-guided control framework to map changes in center-of-gravity projection into perceptible multi-dimensional prompts. This enables prompt modulation in time, space, and intensity dimensions, providing continuous, flexible, and non-coercive guidance for player actions, improving the efficiency and comfort of posture adjustment. Simultaneously, through a center-of-gravity driven response function and a device virtual motion control mechanism, the moving center of gravity is abstracted as the main driving variable of the virtual character and iteratively optimized to achieve synchronous control of the virtual character's movement and device-perceived prompts, ensuring the real-time performance and stability of action feedback and interactive prompts.

[0019] In summary, this invention can significantly improve the accuracy and smoothness of virtual character movement in motion-sensing interactive games, enhance the reliability and immersion of player posture control, optimize the rhythm of game prompts, and reduce player fatigue. It has good application value and promotion prospects. Attached Figure Description

[0020] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of the framework of a virtual device perception-guided motion-sensing game posture prompting method and system according to the present invention.

[0022] Figure 2 This is a schematic diagram of a virtual device perception-guided motion-sensing game posture prompting method and device perception convergence prompting information in the system, according to the present invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0024] Please see Figure 1 A method for providing gesture prompts in motion-sensing games using virtual device perception guidance: S1: Obtain the spatial motion features of the motion sensing device, perform state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtain the device spatial temporal state vector, map the device spatial temporal state vector to the virtual character motion space, construct an implicit motion feature association window, and indirectly constrain the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set. S2: Construct a center-of-gravity guided migration model based on the virtual center-of-gravity coupled migration parameter set, regard the equipment spatial trajectory signal as an observation of the center-of-gravity projection change, and map the observation of the center-of-gravity projection change into a transfer prompt instruction based on the quality-guided control framework. Perform energy level reshaping on the directional sensitivity of the center-of-gravity offset weight to generate equipment perception convergence prompt information. S3: Couple and map the observed changes in the center of gravity projection with the movement behavior of the virtual character in the game, and abstract the movement center of gravity allocation into a computable controllable quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronize the device perception convergence prompt information. S4: Establish a virtual motion control mechanism for the device, iteratively correct the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhance the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the rhythm of game prompts on the mobile sensing terminal.

[0025] In this embodiment, the specific solution process of the stagnation constraint compensation algorithm is as follows: the abnormal stationary signal in the spatial motion characteristics of the haptic device is judged in the time domain, the variance of the device acceleration vector in the continuous time window is calculated, and the variance is used as the motion activity criterion. If the variance is lower than the preset stagnation threshold, the spatial motion characteristics of the haptic device in the current sampling interval are determined to be in a pseudo-static constraint state. Based on the pseudo-static constraint state, the integral drift term of the device state at the current moment is subjected to reverse constraint convergence based on the historical motion trajectory to generate the device spatial temporal state vector.

[0026] In this embodiment, the method for constructing the implicit motion feature association window is as follows: the motion state quantities of multiple frames of virtual characters within the window coverage area are stacked in chronological order, the original multidimensional motion trajectory obtained by stacking is projected onto the feature subspace spanned by the dominant feature vector, the dominant motion mode vector group that maps the player's motion is extracted, and based on the dominant motion mode vector group, the device motion trajectory is mapped to the virtual character motion space to construct the implicit motion feature association window.

[0027] In this embodiment, the method for indirectly constraining the player's posture is as follows: the virtual character's motion state is regarded as a feedback signal, the device position state feedback is calculated by introducing the virtual character's centroid motion parameters, and based on the coupling relationship between the device position state feedback and the device spatial temporal state vector, the posture deviation projection amount of the current device in the implicit motion feature subspace is calculated, the player's current posture is softly constrained, and the game interaction prompt rules are adaptively adjusted based on the posture deviation projection amount to generate game posture prompt information.

[0028] In this embodiment, taking a motion-sensing shooting game as an example, the preparation stage includes system architecture and data acquisition. The hardware environment involves the player using a smartphone as a motion-sensing device, fixed to their arm. The software framework involves building a core module, "PoseGuidanceSystem," in Unity, which includes: a data input layer (acquiring raw spatial motion characteristics from device sensors, including three-axis acceleration vectors and gyroscope data); a computation layer (using mathematical libraries such as Unity's Vector3 and Quaternion to perform state calculations and mapping); and an output layer (outputting posture guidance through UI elements such as visual cues like arrow icons, vibration feedback, and audio cues).

[0029] Parameter initialization: Stagnation threshold: Variance threshold is 0.05 m / s 2 (Based on empirical testing); Stable boundary threshold: center of gravity offset deviation < 0.1 unit (virtual space unit); Initial set of weight coefficients: The initial set of virtual center of gravity coupling migration parameters is {offset weight: 0.5, orientation sensitivity: 0.7, feedback gain: 1.0}; Game scenario: The virtual character is an adventurer, and the center of gravity position is mapped to the center of the character's torso.

[0030] Acquire spatial motion characteristics of motion sensing devices and construct a virtual center of gravity coupling and migration parameter set. Data Acquisition: During the game loop, spatial motion features are read from the device's sensors each frame, including acceleration vector A = (Ax, Ay, Az) and angular velocity vector G = (Gx, Gy, Gz).

[0031] Stasis constraint compensation algorithm: Processes abnormal stationary signals. For example, it calculates the variance of the acceleration vector over a continuous 5-second period (time window = 500 frames). If Var(A) < 0.05, it is determined to be a pseudo-static state (the player may have temporarily stopped due to fatigue). The integral drift term ΔD = ∫(A_err dt) is calculated based on the historical trajectory (data from the previous 10 seconds), and a reverse constraint is applied: the current state vector S_t = S_{t-1} - k * ΔD (k = 0.2 is the convergence coefficient). The result is the device spatial temporal state vector S = [position P, velocity V, attitude Q] (represented by Quaternion).

[0032] Mapping to the virtual character's motion space: Map S to the virtual character's coordinate system using a linear transformation M: VirtualPos = M * P (M is a 4x4 affine matrix, considering device-to-character scaling). Construct an implicit motion feature association window: window size = 10 frames, stacking multiple frames of motion states into a matrix T (dimension: 10x6, including position and velocity). Project onto the dominant feature subspace (using PCA dimensionality reduction, retaining the first 3 dominant pattern vector groups, such as arm swing, jump, and balance patterns). Based on these vectors, implicitly associate the device trajectory with the virtual character's motion.

[0033] Indirect constraints on player posture: The virtual character's motion state serves as feedback, and the device position feedback F = (VirtualCentroid - ExpectedCentroid) is calculated. Based on the coupling between S and F, the posture deviation projection D_proj = ||Proj(F - S)||_2 (in the feature subspace) is calculated. If D_proj > 0.2, then a soft constraint is applied: the game hint rules are adjusted, such as increasing the transparency of arrow hints. A virtual centroid coupling migration parameter set P = {offset weights: calculated from D_proj 0.4-0.8, orientation sensitivity: based on gradient 0.6-0.9, etc.} is generated.

[0034] Construct a center-of-gravity guided migration model and generate device-aware convergence prompts. Center-of-gravity guided migration model construction: Based on P-reconstruction of the target center evolution pattern. For example, in a game, the center-of-gravity projection of a virtual character is C_proj = (Cx, Cy, Cz), considered as an observation. The dominant migration direction (using SVD decomposition of the trajectory matrix's principal singular vectors) and local inertial features (moments of inertia I = m * r) are extracted. 2 (m = virtual mass 1 unit). Mass-guided control framework: The equivalent controlled evolution structure abstracts the role as a point mass, applying smoothing constraints (Kalman filtering to smooth the path) and convergence constraints (PID control converges to the stable region). Deviation-driven feedback structure calculates the deviation gradient. Generate migration correction vector (Proportional-derivative control, k_p=0.5, k_d=0.3). Nonlinear adjustment of transfer weights: weight w_dir = sigmoid(offset angle) * initial weights.

[0035] Mapping to transition cue instructions: Map changes in C_proj to instructions such as "tilt right". Energy level renormalization of direction sensitivity: Sensitivity weight w_sens = exp(-energy E / T), E = offset energy ||V_corr|| 2 T = temperature parameter 1.0.

[0036] Generate device perception convergence prompts, including: Time modulation dimension: based on the rate of change rate = dC_proj / dt; if rate > 0.1, the trigger interval is 0.5s, lasting 1s; short-term channel response abrupt changes (e.g., jump instability), long-term channel decay dependence (decreasing by 0.1 per minute); Spatial pointing dimension: based on... Vectorized allocation, such as output vibration direction (phone vibration API, left / right intensity ratio based on w_dir); intensity modulation dimension: nonlinear scaling of visual brightness (UI arrow Alpha=0.2 + 0.8tanh(E)) and auditory loudness based on deviation energy E. ), tactile amplitude (Vibration.vibrate(intensity ms)).

[0037] Couple mapping and synchronizing device-aware convergence prompts Coupling mapping: Coupling C_proj with the virtual character's movement behavior, such as character speed V_role = f(C_proj) (f is a linear mapping).

[0038] Center of gravity driven response function: Maps the center of gravity offset to the degree-of-freedom subspace. Degree-of-freedom mapping matrix M_fd (3x3 dimension, corresponding to x / y / z degrees of freedom, contribution ratios e.g., [0.4, 0.3, 0.3]). Generate control basis vector B = M_fd * V_offset (V_offset = center of gravity offset vector). Amplitude recalibration: B_scaled = B * (1 + sensitivity * ||V_offset||), sensitivity = 0.2 for values ​​far from stability. Apply saturation constraints (clamp(B, -1,1)) and temporal continuity (Lerpsmooth previous frame). Output is the primary driving variable: character movement Transform.Translate(B_scaled* dt). Based on the bias coordination algorithm, modulate cue: parameter mod = V_offset * B, synchronized to the cue information (e.g., enhanced vibration if mod > 0.15).

[0039] Establish a virtual motion control mechanism for the device and control the rhythm of game prompts. Iterative correction of weight coefficients: Construct a multi-source state fusion layer, using feature alignment (cosine similarity aligns S and the centroid trend) to obtain the controlled observation vector O = fused(S, Trend). If ||O - StableThreshold|| < 0.1, increase the feedback weights (feedback gain += 0.1). Iteratively correct the weights in P: w_new = w_old + η * ∂L / ∂w (η = 0.01 learning rate, L = bias loss ||O - Target|| 2 ).

[0040] Control the pace of game prompts: Prompt frequency freq = base_freq / (1 + occupancy), where occupancy = channel occupancy (short-term: response instability freq = 2Hz, long-term: reduce dependency freq -= 0.1 / min). Adjust based on feedback weight: if the weight > 1.5, implement autonomous transition guidance (e.g., fade-out prompts).

[0041] In this embodiment, the method for constructing the center of gravity-guided migration model is as follows: based on the virtual center of gravity coupled migration parameters, the evolution mode of the target center in the motion space of the virtual character is reconstructed, the dominant evolutionary direction and local inertial features of the trajectory are extracted, based on the mass-guided control framework, the deviation gradient between the current center of gravity projection change and the target stable domain is calculated, a migration correction vector is generated, and according to the adaptive guidance offset of the virtual center of gravity in the multi-dimensional motion space, the motion state of the virtual character is abstracted into controlled mass dynamic parameters, and the migration weights of different offset directions of the virtual character are nonlinearly adjusted to construct the center of gravity-guided migration model.

[0042] In this embodiment, the quality-guided control framework includes an equivalent controlled evolution structure and a deviation-driven feedback structure; The equivalent controlled evolution structure is used to abstract the motion of the virtual character into a multi-dimensional controlled mass point. By introducing equivalent inertial parameters into the motion space of the virtual character, the position state of the virtual center of gravity is modeled. Combined with the historical motion trajectory and local inertial trend, smoothing constraints and convergence constraints are applied to the center of gravity migration path. The deviation-driven feedback structure is used to apply an adjustable gain to the migration correction vectors in different offset directions, calculate the deviation gradient of the virtual center of gravity projection change relative to the target stable domain, adjust the convergence performance of the center of gravity migration trajectory, and cooperate with the equivalent controlled evolution structure as an intermediate station for the adaptive guided migration closed loop of the virtual center of gravity in the multidimensional motion space.

[0043] This embodiment is described in conjunction with a specific game interaction scenario. Assume a virtual character motion-sensing game where the player controls the virtual character's movement in three-dimensional space using a motion-sensing device. The system implements posture cues and control through the following steps: 1. Game interaction prompt rules A three-dimensional cue modulation strategy is adopted, including time dimension, spatial orientation dimension, and intensity dimension: In time-dimension modulation, the trigger interval is indicated. Adjust according to the rate of change of the center of gravity shift, c(t): , Where Δt0 is the initial prompting interval and β is the sensitivity coefficient.

[0044] In spatial orientation dimension modulation, the cue vector ps(t) is distributed along the player's degree of freedom direction: , in, W is the centroid deviation vector. d This is a direction-sensitive weight matrix.

[0045] In intensity-dimensional modulation, the signal strength I(t) and the deviation energy index are indicated. Perform nonlinear mapping: , Where γ is the nonlinear exponent, κ is the threshold, and Imax is the maximum cue intensity.

[0046] 2. Deviation Cooperative Response Algorithm To synchronize the virtual character's movement with device prompts, a deviation co-response function fsync is defined: , Among them, u v (t) represents the main driving variable for the movement of the virtual character, K p ,K d ,K i Here are the proportional, differential, and integral gain matrices.

[0047] The steps are as follows: (1) Calculate the real-time center of gravity deviation Δc(t) and the rate of change c(t); (2) Generate the virtual character driving vector u based on the gain matrix. v (t); (3) will u v (t) Project onto the virtual character's motion freedom subspace and update the character's position. ; (4) According to u v (t) Synchronize and adjust game prompt signals ; (5) For the weighting coefficient w i Iterative corrections are performed to ensure convergence and stability.

[0048] In this embodiment, as Figure 2 The device-sensing convergence prompt information described above includes information in the time modulation dimension, spatial pointing dimension, and intensity modulation dimension. The time modulation dimension involves setting the prompt trigger interval and duration based on the rate of change of the center of gravity projection and the posture convergence trend, and rapidly responding to posture changes through a short-time response channel while gradually attenuating the prompt dependence through a long-time trend channel. The spatial pointing dimension involves vectorizing the prompt signal along the player's degrees of freedom of movement based on the virtual center of gravity deviation gradient vector and the adjustment results of the direction-sensitive weights, forming a direction guidance effect and outputting a non-single forced signal that allows the player to perceive the direction of motion correction. The intensity modulation dimension involves non-linearly scaling the visual brightness, auditory loudness, or tactile amplitude of the prompt signal based on the amplitude and rate of change of the posture deviation energy index, enhancing the prompts for unstable movements and gradually weakening the prompts for stable movements.

[0049] In this embodiment, the execution process of the center of gravity driven response function is as follows: the center of gravity offset component is mapped to the virtual character's motion degree of freedom subspace to generate a control basis vector; the control basis vector is recalibrated according to the center of gravity offset magnitude to assign response sensitivity to the offset state far from the stable region; saturation interval restrictions and time continuity constraints are applied to the control basis vector; the processed control basis vector is output as the main driving variable for the virtual character's movement; and based on the deviation collaborative response algorithm, the center of gravity offset magnitude and the control basis vector are mapped as modulation parameters for the device perception convergence prompt information, and the device perception convergence prompt information is synchronized.

[0050] In this embodiment, the method for iteratively correcting the weight coefficients of the virtual motion control mechanism of the device is as follows: a multi-source state fusion layer is constructed, the device spatial temporal state vector and the virtual center of gravity migration trend are aligned to obtain the control observation state vector, when the control observation state vector approaches the preset approaching stability boundary threshold, the feedback weight of the device perception convergence prompt information is increased, and the weight coefficients of the virtual center of gravity coupling migration parameter set are iteratively corrected based on the degree of dependence of the main driving variables in the vicinity of the stability domain.

[0051] In this embodiment, the method for controlling the rhythm of game prompts on the mobile sensing terminal is as follows: the frequency of prompt stimulation is regarded as a measure of the degree of occupancy of the player's perception channel, and the prompt rhythm parameter is adjusted in two channels: the short-time channel is used to quickly respond to sudden posture instability, and the long-time channel is used to gradually reduce the dependence on prompts. The rhythm of the prompt autonomous transition guidance is adjusted according to the feedback weight of the device's perception convergence prompt information.

[0052] In this embodiment, the control basis vector is based on the degree-of-freedom mapping matrix, which projects the virtual center of gravity offset vector onto the virtual character's motion degree-of-freedom subspace. In the degree-of-freedom mapping matrix, the vector dimension is consistent with the number of controllable motion degrees of freedom of the virtual character, and each component corresponds to the basic driving contribution ratio of the virtual character in different motion degree-of-freedom directions, forming the basic vector skeleton of the virtual character's main driving variable for movement.

[0053] This invention also provides a virtual device-guided motion-sensing game posture prompting system, specifically including: Motion sensing calculation module: acquires the spatial motion features of the motion sensing device, performs state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtains the device spatial temporal state vector, maps the device spatial temporal state vector to the virtual character motion space, constructs an implicit motion feature association window, and indirectly constrains the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set; Perception convergence prompt module: Based on the virtual center of gravity coupling migration parameter set, a center of gravity guided migration model is constructed. The device spatial trajectory signal is regarded as an observation of the center of gravity projection change. Based on the mass guidance control framework, the observation of the center of gravity projection change is mapped into a transfer prompt instruction. The energy level is re-normalized for the directional sensitivity of the center of gravity offset weight to generate device perception convergence prompt information. Center of gravity driven control module: Couples and maps the observed changes in center of gravity projection with the movement behavior of the virtual character in the game, and abstracts the movement center of gravity allocation into a computable control quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronizes the device perception convergence prompt information; The prompt rhythm control module establishes a virtual motion control mechanism for the device, iteratively corrects the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhances the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the game prompt rhythm on the mobile sensing terminal.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for providing gesture prompts in motion-sensing games using virtual device perception guidance, characterized in that, include: S1: Obtain the spatial motion features of the motion sensing device, perform state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtain the device spatial temporal state vector, map the device spatial temporal state vector to the virtual character motion space, construct an implicit motion feature association window, and indirectly constrain the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set. S2: Construct a center-of-gravity guided migration model based on the virtual center-of-gravity coupled migration parameter set, regard the equipment spatial trajectory signal as an observation of the center-of-gravity projection change, and map the observation of the center-of-gravity projection change into a transfer prompt instruction based on the quality-guided control framework. Perform energy level reshaping on the directional sensitivity of the center-of-gravity offset weight to generate equipment perception convergence prompt information. S3: Couple and map the observed changes in the center of gravity projection with the movement behavior of the virtual character in the game, and abstract the movement center of gravity allocation into a computable controllable quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronize the device perception convergence prompt information. S4: Establish a virtual motion control mechanism for the device, iteratively correct the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhance the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the rhythm of game prompts on the mobile sensing terminal.

2. The method according to claim 1, characterized in that, The specific solution process of the stagnation constraint compensation algorithm is as follows: the abnormal stationary signal in the spatial motion characteristics of the somatosensory device is judged in the time domain, the variance of the device acceleration vector in the continuous time window is calculated, and the variance is used as the motion activity criterion. If the variance is lower than the preset stagnation threshold, the spatial motion characteristics of the somatosensory device in the current sampling interval are determined to be in a pseudo-static constraint state. Based on the pseudo-static constraint state, the integral drift term of the device state at the current moment is subjected to reverse constraint convergence based on the historical motion trajectory to generate the device spatial temporal state vector.

3. The method according to claim 1, characterized in that, The method for constructing the implicit motion feature association window is as follows: the motion state quantities of multiple frames of virtual characters within the window coverage area are stacked in chronological order, the original multidimensional motion trajectory obtained by stacking is projected onto the feature subspace spanned by the dominant feature vector, the dominant motion mode vector group that maps the player's motion is extracted, and based on the dominant motion mode vector group, the device motion trajectory is mapped to the virtual character motion space to construct the implicit motion feature association window.

4. The method according to claim 1, characterized in that, The method for indirectly constraining player posture is as follows: the motion state of the virtual character is regarded as a feedback signal, the device position state feedback is calculated by introducing the motion parameters of the virtual character's centroid, and based on the coupling relationship between the device position state feedback and the device spatial temporal state vector, the posture deviation projection amount of the current device in the implicit motion feature subspace is calculated, the player's current posture is softly constrained, and the game interaction prompt rules are adaptively adjusted based on the posture deviation projection amount to generate game posture prompt information.

5. The method according to claim 1, characterized in that, The method for constructing the center-of-gravity guided migration model is as follows: based on the virtual center-of-gravity coupled migration parameters, the evolution mode of the target center in the motion space of the virtual character is reconstructed, the dominant evolutionary direction and local inertial features of the trajectory are extracted, based on the mass-guided control framework, the deviation gradient between the current center-of-gravity projection change and the target stable domain is calculated, a migration correction vector is generated, and based on the adaptive guided offset of the virtual center of gravity in the multi-dimensional motion space, the motion state of the virtual character is abstracted into controlled mass dynamic parameters, and the migration weights of different offset directions of the virtual character are nonlinearly adjusted to construct the center-of-gravity guided migration model.

6. The method according to claim 5, characterized in that, The quality-guided control framework includes an equivalent controlled evolution structure and a deviation-driven feedback structure; The equivalent controlled evolution structure is used to abstract the motion of the virtual character into a multi-dimensional controlled mass point. By introducing equivalent inertial parameters into the motion space of the virtual character, the position state of the virtual center of gravity is modeled. Combined with the historical motion trajectory and local inertial trend, smoothing constraints and convergence constraints are applied to the center of gravity migration path. The deviation-driven feedback structure is used to apply an adjustable gain to the migration correction vectors in different offset directions, calculate the deviation gradient of the virtual center of gravity projection change relative to the target stable domain, adjust the convergence performance of the center of gravity migration trajectory, and cooperate with the equivalent controlled evolution structure as an intermediate station for the adaptive guided migration closed loop of the virtual center of gravity in the multidimensional motion space.

7. The method according to claim 1, characterized in that, The device's convergence prompt information includes information in the time modulation dimension, spatial pointing dimension, and intensity modulation dimension. The time modulation dimension sets the prompt trigger interval and duration based on the rate of change of the center of gravity projection and the posture convergence trend, and rapidly responds to posture changes through a short-time response channel while gradually attenuating the prompt dependence through a long-time trend channel. The spatial pointing dimension vectorizes the prompt signal along the player's degrees of freedom of movement based on the virtual center of gravity deviation gradient vector and the adjustment results of the direction-sensitive weights, forming a direction guidance effect and outputting a non-single forced signal that allows the player to perceive the direction of motion correction. The intensity modulation dimension non-linearly scales the visual brightness, auditory loudness, or tactile amplitude of the prompt signal based on the amplitude and rate of change of the posture deviation energy index, enhancing the prompts for unstable movements and gradually weakening the prompts for stable movements.

8. The method according to claim 1, characterized in that, The execution process of the center of gravity driven response function is as follows: the center of gravity offset component is mapped to the virtual character's motion degree of freedom subspace to generate a control basis vector; the amplitude of the control basis vector is recalibrated according to the center of gravity offset magnitude; response sensitivity is assigned to the offset state far from the stable region; saturation interval restrictions and time continuity constraints are applied to the control basis vector; the processed control basis vector is output as the main driving variable for the virtual character's movement; and based on the deviation collaborative response algorithm, the center of gravity offset magnitude and the control basis vector are mapped as modulation parameters for the device perception convergence prompt information, and the device perception convergence prompt information is synchronized.

9. The method according to claim 1, characterized in that, The method for iteratively correcting the weight coefficients in the virtual motion control mechanism of the device is as follows: a multi-source state fusion layer is constructed, the device spatial temporal state vector and the virtual center of gravity migration trend are feature-aligned to obtain the control observation state vector, when the control observation state vector approaches the preset approaching stability boundary threshold, the feedback weight of the device perception convergence prompt information is increased, and the weight coefficients in the virtual center of gravity coupling migration parameter set are iteratively corrected based on the degree of dependence of the main driving variables in the vicinity of the stability domain.

10. The method according to claim 1, characterized in that, The method for controlling the rhythm of game prompts on the mobile sensing terminal is as follows: the frequency of prompt stimulation is regarded as a measure of the degree of occupancy of the player's perception channel, and the prompt rhythm parameter is adjusted in two channels: the short-time channel is used to quickly respond to sudden posture instability, and the long-time channel is used to gradually reduce the dependence on prompts. The rhythm of the prompt autonomous transition guidance is adjusted according to the feedback weight of the device's perception convergence prompt information.

11. The method according to claim 8, characterized in that, The control basis vector is based on the degree-of-freedom mapping matrix, which projects the virtual center of gravity offset vector onto the virtual character's motion degree-of-freedom subspace. In the degree-of-freedom mapping matrix, the vector dimension is consistent with the number of controllable motion degrees of freedom of the virtual character, and each component corresponds to the basic driving contribution ratio of the virtual character in different motion degree-of-freedom directions, forming the basic vector skeleton of the virtual character's main driving variable for movement.

12. A virtual device-guided motion-sensing game posture prompting system, used to perform the method as described in any one of claims 1-11, characterized in that, include: Motion sensing calculation module: acquires the spatial motion features of the motion sensing device, performs state calculation on the spatial motion features of the motion sensing device based on the stagnation constraint compensation algorithm, obtains the device spatial temporal state vector, maps the device spatial temporal state vector to the virtual character motion space, constructs an implicit motion feature association window, and indirectly constrains the player's posture according to the virtual character's motion state to obtain the virtual center of gravity coupling migration parameter set; Perception convergence prompt module: Based on the virtual center of gravity coupling migration parameter set, a center of gravity guided migration model is constructed. The device spatial trajectory signal is regarded as an observation of the center of gravity projection change. Based on the mass guidance control framework, the observation of the center of gravity projection change is mapped into a transfer prompt instruction. The energy level is re-normalized for the directional sensitivity of the center of gravity offset weight to generate device perception convergence prompt information. Center of gravity driven control module: Couples and maps the observed changes in center of gravity projection with the movement behavior of the virtual character in the game, and abstracts the movement center of gravity allocation into a computable control quantity through the center of gravity driven response function, which serves as the main driving variable for the movement of the virtual character, and synchronizes the device perception convergence prompt information; The prompt rhythm control module establishes a virtual motion control mechanism for the device, iteratively corrects the weight coefficients in the set of virtual center of gravity coupling migration parameters, and enhances the feedback weight of the device perception convergence prompt information when the change in center of gravity projection approaches the stable boundary threshold, thereby controlling the game prompt rhythm on the mobile sensing terminal.

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