Motion sickness evaluation system and method based on accumulative motion sickness value

Abstract

The invention relates to the technical field of automobile motion sickness detection, in particular to a motion sickness evaluation system and method based on an accumulated motion sickness value. According to the system and method, accurate quantitative evaluation is achieved by collecting multi-dimensional motion data through a test dummy. Comprising the following steps: acquiring head and chest three-axis acceleration, inclination angle and vehicle speed data in real time; a dynamic model of a cosine product of vehicle speed and acceleration is introduced when the basic dizziness value is calculated; basic values are divided into danger, transition and harmlessness according to the perceived red and green lines, continuous danger values are weighted according to 5% progressive increase, transition values are attenuated according to 5% progressive decrease, and experience dizziness values are formed. And counting data distribution of each axis through a four-level threshold interval, calculating weighted scores of the head and chest, and fusing the weighted scores into a three-axis comprehensive score. The system integrates a multi-axis sensor, a dynamic calculation module and a grading and assigning engine, multi-dimensional coupling analysis of motion perception and visual perception is achieved, the physiological authenticity and scene adaptability of motion sickness evaluation are improved, and the evaluation accuracy of the motion sickness condition of the vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile motion sickness detection, and in particular to a motion sickness evaluation system and method based on cumulative motion sickness values. Background Art

[0002] Motion sickness is a physiological reaction caused by the conflict between visual, vestibular and proprioceptive information in a moving environment, and is mainly manifested as dizziness, nausea, vomiting and other symptoms. Its core mechanism is the mismatch between the vestibular system and visual perception. For example, in a vehicle or virtual reality scene, the motion signal perceived by the inner ear conflicts with the static image input by the visual input, leading to autonomic nervous system disorder. In the actual process of riding, the passenger's motion sickness is also related to the

[0003] Application number 202311494964.1 discloses a method for constructing a motion sickness degree evaluation model, which involves the fields of brain-computer interaction, motion sickness identification, etc. The method first collects motion sickness signals through motion sickness induction and cerebral blood oxygenation signals, and collects subjective evaluation scores of the motion sickness state. After processing and analysis, characteristic parameters are obtained, and then the characteristic parameters are judged and processed for the construction of the motion sickness degree evaluation model. This method can simply and effectively obtain the motion sickness degree evaluation model. The motion sickness degree evaluation model obtained by this method can be accurately and effectively used to predict or evaluate the degree of motion sickness of a person, and is helpful in quantitatively evaluating and predicting or evaluating the degree of motion sickness of a person in practical applications. By real-time monitoring of changes in physiological signals, the time point when motion sickness symptoms appear can be predicted, and timely intervention measures can be taken to effectively avoid or alleviate the occurrence of motion sickness.

[0004] The above technology evaluates the motion sickness state through the occupants' physical state parameters combined with the occupants' subjective evaluation of their motion sickness state. This evaluation method is subject to many site and facility restrictions and is not suitable for scenarios where motion sickness detection is performed on multiple vehicles. Retesting of motion sickness on the same type of vehicle may also cause distortion of the detection results due to subjective factors of real people. If sensor data is used to simulate the measurement of a person's physiological state, there will be deviations in the actual real-person motion sickness perception. During the occupants' actual ride, the occupants are in the car's sickness field. Depending on the car's driving status, the occupants will not feel motion sickness when the car's motion sickness index is below a certain level. When the car's condition is higher than another motion sickness index, the occupants will not only feel motion sickness, but also, as time goes by, the car's driving status remains unchanged. The motion sickness condition will worsen; if it is between these two indicators, the occupant's motion sickness condition will gradually ease. The two indicators are named the green line and the red line to divide the motion sickness state of the car. For real-person detection, this cumulative motion sickness effect can be obtained through subjective feeling. If only the sensors installed on the vehicle are used to collect and simulate data, the collected data will only be directly used, and the accumulated motion sickness state cannot be cumulatively calculated, which will lead to the final motion sickness result being far from the real perception. Therefore, a technology is needed that can detect and calculate data that may affect the motion sickness state of the vehicle by means of sensors, so as to solve the problem that direct calculation through sensor data cannot represent the deepening / relief of motion sickness in the motion sickness scene, resulting in inaccurate evaluation of the vehicle's motion sickness condition. Summary of the Invention

[0005] The present invention provides a motion sickness evaluation system and method based on cumulative motion sickness values, which can perform motion sickness enhancement / reduction calculations for vehicles in different motion sickness states, thereby improving the accuracy of the final output results.

[0006] In order to solve the above technical problems, the present application provides the following technical solution: a motion sickness evaluation method based on cumulative motion sickness value, comprising the following steps:

[0007] S1: Obtain motion data of the test dummy at every threshold time, the motion data including:

[0008] Acceleration components of the head on the x / y / z axis:

[0009] Vehicle speed: v;

[0010] The tilt angle of the head in the x / y / z axis plane Where k∈(x,y,z);

[0011] S2: Calculate the basic field halo value of the head on the x, y, and z axes;

[0012]

[0013] S3: Classifying the basic field sickness value according to the preset perception green line value and the perception red line value: the basic field sickness value above the perception red line value is marked as a dangerous field sickness value, the basic field sickness value above the perception green line and below the perception red line value is marked as a transitional field sickness value, and the basic field sickness value below the perception green line is marked as a harmless field sickness value;

[0014] S4: Calculate the experience sickness value through the dangerous sickness value, the transition sickness value and the harmless sickness value, wherein the experience sickness value of the points with consecutive dangerous sickness values is calculated by increasing the multiplier by 5% in the order of occurrence based on the basic sickness value, until the upper limit of 150%; the experience sickness value of the points with consecutive transition sickness values is calculated by decreasing the multiplier by 5% in the order of occurrence based on the basic sickness value, until the upper limit of 50%; the harmless sickness value is directly output as the experience sickness value according to the original value;

[0015] S5: Set four levels of field dizziness experience independently for the x / y / z axis;

[0016] [D k1 ,D k2 ),[D k2 ,D k3 ),[D k3 ,D k4 ),[D k4 ,∞);

[0017] And assign a halo coefficient to each level interval: satisfy (j=1,2,3,4);

[0018] S6: Gradual statistics and calculation of the field sickness score rate of each axis:

[0019] Count the number of data points in the four-level intervals of each axis within the preset detection time period:

[0020] Calculate the score rate of each axis:

[0021]

[0022] S7: Weight the three-dimensional axial score rate of the head to generate the comprehensive motion sickness score rate of the three axes of experience;

[0023]

[0024] S8: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the three-axis motion sickness evaluation score N of the car experience xyz .

[0025] The basic principles and beneficial effects of this solution are as follows: This solution uses a test dummy as a vehicle for motion sickness data collection, collecting data from various sensors during driving and using it for subsequent motion sickness assessment. This solution introduces the concept of motion sickness value, which intuitively and simply assesses the vehicle's motion sickness through a comprehensive calculation of speed, acceleration, and tilt angle. Specifically, the larger the motion sickness value, the lower the vehicle's motion sickness score (poor vehicle motion sickness evaluation). Before evaluating the vehicle's motion sickness score, the baseline motion sickness value in step S2 is converted into an experienced motion sickness value using the calculation method in steps S3 and S4, based on the demarcated red and green motion sickness lines. The green and red motion sickness lines are determined as follows: (a) Idle and constant speed—the green baseline. Below this baseline, the human body quickly adapts and does not cause motion sickness. (b) Above the red line (motion sickness event), the user experiences significant discomfort and motion sickness is suppressed. (c) Below the red line and above the green line, the user experiences discomfort, but only high frequencies cause motion sickness. That is, through real-person testing, the red and green line ranges are determined, and then applied to the calculation of the experience of motion sickness in this solution.

[0026] If the basic field halo value at a certain time point is above the red line, and the basic field halo value at a subsequent time point is also above the red line, the basic field halo value at the subsequent time point will be superimposed on the original basis by 5%. If the basic field halo values of the next two time points are both above the red line, the first will be superimposed by 5%, the second by 10%, and so on, until it is superimposed to 50%. If the basic field halo values of subsequent consecutive time points are all above the red line, 50% will still be superimposed on the original basic field halo value until the basic field halo value of a subsequent time point is lower than the value of the field halo red line. For the basic field halo values above the red line and between the red and green lines, corresponding calculations are performed to obtain the experience field halo value. Finally, the score rate of each axis in the entire preset detection time period is calculated according to the preset grade, and then weighted to obtain the total three-axis comprehensive score rate. Finally, the obtained experience three-axis comprehensive motion sickness score rate is matched with the preset score rate and score matching table to obtain the car experience three-axis motion sickness evaluation score N. xyz The output car experience three-axis motion sickness evaluation score N xyz The motion sickness score of the car was used as an evaluation.

[0027] This solution uses test dummies instead of real people to test the motion sickness of cars. First, this solution can complete retesting, and the evaluation criteria are unified, avoiding the subjective influence of human testing, which may lead to inaccurate results. For example, when a car company produces a batch of cars, it can use some cars as test cars through random inspections to evaluate the motion sickness of the vehicles under different road conditions. The test results reduce human subjective interference and make the results more unified. At the same time, during the testing process, by dividing the green and red lines of motion sickness, the basic motion sickness value is calculated to obtain the experienced motion sickness value. The experienced motion sickness value can better reflect the passenger's motion sickness in a continuous environment than the basic motion sickness value. That is, the test results can better reflect the passenger's real perception and the results are more accurate.

[0028] This solution uses a test dummy instead of a real person. Sensors placed on the dummy's head more intuitively simulate the passenger's ride and sense their motion data. This provides automakers with a new method for detecting motion sickness in vehicles. The test generates a vehicle rating under various road conditions, which is communicated to customers, helping them choose the right model when purchasing.

[0029] Although this solution uses a test dummy to calculate the vehicle's motion sickness, it introduces the calculation of the experienced motion sickness value compared to the red and green lines. This solves the problem that direct calculation using sensor data cannot represent the deepening / relief of motion sickness in the motion sickness scene, resulting in inaccurate assessment of the vehicle's motion sickness.

[0030] Further: the motion data in step S1 also includes:

[0031] The acceleration components of the chest on the x / y / z axis are:

[0032] The inclination angle of the chest in the x / y / z axis plane

[0033] Obtain the chest axis score rate through steps S2-S6

[0034] Step S6 also includes weighting the score rates of each axis of the head and chest;

[0035]

[0036] In step S7, the weighted scores of each axis of the head and chest are calculated to obtain the three-axis comprehensive motion sickness score:

[0037]

[0038] Beneficial Effects: Superimposing chest data broadens the data sources involved in the calculation. During a real-world ride, a passenger's head is the focal point of their thoughts and perceptions, while their chest is home to their lungs. Test data from these two locations directly impacts motion sickness. For example, excessive chest shaking or displacement can affect breathing, causing passengers to experience breathing pressure and directly impacting motion sickness. The head has numerous nerves, so excessive or severe shaking can cause perceptual confusion, which can also contribute to motion sickness. This solution simultaneously collects data from both the head and chest to calculate the field sickness value. After calculating the field sickness experience score for each, a weighted calculation is performed. This combined data from both locations further improves the accuracy of the results.

[0039] Furthermore, the sampling frequency of the motion data in step S1 is 10-20 Hz.

[0040] Further, the fourth level experiences the stage sickness interval;

[0041] [D x1 ,D x2 ),[D x2 ,D x3 ),[D x3 ,D x4 ),[D x4 ,∞)=[0,25),[25,50),[50,75),[75,∞);

[0042] [D y1 ,D y2 ),[D y2 ,D y3 ),[D y3 ,D y4 ),[D y4 ,∞)=[0,150),[150,300),[300,450),[450,∞);

[0043] [D z1 ,D z2 ),[D z2 ,D z3 ),[D z3 ,D z4 ),[D z4 ,∞)=[0,250),[250,500),[500,750),[750,∞).

[0044] Furthermore, the method further includes step S9: obtaining and calculating the visual distance value of the test dummy within the preset detection time period, and matching the visual distance value with the preset visual distance and score matching table, obtaining the visual distance score of each data point within the preset detection time period and calculating the average, and then calculating the average visual distance score and the three-axis motion sickness evaluation score N. xyzThe weighted scores are used to obtain the comprehensive evaluation score of the stage sickness experience.

[0045] Beneficial effect: Increasing the calculation of viewing distance means increasing the test dummy's field of view. In the evaluation of motion sickness, the core mechanism of motion sickness is the mismatch between the vestibular system and visual perception. This solution collects the field of view, assigns it a score based on distance, and incorporates it into the weighted calculation of the final score to obtain a comprehensive evaluation score for the experience of motion sickness that superimposes the field of view factor.

[0046] Furthermore, the viewing distance value of each data point in step S9 is output as a viewing distance experience value. The preset viewing distance and score matching table is divided into four threshold intervals according to distance from far to near, and is matched with the four-level field dizziness experience intervals from small to large. If the threshold interval sequence of the field dizziness experience value of a certain data point does not match the threshold interval sequence of the field of view distance of the data point, the two sequences are compared. If the sequence of the threshold interval of the field of view distance is greater than the sequence of the field dizziness experience, the field of view distance of the data point is attenuated by 5-20% and output as the viewing distance experience value. The viewing distance experience value of each data point replaces the original viewing distance value for score calculation.

[0047] Beneficial effects: By dynamically matching viewing distance and field of view halo values, combined with an attenuation mechanism, data-driven scoring optimization is achieved, ensuring that the results are both objective and authentic to the user's subjective experience (using test dummy data to mimic a real person's viewing distance perception).

[0048] Furthermore, if the sequence of the threshold interval where the field of view distance is located is greater than the difference of the sequence where the motion sickness is experienced by 1-3, the field of view distance of the data point is correspondingly attenuated by 5%, 10%, and 20% and output as the viewing distance experience value.

[0049] Furthermore, the visual field distance values of two adjacent data points are compared. If the change interval is within the threshold range, the two data points are named consistent points, and no less than three consecutive consistent points are confirmed as visual perception lines, and the average visual field distance of the data points of the visual perception line is calculated; the average visual field distance of two adjacent visual perception lines is compared. If the difference exceeds the threshold, and the average visual field distances of the two visual perception lines belong to two different threshold intervals in the visual distance and score matching table, the change trend of the average visual field distance of the two adjacent visual perception lines is obtained and arranged in chronological order; if the total number of change trends exceeds 6 within the preset calculation period, and the number of increasing and decreasing trends accounts for more than 1 / 3 of the total number, the visual distance values of all data points within the preset calculation period are determined to be 0.

[0050] Beneficial Effect: Rapid and conflicting changes in visual distance will significantly affect passengers' perception, leading to worsening motion sickness. Using an average value, or simply weighting the visual distance score at each time point to the experienced motion sickness score at that time, only the actual calculated value at that time can be displayed. This value, calculated alone, can reflect the passenger's baseline motion sickness, which is different from the perception under high-frequency alternations in visual distance. This solution corrects the visual distance for this high-frequency change, ensuring the accuracy of the final output motion sickness assessment data.

[0051] A motion sickness evaluation system based on cumulative motion sickness values, comprising:

[0052] Test dummy: Sensor modules are installed on the head and chest of the test dummy to capture the test dummy's motion data in real time during the test. The motion data includes the velocity, acceleration, deflection angle of the test dummy's head and chest on the x-axis, y-axis, and z-axis, as well as the vehicle's speed.

[0053] Data acquisition module: acquires motion data and matches the motion data of each data point in chronological order;

[0054] Experienced motion sickness calculation module: obtains various motion data of each data point, and obtains the basic motion sickness value of each data point by multiplying the velocity, acceleration, cosine value of the deflection angle and vehicle speed. According to the pre-defined red line and green line, if the basic motion sickness values of several consecutive data points are all above the red line, the basic motion sickness values are calculated by increasing the multiplier by 5% in the order of appearance until the upper limit of 150%, and the output is the experienced motion sickness value of the corresponding data point; if the basic motion sickness values of several consecutive data points are all between the red line and the green line, the basic motion sickness values are calculated by decreasing the multiplier by 5% in the order of appearance until the upper limit of 50%, and the output is the experienced motion sickness value of the corresponding data point; the basic motion sickness value below the green line is directly output as the experienced motion sickness value;

[0055] Scoring module: Classify the motion sickness experience value of each data point according to the pre-set four-level motion sickness threshold range, and calculate the motion sickness experience score rate of each axis of the head and chest. After the single-axis corresponding weighting of the motion sickness experience score rate of each axis of the head and chest, the weighted three-axis score rate is weighted and calculated to obtain the three-axis comprehensive motion sickness score rate. The three-axis comprehensive motion sickness score rate is retrieved from the pre-stored score rate and score matching table to obtain the three-axis motion sickness evaluation score of the car experience.

[0056] Furthermore, it also includes a visual distance detection module, which is installed on the test dummy and is used to detect the visual field distance of the test dummy in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1Schematic diagram of the steps of a motion sickness evaluation method based on cumulative motion sickness value;

[0058] Figure 2 Schematic diagram of the calculation of field dizziness experience. DETAILED DESCRIPTION

[0059] The following is further described in detail through specific implementation methods:

[0060] Example 1 is as shown in the attached Figure 1 As shown, a motion sickness evaluation method based on cumulative motion sickness value includes the following steps:

[0061] S1: Obtain motion data of the test dummy at every threshold time, the motion data including:

[0062] Acceleration components of the head and chest on the x / y / z axis: part∈(head, chest);

[0063] Vehicle speed: v;

[0064] The tilt angle of the head and chest in the x / y / z axis plane Where k∈(x,y,z);

[0065] The sampling frequency of the motion data at each time point (data point) in this solution is 10-20 Hz. 10 Hz is selected in this solution.

[0066] S2: Calculate the basic field halo values of the head and chest on the x, y, and z axes;

[0067]

[0068] S3: Classifying the basic field sickness value according to the preset perception green line value and the perception red line value: the basic field sickness value above the perception red line value is marked as a dangerous field sickness value, the basic field sickness value above the perception green line and below the perception red line value is marked as a transitional field sickness value, and the basic field sickness value below the perception green line is marked as a harmless field sickness value;

[0069] S4: Calculate the experience sickness value through the dangerous sickness value, transition sickness value and harmless sickness value, wherein the experience sickness value of the continuous dangerous sickness value point is calculated based on its basic sickness value by increasing the multiplier by 5% in the order of appearance, until the upper limit of 150%; the experience sickness value of the continuous transition sickness value point is calculated based on its basic sickness value by decreasing the multiplier by 5% in the order of appearance, until the upper limit of 50%; the harmless sickness value is directly output as the experience sickness value according to the original value; as shown in the attached figure, Figure 2As shown, the first starting point above the halo red line is E0, the next point is E1*105%, and each subsequent point increases by 5%, with an upper limit of 150%. The first point below the halo red line is E0', the next point is E1'*95%, and each subsequent point decreases by 5%, with a lower limit of 50%.

[0070] The numerical division of the green and red lines in this scheme is shown in Table 1.

[0071] Table 1. Basic halo field green line and red line division

[0072] X-axis halo field base value green line X-axis halo field base value red line Y-axis halo base value green line Y-axis halo base value red line Z-axis halo base value green line Z-axis halo base value red line 10 25 50 150 150 250

[0073] Table 2. Classification of thresholds for experiencing field sickness

[0074]

[0075]

[0076] First-level coefficient: 0.4, second-level coefficient: 0.3, third-level coefficient: 0.2, fourth-level coefficient: 0.1

[0077] S5: Set four levels of field dizziness experience independently for the x / y / z axis;

[0078] [D k1 ,D k2 ),[D k2 ,D k3 ),[D k3 ,D k4 ),[D k4 ,∞); the four-level experience threshold intervals are shown in Table 2.

[0079] Level 4 experience stage sickness interval;

[0080] [D x1 ,D x2 ),[D x2 ,D x3 ),[D x3 ,D x4 ),[D x4 ,∞)=[0,25),[25,50),[50,75),[75,∞);

[0081] [D y1 ,D y2 ),[D y2 ,D y3 ),[D y3 ,D y4 ),[D y4 ,∞)=[0,150),[150,300),[300,450),[450,∞);

[0082] [D z1 ,D z2 ),[D z2 ,D z3 ),[D z3 ,D z4 ),[D z4 ,∞)=[0,250),[250,500),[500,750),[750,∞).

[0083] The data used to determine the motion sickness threshold for this solution is based on real-life data from similar riding environments. All coefficients are verified using a computational model, ensuring an accuracy rate of over 90%.

[0084] And assign a halo coefficient to each level interval: satisfy (j=1,2,3,4); in this scheme, the first-level coefficient is 0.4, the second-level coefficient is 0.3, the third-level coefficient is 0.2, and the fourth-level coefficient is 0.1.

[0085] S6: Gradual statistics and calculation of the field sickness score rate of each axis:

[0086] Count the number of data points in the four-level intervals of each axis within the preset detection time period:

[0087] Calculate the score rate of each axis:

[0088]

[0089] Step S6 also includes weighting the score rates of each axis of the head and chest;

[0090]

[0091] S7: Calculate the weighted scores of each axis for the head and chest to obtain the three-axis comprehensive motion sickness score:

[0092]

[0093] S8: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the three-axis motion sickness evaluation score N of the car experience xyz .

[0094] Table 3. Equal distribution rate and score matching table

[0095]

[0096] S9: Obtain the visual distance value of the test dummy within the preset detection time period, and match the visual distance value with the preset visual distance and score matching table to obtain the visual distance score of each data point within the preset detection time period and calculate the average. Then, the average visual distance score and the three-axis motion sickness evaluation score N are used to calculate the visual distance score. xyz Weighted weighting is performed to obtain a comprehensive evaluation score for the field motion sickness experience. Before calculating the average value, the viewing distance values of each data point in step S9 are output as the viewing distance experience value. A preset viewing distance and score matching table divides the distance into four threshold intervals, from far to near, and corresponds to the four-level field motion sickness experience intervals, from small to large. If the threshold interval sequence of the field motion sickness experience value of a data point does not match the threshold interval sequence of the field motion sickness distance of the data point, the two sequences are compared. If the sequence of the field motion sickness threshold interval sequence is greater than the sequence of the field motion sickness experience, the field motion sickness distance of the data point is attenuated by 5-20% and output as the viewing distance experience value. The viewing distance experience value of each data point is used to replace the original viewing distance value for score calculation. If the difference between the field motion sickness threshold interval sequence and the field motion sickness experience sequence is 1-3, the field motion sickness distance of the data point is attenuated by 5%, 10%, and 20% respectively and output as the viewing distance experience value. For example, if the field sickness experience value of a certain data point is within the first-level field sickness experience threshold interval, if its viewing distance (actual data obtained by the sensor) is within the interval divided by the second, third or fourth level field of view space, then the difference between the interval sequence of the visual space and the threshold interval sequence where the field sickness experience is located is calculated, and the difference may be 1-3 (the field sickness experience is 1, and the viewing distance is 2-4). When the difference is 1, the viewing distance measurement value at that time point is reduced by 5% and output as the viewing distance experience value. If the difference is 2, it is reduced by 10%, and when the difference is 3, it is reduced by 20%.

[0097] Compare the visual field experience values of two adjacent data points. If the change interval is within the threshold range, the change interval range determined in this scheme is (-0.5m, 0.5m), then the two data points (time points) are named consistent points, and the visual distances of subsequent data points are compared with the first data point in turn. If the change interval range is still within the range specified by the change interval, it is also named consistent point. Confirm at least 3 consecutive consistent points as visual perception lines, and calculate the average visual field distance of the data points of the visual perception line; compare the average visual field distances of two adjacent visual perception lines. If the difference exceeds the threshold (0.5m is selected in this scheme), and the average visual field distances of the two visual perception lines belong to two different threshold intervals in the visual distance and score matching table, then obtain the change trend of the average visual field distance of the two adjacent visual perception lines and arrange them in chronological order; if the total number of change trends exceeds 6 within the preset calculation period, and the number of increasing and decreasing trends accounts for more than 1 / 3 of the total number, then the visual distance values of all data points within the preset calculation period are determined to be 0.

[0098] For example, within the preset detection time period (30 seconds in this solution), 10 visual perception lines appeared in chronological order, with average distances of 15m, 21m, 8m, 7m, 12m, 9m, 4m, 3m, 13m, and 8m. The distances that span the visual field are: (15m) Level 2, (21m) Level 1, (8m, 7m) Level 3, (12m) Level 2, (9m) Level 3, (4m, 3m) Level 4, (13m) Level 2, and (8m) Level 3. The trend of change is rise, fall, fall, rise, fall, fall, rise, fall, and the trend change is 7; among them, the rise is 3 and the fall is 4, which meets the 1 / 3 requirement. This shows that the field of view experience value in this period changes rapidly and the number of cross-intervals is large, which can easily cause perceptual conflict for passengers. Although the visual distance of some perception lines is farther, which can promote the improvement of motion sickness, when this alternation between near and far is more frequent, it will cause frequent processing in the brain, and accompanied by frequent visual conflicts, it will aggravate the feeling of motion sickness. Therefore, all the field of view experience values in the preset detection time period are changed to 0 for the subsequent calculation of the average.

[0099] This solution also relates to a motion sickness evaluation system based on cumulative motion sickness values, comprising:

[0100] Test dummy: The test dummy's head and chest are equipped with sensor modules for capturing the test dummy's motion data in real time during the test. The motion data includes the velocity, acceleration, deflection angle, and vehicle speed of the test dummy's head and chest on the x-axis, y-axis, and z-axis, respectively. The sensors can use vibration sensors and attitude sensors to measure the real-time acceleration and tilt angle of the test dummy, or use an IMU (inertial measurement unit) to obtain the following characteristics to meet the requirements: Acceleration measurement: The IMU has a built-in three-axis accelerometer that can directly obtain the x / y / z-axis acceleration components. Tilt angle calculation: By fusing accelerometer and gyroscope data (such as extended Kalman filtering), the attitude angle (roll angle, pitch angle, etc.) can be calculated in real time to meet the tilt angle measurement requirements. Select an IMU (such as the Epson M-G370) to directly output the attitude angle without the need for additional algorithm processing. This solution can select Epson M-G370, MicroStrain 3DM-GX5-AHRS, Honeywell TARS-IMU, or Epson M-G570PR based on actual usage requirements. Those skilled in the art can also select other sensors that meet the requirements to obtain data for this solution.

[0101] Data acquisition module: acquires motion data and matches the motion data of each data point in chronological order;

[0102] Experienced motion sickness calculation module: obtains various motion data of each data point, and obtains the basic motion sickness value of each data point by multiplying the velocity, acceleration, cosine value of the deflection angle and vehicle speed. According to the pre-defined red line and green line, if the basic motion sickness values of several consecutive data points are all above the red line, the basic motion sickness values are calculated by increasing the multiplier by 5% in the order of appearance until the upper limit of 150%, and the output is the experienced motion sickness value of the corresponding data point; if the basic motion sickness values of several consecutive data points are all between the red line and the green line, the basic motion sickness values are calculated by decreasing the multiplier by 5% in the order of appearance until the upper limit of 50%, and the output is the experienced motion sickness value of the corresponding data point; the basic motion sickness value below the green line is directly output as the experienced motion sickness value;

[0103] Scoring module: Classifies the motion sickness experience value of each data point according to the pre-set four-level motion sickness threshold range, calculates the motion sickness experience score rate of each axis of the head and chest, and then weights the motion sickness experience score rate of each axis of the head and chest. The weighted three-axis score rate is then weighted to obtain the three-axis comprehensive motion sickness score rate. The three-axis comprehensive motion sickness score rate is retrieved from the pre-stored score rate and score matching table to obtain the three-axis motion sickness evaluation score of the car. It also includes a visual range detection module, which can use a laser radar to capture the visual field distance in front of the test dummy. The visual detection module is installed on the test dummy and is used to detect the visual field distance of the test dummy in real time.

[0104] The above are only embodiments of the present invention. The invention is not limited to the fields involved in this implementation case. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A motion sickness evaluation method based on cumulative motion sickness value, characterized in that: The following steps are involved: S1: Obtain motion data of the test dummy at every threshold time, the motion data including: Acceleration components of the head on the x / y / z axis: Vehicle speed: v; The tilt angle of the head in the x / y / z axis plane Where k∈(x,y,z); S2: Calculate the basic field halo value of the head on the x, y, and z axes; S3: Classifying the basic field sickness value according to the preset perception green line value and the perception red line value: the basic field sickness value above the perception red line value is marked as a dangerous field sickness value, the basic field sickness value above the perception green line and below the perception red line value is marked as a transitional field sickness value, and the basic field sickness value below the perception green line is marked as a harmless field sickness value; S4: Calculate the experience sickness value through the dangerous sickness value, the transition sickness value and the harmless sickness value, wherein the experience sickness value of the points with consecutive dangerous sickness values is calculated by increasing the multiplier by 5% in the order of occurrence based on the basic sickness value, until the upper limit of 150%; the experience sickness value of the points with consecutive transition sickness values is calculated by decreasing the multiplier by 5% in the order of occurrence based on the basic sickness value, until the upper limit of 50%; the harmless sickness value is directly output as the experience sickness value according to the original value; S5: Set four levels of field dizziness experience independently for the x / y / z axis; [D k1 ,D k2 ),[D k2 ,D k3 ),[D k3 ,D k4 ),[D k4 ,∞); And assign a halo coefficient to each level interval: satisfy S6: Gradual statistics and calculation of the field sickness score rate of each axis: Count the number of data points in the four-level intervals of each axis within the preset detection time period: Calculate the score rate of each axis: S7: Weight the three-dimensional axial score rate of the head to generate the comprehensive motion sickness score rate of the three axes of experience; S8: Match the obtained three-axis comprehensive motion sickness score rate with the preset score rate and score matching table to obtain the three-axis motion sickness evaluation score N of the car experience xyz .

2. The motion sickness evaluation method based on cumulative motion sickness value according to claim 1, characterized in that: The motion data in step S1 also includes: The acceleration components of the chest on the x / y / z axis are: The inclination angle of the chest in the x / y / z axis plane Obtain the chest axis score rate through steps S2-S6 Step S6 also includes weighting the score rates of each axis of the head and chest; In step S7, the weighted scores of each axis of the head and chest are calculated to obtain the three-axis comprehensive motion sickness score:

3. The motion sickness evaluation method based on cumulative motion sickness value according to claim 2, characterized in that: The motion data sampling frequency in step S1 is 10-20 Hz.

4. The motion sickness evaluation method based on cumulative motion sickness value according to claim 2, wherein: Level 4 experience stage sickness interval; [D x1 ,D x2 ),[D x2 ,D x3 ),[D x3 ,D x4 ),[D x4 ,∞)=[0,25),[25,50),[50,75),[75,∞); [D y1 ,D y2 ),[D y2 ,D y3 ),[D y3 ,D y4 ),[D y4 ,∞)=[0,150),[150,300),[300,450),[450,∞); [D z1 ,D z2 ),[D z2 ,D z3 ),[D z3 ,D z4 ),[D z4 ,∞)=[0,250),[250,500),[500,750),[750,∞)。 5. The motion sickness evaluation method based on cumulative motion sickness value according to claim 2, characterized in that: The method further includes step S9: obtaining and calculating the visual distance value of the test dummy within the preset detection time period, and matching the visual distance value with the preset visual distance and score matching table, obtaining the visual distance score of each data point within the preset detection time period and calculating the average, and then calculating the average visual distance score and the three-axis motion sickness evaluation score N. xyz The weighted scores are used to obtain the comprehensive evaluation score of the stage sickness experience.

6. The motion sickness evaluation method based on cumulative motion sickness value according to claim 5, characterized in that: The viewing distance value of each data point in step S9 is output as the viewing distance experience value. The preset viewing distance and score matching table is divided into four threshold intervals based on distance from far to near, and is matched with the four-level experience field dizziness intervals from small to large. If the threshold interval sequence of the field dizziness experience value of a data point does not match the threshold interval sequence of the field dizziness distance of the data point, the two sequences are compared. If the sequence of the threshold interval of the field dizziness distance is greater than the sequence of the field dizziness experience, the field dizziness of the data point is attenuated by 5-20% and output as the viewing distance experience value. The viewing distance experience value of each data point replaces the original viewing distance value for score calculation.

7. The motion sickness evaluation method based on cumulative motion sickness value according to claim 6, characterized in that: If the difference between the threshold interval sequence of the field of view distance and the field of view experience sequence is 1-3, the field of view distance of the data point is correspondingly attenuated by 5%, 10%, and 20% and output as the viewing distance experience value.

8. The motion sickness evaluation method based on cumulative motion sickness values according to any one of claims 5 to 7, characterized in that: Compare the visual field distance values of two adjacent data points. If the change interval is within the threshold range, the two data points are named consistent points, and at least three consecutive consistent points are confirmed as visual perception lines, and the average visual field distance of the data points of the visual perception line is calculated; compare the average visual field distances of two adjacent visual perception lines. If the difference exceeds the threshold, and the average visual field distances of the two visual perception lines belong to two different threshold intervals in the visual distance and score matching table, obtain the change trend of the average visual field distance of the two adjacent visual perception lines and arrange them in chronological order; if the total number of change trends exceeds 6 within the preset calculation period, and the number of increasing and decreasing trends accounts for more than 1 / 3 of the total number, then the visual distance values of all data points within the preset calculation period are determined to be 0.

9. A motion sickness assessment system based on cumulative motion sickness values, characterized by: include: Test dummy: Sensor modules are installed on the head and chest of the test dummy to capture the test dummy's motion data in real time during the test. The motion data includes the velocity, acceleration, deflection angle of the test dummy's head and chest on the x-axis, y-axis, and z-axis, as well as the vehicle's speed. Data acquisition module: acquires motion data and matches the motion data of each data point in chronological order; Experienced motion sickness calculation module: obtains various motion data of each data point, and obtains the basic motion sickness value of each data point by multiplying the velocity, acceleration, cosine value of the deflection angle and vehicle speed. According to the pre-defined red line and green line, if the basic motion sickness values of several consecutive data points are all above the red line, the basic motion sickness values are calculated by increasing the multiplier by 5% in the order of appearance until the upper limit of 150%, and the output is the experienced motion sickness value of the corresponding data point; if the basic motion sickness values of several consecutive data points are all between the red line and the green line, the basic motion sickness values are calculated by decreasing the multiplier by 5% in the order of appearance until the upper limit of 50%, and the output is the experienced motion sickness value of the corresponding data point; the basic motion sickness value below the green line is directly output as the experienced motion sickness value; Scoring module: Classify the motion sickness experience value of each data point according to the pre-set four-level motion sickness threshold range, and calculate the motion sickness experience score rate of each axis of the head and chest. After the single-axis corresponding weighting of the motion sickness experience score rate of each axis of the head and chest, the weighted three-axis score rate is weighted and calculated to obtain the three-axis comprehensive motion sickness score rate. The three-axis comprehensive motion sickness score rate is retrieved from the pre-stored score rate and score matching table to obtain the three-axis motion sickness evaluation score of the car experience.

10. The motion sickness assessment system based on cumulative motion sickness values according to claim 9, characterized in that: It also includes a visual distance detection module, which is installed on the test dummy and is used to detect the visual field distance of the test dummy in real time.

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