Method and apparatus for detecting optical device mis-adaptation

Abstract

An apparatus for detecting maladaptation of an optical device to a wearer, the apparatus comprising a processing circuitry system configured to: - receive and store head data of a wearer's head while wearing and using the optical device over time; - process the head data based on head data patterns associated with known maladaptation of the optical device to the wearer; and - detect maladaptation of the optical device to the wearer by matching the received and stored head data with the head data patterns associated with maladaptation.

Description

Technical Field

[0001] This disclosure relates to apparatus and related methods for detecting maladaptive ophthalmic lenses provided to wearers.

[0002] Furthermore, this disclosure relates to a computer program product comprising one or more stored instruction sequences accessible to a processor. Background Technology

[0003] It is well known that for some wearers, the provided ophthalmic lenses may be maladaptive. This maladaptation may occur over time or immediately after wearing the lenses. For example, adapting to progressive lenses often requires the wearer to adjust their head-eye coordination to correctly use the lens's visual field. One reason for maladaptation is that some wearers do not correctly use the progressive lens's visual field when wearing them.

[0004] Each wearer will adopt a specific body posture to perform a specific task, such as reading. If we consider a seated wearer reading documents on a table in front of them, different wearers will tilt their heads differently to read the documents.

[0005] Eye trackers and motion capture are used in the lab to assess wearers' head-eye coordination. Smart frames exist that can measure head tilts made by wearers at any time of day, for example, when reading or performing any other task.

[0006] Wearers must go to an optics lab and perform tasks in a specific environment to determine if their ophthalmic lenses are ill-fitting. For this reason, ill-fitting lenses may be detected too late or not at all, leading to wearer discomfort or causing them to abandon their ophthalmic devices.

[0007] Therefore, it is necessary to detect the wearer's maladaptation to their ophthalmic lenses in their daily life environment, taking into account the wearer's body posture.

[0008] It is also necessary to address wearers who are not suited to their ophthalmic lenses. Summary of the Invention

[0009] Therefore, this disclosure proposes a device for detecting poor adaptation of an optical device to a wearer, the device including a processing circuit system configured to:

[0010] - Receive and store head data about the wearer's head over time while wearing and using the optical device.

[0011] - Head data is processed based on head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device.

[0012] -Detecting maladaptation of optical devices to wearers by matching received and stored head data with head data patterns associated with maladaptation.

[0013] Advantageously, the device can self-detect optical misfits based on head data. This self-detection of optical misfits does not require knowledge from eye care professionals.

[0014] Furthermore, the device can detect wearer maladaptation based on head data parameters such as head movement and head posture. An example of head movement is tilting the head down. If the recorded head data corresponds to an anomalous head data pattern associated with known maladaptation, then the device detects optical maladaptation.

[0015] According to further embodiments that can be considered individually or in combination:

[0016] - Head data includes at least one of the following: head posture and movement data, eye gaze direction, eyelid opening, pupil size, EEG data, and facial expression; and / or

[0017] - When processing head data, take into account indications of wearer activity to match received and stored head data with head data patterns associated with maladaptation; and / or

[0018] - The optical device has ophthalmic functions, and the processing circuitry is further configured to receive the wearer's ophthalmic prescription and take the ophthalmic prescription into account when comparing the received and stored head data with a head data pattern; and / or

[0019] - The processing circuitry is further configured to alert the wearer or eye care professional to detected optical device maladaptation; and / or

[0020] - The cause of maladaptation is selected from a list consisting of the optical function of the optical device, the installation parameters of the optical device, the wearer's use of the optical device, the wearer's ophthalmological prescription, and the integrity of the optical device; and / or

[0021] - The optical device has progressive additional focal power, and the head data is related to the distribution of the wearer's head tilt angle when using the optical device.

[0022] This disclosure also relates to a method performed by a processing circuitry system of a device, the method comprising:

[0023] - Receive and store head data about the wearer's head over time while wearing and using the optical device.

[0024] - Head data is processed based on head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device.

[0025] -Detecting maladaptation of optical devices to wearers by matching received and stored head data with head data patterns associated with maladaptation.

[0026] Advantageously, by matching received and stored head data with head data patterns associated with maladaptation to detect maladaptation of the optical device in the wearer, the cause of the maladaptation can be determined. Based on the cause of the maladaptation, specialized solutions can be provided to the wearer, such as specific training to improve adaptation to the optical device, or by offering a different optical design.

[0027] This method can automatically detect maladaptation of the optical device in the wearer. Wearers do not need to go to an optics lab or perform specific tasks to determine if the optical device is maladaptive. Based on head data and known patterns of maladaptive head data, the device itself can detect maladaptation.

[0028] According to further embodiments that can be considered individually or in combination:

[0029] - Head data includes at least one of the following: head posture and movement data, eye gaze direction, eyelid opening, pupil size, EEG data, and facial expression; and / or

[0030] - When processing head data, take into account indications of wearer activity to match received and stored head data with head data patterns associated with maladaptation; and / or

[0031] - The optical device has ophthalmic functionality, and the method further includes receiving an ophthalmic prescription from the wearer, and taking the ophthalmic prescription into account when comparing the received and stored head data with a head data pattern; and / or

[0032] -The method further includes alerting the wearer or eye care professional to detected optical device maladaptation; and / or

[0033] - The method further includes determining the cause of optical device misfitting by comparing the received and stored head data with head data patterns; and / or

[0034] - The cause of maladaptation is selected from a list consisting of the optical function of the optical device, the installation parameters of the optical device, the wearer's use of the optical device, and the wearer's ophthalmological prescription; and / or

[0035] - Wherein, the optical device has progressive additional focal power, and the head data is related to the wearer's head tilt angle distribution when using the optical device; and / or

[0036] - Detecting maladaptive behavior includes determining the number of modes in the distribution of head-down angles.

[0037] This disclosure also relates to a method for determining a head pattern, the method comprising:

[0038] - Receive and store wearer data about the wearer of the optical device.

[0039] - Receive and store optical device data about the optical device associated with each wearer.

[0040] - Receive and store head data over time about each wearer's head while wearing and using the associated optical device.

[0041] - Receive and store adaptive data regarding the adaptability of the optical device associated with each wearer.

[0042] - Using a machine learning model to find the correlation between head data (and other optional data) and maladaptation, a large amount of received and stored data from wearers is processed to determine at least one head pattern associated with maladaptation of the optical device, including:

[0043] o Obtain head data, which can consist of several time series of frame measurements.

[0044] Calculate the descriptive statistics for each time series.

[0045] o groups the descriptive statistics into data vectors, thereby summarizing the head data.

[0046] o The data on the fit of the optical device associated with each wearer are grouped into a single number from 1 (incompatible) to 10 (fully compatible) to indicate the degree of fit.

[0047] The machine learning model is launched, where the input data is a data vector for each wearer, and the data to be predicted is a number representing the degree of adaptation.

[0048] o Use machine learning models to define head data patterns associated with maladaptive optics.

[0049] o

[0050] According to further embodiments that can be considered individually or in combination:

[0051] - Head data includes at least one of head posture data, eye gaze direction, eyelid opening, and pupil size; and / or

[0052] - Wearer data includes the wearer's ophthalmological prescription, and the optical device has ophthalmic functionality; and / or

[0053] -Among them, optical device data includes data on the optical function of the optical device and / or the installation parameters of the optical device and / or the wearer's use of the optical device and / or the wearer's ophthalmological prescription.

[0054] This disclosure also relates to a non-transitory computer-readable storage medium containing computer-executable instructions, wherein, when executed by a computer, these instructions cause the computer to perform a method performed by a processing circuitry system of a device according to this disclosure.

[0055] This disclosure also relates to a non-transitory computer-readable storage medium containing computer-executable instructions, wherein, when executed by a computer, these instructions cause the computer to perform a method for determining a header pattern. Attached Figure Description

[0056] Embodiments of this disclosure will now be described by way of example only and with reference to the following accompanying drawings, in which:

[0057] - Figure 1 A flowchart illustrates the steps performed by the processing circuitry of a device used to detect poor adaptation of an optical device to a wearer;

[0058] - Figure 2a and Figure 2b The optical system of the eye and ophthalmic lenses is illustrated schematically.

[0059] - Figure 3 A flowchart illustrating the steps of a method for detecting poor adaptation of an optical device to a wearer;

[0060] - Figure 4 The histogram shows the wearer's head-down angle during the weekdays.

[0061] - Figure 5 The histogram shows the head-down angle of the wearer during Saturday and Sunday.

[0062] - Figure 6 The histogram of the downward angle of a single peak is shown;

[0063] - Figure 7 The histogram of the bimodal peaks' tilt angles is shown; and

[0064] - Figure 8 A flowchart illustrating the steps used to determine the head pattern is shown.

[0065] The elements in the accompanying drawings are shown for simplicity and clarity and are not necessarily drawn to scale. For example, the dimensions of some elements in the drawings may be enlarged relative to other elements to aid in understanding the embodiments of this disclosure. Detailed Implementation

[0066] This disclosure relates to a device for detecting poor adaptation of an optical device to a wearer.

[0067] A first aspect of this disclosure relates to a device for detecting poor adaptation of an optical device to a wearer, the device comprising a processing circuit system configured to:

[0068] - Receive and store head data about the wearer's head over time while wearing and using the optical device.

[0069] - Head data is processed based on head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device.

[0070] -Detecting maladaptation of optical devices to wearers by matching received and stored head data with head data patterns associated with maladaptation.

[0071] Figure 1 It is a flowchart showing the different steps implemented by the processing circuitry system of the device.

[0072] The device including a processing circuitry system performs the receiving step S10, wherein the processing circuitry system initiates the receiving of wearer head data measured by at least one head data sensor. The processing circuitry system stores the measured head data on a data storage medium.

[0073] Furthermore, the device including the processing circuitry system performs processing step S12, wherein the processing circuitry system begins to compare the measured wearer head data with a predefined head data pattern associated with the cause of poor adaptation of the optical device to the wearer.

[0074] Finally, the device including the processing circuitry system performs detection step S14, wherein the processing circuitry system begins to detect maladaptation of the optical device to the wearer. This detection is performed by matching received and stored head data with head data patterns associated with maladaptation.

[0075] In the context of this disclosure, the term "maladaptation" refers to the poor adaptation of an optical device to a wearer. This maladaptation may result from the fact that the ophthalmic lens is designed without taking into account the wearer's body behavior and posture. An example of body behavior and posture is head positioning when the wearer performs a given activity or task.

[0076] According to this disclosure, the term "optical device" can refer to optical lenses, and even more particularly to ophthalmic lenses. An optical device can be a single-vision lens, a bifocal lens, a trifocal lens, a progressive lens, a filter lens, or any other type of optical device that enables a wearer to improve their vision.

[0077] The term "head data" refers to any data that can be measured about a wearer's head over time. For example, head data can refer to the position or orientation of the wearer's head relative to the torso.

[0078] In this way, head data can involve, for example, head tilt or head rotation along a horizontal axis. Head data regarding head tilt can be stored as an angular parameter. This angular parameter can correspond to a head-down angle.

[0079] Head data can be obtained using smart frames. A smart frame corresponds to a frame that includes head data sensors that detect movement of the wearer's head. When the head data under consideration corresponds to head displacement, the smart frame can include, for example, a head data sensor such as an IMU (Inertial Measurement Unit). In this example, the head data sensor, such as an IMU, will detect information about the wearer's head displacement (e.g., tilting or turning of the wearer's head) and transmit that information to the device.

[0080] The device according to this disclosure is configured to receive and store any displacement of the wearer's head when the wearer wears the optical device.

[0081] The device stores wearer head data over time. Once the wearer's head data is stored on the device, it is compared with previously acquired head data patterns associated with optical device maladaptation. Based on this comparison, the device can detect the similarity between the measured wearer head data and the head data patterns associated with optical device maladaptation. If the similarity exceeds a given similarity threshold, the device determines that the optical device is not adapted to the wearer.

[0082] Similarity can be determined using histograms. For example, a histogram is calculated based on wearer head data over a period of time, describing the frequency of each data value. This histogram is then compared to histograms of previously acquired data. The most similar histogram is determined to be the one whose overall shape most closely matches the calculated histogram.

[0083] Similarity can be further determined using machine learning algorithms. For example, a machine learning model is built based on previously acquired data, which determines the wearer's maladaptation to the optical device based on head data. The acquired data is used to train the model. For example, the model could be a decision tree, random forest, state vector machine, neural network, or deep learning algorithm. The wearer's data is then fed into the machine learning model to determine their maladaptation or similarity.

[0084] In cases of maladaptive progressive lenses, the wearer can compensate for the inability to see through specific areas of the progressive lens by moving their head. These specific areas of the optics may, for example, correspond to the near vision zone. The wearer's head data corresponding to the unnatural head posture used to compensate for the inability to see through the specific area is then compared with head data patterns indicating maladaptive optics. Based on this comparison and further similarity to head data patterns indicating maladaptive optics, the device can determine that the optics are not adapted to the wearer.

[0085] Misfit adaptation to progressive lenses can also be determined based on the wearer finding a head posture and the smoothness of looking through the progressive lenses (which provide good vision at any distance). Misfit wearers tend to adopt the same typical and habitual posture.

[0086] Head data can include head posture and movement data, eye gaze direction, eyelid opening, pupil size, EEG (electroencephalogram) data, and various parameters in facial expressions.

[0087] The term "head posture" refers to the head positioning of a specific wearer relative to the torso or a reference position when performing a specific task in daily life.

[0088] The term "head motion data" refers to data measured about head displacement.

[0089] The term "eye gaze direction" is defined by two angles measured about a direct orthogonal basis centered on the rotation center of the right or left eye.

[0090] Figure 2a and Figure 2b This is a schematic diagram of the optical system of the eye and lenses, therefore illustrating the definitions used in this specification. More precisely, Figure 2a The stereo diagram representing this system shows the parameters α and β used to define the gaze direction. Figure 2b It is a view in a vertical plane that is parallel to the front-back axis of the wearer's head and passes through the center of eye rotation, with parameter β equal to 0.

[0091] Mark the center of eye rotation as Q'. Figure 2b The axis Q'F', shown as a dashed line, is a horizontal axis passing through the center of eye rotation and extending to the front of the wearer; that is, it corresponds to the axis Q'F' of the dominant viewing angle. This axis cuts through the front surface of the optics (which can be, for example, a lens) at a point known as the fitting cross, which exists on the lens and allows the optician to position the lens in the frame. The point where the rear surface of the lens intersects the axis Q'F' is point O. If O is located on the rear surface, it can be the fitting cross. The apex ball, with a center Q' and a radius q', is tangent to the rear surface of the lens at a point on the horizontal axis. As an example, a radius q' of 25.5 mm corresponds to a commonly used value and provides satisfactory results when wearing the lens.

[0092] Given the gaze direction (by) Figure 2a The solid line in the diagram represents the position of the eye as it rotates around Q' and the point J on the top of the ball; angle β is the angle formed between the axis Q'F' and the projection of the line Q'J onto the horizontal plane including the axis Q'F'; this angle appears Figure 2b On the schematic diagram. Angle α is the angle formed between the axis Q'J and the projection of the line Q'J onto the horizontal plane including the axis Q'F'; this angle appears Figure 2b The diagram illustrates this. Therefore, a given gaze angle corresponds to point J on the top of the sphere or to a pair (α, β). The larger the value of the gaze drop angle in the positive direction, the greater the gaze drop; conversely, the larger the value in the negative direction, the greater the gaze rise.

[0093] The term "eyelid opening" refers to the space between the lower and upper eyelids when the upper eyelid retracts. Wearers can have different eyelid openings. Considering that the provided optical devices may not be suitable for wearers with small eyelid openings, it is necessary to provide wearers with appropriate devices.

[0094] In another aspect of this disclosure, the device may take into account other physical behaviors or postures of the wearer in daily life, in addition to their head movements and postures.

[0095] The optical device may have ophthalmic functions, and the processing circuitry is further configured to receive an ophthalmic prescription from the wearer and to take the ophthalmic prescription into account when comparing the received and stored head data with a head data pattern.

[0096] The term "processing circuit system" can refer to any electronic control unit, such as a microcontroller.

[0097] The term "ophthalmic prescription" corresponds to a prescription given by an eye care professional to an optical device so as to correct the wearer's impairment whenever the device is worn. Ophthalmic function is the corrective function performed by the optical device.

[0098] Based on the ophthalmic function of the optical device and the head data monitored, it is possible that the optical device is not used correctly by the wearer, or that the optical device is not adapted to the wearer when performing specific activities (such as reading).

[0099] The processing circuitry is further configured to alert the wearer or eye care professional to detected optical device misfit.

[0100] Optical devices can be equipped with warning mechanisms. Warnings can take various forms, such as mechanical vibrations or sounds. Warning signals can notify the wearer of signs that the optical device is not suited to their needs.

[0101] Therefore, when a wearer tries on a new pair of ophthalmic lenses, the warning system can quickly notify the wearer and / or eye care professionals that the optics are not suitable for the wearer.

[0102] Another embodiment of this disclosure will inform the wearer and / or eye care professionals that the optical device provided to the wearer does not correspond to the wearer's current visual impairment and that the wearer needs a new optical device with new ophthalmic functions to continue correcting his / her visual impairment.

[0103] The processing circuitry can be further configured to determine the cause of optical device maladaptation by comparing the received head data with different groups of stored head data (which have head distribution patterns associated with the cause of maladaptation).

[0104] The optical device may include a data storage medium. This medium contains stored head data patterns from different groups associated with the causes of maladaptation. A processing circuitry system compares the monitored wearer head data, which may also be stored on the data storage medium.

[0105] During the comparison, the processing circuitry controls whether there is a likelihood above a given threshold for comparing the head data pattern monitored by the wearer with the head data pattern associated with the cause of maladaptation. It should be understood that the likelihood may correspond to the percentage of similarity between the measured head data and the stored head data pattern associated with the cause of maladaptation. If it is above the threshold, the processing circuitry determines that the cause of maladaptation corresponds to a similar stored head data pattern associated with the cause of maladaptation.

[0106] The causes of maladaptation can also be determined using a machine learning model that was previously built using previously acquired data.

[0107] The causes of maladaptation can be selected from a list consisting of the optical function of the optical device, the installation parameters of the optical device, the wearer's use of the optical device, the wearer's ophthalmological prescription, and the integrity of the optical device.

[0108] In the sense of this disclosure, the optical function corresponds to the function of providing the effect of the optical lens on the light passing through the optical lens for each viewing direction.

[0109] Optical functions may include refractive function, light absorption, spectral function, diffraction function, scattering characteristics, polarization capability, contrast enhancement or reduction, or any other related parameters.

[0110] Refractive function corresponds to the optical lens power (mean power, astigmatism, etc.) that varies with the direction of gaze.

[0111] The term "optical design" is a widely used term known to ophthalmologists to specify a set of parameters that allow for the definition of the refractive function of an ophthalmic lens; each ophthalmic lens designer has their own design, especially for progressive lenses. For example, a progressive lens "design" is the result of optimizing the progressive surface to restore the ability of presbyopia to see clearly at all distances, while also optimally respecting all physiological visual functions (such as foveal visual acuity, exterofoveal visual acuity, and binocular visual acuity) and minimizing unwanted astigmatism. For example, progressive lens designs include:

[0112] - The focal power profile used by the lens wearer along the primary direction of gaze (meridian) in daily activities.

[0113] - The distribution of power (average power, astigmatism, etc.) on the side of the lens, that is, the distribution away from the main direction of gaze.

[0114] These optical properties are part of the “design” defined and calculated by the ophthalmic lens designer and are set with progressive lenses.

[0115] The "optical design" of progressive lenses must be rigorously tested before they can be commercialized.

[0116] The term "installation parameters" refers to the position of the two optical lenses within the frame.

[0117] When a wearer purchases a new optical device, the mechanical aspects of the eyeglass frame may not fit the wearer's head properly.

[0118] Another embodiment of this disclosure relates to the mechanical aspects of an eyeglass frame. The shape of the wearer's head may change over time. Changes in the shape of the wearer's head or skin characteristics may induce displacement of the optical elements. The mechanical structure of the eyeglass frame may also change over time, for example due to wear, improper use or storage, resulting in changes in the positioning of the eyeglass frame on the wearer's face.

[0119] The wearer's use of the optical device corresponds to the manner in which the wearer can use the optical device. In the case of multifocal or progressive lenses, the wearer may use a single portion of the area of ​​the optical lens. For example, if the wearer is unable to lower his / her gaze direction, then even in near vision situations, the wearer may always see through the distance vision portion of the progressive lens. The wearer will then compensate for this insufficient lowering of the gaze direction by further lowering their head. Based on the head data monitored during the use of the optical device, the processing circuitry system can identify abnormal head data during the use of the optical device.

[0120] Another reason for maladaptation may be the integrity of the optical device, such as distortion of the temples relative to the bridge between the lenses. Any deformation of the optical device can lead to changes in the optical function provided by the device, and that function may no longer be adapted to the wearer's visual impairment.

[0121] Optical devices have progressive additional power, and head data correlates with the wearer's head-down angle distribution when using the optical device. As previously mentioned, wearers with insufficient eye-fixation lowering compensate for this insufficiency by further lowering their heads when using optical elements (e.g., progressive lenses) for near vision. Abnormal head data regarding further head lowering by the wearer is data that can determine poor adaptation of the optical device to the wearer.

[0122] Devices for detecting maladaptive conditions may include head data sensors or multiple head data sensors, processing circuitry, and data storage media. The device may be removably attached to a part of an optical device, such as the temple of a pair of glasses.

[0123] In alternative embodiments, the device for detecting maladaptive symptoms can be integrated within the eyeglass frame. Computation can also be performed outside the optical device: in a smartphone application, computer, or cloud server to which head data is transmitted.

[0124] A second aspect of this disclosure relates to a method performed by a processing circuitry system of a device, the method comprising:

[0125] - Receive and store head data about the wearer's head over time while wearing and using the optical device.

[0126] - Head data is processed based on head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device.

[0127] -Detecting maladaptation of optical devices to wearers by matching received and stored head data with head data patterns associated with maladaptation.

[0128] Figure 3 It is a flowchart showing the different steps corresponding to the method executed by the processing circuit.

[0129] The method includes a receiving step S20, wherein the processing circuitry system initiates the reception of wearer head data measured by at least one head data sensor. The processing circuitry system stores the measured head data on a data storage medium.

[0130] Furthermore, the method includes a processing step S22, wherein the processing circuitry system begins to compare the measured wearer head data with a predefined head data pattern associated with the cause of maladaptation of the optical device to the wearer.

[0131] Finally, the method includes a detection step S24, in which the processing circuitry system begins to detect maladaptation of the optical device to the wearer. This detection is performed by matching received and stored head data with head data patterns associated with maladaptation.

[0132] The term "match" should be understood as the likelihood above a given threshold between monitored head data from the wearer and head data patterns associated with causes of maladaptation. It should be understood that likelihood can correspond to the percentage of similarity between measured head data and stored head data patterns associated with causes of maladaptation. This match can be determined using machine learning methods.

[0133] The threshold can correspond to the similarity between a measured head data pattern and a predefined head data pattern, for example, exceeding a given percentage. It is preferably determined using a machine learning method that automatically determines the most probable state (e.g., adapted / maladapted) based on the input head data.

[0134] The method may include an additional warning step during which an eye care professional and / or the wearer is alerted to a misfit of the optical device in their daily activities. The warning may also inform the eye care professional that the optical device is not suitable for the wearer. The eye care professional may then examine the prescribed pupillary distance, the prescription, the frame type, the lens design, or any other parameters related to the optical device.

[0135] This warning can be used to check if the wearer's posture is becoming increasingly closer to the adaptation posture over time. This is why it is necessary to monitor the wearer's use of the optical device over time and in his / her daily life environment, rather than in a fixed environment (which may not correspond to his / her daily life environment).

[0136] This method can take into account the number of modes in the distribution of head-down angles when the wearer is using the optical device.

[0137] A head data sensor records the frame orientation or tilt angle over time. The data acquisition time can be predefined. The recorded frame orientation or tilt angle is then transmitted for processing.

[0138] For several reasons, some of the measured data can be removed before processing:

[0139] - The measured data was considered outlier.

[0140] - The measured data was acquired outside of a specific time period (e.g., not corresponding to working hours).

[0141] - Data not corresponding to the completion of a specific activity, and

[0142] - Data measured at times when the optical device is not worn correctly by the wearer.

[0143] Other parameters can be considered to remove the measured data.

[0144] Examples can be considered of wearers considering optical devices during the time they spend working. Figure 4 Histogram of head-down angles corresponding to Monday through Friday. Figure 5 Histograms showing head-down angles corresponding to Saturdays and Sundays. In the example below, the wearer is assumed to work from 9 AM to 6 PM Monday through Friday and not work on weekends. In this way, the angles corresponding to... Figure 5 The measured data, and for Figure 4 The time period taken into account corresponds to the data on head-down angles measured between 9 a.m. and 6 p.m.

[0145] The mode corresponds to the peak value that is more significant when performing a specific activity (such as working in front of a computer).

[0146] If the distribution of head-down angles follows a curve similar to a Gaussian distribution, then the histogram is unimodal. When the histogram is unimodal, the optics are well-suited to the wearer. Figure 6 A single-peak histogram corresponding to the head-down angle of the wearer when wearing the optical device has been disclosed.

[0147] The distribution of head-down angles can include more than one peak. Therefore, this distribution is considered a multimodal histogram. Figure 7 A bimodal histogram corresponding to the head-down angle of the wearer when wearing the optical device has been disclosed. Figure 7 As shown, the specific range of the head-down angle is not one, but two.

[0148] A bimodal histogram indicates that the wearer is not adapting to the optical device. Wearers with multifocal or progressive lenses may attempt to compensate for the inability to lower their gaze direction by further reducing their head angle.

[0149] In this way, one of the two peaks shown can correspond to abnormal head data, such as a further tilting angle as mentioned in the example above. In particular, wearers equipped with progressive lenses and having a bimodal histogram have optics that are not suited to them.

[0150] To use progressive lenses correctly, wearers must learn to lower their eyes further than naturally when performing intermediate and near vision tasks. Simultaneously, they will need to lower their heads less when performing these tasks compared to a natural head tilt.

[0151] Wearers with a bimodal histogram have not yet adopted the visual strategies required by progressive lenses. These progressive lens wearers tend to view near and intermediate objects through the distance vision zone, without experiencing the appropriate additional focal power required for the task. This leads to poor adaptation and discomfort for the wearer.

[0152] Following optional warning signals, progressive lens wearers who are not suited to progressive lenses may be advised to undergo specific vision training for the use of progressive lenses.

[0153] A third aspect of this disclosure relates to a method for determining a head pattern, the method comprising:

[0154] - Receive and store wearer data about the wearer of the optical device.

[0155] - Receive and store optical device data about the optical device associated with each wearer.

[0156] - Receive and store head data over time about each wearer's head while wearing and using the associated optical device.

[0157] - Receive and store adaptive data regarding the adaptability of the optical device associated with each wearer.

[0158] - Process data received and stored from a large number of wearers to determine at least one head pattern associated with maladaptation of the optical device.

[0159] Figure 8 A flowchart illustrating the different steps involved in determining the head pattern is provided.

[0160] The method includes a first receiving step S30, wherein wearer data about the wearer of the optical device is received and stored.

[0161] The method includes a second receiving step S32, wherein optical device data about the optical device associated with each wearer is received and stored.

[0162] The method includes a third receiving step S34, wherein head data about each wearer’s head when wearing and using the associated optical device is received and stored over time.

[0163] Furthermore, the method includes a fourth receiving step S36, wherein adaptive data regarding the adaptability of the optical device associated with each wearer is received and stored.

[0164] Finally, the method includes a processing step S38, wherein the received and stored data is processed to determine at least one head pattern associated with maladaptation of the optical device.

[0165] The head data according to the third aspect of this disclosure may include head posture data, eye gaze direction, eyelid opening, and pupil size.

[0166] Wearer data may include the wearer's ophthalmological prescription, and the optical device has ophthalmological functions.

[0167] Optical device data may include data on the optical function of the optical device and / or the installation parameters of the optical device and / or the wearer’s use of the optical device and / or the wearer’s ophthalmological prescriptions.

[0168] Another approach to identify at least one head pattern associated with maladaptation to the optical device is to correlate monitored head data with wearer feedback.

[0169] The wearer can provide feedback on the optical device they are wearing, which is equipped with devices to detect maladaptation to the device. Feedback can be provided by completing a questionnaire about the comfort of the optical device and / or the wearer's adaptation to it in daily activities. Other feedback sources are also possible, such as using a smartphone application. The wearer can use this application to notify the source of discomfort or maladaptation when using the optical device.

[0170] By determining posture data patterns related to the wearer's adaptation level to the optical device based on feedback provided by the wearer and monitored data (acquired by at least one head data sensor), new maladaptive posture patterns can be identified. Posture data patterns can be, for example, head data posture patterns.

[0171] According to an embodiment, the pose data pattern is determined using a machine learning model that is used to find the correlation between head data (and other optional data) and maladaptation.

[0172] Header data can consist of several time series of the following measurements:

[0173] - The pitch angle of the eyeglasses, measured by an IMU embedded in the eyeglass frame.

[0174] - The time derivative of the yaw angle of the eyeglasses, measured by an IMU embedded in the eyeglasses frame.

[0175] These two time series were obtained for a large number of n wearers (e.g., i = 1 to 10000).

[0176] For each time series, a set of descriptive statistics is calculated, including:

[0177] - The average value of the measured value over a period of time.

[0178] -The standard deviation of the measured value over a period of time.

[0179] - The range of the measured value over a period of time (maximum value - minimum value).

[0180] According to the embodiment, a histogram of the measured values ​​can be further constructed, from which the center value and width of the main peak can be derived.

[0181] The descriptive statistics are grouped into data vectors to summarize the head data. Each wearer has such a vector, denoted as x_i.

[0182] Data on the fit of the optical device associated with each wearer consists of a single number representing the degree of fit, ranging from 1 (incompatible) to 10 (fully compatible). Each wearer has a value. This value is denoted as v_i.

[0183] Launch a machine learning model where the input data is a data vector for each wearer, and the data to be predicted is a number representing the degree of fitness. The machine learning algorithm can be multiple linear regression, random forest, neural network, or any other relevant algorithm.

[0184] The model is trained and tested using datasets of {x_i} and {v_i} to associate a vector {x_i} with an adaptive value {y_i}:

[0185] Model M(x_i)→v_i

[0186] If the trained model gives good predictions, then it can be used to define head data patterns associated with maladaptive optical devices.

[0187] The threshold T is defined based on the degree of adaptation. A wearer can be considered maladapted in the following situations:

[0188] v_i≤T

[0189] If the model predicts a value below a threshold for this head data, then the head pattern x_non-adapt is associated with maladaptation.

[0190] M(x_non-adapt)=v_non-adapt≤T

[0191] This example does not use wearer data or optical device data. This data can be easily added to the data vector x_i.

[0192] Other data can be monitored and correlated with head data, such as activities performed by the wearer while head data is being monitored. In fact, when a wearer performs a given activity, head patterns may be associated with maladaptation, while when performing different activities, head patterns may not be associated with maladaptation or may be associated with different types of maladaptation. For example, when a wearer walks, postural patterns may be associated with maladaptation, but when the wearer is working at a desk, they may not be. Activities performed by the wearer can be determined based on the wearer's location (e.g., GPS location) or directly on input from the wearer.

[0193] The acquisition of new patterns of maladaptive posture data enables the improvement of devices used to detect maladaption over time. Wearer feedback can identify new causes of maladaption.

[0194] Wearers can be grouped into different groups based on different histograms and / or feedback, such as their ability to perform a given task based on the type of optical lenses they wear and / or their adaptability to using the optical device when performing a specific task (e.g., reading or working in front of a computer). The optical device can be a single-vision lens, a wide progressive lens, or a narrow progressive lens. In a preferred embodiment, the grouping is performed using machine learning methods, where a model is trained based on head data and other optional data to determine the segment to which the wearer belongs, which may be defined by the cause of maladaptation.

[0195] Because of the segmentation of wearers, maladaptation detection methods can predict maladaptation to progressive lenses.

[0196] The fourth aspect of this disclosure relates to an alternative method for detecting poor adaptation to optical devices.

[0197] The fifth aspect of this disclosure relates to a non-transitory computer-readable storage medium.

[0198] The non-transitory computer-readable storage medium may contain computer-executable instructions, wherein, when executed by a computer or processing circuitry system, these instructions cause the computer or processing circuitry system to perform methods for detecting maladaptation and / or methods for determining head patterns associated with maladaptation.

[0199] The method includes a first receiving step S40, in which data about the wearer (e.g., prescription, gender, and / or age) is received and stored in a database.

[0200] The method includes a second receiving step S42, in which data about the optical device is received and stored in a database. The data about the optical device may include optical lens design, AR coating, anti-fouling properties, and / or any other parameters about the optical device.

[0201] The method includes a third receiving step S44, wherein data measured by at least one head data sensor is stored in a database. The measured data may include head movement, posture and tilt angle, light intensity, distance to an object, and / or whether the wearer is wearing an optical device over time.

[0202] The method includes a fourth receiving step S46, in which feedback on maladaptation is received and stored in a database. The feedback may be provided by a smartphone application or an online survey.

[0203] The method may include a fifth and final receiving step S48, in which the cause of maladaptation is received and stored in a database.

[0204] Data for the database can be provided by actual wearers via smartphone apps or by eye care professionals through surveys and / or feedback.

[0205] The data in the database can also be provided based on simulated virtual wearers using virtual avatars with incorrect prescriptions or incorrectly fitted optical devices, and their head posture and / or head data are calculated for various visual tasks.

[0206] The database may include a combination of data related to both real and virtual wearers.

[0207] The method includes a first analysis step S50, in which data stored in a database is analyzed to determine head data patterns specific to a given maladaptive condition.

[0208] The method further includes a model creation step S52, wherein a model is created to predict potential maladaptation and causes of maladaptation based on a database. The model creation can be achieved through machine learning, such as using a neural network.

[0209] During the learning phase of machine learning, obtaining feedback from the wearer can improve convergence to underlying maladaptive behaviors and their causes. Reinforcement learning methods can be used to provide further improvements.

[0210] The method includes a second analysis step S54, wherein, for a new wearer equipped with an optical device (with a device for detecting maladaptive conditions according to the present disclosure), data provided by a head data sensor is analyzed.

[0211] The method includes a warning step S56, wherein if the data measured by the head data sensor corresponds to a maladaptive pattern, then the warning device of the device according to the present disclosure warns the wearer or an eye care professional.

[0212] If the header data pattern allows for the identification of the cause of maladaptation, then this information can be added to the database.

[0213] The method includes an optical device providing step S58, wherein, in the case that the tested optical device is not suitable for the wearer, a new optical device is proposed to the wearer, taking into account the reasons for the maladaptation and data stored in the database.

[0214] Alternatively, the method can suggest readjustments to the optics used in the test or to the vision training based on the cause of maladaptation.

[0215] The readjustment of the optical device can be related to a different design of the optical lens or frame.

[0216] This training can be specific to improve a wearer's adaptation to a particular optical device (such as progressive lenses).

[0217] This method can be executed by a processing circuit system, or it can be processed by a device other than the device according to this disclosure (e.g., in a cloud network).

[0218] The database may include supplementary data, such as data obtained through refraction. This supplementary data may include convergence deficits, adaptation lags, fixation differences, prolonged head posture, or fixation stability, provided by at least a subset of real and / or virtual wearers.

[0219] The head data sensor can be a different type of sensor than an IMU, such as an eye tracker that measures gaze direction. The eye tracker can be integrated into the frame of the optical device.

[0220] Head data sensors can measure pupil size or facial expressions, such as eyebrow position. These sensors can be integrated with a camera.

[0221] The head data sensor can measure EEG (electroencephalogram) data.

[0222] Head data sensors can be used to determine the duration of time the optical device is worn and not worn, as well as the type of task optionally performed by the wearer. This parameter allows for the detection of maladaptive conditions. Wearers may not wear the optical device frequently because, once worn, the shape of the device can induce discomfort. Shape issues may be due to the size of the frame at temple level or the shape and / or size of the nose pads. Based on this data, eye care professionals can be alerted to the need for optical readjustment.

[0223] This disclosure relates to head data. However, head data can be replaced by other types of data related to the wearer's body. This data may, for example, relate to the posture or movement of a part of the wearer's body (not the head).

[0224] Many other modifications and variations will be apparent to those skilled in the art when referring to the foregoing illustrative embodiments. These embodiments are given by way of example only and are not intended to limit the scope of this disclosure, which is defined only by the appended claims. In particular, this disclosure can be applied to active lenses, wherein the optical design of the active lens can be modified to improve wearer comfort. This disclosure can also be applied to active filters, wherein optical transmission functionality can be adapted with wearer comfort in mind.

[0225] In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude a plural. The mere fact that different features are described in mutually different dependent claims does not imply that combinations of these features cannot be used advantageously. No reference numerals in the claims should be construed as limiting the scope of this disclosure.

Claims

1. A device for detecting maladaptive adaptation of an optical device to a wearer, the device comprising a processing circuit system configured to: - Receive and store head data about the wearer's head while wearing and using the optical device over time. - The head data is processed by comparing it with head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device. - The maladaptation of the optical device to the wearer is detected by matching received and stored head data with head data patterns associated with maladaptation. in, The head data includes head posture and motion data, and the optical device refers to progressive lenses.

2. The device according to claim 1, wherein, The head data also includes at least one of the following: eye gaze direction, eyelid opening, pupil size, EEG data, and facial expression.

3. The device according to claim 1, wherein, The optical device has ophthalmic functions, and the processing circuitry is further configured to receive the wearer's ophthalmic prescription and take the ophthalmic prescription into account when comparing the received and stored head data with a head data pattern.

4. The device according to claim 1, wherein, The processing circuitry is further configured to determine the cause of maladaptation of the optical device by comparing the received head data with stored head data of different groups having head distribution patterns associated with causes of maladaptation.

5. The device according to claim 1, wherein, The optical device has progressive additional focal power, and the head data is related to the distribution of the wearer's head tilt angle when using the optical device.

6. The device according to any one of claims 1 to 5, wherein, The processing circuitry is further configured to determine header data patterns associated with maladaptation through the following steps: - Receive and store wearer data about the wearer of the optical device. - Receive and store optical device data about the optical device associated with each wearer. - Receive and store head data over time about each wearer's head while wearing and using the associated optical device. - Receive and store adaptation data regarding the adaptability of the optical device associated with each wearer. - A machine learning model is used to find the correlation between head data and maladaptation to process a large amount of data received and stored from a large number of wearers to identify at least one head data pattern associated with maladaptation of the optical device.

7. A method executed by a processing circuitry system of a device, the method comprising: - Receives and stores head data about the wearer's head over time while wearing and using the optical device. - The head data is processed by comparing it with head data patterns associated with known maladaptive behaviors of the optical device to the wearer of the optical device. - The maladaptation of the optical device to the wearer is detected by matching received and stored head data with head data patterns associated with maladaptation. The head data includes head posture and motion data, and the optical device refers to progressive lenses.

8. The method according to claim 7, wherein, The head data also includes at least one of the following: eye gaze direction, eyelid opening, pupil size, EEG data, and facial expression.

9. The method according to claim 7, wherein, The optical device has ophthalmic functions, and the method further includes receiving an ophthalmic prescription from the wearer and taking the ophthalmic prescription into account when comparing the received and stored head data with a head data pattern.

10. The method according to claim 7, wherein, The method further includes determining the cause of the optical device's poor adaptation by comparing the received and stored head data with a head data pattern.

11. The method according to claim 7, wherein, The optical device has progressive additional focal power, and the head data is related to the distribution of the wearer's head tilt angle when using the optical device.

12. The method according to claim 11, wherein, Detecting maladaptive behavior includes determining the number of modes in the head-down angle distribution.

13. The method according to any one of claims 7 to 12, wherein, The method also includes determining head data patterns associated with maladaptation through the following steps: - Receive and store wearer data about the wearer of the optical device. - Receive and store optical device data about the optical device associated with each wearer. - Receive and store head data over time about each wearer's head while wearing and using the associated optical device. - Receive and store adaptation data regarding the adaptability of the optical device associated with each wearer. - A machine learning model is used to find the correlation between head data and maladaptation to process a large amount of data received and stored from a large number of wearers to identify at least one head data pattern associated with maladaptation of the optical device.

14. A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium comprising computer-executable instructions, wherein, When executed by a computer, the instructions cause the computer to perform the method according to any one of claims 7 to 13.

Images