Wearer load evaluation system and wearer load evaluation method

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The system evaluates and adjusts eyeglass lens performance based on wearer posture changes using a camera and LiDAR sensor to enhance comfort by minimizing posture-related burden.

WO2026126580A1PCT designated stage Publication Date: 2026-06-18NIKON ESSILOR

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Patent Information

Authority / Receiving Office: WO · WO
Patent Type: Applications
Current Assignee / Owner: NIKON ESSILOR
Filing Date: 2025-08-22
Publication Date: 2026-06-18

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Abstract

[Problem] To make it possible to quantitatively evaluate the posture load of a wearer. [Solution] Provided is a wearer load evaluation system for reducing a load on the posture of a wearer of spectacles, the wearer load evaluation system comprising: a processing unit that, on the basis of reference posture data indicating a reference posture of the wearer and specific posture data indicating a specific posture of the wearer, calculates a load amount applied to the wearer by a posture change from the reference posture to the specific posture; and an output unit that outputs the load amount.

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Description

User burden evaluation system and user burden evaluation method 【0001】 The present disclosure relates to a user burden evaluation system and a user burden evaluation method. 【0002】 Glasses are widely used devices for vision correction, and various lens performances have been developed to ensure a comfortable visual field for the wearer. On the other hand, when aligning the line of sight with a specific object, the posture and head movement of the wearer may change, and such posture changes may impose a burden on the wearer. There is an increasing need for a mechanism to appropriately evaluate and reduce such a burden. 【0003】 Japanese Patent Application Laid-Open No. 2008-89618 【0004】 According to a first aspect of the present disclosure, there is provided a user burden evaluation system for reducing the burden on the posture of a wearer of glasses, including a processing unit that calculates the amount of burden imposed on the wearer due to a posture change from the reference posture to the specific posture based on reference posture data indicating the reference posture of the wearer and specific posture data indicating the specific posture of the wearer, and an output unit that outputs the amount of burden. 【0005】 According to a second aspect of the present disclosure, there is provided a user burden evaluation method for reducing the burden on the posture of a wearer of glasses, including calculating the amount of burden imposed on the wearer due to a posture change from the reference posture to the specific posture based on reference posture data indicating the reference posture of the wearer and specific posture data indicating the specific posture of the wearer, and outputting the amount of burden. 【0006】This figure shows an example of the schematic configuration of the wearer burden evaluation system according to this embodiment. This figure shows an example of 2D image data according to this embodiment. This figure schematically shows an example of depth data according to this embodiment. This is an example of a schematic block diagram of the information processing device according to this embodiment. These are the functional blocks of the processor according to this embodiment. This figure shows integrated feature points in 2D image data according to this embodiment. This figure shows integrated feature points in depth data according to this embodiment. This figure shows an example of three-dimensional posture data according to this embodiment. This figure shows an example of a reference posture according to this embodiment. This figure shows an example of a specific posture according to this embodiment. This figure shows the analysis feature points in the reference posture according to this embodiment viewed from the right ear side. This figure shows the analysis feature points in the specific posture according to this embodiment viewed from the right ear side. This figure explains the method for calculating the amount of burden according to this embodiment. This figure explains the method for determining the pupil position according to this embodiment. This figure shows astigmatism on the spectacle lens according to this embodiment using contour lines. This figure shows the add power on the spectacle lens according to this embodiment using contour lines. This figure explains the line of sight distance and line of sight vector according to this embodiment. This figure shows a first example of an astigmatism contour plot according to this embodiment. This figure shows a first example of an add power contour plot according to this embodiment. This figure shows a second example of an astigmatism contour plot according to this embodiment. This figure shows a second example of the join degree contour map according to this embodiment. This figure illustrates the first dataset according to this embodiment. This figure illustrates the second dataset according to this embodiment. This figure shows the estimated data for the intermediate posture according to this embodiment. This figure shows the estimated data for a third specific posture other than the intermediate posture according to this embodiment. This is a table showing contour map data at the confirmation coordinate points according to this embodiment. This is a flowchart of the feature point extraction process according to this embodiment. This is a flowchart of the burden amount calculation process according to this embodiment. This is a flowchart of the line-of-sight data calculation process according to this embodiment. This is a flowchart of the data estimation process according to this embodiment. 【0007】 The present invention will be described below through embodiments, but these embodiments are not intended to limit the invention as defined in the claims. Furthermore, not all combinations of features described in the embodiments are necessarily essential to the solution of the invention. 【0008】 Conventional eyeglasses and eye-correction technologies sometimes lack sufficient capabilities to quantitatively evaluate the burden on the wearer due to changes in their gaze and posture, and to adjust lens performance and the wearer's posture based on that evaluation. Therefore, there is a need for systems and methods that reduce the burden on the wearer and enable more comfortable use. 【0009】 The wearer burden evaluation system 100 according to this embodiment is a system that evaluates the changes in posture that occur when a wearer of eyeglasses 50 focuses their gaze on a specific object (hereinafter referred to as "specific object"), and seeks information to reduce that burden. For example, the wearer burden evaluation system 100 can calculate the amount of burden on the wearer based on the wearer's posture data, and can also estimate changes in posture and neck burden due to lens performance. 【0010】 Figure 1 is a diagram showing an example of the schematic configuration of the wearer burden evaluation system 100 according to this embodiment. As shown in Figure 1, the wearer burden evaluation system 100 comprises a measurement unit 200, a display device 300, and an information processing device 400. 【0011】 The measurement unit 200 measures posture data, including the wearer's body. The posture data may be two-dimensional or three-dimensional. For example, the measurement unit 200 may be a single device or a configuration having multiple devices. The measurement unit 200 may include, for example, a camera 210 and a LiDAR sensor 220. The measurement unit 200 may be, for example, a single device having the functions of both a camera 210 and a LiDAR sensor 220. The measurement unit 200 measures the wearer's posture using the camera 210 and the LiDAR sensor 220. This measurement by the measurement unit 200 is sometimes referred to as "posture measurement". 【0012】The camera 210 is installed, for example, at a certain distance in front of a wearer wearing glasses 50. The camera 210 captures a two-dimensional image including part of the wearer's body (for example, the head and upper body) or the whole. The two-dimensional image is an example of posture data. Figure 2 is a diagram showing an example of an image captured by the camera 210 according to this embodiment (hereinafter referred to as "2D image data"). The 2D image data captured by the camera 210 illustrated in Figure 2 is an image of a wearer wearing glasses 50 holding a tablet terminal TB and looking at the display screen of the tablet terminal TB. The tablet terminal TB is an example of a specific object. The 2D image data captured by the camera 210 is transmitted to the information processing device 400. 【0013】 Furthermore, the term "specific object" is not limited to tablet devices (TB); it can be anything the wearer can focus on, regardless of its shape or type. For example, objects held in the hand, stationary devices, ordinary ornaments, and other visible objects can be used as specific objects as long as they are appropriate targets for the wearer's gaze. 【0014】 The LiDAR sensor 220 is installed, for example, at a certain distance in front of a wearer wearing glasses 50. The LiDAR sensor 220 is a sensor that measures the distance to an object within its detection area (hereinafter referred to as "depth data") by utilizing the reflection of light. The detection area of the LiDAR sensor 220 includes a part of the wearer's body (for example, the head and upper body) or the whole. Figure 3 is a schematic diagram showing an example of depth data measured by the LiDAR sensor 220 according to this embodiment. The depth data exemplified in Figure 3 is an image taken of a wearer holding a tablet terminal TB and looking at the display screen of the tablet terminal TB. In Figure 3, darker colors indicate that the object is closer. The depth data measured by the LiDAR sensor 220 is transmitted to the information processing device 400. 【0015】Here, in a single posture measurement by the measurement unit 200, the posture of the wearer as seen in the 2D image data of the wearer's posture and the posture of the wearer measured by the depth data are the same or nearly the same. For example, it is desirable that the timing of imaging by the camera 210 and measurement by the LiDAR sensor 220 be simultaneous or nearly simultaneous. 【0016】 For example, the measurement unit 200 is a device equipped with a camera 210 and a LiDAR sensor 220. As an example, an application installed on this device (measurement unit 200) is launched. This application starts measuring depth data by the LiDAR sensor 220 at that time in conjunction with the operation of pressing the record button on the camera 210. The record button is the operation unit that starts imaging with the camera 210, for example, a shutter button. When the record button on the camera 210 is pressed, 2D image data and depth data measured of a wearer in the same or nearly the same posture are transmitted to the information processing device 400. 【0017】 In this case, acquiring depth data may take time depending on the performance of the LiDAR sensor 220. To address this problem, a delay time may be set between the time the camera's recording button is pressed and the time the depth data is acquired. For example, the delay time may be set within the application. This may improve the measurement accuracy of the LiDAR sensor 220. 【0018】 For example, due to the performance of the LiDAR sensor 220, the measurement by the LiDAR sensor 220 may not be stable at the moment the recording button on the camera 210 is pressed. By introducing the above-mentioned delay time, the LiDAR sensor 220 can acquire depth data in a stable state. As a result, noise is less likely to be introduced into the depth data, and highly accurate depth data can be acquired. In addition, fluctuations in the LiDAR sensor 220 caused by hand shake or movement that occur immediately after pressing the recording button can be reduced. As a result, the depth of the object can be reliably and accurately acquired, and measurement errors are reduced. 【0019】The delay time can be adjusted arbitrarily. For example, the delay time can be adjusted according to the specifications of the LiDAR sensor 220 and the measurement conditions. The measurement unit 200 may also measure depth data an arbitrary number of times within the delay time and obtain the final depth data by averaging that data. The final depth data is the depth data to be transmitted to the information processing device 400. The measurement unit 200 may also measure depth data an arbitrary number of times within the delay time and apply a process to weight that depth data. The measurement unit 200 may then obtain the final depth data by averaging the weighted depth data set. 【0020】 The display device 300 is connected to the information processing device 400. The display device 300 displays data transmitted from the information processing device 400 on its display screen. The display device 300 is a monitor for displaying data. The display device 300 and the information processing device 400 may be configured as a single unit. 【0021】 The information processing device 400 comprehensively processes the data acquired from the camera 210 and the LiDAR sensor 220 to calculate the burden amount, which indicates the amount of change in the wearer's posture. Based on the integrated data, the information processing device 400 calculates the wearer's gaze direction and posture changes. Figure 4 is an example of a schematic block diagram of the information processing device 400 according to this embodiment. 【0022】 As shown in Figure 4, the information processing device 400 comprises a communication interface 410, a storage device 420, and a processor 430. Note that the storage device 420 may be an external storage device rather than being part of the information processing device 400. If the storage device 420 is an external storage device, the information processing device 400 is connected to the storage device 420 by wire or wireless connection and transmits and receives information with the storage device 420. The storage device 420 is an example of a storage unit. 【0023】 Communication I / F 410 is a communication interface for communicating with external devices (for example, the display device 300 or an external communication terminal). The communication network through which communication I / F 410 communicates may be wired, wireless, or both. 【0024】The storage device 420 includes, for example, non-volatile memory such as ROM (Read Only Memory), an HDD (Hard Disk Drive), and an SSD (Solid State Drive). The storage device 420 stores a processing program. The processing program is a software program that calculates the wearer's burden based on the wearer's posture data and estimates changes in posture and neck burden due to lens performance. The processing program may be provided by a computer-readable storage medium or by an external device via a wired or wireless communication network. The provided processing program is stored in the storage device 420 and executed by the processor 430. 【0025】 Examples of computer-readable storage media mentioned above may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (ERPOM or flash memory), electrically erasable programmable read-only memory (EERPOM), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital multipurpose disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, etc. 【0026】 The processor 430 reads processing programs and the like stored in the storage device 420 to calculate the wearer's burden based on the wearer's posture data, and estimates changes in posture and neck burden due to lens performance. The processor 430 includes, for example, at least one of a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). Alternatively, the processor 430 may be a microcontroller unit. The processor 430 is an example of a processing unit. 【0027】Figure 5 shows the functional blocks of the processor 430 according to this embodiment. As shown in Figure 5, the processor 430 comprises a data processing unit 500, an extraction unit 510, a workload calculation unit 520, a setting unit 530, a gaze parameter calculation unit 540, an estimation unit 550, and an output unit 560. The data processing unit 500, the extraction unit 510, the workload calculation unit 520, the setting unit 530, the gaze parameter calculation unit 540, the estimation unit 550, and the output unit 560 are software modules realized by the processor 430 executing a processing program stored in the storage device 420. 【0028】 The data processing unit 500 acquires 2D image data and depth data from the measurement unit 200. The data processing unit 500 generates three-dimensional spatial coordinate data using the acquired 2D image data and depth data. When the data processing unit 500 generates three-dimensional spatial coordinate data, the resolution of the 2D image data and depth data may not match. As a result, it may be difficult to simply superimpose the two-dimensional spatial coordinates of both the 2D image data and depth data. In such cases, the data processing unit 500 detects multiple feature points (hereinafter referred to as "integrated feature points") fp obtained from the 2D image data and depth data respectively, and calculates the ratio of the lengths between these integrated feature points fp. Based on the calculated ratio, the data processing unit 500 performs a process to match the two-dimensional spatial coordinates of the depth data with the two-dimensional spatial coordinates of the 2D image data (hereinafter referred to as "coordinate matching process"). 【0029】 Here, it is generally known that depth data is more accurate in areas closer to the LiDAR sensor 220. Therefore, by preferentially adopting two or more integrated feature points fp detected at positions close to the LiDAR sensor 220 from among the multiple integrated feature points fp obtained from depth data, it is possible to generate highly accurate three-dimensional spatial coordinate data. 【0030】The integrated feature point fp may be set in advance. For example, in this embodiment, two integrated feature points, a first integrated feature point fp1 and a second integrated feature point fp2, are set. The first integrated feature point fp1 is, for example, an arbitrary position on the head. The first integrated feature point fp1 is, for example, the center position of the head. The second integrated feature point fp2 is, for example, an arbitrary position on the hand. The second integrated feature point fp2 is, for example, the center position of the hand. The center position of the head may be, for example, the geometric centroid of the head contour. The center position of the hand may be, for example, the geometric centroid of the hand contour. 【0031】 The first integrated feature point fp1 may be at a predetermined location on the human body, or at a predetermined location on eyeglasses such as the eyeglass frame or lenses. The second integrated feature point fp2 may be at a predetermined location on the human body, or at a predetermined location on eyeglasses such as the eyeglass frame or lenses. However, the first integrated feature point fp1 and the second integrated feature point fp2 are not at the same location, but at different locations. 【0032】 When detecting feature points, it is conceivable to use a pre-trained model that has undergone machine learning in advance. In this embodiment, for example, when the data processing unit 500 acquires a first integrated feature point fp1 from 2D image data, it has a first pre-trained model that has been pre-trained using a dataset of past 2D image data and the location of the first integrated feature point fp1 as training data. The data processing unit 500 uses the first pre-trained model to identify the location of the first integrated feature point fp1 within the 2D image data from the input 2D image data. 【0033】 In this embodiment, the data processing unit 500, for example, when acquiring a second integrated feature point fp2 from 2D image data, has a second pre-trained model that has been pre-trained using a dataset of past 2D image data and the location of the second integrated feature point fp2 as training data. The data processing unit 500 uses the second pre-trained model to identify the location of the second integrated feature point fp2 within the 2D image data from the input 2D image data. The first pre-trained model and the second pre-trained model may be collectively referred to as the "pre-trained model for 2D image data". 【0034】In this embodiment, the data processing unit 500, for example, when acquiring a first integrated feature point fp1 from depth data, has a third pre-trained model that has been pre-trained using a dataset of past depth data and the location of the first integrated feature point fp1 as training data. The data processing unit 500 uses the third pre-trained model to identify the location of the first integrated feature point fp1 within the depth data from the input depth data. 【0035】 In this embodiment, the data processing unit 500, for example, when acquiring a second integrated feature point fp2 from depth data, has a fourth pre-trained model that has been pre-trained using a dataset of past depth data and the location of the second integrated feature point fp2 as training data. The data processing unit 500 uses the fourth pre-trained model to identify the location of the second integrated feature point fp2 within the depth data from the input depth data. The third pre-trained model and the fourth pre-trained model may be collectively referred to as the "pre-trained model for depth data". 【0036】 In this embodiment, the analysis focuses on eyeglass lenses and the posture of the person wearing them. Eyeglass lenses are distinctive objects located on the head, and by performing machine learning based on a head model, it is possible to efficiently detect the position and shape of the eyeglass lenses. This approach allows for high-precision extraction of feature points of eyeglasses, including the lenses, enabling effective analysis of the wearer's posture and assessment of their burden. 【0037】 An example of coordinate fitting processing according to this embodiment is described below. For example, when the data processing unit 500 acquires 2D image data from the measurement unit 200, it uses a trained model for 2D image data to identify a first integrated feature point fp1 and a second integrated feature point fp2 in the 2D image data. Also, when the data processing unit 500 acquires depth data from the measurement unit 200, it uses a trained model for depth data to identify a first integrated feature point fp1 and a second integrated feature point fp2 in the depth data. Figure 6 shows the first integrated feature point fp1 and the second integrated feature point fp2 in the 2D image data according to this embodiment. Figure 7 shows the first integrated feature point fp1 and the second integrated feature point fp2 in the depth data. 【0038】 As shown in Figure 6, the data processing unit 500 calculates the length L1 between the first integrated feature point fp1 and the second integrated feature point fp2 in the 2D image data. As shown in Figure 7, the data processing unit 500 calculates the length L2 between the first integrated feature point fp1 and the second integrated feature point fp2 in the depth data. 【0039】 The data processing unit 500 determines the ratio r of the distance between the corresponding first integrated feature point fp1 and second integrated feature point fp2 in the 2D image data and depth data, respectively. That is, the data processing unit 500 calculates the ratio r = L1 / L2 using lengths L1 and L2. The ratio r is a coefficient used to enlarge or reduce the 2D image data and depth data to match them, for example, when their resolutions or scales are different. The data processing unit 500 enlarges or reduces the coordinates in the 2D space of the depth data by the ratio r so that they can be superimposed on the coordinates in the 2D space of the 2D image data. This completes the coordinate matching process. 【0040】 Furthermore, in calculating the ratio r, it is desirable to select integrated feature points where the distance between the first integrated feature point fp1 and the second integrated feature point fp2 is as large as possible in order to minimize calculation errors. For example, if the first integrated feature point fp1 is the center of the head and the second integrated feature point fp2 is the center of the hand, these integrated feature points generally have a large distance between them, which is suitable for improving the accuracy of ratio calculations. In addition, it has been confirmed that in most cases, when the gaze is directed towards an object, the center of the head and the center of the hand are naturally positioned at a sufficient distance from each other. Moreover, the center of the head and the center of the hand are feature points that are easy to detect even in depth data. This makes it possible to efficiently and accurately adjust the scale between depth data and 2D image data. 【0041】The coordinate fitting process ensures that the scales of the 2D image data and the depth data match, enabling the superposition of the 2D image data and the depth data. Once the coordinate fitting process is complete, the data processing unit 500 generates three-dimensional pose data (hereinafter referred to as "three-dimensional pose data") by superimposing the 2D image data and the depth data. Figure 8 shows an example of three-dimensional pose data according to this embodiment. 【0042】 As shown in Figure 8, the extraction unit 510 extracts feature points (hereinafter referred to as "analysis feature points") AP from the three-dimensional posture data for posture analysis and estimation of gaze direction. There may be one or more analysis feature points AP, and they may be at any position on the eyeglass frame, any position on the wearer's body, or both. In the example shown in Figure 8, the analysis feature points AP are predefined as specific positions on the eyeglass frame, ear, nose, center of the hand, and tablet terminal TB. In this case, the extraction unit 510 extracts each specific position as an analysis feature point AP from the three-dimensional posture data. The extraction unit 510 saves the analysis feature points AP extracted from the three-dimensional posture data in association with the wearer's posture. Note that the process of generating three-dimensional posture data and saving the analysis feature points AP extracted from that three-dimensional posture data may be referred to as the "feature point extraction process". 【0043】 Here, one example of a method by which the extraction unit 510 extracts analytical feature points AP from the 3D pose data is a feature point extraction method that utilizes machine learning. For example, the extraction unit 510 can extract all analytical feature points AP from the 3D pose data with high accuracy by using a pre-trained model that has been trained in advance on a dataset of past 3D pose data and each analytical feature point AP. Another method for extracting analytical feature points AP is an extraction method based on the shape and structure of the 3D data. 【0044】In this extraction method, as an example, it is conceivable to calculate the curvature of the surface of the pose three-dimensional data and extract the locations with high curvature as feature points. For example, the tip of the nose, the edges of the ears, etc. have significant curvature changes and may be suitable as feature points. Also, in some cases, it is possible to use a method where a region with a rapid height change is detected as an edge and its endpoints are used as feature points. Furthermore, for a specific region within the data, it is also conceivable to calculate its centroid and consider the centroid as the reference point for the analysis feature point AP. 【0045】 Also, it is conceivable to use the method of template matching. In this method, a pre-defined template is applied to the three-dimensional data, and the locations that match the template are specified as the analysis feature points AP. For example, when extracting the analysis feature points AP of the face, by applying a standard face template, it may be possible to efficiently detect the positions of both eyes, nose, mouth, etc. Similarly, when extracting the analysis feature points AP of the hand, the finger joints, center points, etc. can be easily specified based on the template. 【0046】 Note that these methods for extracting the analysis feature points AP are merely examples, and depending on the specific data and analysis purpose, it is conceivable to select or combine any of them for use. In this embodiment, the method for extracting the analysis feature points AP is not limited to a specific method, and the method for extracting the analysis feature points AP may be extracted using the methods shown above or other appropriate methods. 【0047】 The analysis feature points AP can also be extracted from only 2D image data. However, when only using 2D image data, errors may occur due to reflections of the background or other objects, light intensity changes, etc. As a result, the analysis feature points AP may not be accurately detected, or the coordinate accuracy may decrease. On the other hand, the analysis feature points AP obtained from a model that has been machine-learned using pose three-dimensional data may be able to avoid these problems. Specifically, feature points based on three-dimensional data are less affected by the background and light, so it is possible to obtain feature points with high coordinate accuracy. 【0048】For example, even if an object located far away is erroneously detected as the analysis feature point AP, it is possible to infer that it is not the target object and exclude it. In particular, since the depth data is not affected by the addition or subtraction of light, it reduces the risk of such erroneous detection and contributes to improving the accuracy of the analysis feature point AP. Here, the analysis feature points AP such as the above-described eyeglass frame, ears, nose, and center of the hand are not affected by expressions and have little change in position, and are analysis feature points AP suitable for pose analysis using eyeglass lenses in the present embodiment. 【0049】 Here, the extraction unit 510 executes feature point extraction processing for each pose three-dimensional data. In other words, the extraction unit 510 extracts the analysis feature point AP for each pose of the wearer, and stores the data of the extracted analysis feature point AP in association with the pose of the wearer (for example, pose three-dimensional data). 【0050】 For example, the measurement unit 200 performs pose measurement in each of the state where the wearer is in the reference pose and the state where the wearer is in the specific pose. In this case, the processor 430 executes feature point extraction processing in each of the reference pose of the wearer as illustrated in FIG. 9 and the specific pose of the wearer as illustrated in FIG. 10. 【0051】 Specifically, as shown in FIG. 9, the extraction unit 510 extracts the analysis feature point AP from the pose three-dimensional data corresponding to the reference pose, and stores the extracted analysis feature point AP and the data indicating the reference pose in association with each other. Further, as shown in FIG. 10, the extraction unit 510 extracts the analysis feature point AP from the pose three-dimensional data corresponding to the specific pose, and stores the extracted analysis feature point AP and the data indicating the specific pose in association with each other. The specific pose is, for example, a pose in which the wearer is looking at the tablet terminal TB. 【0052】 The reference pose is a pose in which the wearer is not looking at the tablet terminal TB. The reference pose is a pose with less physical burden on the neck, shoulders, etc. than the specific pose, and is, for example, a pose facing the front side of the measurement unit 200. Here, the analysis feature point AP extracted from the pose three-dimensional data corresponding to the reference pose is referred to as a "reference point", and the analysis feature point AP extracted from the pose three-dimensional data corresponding to the specific pose is referred to as a "specific point", and they may be distinguished from each other. 【0053】 The burden calculation unit 520 calculates the burden on the wearer based on the change in posture from the standard posture to the specific posture, using standard posture data indicating the wearer's standard posture and specific posture data indicating the wearer's specific posture. The standard posture data and specific posture data may be three-dimensional posture data, or they may be 2D image data and depth data, or both. 【0054】 Postural changes include, for example, the head. The reference posture is the posture used as a reference when determining the postural change. For example, the burden calculation unit 520 calculates the change in posture between a reference point and a specific point corresponding to that reference point. For example, the burden calculation unit 520 may calculate the change in posture between a reference point of the eyeglass frame and a specific point of the eyeglass frame corresponding to that reference point. The burden calculation unit 520 may use this change in posture as the burden, or it may calculate the burden based on this change in posture. 【0055】 For example, the load calculation unit 520 performs alignment using all or one of the following for a reference point and a specific point corresponding to that reference point: coordinate rotation, translation, and scaling. This alignment is performed, for example, by superimposing the three-dimensional posture data of the eyeglass frame in the reference posture with the three-dimensional posture data of the eyeglass frame in the specific posture, or by adjusting the distance between the two to the minimum. The parameters obtained by the alignment (hereinafter referred to as "adjustment parameters") can be treated as changes in posture. The adjustment parameters are, for example, all or one of the following: rotation amount, translation amount, and scaling amount. 【0056】The load calculation unit 520 converts the amount of rotation of the coordinates obtained by alignment into Euler angles, thereby obtaining the amount of change in the head in each of the roll, pitch, and yaw directions. The amount of change in the head is an example of the amount of change in posture. The amount of rotation of the coordinates represents the magnitude of the amount of rotation of the coordinates in three-dimensional space from the reference posture to the posture in which the gaze is directed towards the tablet terminal TB. The rotation of the coordinates can be expressed, for example, as an Euler angle or a quaternion. When the amount of rotation is an Euler angle, it can be decomposed into the amount of rotation around three axes: roll angle, pitch angle, and yaw angle. The roll angle is, for example, the angle of left-right rotation of the head (head tilting motion). The pitch angle is, for example, the angle of forward-backward rotation of the head (nodding motion). The yaw angle corresponds to, for example, the angle of left-right rotation of the head (head shaking motion). 【0057】 Translational quantity represents the parallel movement of a position in three-dimensional space between a reference posture and a specific posture. For example, translational quantity can be expressed as the distance traveled along the X, Y, and Z axes in an XYZ spatial coordinate system. Translational quantity is used, for example, to quantitatively determine how much the wearer's head or body has moved. 【0058】 The scale quantity represents the scaling factor used when an object needs to be enlarged or reduced between a reference pose and a specific pose. The scale quantity is used, for example, to ensure accurate consistency between three-dimensional data from different viewpoints or poses. 【0059】 The burden calculation unit 520 may directly acquire the amount of change in the adjustment parameters obtained by alignment as the burden amount, or it may calculate a more specific burden amount by combining it with other calculation formulas or parameters other than the adjustment parameters. In other words, the burden imposed on the wearer due to a change in posture from a standard posture to a specific posture may be the amount of change in the adjustment parameters obtained by alignment, or it may be a value obtained by combining the amount of change in the adjustment parameters with other calculation formulas or parameters other than the adjustment parameters. 【0060】Figure 11 is a view of the analysis feature point AP (reference point) in the reference posture according to this embodiment, viewed from the right ear side. Figure 12 is a view of the analysis feature point AP (specific point) in a specific posture according to this embodiment, viewed from the right ear side. For example, the load calculation unit 520 converts the amount of rotation of the coordinates obtained by alignment into Euler angles. Of the Euler angles, the pitch angle can be treated as the change in tilt of the head in the anterior-posterior tilt direction, but as shown in Figure 13, it is also possible to treat the pitch angle as the tilt of the center of gravity position from the neck joint position. 【0061】 In the following explanation, we will use the example shown in Figure 13, where the pitch angle is defined as the inclination of the center of gravity from the neck joint. When the head is supported by the muscles at the back of the neck, the load calculation unit 520 calculates the rotational moment R of the head due to gravity by multiplying the distance from the neck joint to the center of gravity by the angle of forward and backward flexion of the neck. Here, we consider the rotational moment in the opposite direction to the rotational moment R, which is pulled by the muscles at the back of the neck. Specifically, assuming that the length of the perpendicular from the neck joint to the line of action of the muscle force is 4 cm, and the muscle force is M, the following equation (1) holds. 【0062】 M × 4 = Gravity × (Distance from the neck joint to the center of gravity) ... (1) 【0063】 Assuming the head weighs 5 kg and the pitch angle is 45 degrees, the distance from the neck joint to the center of gravity is 14.14 cm. Therefore, equation (1) becomes equation (2) below. 【0064】 M × 4 = 5 × 14.14 … (2) 【0065】From equation (2), the muscle strength M is 17.68 kg. The load calculation unit 520 may calculate the load as muscle strength M = 17.68 kg. The load calculation unit 520 stores the calculated load. For example, the load calculation unit 520 stores the calculated load in association with data for a specific posture. In other words, the load calculation unit 520 stores the calculated load for each specific posture. Thus, for example, the load imposed on the wearer due to a change in posture may be the amount of change in posture, or it may be the muscle load calculated based on the amount of change in posture. For example, the amount of change in posture is used as an indicator of the basic load. On the other hand, for example, the above moment calculation is used as a complementary method to improve the accuracy of evaluating a specific load. 【0066】 Next, the eye-tracking measurement according to this embodiment will be described. The eye-tracking measurement process is mainly performed by the setting unit 530 and the eye-tracking parameter calculation unit 540. 【0067】 For eye-tracking, it is necessary to set two positions, for example, the starting position SP of the wearer's gaze and the ending position EP of the wearer's gaze. The setting unit 530 may, for example, set a specific position on the lens of the eyeglasses as the starting position SP of the wearer's gaze, and the center of the tablet terminal TB or the position of the content displayed on the tablet terminal TB (content position) as the ending position EP of the wearer's gaze. 【0068】 For example, the operator can confirm, through communication with the wearer, which part of the lens the wearer is using to view the tablet terminal TB, and set that position as the starting point of the line of sight SP. In this case, the setting unit 530 sets the position on the lens input by the operator via the input device as the starting point of the line of sight SP. It is also possible to verify the lens usage position in advance and use that position as the starting point of the line of sight SP. The lens usage position is the position of the line of sight that passes through the lens when the wearer of glasses looks in a specific line of sight direction. For example, the lens usage position refers to a point on or inside the lens through which the line of sight passes. 【0069】The position of the lens may be measured in advance using known techniques such as eye tracking. As a method for obtaining the position of the lens with high accuracy, it is conceivable to place the measurement unit 200 on or near the tablet terminal TB. The setting unit 530 then obtains 2D image data and depth data from the measurement unit 200 and uses a model (e.g., a trained model) capable of extracting pupil feature points based on these to identify the pupil position through the lens. Figure 14 shows the pupil position identified from the 2D image data and depth data. As shown in Figure 14, the setting unit 530 identifies the pupil position and determines the position of the lens from the identified pupil position. For example, the setting unit 530 may use the pupil position as the starting point, calculate the point where the extension line along the line of sight hits the lens, and set that point as the position of the lens. 【0070】 Furthermore, the setting unit 530 may set the starting position SP of the line of sight based on the performance of the lens. For example, the setting unit 530 may set the starting position SP of the line of sight to the position that has the least astigmatism and the most suitable power when viewing the tablet terminal TB. For example, if the eyeglass lens is a single-focus lens, the setting unit 530 may set the prism reference point (optical center of the lens) to the starting position SP of the line of sight. 【0071】 Furthermore, in the case of progressive lenses, when the wearer is set to look at a tablet terminal TB at a distance of, for example, 50 cm, the setting unit 530 may set the starting position SP of the line of sight to the position with the least astigmatism at an add power of 2.00 dp. Figure 15 is a diagram showing the astigmatism on the lens according to this embodiment using contour lines. Figure 16 is a diagram showing the add power on the lens according to this embodiment using contour lines. Position 60 in Figures 15 and 16 indicates the position with the least astigmatism at an add power of 2.00 dp. 【0072】Next, a method for identifying the end position EP of the gaze will be described. The setting unit 530 may set the center of the tablet terminal TB or the position of the content displayed on the tablet terminal TB as the end position EP of the gaze. When the center of the tablet terminal TB is set as the end position EP of the gaze, the setting unit 530 may, for example, use a model (e.g., a trained model) that extracts feature points of the tablet terminal TB from the three-dimensional posture data to obtain feature points, and set the center of those feature points as the end position EP. Alternatively, when the position of the content displayed on the tablet terminal TB is set as the end position EP of the gaze, the setting unit 530 may, for example, convert an arbitrary position of that content onto three-dimensional data, and set that converted position as the end position EP of the gaze. 【0073】 The gaze parameter calculation unit 540 calculates either or both the distance from the starting position SP of the gaze to the ending position EP of the gaze (hereinafter referred to as "gaze distance") and the gaze vector (hereinafter referred to as "gaze vector") set by the setting unit 530. The gaze distance and gaze vector, or either or both, may be referred to as gaze data. 【0074】 The method for calculating the line of sight vector is described below. Figure 17 is a diagram illustrating the line of sight distance and line of sight vector according to this embodiment. As shown in Figure 17, the line of sight parameter calculation unit 540 connects the starting position SP and ending position EP of the line of sight on the three-dimensional posture data, and calculates the line of sight distance and line of sight vector from the starting position SP to the ending position EP from the connected line. 【0075】 For example, the gaze parameter calculation unit 540 connects the starting position SP1 and ending position EP of the left eye on the three-dimensional posture data, and calculates the gaze distance and gaze vector from the starting position SP1 to the ending position EP of the left eye using the connected line H1. The gaze parameter calculation unit 540 also connects the starting position SP2 and ending position EP of the right eye on the three-dimensional posture data, and calculates the gaze distance and gaze vector from the starting position SP2 to the ending position EP of the right eye using the connected line H2. 【0076】By utilizing both or either of the above-mentioned burden amount and gaze data calculation results, it is possible to adjust the performance of eyeglass lenses to reduce the burden on the wearer. The performance of eyeglass lenses is related to the wearer's posture. For example, changing eyeglass lenses changes the lens performance, and when the lens performance changes, the wearer will change their gaze distance or the position in which they use the lenses in order to comfortably view objects through those lenses. In other words, when the lens performance changes, the wearer's posture may change. To put it another way, it is possible to reduce the wearer's posture by changing the performance of eyeglass lenses. 【0077】 For example, consider a situation where the line of sight distance of the left eye (distance H1) and the line of sight distance of the right eye (distance H2) are both 50 cm. Figure 18 shows the first example of an astigmatism contour plot applied to an eyeglass lens. Figure 19 shows the first example of an add power contour plot applied to an eyeglass lens. As shown in Figures 18 and 19, when the current pupil position PE is located on the lower side of the lens, the wearer can see a specific object such as a tablet device TB clearly, but their field of view is narrowed, forcing them to adopt a posture (specific posture) with their head tilted upward and their gaze directed downward. As a result, it is expected that the change in the analytical feature points AP in the specific posture will be large compared to the analytical feature points AP in the standard posture which is not burdensome or has little burden. 【0078】 To reduce the burden on the wearer, the following adjustments to lens performance can be considered. Figure 20 shows a second example of an astigmatism contour map applied to an eyeglass lens. Figure 21 shows a second example of an add power contour map applied to an eyeglass lens. The pupil position PE shown in Figures 20 and 21 is the pupil position after adjusting the lens performance. For example, when a wearer views an object 50 cm away, the lens design can be modified so that the middle part of the lens can be used. For example, a progressive lens can be adopted that has a faster rise in add power, widens the area of 2.00 dp add power, and suppresses aberrations in the middle part. As a result, the pupil position moves upward, widening the wearer's field of view and increasing the area in which objects can be seen clearly. 【0079】Furthermore, by adjusting the lens performance, the wearer's face is positioned parallel to the object's plane. This causes the wearer's line of sight to intersect perpendicularly with the object's center in a straight line. Specifically, this adjustment eliminates the need to tilt the face unnaturally upward or downward, allowing the wearer to maintain a natural posture without straining the neck or back, while maintaining a line of sight perpendicular to the object. In this state, the field of view widens, enabling comfortable viewing. As a result, the amount of change between the reference posture and the posture with the gaze directed at the object (specific posture) decreases, making the wearer feel more comfortable. This effectively reduces strain and improves comfort for the wearer. 【0080】 To reduce the burden on the wearer, it is desirable to understand the relationship between lens performance and changes in the wearer's posture from multiple perspectives. The estimation unit 550 constructs a model that can understand the relationship between lens performance and changes in the wearer's posture, for example, based on three-dimensional posture data obtained under conditions using lenses with different performance characteristics. 【0081】 For example, suppose posture measurements are taken for a first specific posture when the wearer is wearing a lens with first performance, and for a second specific posture when the wearer is wearing a lens with second performance that is different from the first performance. In this case, the storage device 420 stores a first dataset as basic data, which includes multiple analysis feature points AP of the three-dimensional posture data in the first specific posture, multiple feature points of the pupil, and data for the first performance. The storage device 420 also stores a second dataset as basic data, which includes multiple analysis feature points AP of the three-dimensional posture data in the second specific posture, multiple feature points of the pupil, and data for the second performance. 【0082】The estimation unit 550 estimates the lens performance (hereinafter referred to as "third lens performance") necessary to make the wearer's posture when looking at an object a third specific posture that is different from both the first specific posture and the second specific posture, using at least the first and second data sets. The estimation unit 550 can also estimate the analytical feature points AP of the posture three-dimensional data and the pupil feature points of the third specific posture using at least the first and second data sets. The data of the third specific posture estimated by the estimation unit 550 may be referred to as "estimated data". The estimated data includes, for example, the third lens performance, the analytical feature points AP of the posture three-dimensional data in the third specific posture, and one or more pupil feature points. 【0083】 The third specific posture may be one specific posture or multiple specific postures. For example, the third specific posture may be any posture between the first specific posture and the second specific posture, a posture outside the range between the first and second specific postures, or both. 【0084】 Figure 22 is a diagram illustrating the first dataset according to this embodiment. In the example shown in Figure 22, the wearer is wearing a lens with first performance, and the diagram shows the analytical feature point cloud of the eyeglass frame, ears, and nose (Figure 22(A)), the feature point cloud of the pupil (Figure 22(B)), and the first performance (Figure 22(C)), which is the lens performance, obtained from the three-dimensional posture data of the wearer's posture at that time (first specific posture). 【0085】 Figure 23 illustrates the second dataset according to this embodiment. In the example shown in Figure 23, the wearer is wearing a lens with second performance, and the figure shows the analysis feature point cloud of the eyeglass frame, ears, and nose (Figure 23(A)), the feature point cloud of the pupil (Figure 23(B)), and the second performance (Figure 23(C)), which is the lens performance, obtained from the three-dimensional posture data of the wearer's posture at that time (second specific posture). 【0086】 The data shown in Figures 22 and 23 are measured data obtained by wearing lenses with different performance characteristics, representing combinations of lens performance and feature point clouds corresponding to each posture. This data will be used as the basic data for the model. 【0087】 Based on this basic data, the estimation unit 550 estimates the lens performance (Figure 24(C)) such that the wearer's posture is in an intermediate position between the first and second specific postures (hereinafter referred to as the "intermediate posture"), along with a plurality of analytical feature points (Figure 24(A)) and a plurality of feature points of the pupil (Figure 24(B)). Furthermore, based on this basic data, the estimation unit 550 estimates the lens performance (Figure 25(C)) such that the wearer's posture is a third specific posture other than the intermediate posture, along with a plurality of feature points (Figure 25(A)) and a plurality of feature points of the pupil (Figure 25(B)). The third specific posture other than the intermediate posture is, for example, a posture inferred from the trend of the basic data. The third specific posture other than the intermediate posture is, for example, a posture that falls outside the range between the first and second specific postures. 【0088】 Here, the estimation unit 550 may use multiple regression analysis as a method for calculating the third specific posture. When using multiple regression analysis, the dependent variable must be narrowed down to one. For example, the angle of the head tilt in the forward direction may be set as the dependent variable. For example, the estimation unit 550 uses the first and second datasets, which are the basic data, as explanatory variables, and generates a multiple regression model using the angle of the head tilt in the forward direction calculated from the feature points of the reference posture and the specific posture (both or either of the first and second specific postures) as the dependent variable. 【0089】 Next, we will show an example of treating lens performance as an explanatory variable. Lens performance is represented by the distribution of astigmatism and add power, overlaid on the lens surface as a contour map based on the lens design data. As an example, Figure 15 above shows an example of representing the distribution of astigmatism as a contour map overlaid on the lens surface. Figure 16 above shows an example of representing the distribution of add power as a contour map overlaid on the lens surface. 【0090】When using lens performance as an explanatory variable, one possible method is to use contour map data at important coordinate points (hereinafter referred to as "verification coordinate points") to verify the lens performance. Figure 26 is an example of a table showing contour map data at verification coordinate points. The table shown in Figure 26 is a coordinate table of astigmatism contour lines, showing each important coordinate point and the astigmatism at each coordinate point. The coordinate points are represented in a two-dimensional coordinate system of X and Y, and for example, the center of this coordinate system is set to the optical center (prism reference point). It is also possible to generate contour map data of the add degree using the same coordinate system as the table of contour map data showing astigmatism, and both or either astigmatism and add degree can be used as explanatory variables. 【0091】 As coordinate points for verifying lens performance, the coordinate points of the areas used for distance and near vision during wear can be considered. These areas vary depending on the type and design of the lens, but it is possible to use the distance and near vision coordinate points defined for the relevant lens. In this embodiment, the wearer may also perform a simulation in advance and add the coordinate points of the usage area obtained as a result to the above-mentioned verification coordinate points. This makes it possible to reflect the actual usage situation in the coordinate points that serve as the design standard. The coordinate points of the usage area are, for example, the coordinate points of the area that the wearer passes through on the lens when moving their gaze. 【0092】 Furthermore, the measurement unit 200 is placed near or on a specific object, and the information processing device 400 acquires 2D image data and depth data from the measurement unit 200 when the wearer views the specific object. Then, the processor 430 determines the pupil position through the lens based on the specific posture data. For example, the processor 430 may determine the pupil position through the lens by utilizing a pre-built model (e.g., a trained model) for acquiring pupil feature points based on the 2D image data and depth data. 【0093】The two-dimensional coordinates (X, Y) of the pupil position can be used as a reference coordinate point to check lens performance by overlaying them on the lens surface. Furthermore, parameters such as the tilt angle, frame curve, and corneal vertex distance measured during fitting can be used to connect the pupil position to a specific object with a linear vector. The coordinates where this vector touches the lens can then be used as a reference coordinate point to check lens performance. 【0094】 The output unit 560 outputs data calculated by the processor 430. Output by the output unit 560 may, for example, be displayed on the display screen of the display device 300 by outputting the data to the display device 300, or it may be transmitted to an external communication terminal. The output unit 560 outputs the load amount calculated by the load amount calculation unit 520. The output unit 560 can output gaze data calculated by the gaze parameter calculation unit 540. The output unit 560 outputs estimated data estimated by the estimation unit 550. For example, the output unit 560 outputs one or more of the third lens performance, the analysis feature point AP of the posture three-dimensional data in the third specific posture, and the pupil feature point. 【0095】 The following describes the operation flow of the wearer burden evaluation system according to this embodiment. First, the feature point extraction process will be described. Figure 27 is a flowchart of the feature point extraction process according to this embodiment. The measurement unit 200 measures the wearer's posture using the camera 210 and the LiDAR sensor 220 (step S101). The information processing device 400 acquires 2D image data and depth data from the measurement unit 200 and performs coordinate matching processing to match the two-dimensional spatial coordinates of the 2D image data with the two-dimensional spatial coordinates of the 2D image data (step S102). 【0096】The information processing device 400 generates three-dimensional pose data by superimposing the 2D image data after coordinate fitting processing and depth data (step S103). The information processing device 400 extracts analytical feature points AP from the generated three-dimensional pose data (step S104). Then, the information processing device 400 associates the extracted analytical feature point AP data with data indicating the wearer's posture and stores it in the storage device 420 (step S105). In step S105, the information processing device 400 may also associate the analytical feature point AP data with data indicating the wearer's posture and data on the lens performance of the eyeglasses worn by the wearer and store it in the storage device 420. 【0097】 Next, the flow of the load calculation process according to this embodiment (hereinafter referred to as the "load calculation process") will be described. Figure 28 is a flowchart of the load calculation process according to this embodiment. The information processing device 400 performs a specific point extraction process in the reference posture (step S201). The information processing device 400 performs a specific point extraction process in the specific posture (step S202). The information processing device 400 aligns the analysis feature points (reference points) in the reference posture with the analysis feature points (specific points) in the specific posture (step S203). 【0098】 The information processing device 400 calculates the amount of burden based on the change in the adjustment parameters obtained by the alignment (step S204). The information processing device 400 stores the amount of burden in the storage device 420 in association with the wearer's specific posture (step S205). In step S205, the information processing device 400 may further store data on the lens performance of the eyeglasses worn by the wearer in the storage device 420 in association with the amount of burden calculated in step S204. The storage device 420 may store gaze data and the amount of burden for each lens performance. 【0099】Next, the flow of the gaze data calculation process according to this embodiment (hereinafter referred to as "gaze data calculation process") will be described. Figure 29 is a flowchart of the gaze data calculation process according to this embodiment. The information processing device 400 sets the starting position of the wearer's gaze in a certain posture (step S301). The information processing device 400 sets the ending position of the wearer's gaze in a certain posture (step S302). The information processing device 400 calculates gaze data from the starting position to the ending position of the gaze (step S303). Then, the information processing device 400 stores the calculated gaze data in the storage device 420 in association with the wearer's posture (step S304). In step S305, the information processing device 400 may further store data on the lens performance and burden of the eyeglasses worn by the wearer in association with the gaze data. 【0100】 Next, the flow of the data estimation process for the third specific posture according to this embodiment (hereinafter referred to as "data estimation process") will be described. Figure 30 is a flowchart of the data estimation process according to this embodiment. The information processing device 400 performs a specific point extraction process in the first specific posture (step S401). The information processing device 400 performs a specific point extraction process in the second specific posture (step S402). 【0101】 The information processing device 400 estimates the lens performance necessary to make the posture in which the wearer is looking at an object the third specific posture, based on the analysis feature points in the first specific posture and the analysis feature points in the second specific posture (step S403). The information processing device 400 stores the estimated lens performance in the storage device 420, associating it with data indicating the third specific posture (step S404). 【0102】 The measurement unit 200 may measure posture data from the left and right directions in addition to posture data from the front of the person wearing glasses. 【0103】The execution order of operations, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before," "prior to," etc. Furthermore, it should be noted that the execution order of each process can be any order, unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc. for convenience, this does not mean that it is essential to perform the operations in that order. Also, to the extent permitted by law, the disclosures of all documents cited in Japanese Patent Application No. 2024-217520 and the embodiments, etc., shall be incorporated and constitute part of the description in this specification. 【0104】 100... Wearer burden evaluation system, 200... Measurement unit, 300... Display device, 400... Information processing unit, 430... Processor 430, 500... Data processing unit, 510... Extraction unit, 520... Burden amount calculation unit, 530... Setting unit, 540... Eye gaze parameter calculation unit, 550... Estimation unit, 560... Output unit

Claims

1. A wearer burden evaluation system for reducing the postural burden on eyeglass wearers, comprising: a processing unit that calculates the amount of burden on the wearer due to a change in posture from the standard posture to the specific posture, based on standard posture data indicating the wearer's standard posture and specific posture data indicating the wearer's specific posture; and an output unit that outputs the amount of burden.

2. The wearer burden evaluation system according to claim 1, wherein the reference posture data and the specific posture data are two-dimensional or three-dimensional data including the eyeglass frame and the wearer's body, respectively.

3. A wearer burden evaluation system according to claim 2, wherein one or more characteristic points are pre-set on the eyeglass frame and the wearer's body, and the processing unit comprises: an extraction unit that extracts a reference point which is the position of the characteristic point in the reference posture from the reference posture data, and a specific point which is the position of the characteristic point in the specific posture from the specific posture data; and a burden calculation unit that calculates the amount of change between the reference point and the specific point as the burden amount.

4. The wearer burden evaluation system according to claim 3, wherein the specific posture is the posture in which the wearer is looking at a specific object.

5. The wearer burden evaluation system according to claim 4, wherein the processing unit comprises: a setting unit that sets a specific position on the lens of the eyeglasses as the starting position of the wearer's gaze and the center of the object or the position of the content displayed on the object as the ending position of the wearer's gaze; and a gaze parameter calculation unit that calculates gaze data which is the distance and / or a vector from the starting position to the ending position, and the output unit is capable of outputting the gaze data.

6. The wearer burden evaluation system according to claim 5, comprising a storage unit for storing the gaze data and the burden amount for each lens performance, wherein the lens performance includes both or either astigmatism and add power.

7. The wearer burden evaluation system according to claim 6, comprising a measurement unit for measuring the reference posture data and the specific posture data, wherein the measurement unit transmits the measured data to the processing unit.

8. The wearer burden evaluation system according to claim 7, wherein the processing unit identifies the position of the wearer's pupil through the lens based on the specific posture data.

9. The wearer burden evaluation system according to claim 8, wherein the storage unit stores a first data set including a first specific posture in which the wearer is wearing glasses in which the lens performance is a first specific posture, and the specific point and pupil position calculated based on the specific posture data of the first specific posture measured by the measurement unit, and data indicating the first performance; and a second data set including a second specific posture in which the wearer is wearing glasses in which the lens performance is a second specific posture different from the first performance, and the specific point and pupil position calculated based on the specific posture data of the second specific posture measured by the measurement unit, and data indicating the second performance; and the processing unit estimates the lens performance necessary to make the wearer's posture when looking at the object a third specific posture different from both the first and second specific postures, using at least the first data set and the second data set.

10. A method for evaluating wearer burden to reduce the postural burden on wearers of eyeglasses, comprising: calculating the amount of burden on the wearer due to a change in posture from the standard posture to the specific posture, based on standard posture data indicating the wearer's standard posture and specific posture data indicating the wearer's specific posture; and outputting the amount of burden.