Micro-motion lagging optimization method based on television remote controller

Abstract

The invention discloses a micro-motion jamming optimization method based on a television remote controller, which realizes filter coefficient self-adaption under micro-motion by considering parameters such as data stability of an IMU (Inertial Measurement Unit) sensor and filter cut-off frequency under a micro-motion mode. Firstly, stability analysis is carried out on IMU sensor data, and then a filter coefficient is adjusted according to an analysis result so as to adapt to special requirements of a micro-motion mode. And then, through acceleration and adaptive zero speed correction in the micro-motion mode, in combination with a multi-direction independent filtering technology and an ultra wide band (UWB) calibration strategy, the fluency of the remote controller in the micro-motion mode is further improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent interaction technology, and in particular to a method for optimizing micro-motion freezes of a television remote controller. Background Art

[0002] When using a remote control, micro-operations (such as slight movement of a button, sliding gestures, or subtle operations of the confirmation button, etc.) are common operations. However, when processing such micro-operations, the remote control often freezes, which means that the user's operation cannot be reflected on the TV immediately and accurately, resulting in delayed or inaccurate responses. This phenomenon has a serious negative impact on the user's operating experience.

[0003] Currently, when dealing with micro-motion stuttering problems, if the Kalman filter algorithm is used, the micro-motion signal is easily filtered out, which in turn causes stuttering problems. Based on this, it is necessary to design an optimization method specifically for the inertial measurement unit (IMU) sensor data in micro-motion mode, and combine the Kalman filter algorithm to process the micro-motion data, so as to reduce the sensor error in micro-motion mode and prevent the data from being over-smoothed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing the micro-motion jamming of a TV remote control in order to solve the above-mentioned problems. The present invention uses an inertial measurement unit (IMU) sensor to capture the motion data of the remote control user during operation, and sets a speed threshold to identify whether the user is in a micro-motion state. Once the micro-motion state is confirmed, the acceleration data is adaptively corrected to zero speed and processed in the micro-motion mode. The stability evaluation index of the accelerometer and gyroscope data in the IMU sensor, namely the standard deviation, is calculated and incorporated into the adaptive coefficient of the filter. This step is crucial to improving the performance of the filter. Considering the low-frequency characteristics in the micro-motion mode, the cutoff frequency of the filter is optimized to better meet the needs of micro-motion operation. A set of smoothness evaluation indicators for the micro-motion mode of the filter is designed to evaluate the performance of the optimized remote control in the micro-motion mode.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A method for optimizing micro-motion freeze of a TV remote controller, comprising the following steps:

[0007] Step 1: Determine whether the remote controller is in a micro-motion state according to the speed threshold;

[0008] Step 2: Perform acceleration and adaptive zero speed correction in the inching mode;

[0009] Step 3: Calculate the standard deviation of the accelerometer and gyroscope data of the IMU sensor in a time window as a stability judgment mark;

[0010] Step 4: Using the calculated standard deviation of the gyroscope and accelerometer and the manually set stability judgment threshold of the gyroscope and accelerometer as parameters, an adaptive coefficient of the complementary filter is calculated according to Formula 4;

[0011] Step 5: Secondly, the micro-motion mode usually involves subtle changes in low frequencies, so the complementary filter coefficient α is adjusted as Formula 6, by adjusting the cutoff frequency f c Filter the remote control sensor output data under micro-motion;

[0012] Step 6: Use UWB distance calibration information and multi-directional acceleration independent filtering fusion method to reduce data drift in IMU micro-motion mode;

[0013] Step 7: Design a fluency evaluation index δ of the remote control in the micro-motion mode, and perform micro-motion evaluation on the optimized remote control.

[0014] A further solution is that in step 1, the micro-motion mode is determined according to a speed threshold, and when the speed output by the IMU is less than a certain threshold, it is determined to be in micro-motion mode.

[0015] A further solution is that in step 2, in order to reduce the influence of noise on speed calculation, an acceleration threshold processing method is used in combination with adaptive zero speed correction;

[0016] Acceleration threshold correction: If the measured acceleration |a(t)| is less than a certain threshold a thr , then treat it as noise and set the acceleration to zero:

[0017]

[0018] This can effectively eliminate errors caused by hand shaking and sensor noise;

[0019] Adaptive zero velocity correction: When the IMU detects that the acceleration and angular velocity are close to zero, the velocity is adaptively corrected according to the stationary time. Assume that when |a(t)|≤a thr and|ω(t)|≤ω thr hour:

[0020]

[0021] v(t 0 ) is the speed when the approximate stationary state is initially detected, λ is the attenuation coefficient, which determines how fast the speed decays, T is the stationary time, T thris a manually set approximate stationary time threshold; this process reduces estimation errors caused by data instability.

[0022] A further solution is that in step 3, the volatility of the data is evaluated using the standard deviation and variance, and the standard deviation of the remote control gyroscope and accelerometer is calculated in a time window, and then the calculated value is compared with the standard deviation threshold. When the variance of the value in this time window is less than the judgment threshold, the sensor output data is considered stable, and the contribution of the sensor to the final attitude estimation can be increased. If the calculated values ​​are all greater than the set threshold, this indicates that the IMU data at this time is unreliable, and the remote control needs to be calibrated.

[0023] A further solution is that in step 3, the complementary filtering algorithm is combined with the Kalman filter to process the remote control IMU output data. The formula of complementary filtering is as follows:

[0024] angle new =α(angle old +gyro·dt)+(1-α)·accel Formula (3)

[0025] Assume that angle is the current attitude estimate, gyro is the angular velocity of the gyroscope, accel is the proportional coefficient of the filter, dt is the IMU sampling time interval, which is related to the manually set sampling frequency; α is the filter coefficient, which determines the weight of the accelerometer and gyroscope data. A larger α value makes the filter more dependent on the gyroscope data and is suitable for dynamic environments; a smaller α value makes the filter more dependent on the accelerometer data and is suitable for static or slowly changing scenes.

[0026] A further solution is that in step 4, affected by the angular velocity output stability of the gyroscope and the accelerometer, when σ accel ≤σ accel_std and σ gyro ≤σ gyro_std When the output stability of a sensor is poor, the adaptive threshold is set to reduce the contribution of the sensor to the final output angle; the adaptive coefficient calculation formula is as follows:

[0027]

[0028] The improved angle calculation formula is as follows:

[0029] angle new =σα(angle old +gyro·dt)+(1-σα)·accel formula (5).

[0030] A further solution is that in step 5, the micro-motion mode involves subtle changes in low frequency. By lowering the cutoff frequency of the filter, the impact on high-frequency noise can be reduced to avoid over-smoothing; because accelerometers are usually used to measure low-frequency movement, and gyroscopes are used to measure high-frequency movement. Therefore, the filter coefficient calculation formula in the complementary filter algorithm is as follows:

[0031] α=1 / (1+τ / dt) Formula (6)

[0032] Where τ is the time constant of the filter and dt is the sampling interval of the data. The calculation formula is as follows:

[0033]

[0034] where f c is the cut-off frequency.

[0035] A further solution is that in step 6, the accelerations in different directions of XYZ are filtered independently and then fused to improve the responsiveness to complex motions;

[0036] By adding UWB distance calibration information, the real-time distance measurement provided by UWB is used as a correction reference for the IMU data. By comparing the distance information of UWB with the predicted position of the IMU, the drift effect of the IMU is reduced, thereby maintaining the positioning accuracy.

[0037] A further solution is that in step 7, the fluency evaluation index δ of the remote control in the micro-motion mode is specified: on the TV interface, a label trajectory L1 of the remote control is pre-drawn through a drawing board, and the remote control is micro-moved along the same trajectory, and the remote control motion trajectory L2 in the micro-motion mode is obtained at the same time; the two trajectories are sampled at equal intervals, and the sampling coordinate point set of the label trajectory L1 is {(x1, y1), (x2, y2), … (xn, yn)}, and the corresponding coordinate set on the remote control trajectory L2 is {(a1, b1), (a2, b2), … (an, bn)}, and the coordinate deviation is calculated as:

[0038]

[0039] The beneficial effects of the present invention are:

[0040] The present invention is a method for optimizing the micro-motion jamming of a TV remote control, which aims to realize the adaptive filter coefficient under micro-motion by considering the data stability of the IMU (inertial measurement unit) sensor and the filter cutoff frequency in the micro-motion mode and other parameters. The method first performs a stability analysis on the IMU sensor data, and then adjusts the filter coefficient according to the analysis results to meet the special needs of the micro-motion mode. Then, through the acceleration and adaptive zero-speed correction in the micro-motion mode, combined with multi-directional independent filtering technology and ultra-wideband (UWB) calibration strategy, the fluency of the remote control in the micro-motion mode is further improved. Finally, the present invention proposes a set of evaluation indicators for more accurately evaluating the directional performance of the remote control.

[0041] Improved accuracy: By comprehensively considering the data stability of the IMU sensor, adaptive adjustment of the filter coefficients is achieved. In the micro-motion mode, the remote control fluency is significantly improved through acceleration and adaptive zero-speed correction, combined with multi-directional independent filtering technology and UWB calibration.

[0042] Enhanced recognition reliability: For micro-motion mode, a complementary filter is designed and combined with a Kalman filter to improve the remote control's ability to handle fine movements. This combination strategy effectively reduces error accumulation and avoids cursor freezes or drift, thereby enhancing the remote control's recognition reliability.

[0043] Optimized user experience: In micro-motion mode, the user experience is significantly improved by improving accuracy. Whether it is voice control or game control, users can enjoy a smoother operation experience. This optimization not only enhances user satisfaction, but also broadens the application scope of the remote control in scenarios with high precision requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0046] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0047] In any embodiment, if Figure 1 As shown, a method for optimizing micro-motion freeze of a TV remote controller according to the present invention comprises:

[0048] The micro-motion mode is determined based on the speed threshold. When the speed output by the IMU is less than a certain threshold, it can be determined as micro-motion mode.

[0049] In order to reduce the impact of noise on speed calculation, this patent adopts acceleration threshold processing method combined with adaptive zero speed correction.

[0050] Acceleration threshold correction: if the measured acceleration |a(t)| is less than a certain threshold a thr , it is considered as noise and the acceleration is set to zero:

[0051]

[0052] This can effectively eliminate errors caused by hand shake and sensor noise.

[0053] Adaptive zero velocity correction: When the IMU detects that the acceleration and angular velocity are close to zero, the velocity can be adaptively corrected according to the stationary time. Here, it is assumed that when |a(t)|≤a thr and|ω(t)|≤ω thr hour,

[0054]

[0055] v(t 0 ) is the speed when the approximate stationary state is initially detected, λ is the attenuation coefficient, which determines how fast the speed decays. T is the stationary time, T thr is a manually set approximate stationary time threshold. This can reduce estimation errors caused by unstable data.

[0056] The stability of sensor data is the key to ensure that the system can be effectively controlled in micro-motion mode. The standard deviation and variance are usually used to evaluate the volatility of the data. The standard deviation of the remote control gyroscope and accelerometer in a time window is calculated respectively, and the standard deviation threshold is judged. When the value variance in the window is less than the judgment threshold, the sensor output data is considered stable, which can improve the contribution of the sensor to the final attitude estimation. If both are greater than the set threshold, it means that the IMU data at this time is unreliable, and the remote control needs to be calibrated.

[0057] The complementary filtering algorithm is combined with the Kalman filter to process the remote control IMU output data. The formula of complementary filtering is as follows:

[0058] angle new=α(angle old +gyro·dt)+(1-α)·accel Formula (3)

[0059] Assume that angle is the current attitude estimate, gyro is the angular velocity of the gyroscope, accel is the proportional coefficient of the filter, and dt is the IMU sampling time interval, which is related to the manually set sampling frequency. α is the filter coefficient, which determines the weight of the accelerometer and gyroscope data. A larger α value makes the filter more dependent on the gyroscope data and is suitable for dynamic environments. A smaller α value makes the filter more dependent on the accelerometer data and is suitable for static or slowly changing scenes.

[0060] Affected by the stability of the angular velocity output of the gyroscope and accelerometer, when σ accel ≤σ accel_std and σ gyro ≤σ gyro_std When the output stability of a sensor is poor, the adaptive threshold can be set to reduce the contribution of the sensor to the final output angle. The adaptive coefficient calculation formula is as follows:

[0061]

[0062] The improved angle calculation formula is as follows:

[0063] angle new =σα(angle old +gyro·dt)+(1-σα)·accel Formula (5)

[0064] Secondly, the micro-motion mode usually involves subtle changes in low frequencies. By lowering the cutoff frequency of the filter, the impact on high-frequency noise can be reduced and over-smoothing can be avoided. Because accelerometers are usually used to measure low-frequency motion and gyroscopes are used to measure high-frequency motion. Therefore, the filter coefficient calculation formula in the complementary filter algorithm is as follows:

[0065] α=1 / (1+τ / dt) Formula (6)

[0066] Where τ is the time constant of the filter and dt is the sampling interval of the data. The calculation formula is as follows:

[0067]

[0068] where f c is the cut-off frequency.

[0069] Secondly, the accelerations in different directions of XYZ can be filtered independently and then fused to improve the response capability to complex motions.

[0070] By adding UWB distance calibration information, the real-time distance measurement provided by UWB can be used as a correction reference for IMU data. By comparing the distance information of UWB with the predicted position of IMU, the drift effect of IMU can be reduced, thereby maintaining the positioning accuracy.

[0071] Specify the fluency evaluation index δ of the remote control in the micro-motion mode: Use the drawing board on the TV interface to pre-draw the remote control moving label trajectory L1, let the remote control micro-move along the same trajectory, and obtain the remote control movement trajectory L2 in the micro-motion mode. The two trajectories are sampled at equal intervals. The sampling coordinate point set of the label trajectory L1 is {(x1, y1), (x2, y2), ... (xn, yn)}, and the corresponding coordinate set on the remote control trajectory L2 is {(a1, b1), (a2, b2), ... (an, bn)}. The coordinate deviation is calculated as:

[0072]

[0073] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further explain various possible combinations. In addition, the various different embodiments of the present invention can also be combined arbitrarily, as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for optimizing the micro-motion freeze of a TV remote control, characterized in that: The following steps are involved: Step 1: Determine whether the remote controller is in a micro-motion state according to the speed threshold; Step 2: Perform acceleration and adaptive zero speed correction in the inching mode; Step 3: Calculate the standard deviation of the accelerometer and gyroscope data of the IMU sensor in a time window as a stability judgment mark; Step 4: By using the calculated standard deviation of the gyroscope and accelerometer and the manually set stability judgment threshold of the gyroscope and accelerometer as parameters, the adaptive coefficient of the IMU sensor's contribution to the final output angle is obtained; Step 5: By adjusting the cutoff frequency f c Filter the remote control sensor output data under micro-motion to obtain the filter coefficients in the complementary filter algorithm to avoid over-smoothing of the filter; Step 6: Use UWB distance calibration information and multi-directional acceleration independent filtering fusion method to reduce data drift in IMU micro-motion mode; Step 7: Design a fluency evaluation index δ of the remote control in the micro-motion mode, and perform micro-motion evaluation on the optimized remote control.

2. A TV remote controller micro-motion freeze optimization method as claimed in claim 1, characterized in that: In the step 1, the micro-motion mode is determined according to the speed threshold, and when the speed output by the IMU is less than a certain threshold, it is determined to be the micro-motion mode.

3. A TV remote controller micro-motion freeze optimization method as claimed in claim 1, characterized in that: In step 2, in order to reduce the influence of noise on speed calculation, an acceleration threshold processing method is used in combination with adaptive zero speed correction; Acceleration threshold correction: If the measured acceleration |a(t)| is less than a certain threshold a thr , then treat it as noise and set the acceleration to zero: This can effectively eliminate errors caused by hand shaking and sensor noise; Adaptive zero velocity correction: When the IMU detects that the acceleration and angular velocity are close to zero, the velocity is adaptively corrected according to the stationary time. Assume that when |a(t)|≤a thr and|ω(t)|≤ω thr hour: v(t0) is the initial speed detected when it is approximately stationary, λ is the attenuation coefficient, which determines how fast the speed decays, T is the stationary time, T thr is a manually set approximate inactivity time threshold.

4. The method for optimizing the micro-motion freeze of a TV remote controller according to claim 1, characterized in that: In step 3, the volatility of the data is evaluated using the standard deviation and variance, and the standard deviation of the remote control gyroscope and accelerometer is calculated in a time window, and then the calculated value is compared with the standard deviation threshold. When the variance of the value in this time window is less than the judgment threshold, the sensor output data is considered stable, and the contribution of the sensor to the final attitude estimation can be increased. If the calculated values ​​are all greater than the set threshold, this indicates that the IMU data at this time is unreliable, and the remote control needs to be calibrated.

5. The method for optimizing the micro-motion freeze of a TV remote controller according to claim 1, characterized in that: In step 3, the complementary filtering algorithm is combined with the Kalman filter to process the remote control IMU output data. The formula of complementary filtering is as follows: angle new = α(angle old + gyro·dt) + (1 - α)·accel Equation (3) Assume that angle is the current attitude estimate, gyro is the angular velocity of the gyroscope, accel is the proportional coefficient of the filter, dt is the IMU sampling time interval, which is related to the manually set sampling frequency; α is the filter coefficient, which determines the weight of the accelerometer and gyroscope data. A larger α value makes the filter more dependent on the gyroscope data and is suitable for dynamic environments; a smaller α value makes the filter more dependent on the accelerometer data and is suitable for static or slowly changing scenes.

6. A method for optimizing micro-motion freeze of a TV remote controller as claimed in claim 1, characterized in that: In step 4, when σ is satisfied at the same time accel ≤σ accel_std and σ gyro ≤σ gyro_std When , the angle calculation is shown in Formula 3. When the output stability of a certain sensor is poor, an adaptive threshold is set to reduce the contribution of the sensor to the final output angle; The adaptive coefficient calculation formula is as follows: The improved angle calculation formula is as follows: angle new = σα(angle old + gyro·dt) + (1 - σα)·accel Equation (5).

7. The method for optimizing the micro-motion freeze of a TV remote controller according to claim 1, characterized in that: In step 5, the impact on high-frequency noise is reduced by lowering the cutoff frequency of the filter to avoid oversmoothing; the filter coefficient calculation formula in the complementary filter algorithm is as follows: α=1 / (1+τ / dt) Formula (6) Where τ is the time constant of the filter and dt is the sampling interval of the data. The calculation formula is as follows: where f c is the cut-off frequency.

8. The method for optimizing the micro-motion freeze of a TV remote controller according to claim 1, characterized in that: In step 6, the accelerations in different directions of XYZ are filtered independently and then fused to improve the responsiveness to complex motions; By adding UWB distance calibration information, the real-time distance measurement provided by UWB is used as a correction reference for the IMU data. By comparing the distance information of UWB with the predicted position of the IMU, the drift effect of the IMU is reduced, thereby maintaining the positioning accuracy.

9. The method for optimizing the micro-motion freeze of a TV remote controller according to claim 1, characterized in that: In step 7, the fluency evaluation index δ of the remote control in the micro-motion mode is specified: on the TV interface, a remote control moving label trajectory L1 is pre-drawn through a drawing board, and the remote control is micro-moved along the same trajectory, and the remote control movement trajectory L2 in the micro-motion mode is obtained at the same time; the two trajectories are sampled at equal intervals, and the sampling coordinate point set of the label trajectory L1 is {(x1, y1), (x2, y2), ... (xn, yn)}, and the corresponding coordinate set on the remote control trajectory L2 is {(a1, b1), (a2, b2), ... (an, bn)}, and the coordinate deviation is calculated as:

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