Glasses wearing comfort evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a glasses wearing comfort evaluation method and device, electronic equipment and a storage medium, and belongs to the field of comfort evaluation, the method comprises the following steps: determining pressure data sets corresponding to a plurality of simulated heads, each pressure data set comprising pressure data of smart glasses wearing positions of different heads, different simulated heads correspond to different basic information; fitting is carried out based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head, and a fitting result corresponding to the simulated head is obtained; and based on the fitting result corresponding to each simulated head, determining the wearing comfort of the intelligent glasses of the simulated head. According to the glasses wearing comfort evaluation method and device, the electronic equipment and the storage medium provided by the invention, the accuracy and effectiveness of glasses wearing comfort evaluation can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of comfort evaluation, and more specifically, relates to a method and device for evaluating the wearing comfort of glasses, an electronic device, and a storage medium. Background Art

[0002] With the rapid development of intelligent technology, smart glasses have gradually become popular and integrate multiple functions. However, the current market focuses on the realization of multi-focus functions of smart glasses, but ignores the key indicator of wearing comfort. Traditional glasses comfort evaluation methods are not suitable for smart glasses with more complex structures and weight distributions; consumer evaluations rely on subjective judgments, the comfort evaluation is not accurate enough, and there is a lack of unified quantitative standards, making it difficult to accurately locate the factors affecting discomfort. Therefore, an accurate and effective method for evaluating the wearing comfort of smart glasses is needed. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for evaluating the wearing comfort of glasses, an electronic device, and a storage medium, so as to improve the accuracy and effectiveness of the evaluation of the wearing comfort of glasses.

[0004] According to a first aspect of the embodiments of the present disclosure, a method for evaluating wearing comfort of glasses is provided, comprising: Determine a plurality of pressure data sets corresponding to simulated heads, each of which includes pressure data of different head smart glasses wearing positions, and different simulated heads have different corresponding basic information; Fitting is performed based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head to obtain a fitting result corresponding to the simulated head; The wearing comfort of the smart glasses on each simulated head is determined based on the fitting results corresponding to the simulated head.

[0005] According to a second aspect of the embodiments of the present disclosure, there is provided a device for evaluating wearing comfort of glasses, comprising: A data acquisition module is used to determine pressure data sets corresponding to multiple simulated heads, each of which includes pressure data of different head smart glasses wearing positions, and different simulated heads have different basic information corresponding to them; A data fitting module, used for fitting based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head, to obtain a fitting result corresponding to the simulated head; The comfort evaluation module is used to determine the wearing comfort of the smart glasses on each simulated head based on the fitting results corresponding to the simulated head.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for evaluating wearing comfort of glasses when executing the computer program.

[0007] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for evaluating the wearing comfort of glasses are implemented.

[0008] The beneficial effects of the glasses wearing comfort evaluation method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are: The present disclosure determines the pressure data sets corresponding to multiple simulated heads, including the pressure data of different head smart glasses wearing positions, and combines different basic information to comprehensively simulate the characteristics of diverse user groups, including factors such as different ages, genders, and head circumferences, so that the evaluation is extremely comprehensive and representative, and can accurately reflect the actual situation of various groups of people wearing glasses. Secondly, the present disclosure fully considers the inherent connection and mutual influence between various factors to make the evaluation more objective and accurate. Finally, the wearing comfort of smart glasses for each simulated head is determined based on the fitting results, which can intuitively provide a clear direction and quantitative basis for the design, improvement and optimization of the glasses, thereby effectively improving the wearing comfort of the glasses and enhancing the user experience. Therefore, the present disclosure can improve the accuracy and effectiveness of the evaluation of the wearing comfort of glasses. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a method for evaluating the wearing comfort of glasses provided in one embodiment of the present disclosure; Figure 2 A structural block diagram of a device for evaluating wearing comfort of glasses provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 The present invention provides a flowchart of a method for evaluating the wearing comfort of glasses according to an embodiment of the present invention. The method comprises: S101: Determine a plurality of pressure data sets corresponding to simulated heads, each of which includes pressure data of different head smart glasses wearing positions, and different simulated heads correspond to different basic information.

[0014] In this embodiment, in order to comprehensively evaluate the wearing comfort of smart glasses for different groups of people, it is necessary to construct multiple simulated head models, which represent different types of users. For each simulated head, the pressure data generated by the smart glasses at each wearing position is collected to form a pressure data set. Therefore, for each simulated head, there is a set of corresponding pressure data sets, which can reflect the pressure conditions of each contact point of the simulated head when wearing the smart glasses.

[0015] This embodiment collects data by setting pressure sensors at contact positions on both sides of the nose, above the ears, and on both sides of the ears. In addition, this embodiment collects data at the measurement points, and the data can be converted into gravity or pressure information through current signals to obtain the pressure of different frames on the nose, ears, and head when worn. At the same time, the position of the pressure sensor set on the nose pad can be adjusted up and down to simulate the relative positions of different noses and ears; the position of the pressure sensor set on both sides of the ear can be moved laterally to simulate different head widths; the height of the pressure sensor set at the ear position can be adjusted to simulate different ear-nose distances.

[0016] For example, suppose three simulated heads are constructed to simulate children, adult women, and adult men respectively. For the simulated child's head, pressure sensors are installed on the common wearing positions of smart glasses, such as the bridge of the nose, temples, and ears. The simulated child's head is made to wear a specific smart glasses, and the pressure values ​​at these positions are recorded over time, thereby obtaining a pressure data set for the simulated child's head. The same method is used to operate the simulated adult female and adult male heads to obtain their corresponding pressure data sets.

[0017] In the pressure data set of each simulated head, the pressure information of different contact parts between the smart glasses and the head is recorded. The contact parts can be the places where the smart glasses directly contact the face and may generate pressure, such as the pressure at the contact between the nose pads and the nose bridge, the pressure at the contact between the temples and the temples and around the ears, etc. This embodiment can fully understand the pressure distribution of the smart glasses on the simulated head by accurately measuring the pressure at the above positions, and then analyze the impact of the smart glasses on wearing comfort.

[0018] Basic information refers to characteristic parameters that can distinguish different types of users, such as age, gender, head circumference, facial contour shape, etc. Considering that different basic information will affect the wearing method and fit of smart glasses, as well as the human body's perception and tolerance of pressure, different simulated heads correspond to different basic information, which can simulate diverse user groups in the real world, so as to more accurately evaluate the wearing comfort of smart glasses among different groups of people.

[0019] S102: performing fitting based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head to obtain a fitting result corresponding to the simulated head.

[0020] In this embodiment, fitting refers to a method of constructing a mathematical model, using known pressure data sets and basic information, to find model parameters that best reflect the internal relationship of the above data, so that the mathematical model can describe or predict the actual situation as accurately as possible. Specifically, it is to establish a reasonable correlation between pressure data, basic information and the wearing comfort to be determined.

[0021] The fitting result is the output content obtained after fitting that can reflect the relationship between the input data (pressure data set and basic information) and the target result (wearing comfort). The fitting result can be a specific value, such as a quantitative score of wearing comfort; it can also be an expression that contains the coefficients of various factors, etc., which can be used for subsequent calculation of wearing comfort; it can also be a functional relationship with a certain regularity, which is used as a basis for subsequent judgment of the comfort of the simulated head wearing smart glasses.

[0022] For each simulated head representing a different type of user, the collected data set reflecting the pressure of wearing smart glasses and the basic information describing the characteristics of the simulated head are used as input content, and fitting operations are performed using appropriate mathematical methods, statistical models, etc., to ultimately obtain a fitting result for this simulated head that can reflect the comfort of wearing smart glasses.

[0023] S103: Determine the wearing comfort of the smart glasses on each simulated head based on the fitting result corresponding to the simulated head.

[0024] In this embodiment, if the fitting result corresponding to each simulated head is less than the first threshold, the wearing comfort of the smart glasses of the simulated head is the first level of comfort; If the fitting result corresponding to each simulated head is greater than or equal to the first threshold and less than the second threshold, the wearing comfort of the smart glasses of the simulated head is the second level of comfort; If the fitting result corresponding to each simulated head is greater than or equal to the second threshold, the wearing comfort of the smart glasses of the simulated head is the third level of comfort; Among them, the first level of comfort is lower than the second level of comfort, the second level of comfort is lower than the third level of comfort, and the first threshold and the second threshold are both comfort evaluation thresholds dynamically set according to actual conditions.

[0025] The total range of comfort levels can be [0, 10], the first level of comfort is [0, 3], the second level of comfort is (3, 7), and the third level of comfort is [7, 10].

[0026] In this embodiment, according to step S102, an output value can be obtained by fitting the pressure data set of each simulated head and its corresponding basic information. The comfort of wearing smart glasses is an evaluation index of the comfort level of the simulated head wearing smart glasses to be determined in the end, which can be expressed by a qualitative level or a quantitative numerical range, reflecting the actual experience of the user under the combined effect of subjective feelings and objective pressure factors during the wearing process.

[0027] From the above, it can be concluded that the present disclosure determines the pressure data sets corresponding to multiple simulated heads, including the pressure data of different head smart glasses wearing positions, and then combines different basic information to comprehensively simulate the characteristics of diverse user groups, including different ages, genders, head circumferences and other factors, so that the evaluation is extremely comprehensive and representative, and can accurately reflect the actual situation of various groups of people when wearing glasses. Secondly, the present disclosure fully considers the inherent connection and mutual influence between various factors to make the evaluation more objective and accurate. Finally, the wearing comfort of smart glasses for each simulated head is determined based on the fitting results, which can intuitively provide a clear direction and quantitative basis for the design, improvement and optimization of the glasses, thereby effectively improving the wearing comfort of the glasses and enhancing the user experience. Therefore, the present disclosure can improve the accuracy and effectiveness of the evaluation of the wearing comfort of glasses.

[0028] In one embodiment of the present disclosure, fitting is performed based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head to obtain a fitting result corresponding to the simulated head, including: Determine a pressure data set corresponding to each simulated head as a pressure data set; Determine the basic information corresponding to each simulated head as basic data; Fitting is performed based on the pressure data set and the basic data to obtain the fitting results corresponding to each simulated head.

[0029] In one embodiment of the present disclosure, fitting is performed based on the pressure data set and the basic data to obtain a fitting result corresponding to the simulated head, including: The pressure data set and the basic data are input into the first fitting formula to obtain the fitting result corresponding to each simulated head; wherein the first fitting formula is a formula obtained by fitting the historical pressure data set and the historical basic data.

[0030] In this embodiment, the pressure data set includes the pressure value at the nose pad , the average pressure value at both temples , the average pressure value at both ears , basic data includes age value , gender value (Male is 0, female is 1), head circumference value , fitting results .

[0031] The expression of the first fitting formula is:

[0032] in, It represents the time value from the beginning of wearing the smart glasses, and is used to describe the time process during the wearing process; Represents the time interval value, which is used to discretize the continuous time process so as to analyze the pressure data at different moments; All are fitting coefficients; is a constant term, which represents the basic comfort level without considering pressure and basic information; The coefficients representing age, gender, and head circumference, respectively, can measure the linear influence of basic information on wearing comfort; It represents the coefficient related to the pressure at the nose pad, which is used to measure the cumulative effect of the square of the pressure at the nose pad at different times on the wearing comfort; represents the coefficient related to the change of temple pressure at the bilateral temples; represents the coefficient related to the temple pressure at the bilateral ears; are time-dependent coefficients, Represents weight.

[0033] Indicates that at time The pressure values ​​at the nose pads are recorded at all times. In this embodiment, the squares of the nose pad pressures at different times are accumulated to comprehensively consider the cumulative effect of the nose pad pressure during long-term wearing and its nonlinear effect on wearing comfort.

[0034] and Indicates that at two adjacent time points and The temple pressure values ​​at both sides of the temple were recorded, and the square root of the absolute value of their difference was calculated to measure the impact of the change in temple pressure on wearing comfort.

[0035] Indicates that at time The temple pressure values ​​at both ears are recorded at all times. In this embodiment, a sine function is used to simulate the complex nonlinear relationship between ear pressure and comfort. The influence at different times is comprehensively considered by accumulating coefficients.

[0036] Represents a random error term, which is used to consider the impact of other factors not included in the model on wearing comfort. For example, factors such as individual mood changes during wearing, slight fluctuations in ambient temperature and humidity, and slight displacement of glasses may affect comfort, but are not reflected in the main factors of the above model.

[0037] In this embodiment, the pressure data set of each simulated head when wearing the smart glasses is collected by the pressure sensor, and the basic information of each simulated head can also be entered as basic data, and then the historical pressure data set, the historical basic data and the corresponding historical wearing comfort are fitted to obtain the first fitting formula. At the same time, this embodiment can obtain a fitting result through the first fitting formula, and the fitting result is the wearing comfort.

[0038] From the above, it can be concluded that this embodiment achieves accurate simulation of different types of users by determining the pressure data set and basic information corresponding to the simulated head, whether it is children, adults or the elderly, people of different genders and head circumferences can be covered, making the evaluation very targeted. Secondly, fitting results are obtained based on these data, which can quantify the complex wearing experience and intuitively present the comfort status. Furthermore, by using the first fitting formula fitted based on the historical pressure data set and the historical basic data, we can fully draw on past experience to make the comfort evaluation of the new simulated head more scientific and accurate, greatly saving time and energy to re-explore the model, and efficiently assisting in the design optimization of smart glasses and improving the user wearing experience.

[0039] In one embodiment of the present disclosure, determining the wearing comfort of smart glasses of each simulated head based on the fitting result corresponding to the simulated head includes: The fitting results corresponding to each simulated head are input into the comfort evaluation model to obtain the wearing comfort of the smart glasses for the simulated head; wherein the comfort evaluation model is a wearing comfort model for the smart glasses determined according to the fitting results corresponding to each historical simulated head and the wearing comfort of the smart glasses corresponding to the historical simulated head.

[0040] In this embodiment, the comfort evaluation model is a trained neural network model, and its purpose is to output the corresponding smart glasses wearing comfort evaluation according to the input data set, wherein the input data set is the fitting result corresponding to each simulated head. The historical simulated head is a simulated head used in past research, experiments or data collection processes, and the corresponding relationship between the fitting result and the smart glasses wearing comfort is known, so the comfort evaluation model can be obtained after model training.

[0041] The comfort evaluation model can be obtained by training the neural network model, as follows: First, the neural network structure.

[0042] (1) Input layer: The number of neurons corresponds to the dimension of the fitting result of the simulated head. Assume that the fitting result contains multiple feature values ​​extracted from the pressure data set and basic information, such as age-standardized values, gender code (0 or 1), head circumference-standardized values, average nose pad pressure, average temple pressure, average ear pressure, and some possible pressure change rate features, etc., for a total of features, then the input layer has neurons, each neuron receives a corresponding feature value as input.

[0043] (2) Hidden layer: You can set one or more hidden layers. For example, if you set two hidden layers, the first hidden layer has neurons, and the second hidden layer has The activation function of the hidden layer can choose the commonly used ReLU function, that is, , it can increase the nonlinear expression ability of the neural network, so that the model can learn the complex relationship between the input features, and thus better fit the potential laws between comfort and these features.

[0044] (3) Output layer: The output layer has only one neuron, which is used to output the comfort rating of the smart glasses simulating the head. For example, it outputs a value between 0 and 10 to indicate the degree of comfort, with the larger the value, the more comfortable it is. The activation function of the output layer can select the Sigmoid function to map the output value to the interval of 0 to 1, and then convert it to the range of 0 to 10 through linear transformation to meet the setting of the comfort rating.

[0045] Second, the training process.

[0046] (1) Data preparation: Collect a large amount of historical simulated head data, including the fitting results of each historical simulated head (as input features) and the corresponding known smart glasses wearing comfort scores (as target output). Divide these data into training set, validation set and test set, usually in a ratio of 70%, 15% and 15%.

[0047] (2) Forward propagation: During the training process, neurons in the input layer receive feature values ​​from the simulated head fitting results and then pass these values ​​to the hidden layer. The neurons in the hidden layer perform weighted summation based on the received input values ​​and the corresponding weights, and perform nonlinear transformation through the activation function, and then pass the transformed results to the next hidden layer or output layer. Finally, the neurons in the output layer calculate a predicted comfort score based on the input values.

[0048] (3) Calculation of loss function: Use mean square error as the loss function to calculate the error between the predicted comfort score and the actual comfort score. That is, for each training sample, calculate ,in is the predicted comfort score, is the actual comfort rating.

[0049] (4) Back propagation and weight update: Based on the calculated loss value, the gradient of each neuron is calculated through the back propagation algorithm, and then the weight parameters in the neural network are updated according to the gradient descent method, so that the loss function gradually decreases, thereby continuously optimizing the prediction ability of the model. Specifically, the weight update formula is ,in represents the weight, is the learning rate, which controls the step size of weight update.

[0050] Third, model evaluation and application.

[0051] (1) Model evaluation: During the training process, the validation set is used to monitor the performance of the model and prevent overfitting. After a certain number of training iterations (for example, every 100 iterations), the validation set data is used for forward propagation, and the loss value and other evaluation indicators (such as mean absolute error MAE) on the validation set are calculated. If the loss value on the validation set is no longer decreasing or other evaluation indicators deteriorate, training is stopped to prevent the model from overfitting on the training set and losing its ability to generalize to new data.

[0052] (2) Model application: After the model training is completed, the fitting results of the new simulated head are input into the trained neural network model. After forward propagation calculation, the output layer will output the wearing comfort score of the smart glasses for the simulated head, thereby realizing the comfort evaluation of the new simulated head and providing a quantitative basis for the design improvement of the glasses.

[0053] From the above, it can be concluded that this embodiment builds a model through historical simulated head data, which can well learn the relationship features and make the comfort evaluation model reliable. Afterwards, this embodiment can input the fitting results of the new simulated head into the model to determine the comfort, which can improve the efficiency of judgment. Furthermore, considering that different simulated heads correspond to different basic information, it can fully cover the wearing scenarios of various groups of people, whether children, adults or the elderly, and can accurately evaluate them, providing a key basis for the personalized design, optimization and improvement of smart glasses, and greatly improving the user's wearing experience.

[0054] In one embodiment of the present disclosure, the method for evaluating the wearing comfort of glasses further includes: Determine a comprehensive loss function for comfort evaluation based on the fitting result corresponding to each simulated head and the basic information corresponding to the simulated head; Construct a wearing comfort model of smart glasses based on comprehensive loss function.

[0055] In this embodiment, considering that the basic information of each simulated head has a great influence on the accuracy of comfort evaluation, the loss function can be improved to establish a comprehensive loss function.

[0056] The expression of the comprehensive loss function is:

[0057]

[0058] Among them, basic information includes age value , gender value (0 for males and 1 for females), head circumference value ; It represents the wearing comfort predicted by the fitting results; Indicates actual wearing comfort; Both represent weight coefficients.

[0059] Item 1 It is the mean square error term between the predicted value of the fitting result and the actual comfort score. It mainly measures the direct difference between the predicted value of the fitting result and the actual comfort score, so that the model can be as close to the real comfort score as possible. By minimizing this term, the model can learn how to accurately infer the comfort score from the fitting results.

[0060] Item 2 It is the mean square error term between the basic information and the actual comfort score.

[0061] function is a function that predicts comfort based on basic information, where Represents the parameters to be learned. The purpose of this item is to consider the independent impact of the basic information itself on the comfort level. Even if there may be deviations in the fitting results, it can ensure that the model's dependence on the basic information is consistent with the actual situation, and avoid the model completely ignoring the basic information and relying only on the fitting results.

[0062] Item 3 It is the absolute error term between the predicted value of the fitting result and the predicted value based on the basic information. This term is used to balance the relationship between the fitting result and the basic information to prevent the difference between the two from being too large, so that the model can coordinate the contribution of these two factors to the comfort during the learning process, and avoid the situation where the basic information is ignored due to overfitting results, or the situation where the basic information is overly relied on and the important information such as the pressure data contained in the fitting results is ignored.

[0063] The comprehensive loss function is brought into the wearing comfort model of smart glasses for training. After the training is completed, a comfort evaluation model can be obtained.

[0064] From the above, we can conclude that, on the one hand, the comprehensive loss function incorporates the fitting results and basic information, indicating that the pressure data and individual characteristics are fully considered when the model is constructed, and the complex relationship between factors such as age, gender, head circumference and comfort can be accurately quantified to avoid a single factor dominating the evaluation. On the other hand, the wearing comfort model constructed based on this is more scientific and accurate. It can not only adapt to the characteristics of different groups of people, but also dynamically optimize based on feedback. For example, if the fitting results of the simulated head of a certain group of people are found to have large deviations, the model parameters can be adjusted accordingly. Ultimately, it points out the direction for the design and improvement of smart glasses and effectively improves the wearing comfort of different users.

[0065] In one embodiment of the present disclosure, the method for evaluating the wearing comfort of glasses further includes: Adjusting the smart glasses based on the comfort level of wearing the smart glasses; If the comfort level meets the standard comfort level, the corresponding smart glasses production parameters are retained; If the comfort level does not meet the standard comfort level, the corresponding smart glasses production parameters are adjusted.

[0066] In this embodiment, the standard comfort level is a pre-set reference indicator for measuring whether the smart glasses are comfortable to wear. It is determined based on factors such as market demand, ergonomic principles, and industry experience, and represents the level of comfort that most users can accept or expect to achieve. For example, the standard comfort level is set to a score of 7 or above or at the "comfortable" level.

[0067] Smart glasses production parameters refer to various technical indicators and design parameters involved in the manufacturing process of smart glasses, including but not limited to the material, shape, size of the frame, the length, elasticity, curvature of the temples, the material, shape, height of the nose pads, and the weight and optical center position of the lenses. The above parameters directly affect the comfort and adaptability of smart glasses when worn.

[0068] When the evaluation determines that the wearing comfort of smart glasses reaches or exceeds the preset standard comfort level, it means that the current smart glasses design is qualified in terms of comfort and there is no need to make large-scale changes to its production parameters. At this time, these parameters are recorded and retained for continued use in the subsequent production process to ensure that the smart glasses produced can maintain this good comfort level and meet market demand and user expectations.

[0069] If the evaluation finds that the wearing comfort of smart glasses is lower than the standard comfort, it is necessary to analyze and modify the production parameters that affect the comfort. For example, if the comfort is reduced due to excessive pressure on the nose pads, it may be necessary to adjust the material of the nose pads to make them softer, change the shape of the nose pads to better fit the bridge of the nose, or lower the height of the nose pads; if the temples are too tight or too loose, it is necessary to adjust the length, elasticity or curvature of the temples and other parameters. By optimizing these parameters, redesign and manufacture smart glasses, and then conduct comfort evaluation again until the standard comfort is achieved.

[0070] From the above, we can conclude that, first, timely adjustment of smart glasses based on wearing comfort can accurately locate problems during use, such as nose pad indentation, temple clamp problems, etc., to ensure that the actual user experience is optimized. Second, setting standard comfort as a measurement benchmark gives the evaluation a clear direction and avoids blind improvements. When the comfort level meets the standard, retaining the production parameters is conducive to ensuring the stability and consistency of product quality and reducing production costs. If the standard is not met, the parameters are adjusted in a targeted manner. Whether it is the frame material, temple design or nose pad structure, they can be optimized as needed, and finally create smart glasses that meet the needs of diverse users and have outstanding comfort.

[0071] In one embodiment of the present disclosure, the method for evaluating the wearing comfort of glasses further includes: Determine the special information corresponding to each simulated head, the special information including the gap distance between the temple position of the temple and the tail of the temple; Determine the range of head circumferences suitable for wearing smart glasses based on the gap distance.

[0072] In this embodiment, the special information is the gap distance between the temple position of the temple and the tail of the temple. The gap distance reflects the adaptation of the temple on the simulated head, has a certain impact on the comfort and stability of the smart glasses, and is also potentially related to the head circumference. When the smart glasses are worn on the simulated head, the spatial distance between the two key positions where the temple contacts the head. The temple position is the position where the temple fits the side of the head, and the tail of the temple is the position close to the back of the ear. The distance between the two can be obtained by a distance sensor, and its numerical value will vary depending on the head circumference of the simulated head and the design of the glasses. The head circumference value range suitable for wearing the smart glasses determined by the gap distance of the temples, that is, which people with which head circumference size can get a relatively good wearing experience, including comfort, stability and normal use of the glasses.

[0073] By using the measured temple gap distance data, through data analysis, statistical laws or a pre-established mathematical model, the relationship between the gap distance and the head circumference is found, thereby determining an approximate head circumference range. People within this range can achieve a relatively reasonable state in terms of temple adaptation when wearing the smart glasses, which helps to improve the overall wearing comfort and use effect.

[0074] From the above, we can conclude that, firstly, determining the special information of the gap between the temple position and the tail of the temple adds key details to the study of glasses adaptability. It directly reflects the fit between the temple and the head, making up for the shortcomings of evaluation based on conventional information such as head circumference and age. Secondly, using this gap distance to accurately locate the range of people with suitable head circumferences for smart glasses makes the design and production of glasses more targeted. Consumers can also use this to quickly filter when purchasing, increase the probability of choosing comfortable glasses, and improve the overall user experience.

[0075] Corresponding to the glasses wearing comfort evaluation method of the above embodiment, Figure 2 This is a structural block diagram of a device for evaluating the wearing comfort of glasses provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The glasses wearing comfort evaluation device 20 includes: a data acquisition module 21, a data fitting module 22 and a comfort evaluation module 23.

[0076] The data acquisition module 21 is used to determine the pressure data sets corresponding to the multiple simulated heads, each of which includes the pressure data of different head smart glasses wearing positions, and the basic information corresponding to different simulated heads is different; A data fitting module 22, used to perform fitting based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head, to obtain a fitting result corresponding to the simulated head; The comfort evaluation module 23 is used to determine the wearing comfort of the smart glasses on each simulated head based on the fitting result corresponding to the simulated head.

[0077] In one embodiment of the present disclosure, the data fitting module 22 is specifically configured to: Determine a pressure data set corresponding to each simulated head as a pressure data set; Determine the basic information corresponding to each simulated head as basic data; Fitting is performed based on the pressure data set and the basic data to obtain the fitting results corresponding to each simulated head.

[0078] In one embodiment of the present disclosure, the data fitting module 22 is further configured to: The pressure data set and the basic data are input into the first fitting formula to obtain the fitting result corresponding to each simulated head; wherein the first fitting formula is a formula obtained by fitting the historical pressure data set and the historical basic data.

[0079] In one embodiment of the present disclosure, the comfort evaluation module 23 is specifically used to: The fitting results corresponding to each simulated head are input into the comfort evaluation model to obtain the wearing comfort of the smart glasses for the simulated head; wherein the comfort evaluation model is a wearing comfort model for the smart glasses determined according to the fitting results corresponding to each historical simulated head and the wearing comfort of the smart glasses corresponding to the historical simulated head.

[0080] In one embodiment of the present disclosure, the eyeglasses wearing comfort evaluation device 20 further includes: a model building module; A model building module, used for determining a comprehensive loss function for comfort evaluation based on a fitting result corresponding to each simulated head and basic information corresponding to the simulated head; Construct a wearing comfort model of smart glasses based on comprehensive loss function.

[0081] In one embodiment of the present disclosure, the glasses wearing comfort evaluation device 20 further includes: a parameter adjustment module; A parameter adjustment module, used for adjusting the smart glasses based on the wearing comfort of the smart glasses; If the comfort level meets the standard comfort level, the corresponding smart glasses production parameters are retained; If the comfort level does not meet the standard comfort level, the corresponding smart glasses production parameters are adjusted.

[0082] In one embodiment of the present disclosure, the glasses wearing comfort evaluation device 20 further includes: an application range determination module; An application range determination module is used to determine the special information corresponding to each simulated head, the special information including the gap distance between the temple position of the temple and the tail of the temple; Determine the range of head circumferences suitable for wearing smart glasses based on the gap distance.

[0083] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0084] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0086] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0087] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the glasses wearing comfort evaluation method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0088] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0089] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0094] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0095] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for evaluating the wearing comfort of glasses, characterized in that: include: Determine a plurality of pressure data sets corresponding to simulated heads, each of which includes pressure data of different head smart glasses wearing positions, and different simulated heads have different corresponding basic information; Fitting is performed based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head to obtain a fitting result corresponding to the simulated head; The wearing comfort of the smart glasses on each simulated head is determined based on the fitting results corresponding to the simulated head.

2. The method for evaluating wearing comfort of glasses according to claim 1, characterized in that: Fitting is performed based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head to obtain a fitting result corresponding to the simulated head, including: Determine a pressure data set corresponding to each simulated head as a pressure data set; Determine the basic information corresponding to each simulated head as basic data; Fitting is performed based on the pressure data set and the basic data to obtain a fitting result corresponding to each simulated head.

3. The method for evaluating wearing comfort of glasses according to claim 2, characterized in that: The fitting based on the pressure data set and the basic data to obtain a fitting result corresponding to the simulated head includes: The pressure data set and the basic data are input into a first fitting formula to obtain a fitting result corresponding to each simulated head; wherein the first fitting formula is a formula obtained by fitting the historical pressure data set and the historical basic data.

4. The method for evaluating wearing comfort of glasses according to claim 1, characterized in that: The determining the wearing comfort of the smart glasses of each simulated head based on the fitting result corresponding to the simulated head includes: The fitting result corresponding to each simulated head is input into the comfort evaluation model to obtain the wearing comfort of the smart glasses for the simulated head; wherein the comfort evaluation model is a wearing comfort model for the smart glasses determined according to the fitting result corresponding to each historical simulated head and the wearing comfort of the smart glasses corresponding to the historical simulated head.

5. The method for evaluating wearing comfort of glasses according to claim 4, characterized in that: Also includes: Determine a comprehensive loss function for comfort evaluation based on the fitting result corresponding to each simulated head and the basic information corresponding to the simulated head; A wearing comfort model of the smart glasses is constructed based on the comprehensive loss function.

6. The method for evaluating wearing comfort of glasses according to claim 1, characterized in that: Also includes: Adjusting the smart glasses based on the wearing comfort of the smart glasses; If the comfort level meets the standard comfort level, the corresponding smart glasses production parameters are retained; If the comfort level does not meet the standard comfort level, the corresponding smart glasses production parameters are adjusted.

7. The method for evaluating wearing comfort of glasses according to claim 1, characterized in that: Also includes: Determine the special information corresponding to each simulated head, wherein the special information includes the gap distance between the temple position of the temple and the tail of the temple; The range of people with head circumferences suitable for wearing the smart glasses is determined based on the gap distance.

8. A device for evaluating wearing comfort of glasses, characterized in that: include: A data acquisition module is used to determine pressure data sets corresponding to multiple simulated heads, each of which includes pressure data of different head smart glasses wearing positions, and different simulated heads have different basic information corresponding to them; A data fitting module, used for fitting based on the pressure data set corresponding to each simulated head and the basic information corresponding to the simulated head, to obtain a fitting result corresponding to the simulated head; The comfort evaluation module is used to determine the wearing comfort of the smart glasses on each simulated head based on the fitting results corresponding to the simulated head.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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