Display anti-shake method and system for riding intelligent glasses

Abstract

The invention discloses a display anti-shake method and system for intelligent riding glasses, and the method comprises the steps: carrying out the time alignment of an angular velocity and an accelerated velocity collected by an inertial measurement unit, and estimating and compensating an offset and scale factor; performing dual-channel adaptive filtering on the inertial data stream; detecting and identifying a glancing or turning intention of the wearer based on an angular velocity threshold value, acceleration abrupt change and short-time pause, and generating a gating coefficient for continuously adjusting the image stabilization intensity; according to the low-frequency attitude component and the gating coefficient, view field transformation and viewport offset used for stabilizing the superposed content are solved; and according to the angular motion state, performing prediction compensation to the display moment on the view field transformation to offset the rendering and display link time delay, taking the relative displacement of the key content of the adjacent frames as stability feedback, and updating offset and parameters on line to realize long-term self-correction. According to the technical scheme of the invention, the readability and security of the superposed information can be integrally improved.

Description

Technical Field

[0001] This invention relates to the field of smart glasses technology, and in particular to a display stabilization method and system for cycling smart glasses. Background Technology

[0002] Smart cycling glasses are used to overlay navigation prompts, speed / power / heart rate, and other key information within the user's field of vision. During cycling, the bike and head are simultaneously subjected to road surface excitation and human movement, resulting in broad-spectrum vibrations, compounded by the wearer's active glancing and rapid head turns. Meanwhile, the link between sensor sampling, image rendering, and display scanning has inherent time delays and phase errors, and inertial devices experience slow drift due to temperature and wearing conditions. Existing technologies typically employ electronic image stabilization or fixed-parameter low-pass filters to uniformly smooth the display coordinates, but these methods struggle to balance strong vibrations and rapid visual motion: firstly, it's difficult to distinguish the different frequency bands of road surface excitation and head posture, requiring a trade-off between suppressing high-frequency jitter and maintaining low-frequency response; secondly, the lack of adaptive parameter tuning based on road conditions and pedal frequency, as well as gating of gaze / head-turning intentions, makes it difficult to avoid lag and dizziness. Furthermore, different display content layers have varying sensitivities to stability and different anchor references. Without layered image stabilization and anchor point management, text value jitter, navigation anchor point drift, or overall slowdown can easily occur. Without a unified time base and look-ahead prediction compensation, phase lag under link latency further amplifies jitter, and continues to degrade due to bias drift over long-term use. Consequently, overlaid information in cycling scenarios often exhibits jitter, ghosting, and drift, making readability and safety difficult to guarantee. Summary of the Invention

[0003] The purpose of this application is to propose a display anti-shake method and system for cycling smart glasses, so as to solve the technical problems of shaking, ghosting and drifting of the content displayed by smart glasses.

[0004] To address the aforementioned technical problems, this application provides a display stabilization method for smart cycling glasses, employing the following technical solution:

[0005] A display stabilization method for smart glasses used in cycling includes the following steps:

[0006] S101. Under the display refresh clock, the angular velocity and acceleration collected by the inertial measurement unit are time-aligned, and the bias and scaling factor are estimated and compensated to form an inertial data stream based on a unified time reference.

[0007] S102. Perform dual-channel adaptive filtering on the inertial data stream to separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation change. Identify riding conditions based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly.

[0008] S103. Based on angular velocity threshold, acceleration change and short pause detection, identify the wearer's saccade or head turning intention, generate a gating coefficient for continuously adjusting the image stabilization intensity, reduce or bypass high frequency suppression during the intention occurrence, and increase the image stabilization intensity during the short pause phase after the intention ends to stabilize the gaze point.

[0009] S104. Based on the low-frequency attitude components and the gating coefficients, solve the field transformation and viewport offset used to stabilize the overlay content. Use head-locking anchoring for the text / numerical layer and head-locking and / or world-locking anchoring for the navigation or environment overlay layer according to the scene to complete the coordinate remapping and rendering by level.

[0010] S105. Based on the angular motion state, predictive compensation is performed on the aforementioned field of view transformation up to the display time to offset the rendering and display link delay, and the relative displacement of key content in adjacent frames is used as stability feedback to update the bias and parameters online to achieve long-term self-correction.

[0011] In one possible implementation, the step S101 is followed by:

[0012] The total time delay Δ from inertia to display is estimated by using the phase difference between the attitude trajectory obtained by integrating angular velocity and the superimposed coordinates of the rendered frame as an observation;

[0013] The total delay Δ is decomposed into three components: sampling delay, rendering queuing delay, and display scanning delay, and the slow drift of each component is tracked using a Kalman / PLL structure.

[0014] The field-of-view transformation is extrapolated based on the relative weights of the three components, so that the prediction compensation corrects for the time delay from different sources.

[0015] In one possible implementation, step S102 specifically includes:

[0016] The main spectral peaks are tracked in real time in the power spectrum of the vertical component of acceleration, the peak frequency is set as the notch center, and the Q factor is adaptively adjusted according to the frequency band energy ratio.

[0017] When an external cadence or speed sensor is available, cross-calibrate the external measurement with the spectral peak frequency to confirm the source of the spectral peak.

[0018] When strong resonance or cobblestone road conditions are confirmed, the notch bandwidth is dynamically widened and the low-pass cutoff frequency is raised to suppress residual jitter caused by structural resonance.

[0019] The suppressed high-frequency residuals are used as one of the operating condition features and fed back to the operating condition classifier to improve the robustness of recognition.

[0020] In one possible implementation, prior to step S103, the method further includes:

[0021] Calculate the instantaneous angular velocity and angular acceleration of the field of view transformation, and compare them with the preset comfort thresholds to obtain the available adjustment margin;

[0022] First-order and second-order combined limiting is applied to the rate of change of the field of view transformation to suppress dizziness caused by excessively rapid compensation.

[0023] The rising slope and maximum amplitude of the gating coefficient are constrained by the available adjustment margin to ensure that the image does not overshoot in strong vibration or emergency stop scenarios.

[0024] In one possible implementation, step S104 includes:

[0025] Set strong / weak anchoring levels and maximum allowable relative displacement thresholds for each display layer;

[0026] Strong earthquake events are detected based on a combined threshold of acceleration jump and angular acceleration, and safety freezing is triggered first for strong anchoring layers to maintain the most recent stable coordinates.

[0027] During the freeze period, reduce the refresh rate of strong anchored layers and maintain the limited smooth update of weak anchored layers;

[0028] When the stability index returns to within the threshold, the image is unfrozen and reset to normal stability using a time window fade-in method.

[0029] In one possible implementation, step S104 further includes:

[0030] Calculate an environmental quality score, which is obtained by combining texture density, feature point traceability, and image signal-to-noise ratio;

[0031] By fusing the environmental quality score with the confidence level of visual inertial odometry, a world-locking availability criterion is obtained.

[0032] Based on the aforementioned criteria, the system switches between world lock and head lock, and sets hysteresis zones and fade-in / fade-out time windows to avoid boundary jitter.

[0033] When the availability criterion is below the threshold, it degenerates into a stable image based on inertial data and maintains the head lock of the text / numerical layer.

[0034] In one possible implementation, step S105 includes:

[0035] Construct a stability cost function with the objective function being the weighted sum of the squared relative displacements of key content in adjacent frames and the squared compensation angular accelerations;

[0036] Based on the gradient of the cost function, the low-pass cutoff frequency, notch depth, bias estimation step size and gating threshold are updated online, and upper / lower limits are set for each parameter;

[0037] Maintain individualized parameter profiles based on user identification and wearing status, and load them upon reuse to accelerate convergence;

[0038] When a sudden change in environment or user discomfort is detected, the parameters are triggered to fall back to the safe baseline and re-enter self-calibration.

[0039] To address the aforementioned technical problems, this application also provides a display stabilization system for cycling smart glasses, employing the following technical solution:

[0040] A display stabilization system for smart cycling glasses includes:

[0041] The acquisition module is used to time-align the angular velocity and acceleration acquired by the inertial measurement unit under the display refresh clock, and to estimate and compensate for the bias and scaling factor to form an inertial data stream based on a unified time reference.

[0042] The filtering module is used to perform dual-channel adaptive filtering on the inertial data stream, separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation change, identify riding conditions based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly.

[0043] The generation module is used to detect and identify the wearer's saccade or head-turning intentions based on angular velocity threshold, acceleration change and short pause. It generates a gating coefficient for continuously adjusting the image stabilization intensity, reducing or bypassing high-frequency suppression during the intention occurrence, and increasing the image stabilization intensity during the short pause phase after the intention ends to stabilize the gaze point.

[0044] The solution module is used to solve the field transformation and viewport offset for stabilizing the overlay content based on the low-frequency attitude components and the gating coefficients. It adopts a head-locking anchoring method for the text / numerical layer and a head-locking and / or world-locking anchoring method for the navigation or environment overlay layer according to the scene, and completes the coordinate remapping and rendering by layer.

[0045] The correction module is used to predict and compensate for the aforementioned field of view transformation up to the display time based on the angular motion state in order to offset the rendering and display link delay. It also uses the relative displacement of key content in adjacent frames as stability feedback to update the bias and parameters online to achieve long-term self-correction.

[0046] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0047] A computer device includes a memory and a processor, the memory storing computer-readable instructions, the processor executing the computer-readable instructions to implement the steps of the display stabilization method for cycling smart glasses as described above.

[0048] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0049] A computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the display stabilization method for cycling smart glasses as described above.

[0050] Compared with the prior art, the embodiments of this application have the following main advantages:

[0051] The display stabilization method for smart cycling glasses disclosed in this application achieves "shake suppression without lag" and "motion-responsiveness without dizziness" in cycling scenarios where strong vibrations and rapid visual motion coexist. It achieves the synergistic effect of unified time base, frequency band separation and adaptive, intent gating, hierarchical anchoring and predictive compensation / self-correction. It significantly reduces residual shaking and phase lag, prioritizes the stabilization of key text / numerical information and maintains navigation space consistency, takes into account responsiveness and comfort, and can automatically maintain a stable effect even after long-term use and under different road conditions / wearing differences, thereby improving the overall readability and security of overlaid information. Attached Figure Description

[0052] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of one embodiment of the display stabilization method for cycling smart glasses according to this application;

[0054] Figure 2 This is a schematic diagram of a display stabilization system for cycling smart glasses according to an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] refer to Figure 1 A flowchart illustrating an embodiment of a display stabilization method for cycling smart glasses according to this application is shown. The display stabilization method for cycling smart glasses includes the following steps:

[0058] In step S101, under the display refresh clock, the angular velocity and acceleration collected by the inertial measurement unit are time-aligned, and the bias and scaling factor are estimated and compensated to form an inertial data stream based on a unified time reference.

[0059] In this embodiment, the electronic device on which the display stabilization method for cycling smart glasses operates can send or receive data via a wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultrawideband) connections, and other currently known or future-developed wireless connection methods.

[0060] In this embodiment, the display refresh clock is used as the unified master time base to perform time alignment and numerical calibration on the angular velocity and acceleration output by the inertial measurement unit. To eliminate the sampling phase difference between the sensor and the display link, the original timestamp is first mapped to the display time base, and interpolation or small-range time resampling is used to eliminate the discrete errors caused by jitter and sample loss. Then, without interrupting normal riding, the gyroscope bias and scaling factor are estimated through static segment detection and short-time steady-state window, and the asymptotic update rate is set according to the characteristics of temperature drift and slow drift under wearing conditions, so that the calibration process can correct in a timely manner and avoid excessive tracking of transient noise. To ensure the reliability of the acceleration used for subsequent attitude and operating condition characteristics, this step also completes the coarse estimation of gravity direction and coordinate system unification, so that the aligned and compensated angular velocity and acceleration form a continuous inertial data stream with equal time steps under the same time base and the same coordinate convention, providing stable input for subsequent vibration separation, intent recognition and image stabilization.

[0061] Step S102: Perform dual-channel adaptive filtering on the inertial data stream to separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation changes. Identify riding conditions based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly.

[0062] In this embodiment, low-frequency components characterizing slow head posture changes are extracted through a low-pass channel with an adjustable cutoff frequency, and high-frequency components characterizing road excitation and vehicle structure resonance are obtained as residuals. To ensure the separation remains effective under complex road conditions, the system uses robust features such as frequency band energy ratio, spectral peak density, and angular acceleration variance as inputs to identify the current operating condition category, such as flat road, cobblestone, or bumpy sprint, and adaptively adjusts the low-pass cutoff, notch center and quality factor, as well as subsequent image stabilization intensity and limiting parameters accordingly. Since cadence and specific road conditions form stable spectral peaks in vertical acceleration, this step can track peak values ​​and dynamically narrow energy leakage without relying on external sensors. If available external cadence or vehicle speed measurements exist, they are checked for consistency with the spectral peak frequencies to improve classification confidence. Through this separation and identification, the system continuously outputs smooth and reliable low-frequency posture components and sensitive high-frequency vibration indications, while providing a current operating condition label, providing a basis for gating and image stabilization parameter scheduling.

[0063] Step S103: Based on the angular velocity threshold, acceleration change and short pause detection, identify the wearer's saccade or head turning intention, generate a gating coefficient for continuously adjusting the image stabilization intensity, reduce or bypass high frequency suppression during the intention occurrence, and increase the image stabilization intensity during the short pause phase after the intention ends to stabilize the gaze point.

[0064] In this embodiment, when the angular velocity crosses the adaptive threshold and exhibits a sustained single-peak or double-peak structure, accompanied by a phase-consistent transition in acceleration, it is determined that a saccade or head-turning intention has begun. The subsequent drop in angular velocity followed by a short, stable pause is considered the end of the intention and the establishment of the fixation point. Based on this, the system generates continuously variable gating coefficients to time-varyingly schedule the image stabilization intensity: during the intention's occurrence, the weights of high-frequency suppression and angular velocity compensation are reduced to ensure smooth movement of the superimposed content, avoiding motion blur and a "head-pulling" sensation; during the short pause phase, the image stabilization intensity is rapidly increased to "lock" the new fixation point, reducing oscillations. To avoid secondary oscillations caused by the gating itself, the coefficient increase / decrease uses a hysteresis-based gradual variation law and is constrained by comfort limits, ensuring a natural visual transition without abrupt jumps.

[0065] Step S104: Based on the low-frequency attitude components and the gating coefficients, solve the field of view transformation and viewport offset used to stabilize the overlay content. Use head-locking anchoring for the text / numerical layer and head-locking and / or world-locking anchoring for the navigation or environment overlay layer according to the scene to complete the coordinate remapping and rendering by level.

[0066] In this embodiment, after obtaining the low-frequency attitude components and gating coefficients, this step solves for the field-of-view transformation and viewport offset used to stabilize the overlaid content, and implements layered anchoring and remapping based on the functional differences of the displayed content. Specifically, the system constructs a compensation transformation of the head reference system relative to the display coordinates using low-frequency angular motion, so that key information such as text and numerical values ​​remain stable under the head reference, thereby maintaining high readability during strong vibrations. For content such as navigation arrows or environmental prompts, the anchoring reference is selected between head locking and world locking based on scene availability: when the external environment texture and visual inertial calculation confidence are sufficient, the layer is anchored to the world reference to avoid drifting with head movements; when visual conditions degrade, it smoothly reverts to head locking to ensure continuity. All layers complete coordinate remapping and rendering on the same time base, and the layers follow priority and maximum allowable relative displacement constraints to avoid interference with the stable display of high-priority layers due to overcompensation of low-priority layers.

[0067] Step S105: Based on the angular motion state, predictive compensation is performed on the aforementioned field transformation up to the display time to offset the rendering and display link delay. The relative displacement of key content in adjacent frames is used as stability feedback to update the bias and parameters online to achieve long-term self-correction.

[0068] In this embodiment, due to the non-negligible end-to-end delay in the link formed by sensor sampling, image rendering, and panel scanning, this step extrapolates the image stabilization transformation to the target moment before display, based on the known angular motion state. This ensures that the superimposed content is in the correct, compensated position when the pixels are lit. To maintain long-term consistency between the extrapolation and the actual display, the system uses the relative displacement of key content in adjacent frames, steady-state residuals, and user subjective discomfort events as stability feedback to continuously and gradually adjust key parameters such as bias, scaling factor, filter bandwidth, gating threshold, and amplitude limit. To avoid overfitting and divergence, online updates are constrained by boundary and step size scheduling, and personalized parameter profiles can be maintained according to the user and wearing status to accelerate convergence upon reuse. When a sudden change in environment or a sharp drop in confidence is detected, the system triggers a safe fallback to a conservative parameter set, and then re-enters self-calibration after stability is restored, thereby maintaining a balance between jitter suppression and response sensitivity throughout the entire lifecycle.

[0069] This application achieves "shake suppression without lag" and "motion-responsiveness without dizziness" in cycling scenarios with both strong vibrations and rapid visual motion through the synergistic effects of unified time base, frequency band separation and adaptive, intent gating, hierarchical anchoring and predictive compensation / self-correction; it significantly reduces residual shaking and phase lag, prioritizes the stabilization of key text / numerical information and maintains navigation space consistency, taking into account both responsiveness and comfort, and can automatically maintain stability even after long-term use and under different road conditions / wearing differences, thereby improving the overall readability and security of overlaid information.

[0070] In some optional implementations of this embodiment, the method further includes the following after step S101:

[0071] The total time delay Δ from inertia to display is estimated by using the phase difference between the attitude trajectory obtained by integrating angular velocity and the superimposed coordinates of the rendered frame as an observation;

[0072] The total delay Δ is decomposed into three components: sampling delay, rendering queuing delay, and display scanning delay, and the slow drift of each component is tracked using a Kalman / PLL structure.

[0073] The field-of-view transformation is extrapolated based on the relative weights of the three components, so that the prediction compensation corrects for the time delay from different sources.

[0074] In this embodiment, under a unified time reference, the attitude trajectory obtained based on angular velocity integration is compared with the superimposed coordinate sequence generated in the previous rendering cycle. The phase difference on the characteristic frequency band (e.g., the main readable content jitter band of 2–8Hz) is used as the observation, and the total delay Δ is estimated as a continuous quantity through cross-correlation or phase-locked loop. To avoid compensation mismatch caused by simply merging all lags, the system further utilizes observable information such as rendering queue length, frame scheduling beat, and panel scan timing to decompose Δ into three components: sampling delay, rendering queue delay, and display scan delay. These components are then tracked slowly using a Kalman structure with process noise or a phase-locked loop. Subsequently, during the generation of the image stabilization transformation, the target time to be displayed is extrapolated forward. Instead of extrapolating with a single Δ, the three components are corrected separately according to their relative weights: the sampling component affects the state estimation time, the rendering component determines the predicted field of view for rendering startup, and the scan component constrains the final geometry of pixel illumination. This ensures that compensation is specifically offset across lags from different sources, thereby significantly reducing residual jitter and phase lag caused by delay mismatch.

[0075] In some optional implementations of this embodiment, step S102 specifically includes:

[0076] The main spectral peaks are tracked in real time in the power spectrum of the vertical component of acceleration, the peak frequency is set as the notch center, and the Q factor is adaptively adjusted according to the frequency band energy ratio.

[0077] When an external cadence or speed sensor is available, cross-calibrate the external measurement with the spectral peak frequency to confirm the source of the spectral peak.

[0078] When strong resonance or cobblestone road conditions are confirmed, the notch bandwidth is dynamically widened and the low-pass cutoff frequency is raised to suppress residual jitter caused by structural resonance.

[0079] The suppressed high-frequency residuals are used as one of the operating condition features and fed back to the operating condition classifier to improve the robustness of recognition.

[0080] In this embodiment, the system can use short-time Fourier transform or recursive autoregressive spectrum estimation to track the main peak frequency in real time on a sliding window, and use this frequency as the notch center. Then, based on the high / low frequency energy ratio to estimate the vibration mode, the quality factor Q of the notch filter is dynamically adjusted to adaptively balance interception depth and bandwidth between "single strong peak" and "multi-peak broadband". When external cadence or vehicle speed sensors are available, external measurements are cross-validated with the spectral peaks to confirm the peak source and avoid mistaking random road surface disturbances for cadence. If strong resonance scenarios such as cobblestones or vehicle body resonance are detected, the notch bandwidth is temporarily widened and the low-pass cutoff frequency is appropriately raised while maintaining overall readability, so that high-frequency suppression and attitude fidelity are simultaneously achieved. The remaining high-frequency residuals after processing are fed back as additional features for condition classification, forming a closed loop of "suppression-identification-re-suppression", improving robustness under complex road conditions and providing a more reliable basis for subsequent image stabilization intensity scheduling.

[0081] In some optional implementations of this embodiment, before step S103, the following steps are also included:

[0082] Calculate the instantaneous angular velocity and angular acceleration of the field of view transformation, and compare them with the preset comfort thresholds to obtain the available adjustment margin;

[0083] First-order and second-order combined limiting is applied to the rate of change of the field of view transformation to suppress dizziness caused by excessively rapid compensation.

[0084] The rising slope and maximum amplitude of the gating coefficient are constrained by the available adjustment margin to ensure that the image does not overshoot in strong vibration or emergency stop scenarios.

[0085] In this embodiment, the system first derives the instantaneous angular velocity and angular acceleration from the current field of view transformation, and compares them with a comfort threshold set based on the vestibular-visual coupling characteristics of the human eye, thereby obtaining the currently available adjustment margin. Subsequently, a series of rate limiters and jerk limiters are applied to the time derivative and second derivative of the field of view transformation to control the slope and curvature of the compensation over time, avoiding mismatch between high-frequency mechanical perception and visual input. The rising slope and maximum amplitude of the gating coefficient are further constrained by the aforementioned margin, preventing secondary oscillations such as compensation overshoot and reverse oscillation even under strong vibrations or sudden stops. Through this pre-processing step, subjective comfort and objective stability are unified, and subsequent gating scheduling is smoother and more controllable.

[0086] In some optional implementations of this embodiment, step S104 includes:

[0087] Set strong / weak anchoring levels and maximum allowable relative displacement thresholds for each display layer;

[0088] Strong earthquake events are detected based on a combined threshold of acceleration jump and angular acceleration, and safety freezing is triggered first for strong anchoring layers to maintain the most recent stable coordinates.

[0089] During the freeze period, reduce the refresh rate of strong anchored layers and maintain the limited smooth update of weak anchored layers;

[0090] When the stability index returns to within the threshold, the image is unfrozen and reset to normal stability using a time window fade-in method.

[0091] In this embodiment, the anchor point manager can set anchoring strength and maximum allowable relative displacement thresholds for text value layers, icon layers, and navigation / environment overlay layers, respectively. The system continuously monitors the joint index of acceleration jumps and angular acceleration peaks, and immediately determines a strong earthquake event when the threshold is exceeded. To ensure the stability of key readable information, the system prioritizes triggering a safety freeze for strong anchoring layers. During the freeze, the most recent stable coordinates are maintained and the refresh rate is reduced to suppress high-frequency flickering caused by severe vibrations. For weak anchoring layers, limited and smooth updates are maintained to maintain overall visual continuity. When the stability index falls back below the threshold, the frozen state fades in and gradually returns to a stable image through a set time window, avoiding visual inconsistencies caused by "instantaneous jumps." This mechanism ensures that key layers are not lost under the most demanding road conditions, and the overall interface remains usable.

[0092] In some optional implementations of this embodiment, step S104 further includes:

[0093] Calculate an environmental quality score, which is obtained by combining texture density, feature point traceability, and image signal-to-noise ratio;

[0094] By fusing the environmental quality score with the confidence level of visual inertial odometry, a world-locking availability criterion is obtained.

[0095] Based on the aforementioned criteria, the system switches between world lock and head lock, and sets hysteresis zones and fade-in / fade-out time windows to avoid boundary jitter.

[0096] When the availability criterion is below the threshold, it degenerates into a stable image based on inertial data and maintains the head lock of the text / numerical layer.

[0097] In this embodiment, environmental quality scores can be constructed using indicators such as traceable feature point density, corner strength / repetition tracking rate, and brightness signal-to-noise ratio. These scores are then fused with the state covariance or residual statistics of VIO to obtain a usability criterion for "world-lockable". When this criterion consistently exceeds an upper threshold, the system switches navigation and other objects to world-locked mode using a time window fade-in method and gradually increases the anchoring strength. When the criterion falls below a lower threshold, the system smoothly fades back to head-locked mode. During this process, all coordinate mappings remain continuous and constrained by amplitude limiters to avoid frequent jittering and jumps under boundary conditions. Simultaneously, the text / numerical layer can consistently use head-locked mode to ensure that core readability is maintained even when visual degradation occurs.

[0098] In some optional implementations of this embodiment, step S105 includes:

[0099] Construct a stability cost function with the objective function being the weighted sum of the squared relative displacements of key content in adjacent frames and the squared compensation angular accelerations;

[0100] Based on the gradient of the cost function, the low-pass cutoff frequency, notch depth, bias estimation step size and gating threshold are updated online, and upper / lower limits are set for each parameter;

[0101] Maintain individualized parameter profiles based on user identification and wearing status, and load them upon reuse to accelerate convergence;

[0102] When a sudden change in environment or user discomfort is detected, the parameters are triggered to fall back to the safe baseline and re-enter self-calibration.

[0103] In this embodiment, the squared relative displacement of key content in adjacent frames can be considered as a "residual jitter" metric, and the squared compensation angular acceleration can be considered as a "compensation intensity / comfort" penalty. The weighted sum of the two constitutes a stability cost function. The system calculates the incremental gradient of this cost in each refresh cycle and performs small-step, upper and lower bounded stochastic gradient updates on core parameters such as low-pass cutoff frequency, notch depth, bias estimation step size, and gating threshold to balance convergence speed and stability. To adapt to different users and wearing methods, the system maintains individualized parameter profiles and directly loads historical stable solutions upon reuse to shorten the warm-up period. When a sudden change in environment, metric divergence, or user discomfort feedback is detected, the parameters are triggered to fall back to a safe baseline and re-enter the self-calibration process, ensuring that the system maintains a balance between jitter suppression and response sensitivity throughout its entire lifecycle.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0105] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0106] Further reference Figure 2 As a response to the above Figure 1 The implementation of the method shown in this application provides an embodiment of a display image stabilization system for cycling smart glasses, which is similar to... Figure 1 Corresponding to the method embodiments shown, the system can be specifically applied to various electronic devices.

[0107] like Figure 2 As shown, the display stabilization system 200 for cycling smart glasses described in this embodiment includes: a data acquisition module 201, a filtering module 202, a generation module 203, a solution module 204, and a correction module 205. Wherein:

[0108] The acquisition module 201 is used to time-align the angular velocity and acceleration acquired by the inertial measurement unit under the display refresh clock, and to estimate and compensate for the bias and scaling factor to form an inertial data stream based on a unified time reference.

[0109] The filtering module 202 is used to perform dual-channel adaptive filtering on the inertial data stream, separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation change, identify the riding condition based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly.

[0110] The generation module 203 is used to detect and identify the wearer's saccade or head-turning intentions based on angular velocity threshold, acceleration change and short pause detection, and generate a gating coefficient for continuously adjusting the image stabilization intensity, so that high frequency suppression is reduced or bypassed during the intention occurrence, and the image stabilization intensity is increased during the short pause stage after the intention ends to stabilize the gaze point.

[0111] The solver module 204 is used to solve the field of view transformation and viewport offset for stabilizing the overlay content based on the low-frequency attitude components and the gating coefficients. It adopts a head-locking anchoring method for the text / numerical layer and a head-locking and / or world-locking anchoring method for the navigation or environment overlay layer according to the scene, and completes the coordinate remapping and rendering by layer.

[0112] The correction module 205 is used to predict and compensate the aforementioned field of view transformation up to the display time based on the angular motion state to offset the rendering and display link delay, and to update the bias and parameters online using the relative displacement of key content in adjacent frames as stability feedback to achieve long-term self-correction.

[0113] The display stabilization system for cycling smart glasses provided in this embodiment of the invention can realize all the processes of the display stabilization method for cycling smart glasses in the above embodiments. The functions and technical effects of each module in the device are the same as those of the display stabilization method for cycling smart glasses in the above embodiments, and will not be repeated here.

[0114] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed] for details. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0115] The computer device 3 includes a memory 31, a processor 32, and a network interface 33 that are interconnected via a system bus. It should be noted that only the computer device 3 with components 31-33 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0116] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0117] The memory 31 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 31 may be an internal storage unit of the computer device 3, such as the hard disk or memory of the computer device 3. In other embodiments, the memory 31 may also be an external storage device of the computer device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 3. Of course, the memory 31 may also include both the internal storage unit and its external storage device of the computer device 3. In this embodiment, the memory 31 is typically used to store the operating system and various application software installed on the computer device 3, such as computer-readable instructions for display image stabilization methods for cycling smart glasses. In addition, the memory 31 can also be used to temporarily store various types of data that have been output or will be output.

[0118] In some embodiments, the processor 32 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 32 is typically used to control the overall operation of the computer device 3. In this embodiment, the processor 32 is used to execute computer-readable instructions stored in the memory 31 or to process data, for example, to execute computer-readable instructions for the display stabilization method for cycling smart glasses.

[0119] The network interface 33 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 3 and other electronic devices.

[0120] The computer device provided in this application achieves "smooth and lag-free" and "motion-responsive and dizzy-free" cycling scenarios with strong vibrations and rapid visual motion through the synergistic effects of unified time base, frequency band separation and adaptive, intent gating, hierarchical anchoring and predictive compensation / self-correction. It significantly reduces residual vibration and phase lag, prioritizes the stabilization of key text / numerical information and maintains navigation space consistency, taking into account both responsiveness and comfort. Moreover, it can automatically maintain a stable effect even after long-term use and under different road conditions / wearing differences, thereby improving the overall readability and security of overlaid information.

[0121] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the display stabilization method for cycling smart glasses as described above.

[0122] The computer-readable storage medium provided in this application achieves "smooth and lag-free" and "motion-responsive and dizzy-free" cycling scenarios with strong vibrations and rapid visual motion through the synergistic effects of unified time base, frequency band separation and adaptive, intent gating, hierarchical anchoring and predictive compensation / self-correction. It significantly reduces residual jitter and phase lag, prioritizes the stabilization of key text / numerical information and maintains navigation space consistency, takes into account responsiveness and comfort, and can automatically maintain stability under long-term use and different road conditions / wearing differences, thereby improving the overall readability and security of overlaid information.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0124] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A display stabilization method for smart cycling glasses, characterized in that, Includes the following steps: S101. Under the display refresh clock, the angular velocity and acceleration collected by the inertial measurement unit are time-aligned, and the bias and scaling factor are estimated and compensated to form an inertial data stream based on a unified time reference. S102. Perform dual-channel adaptive filtering on the inertial data stream to separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation change. Identify riding conditions based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly. S103. Based on angular velocity threshold, acceleration change and short pause detection, identify the wearer's saccade or head turning intention, generate a gating coefficient for continuously adjusting the image stabilization intensity, reduce or bypass high frequency suppression during the intention occurrence, and increase the image stabilization intensity during the short pause phase after the intention ends to stabilize the gaze point. S104. Based on the low-frequency attitude components and the gating coefficients, solve the field transformation and viewport offset used to stabilize the overlay content. Use head-locking anchoring for the text / numerical layer and head-locking and / or world-locking anchoring for the navigation or environment overlay layer according to the scene to complete the coordinate remapping and rendering by level. S105. Based on the angular motion state, predictive compensation is performed on the aforementioned field of view transformation up to the display time to offset the rendering and display link delay, and the relative displacement of key content in adjacent frames is used as stability feedback to update the bias and parameters online to achieve long-term self-correction.

2. The display stabilization method for smart cycling glasses according to claim 1, characterized in that, The process following step S101 also includes: The total time delay Δ from inertia to display is estimated by using the phase difference between the attitude trajectory obtained by integrating angular velocity and the superimposed coordinates of the rendered frame as an observation; The total delay Δ is decomposed into three components: sampling delay, rendering queuing delay, and display scanning delay, and the slow drift of each component is tracked using a Kalman / PLL structure. The field-of-view transformation is extrapolated based on the relative weights of the three components, so that the prediction compensation corrects for the time delay from different sources.

3. The display stabilization method for cycling smart glasses according to claim 1, characterized in that, Step S102 specifically includes: The main spectral peaks are tracked in real time in the power spectrum of the vertical component of acceleration, the peak frequency is set as the notch center, and the Q factor is adaptively adjusted according to the frequency band energy ratio. When an external cadence or speed sensor is available, cross-calibrate the external measurement with the spectral peak frequency to confirm the source of the spectral peak. When strong resonance or cobblestone road conditions are confirmed, the notch bandwidth is dynamically widened and the low-pass cutoff frequency is raised to suppress residual jitter caused by structural resonance. The suppressed high-frequency residuals are used as one of the operating condition features and fed back to the operating condition classifier to improve the robustness of recognition.

4. The display stabilization method for cycling smart glasses according to claim 1, characterized in that, Before step S103, the method further includes: Calculate the instantaneous angular velocity and angular acceleration of the field of view transformation, and compare them with the preset comfort thresholds to obtain the available adjustment margin; First-order and second-order combined limiting is applied to the rate of change of the field of view transformation to suppress dizziness caused by excessively rapid compensation. The rising slope and maximum amplitude of the gating coefficient are constrained by the available adjustment margin to ensure that the image does not overshoot in strong vibration or emergency stop scenarios.

5. The display stabilization method for cycling smart glasses according to claim 1, characterized in that, Step S104 includes: Set strong / weak anchoring levels and maximum allowable relative displacement thresholds for each display layer; Strong earthquake events are detected based on a combined threshold of acceleration jump and angular acceleration, and safety freezing is triggered first for strong anchoring layers to maintain the most recent stable coordinates. During the freeze period, reduce the refresh rate of strong anchored layers and maintain the limited smooth update of weak anchored layers; When the stability index returns to within the threshold, the image is unfrozen and reset to normal stability using a time window fade-in method.

6. The display stabilization method for cycling smart glasses according to claim 5, characterized in that, Step S104 further includes: Calculate an environmental quality score, which is obtained by combining texture density, feature point traceability, and image signal-to-noise ratio; By fusing the environmental quality score with the confidence level of visual inertial odometry, a world-locking availability criterion is obtained. Based on the aforementioned criteria, the system switches between world lock and head lock, and sets hysteresis zones and fade-in / fade-out time windows to avoid boundary jitter. When the availability criterion is below the threshold, it degenerates into a stable image based on inertial data and maintains the head lock of the text / numerical layer.

7. The display stabilization method for cycling smart glasses according to claim 1, characterized in that, Step S105 includes: Construct a stability cost function with the objective function being the weighted sum of the squared relative displacements of key content in adjacent frames and the squared compensation angular accelerations; Based on the gradient of the cost function, the low-pass cutoff frequency, notch depth, bias estimation step size and gating threshold are updated online, and upper / lower limits are set for each parameter; Maintain individualized parameter profiles based on user identification and wearing status, and load them upon reuse to accelerate convergence; When a sudden change in environment or user discomfort is detected, the parameters are triggered to fall back to the safe baseline and re-enter self-calibration.

8. A display stabilization system for smart cycling glasses, characterized in that, include: The acquisition module is used to time-align the angular velocity and acceleration acquired by the inertial measurement unit under the display refresh clock, and to estimate and compensate for the bias and scaling factor to form an inertial data stream based on a unified time reference. The filtering module is used to perform dual-channel adaptive filtering on the inertial data stream, separate the high-frequency component representing road surface excitation and the low-frequency component representing head orientation change, identify riding conditions based on features not limited to frequency band energy ratio, spectral peak density, and angular acceleration variance, and automatically adjust the filtering parameters and image stabilization parameters accordingly. The generation module is used to detect and identify the wearer's saccade or head-turning intentions based on angular velocity threshold, acceleration change and short pause. It generates a gating coefficient for continuously adjusting the image stabilization intensity, reducing or bypassing high-frequency suppression during the intention occurrence, and increasing the image stabilization intensity during the short pause phase after the intention ends to stabilize the gaze point. The solution module is used to solve the field transformation and viewport offset for stabilizing the overlay content based on the low-frequency attitude components and the gating coefficients. It adopts a head-locking anchoring method for the text / numerical layer and a head-locking and / or world-locking anchoring method for the navigation or environment overlay layer according to the scene, and completes the coordinate remapping and rendering by layer. The correction module is used to predict and compensate for the aforementioned field of view transformation up to the display time based on the angular motion state in order to offset the rendering and display link delay. It also uses the relative displacement of key content in adjacent frames as stability feedback to update the bias and parameters online to achieve long-term self-correction.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the display stabilization method for cycling smart glasses as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the display stabilization method for cycling smart glasses as described in any one of claims 1 to 7.

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