Method and device for predicting the evolution of visual acuity-related parameters over time - Patents.com

Abstract

The method for predicting the evolution of at least one vision-related parameter over time for at least one person includes obtaining (30) continuous values ​​for the person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the person, and predicting (36) by at least one processor the evolution of the person's vision-related parameter over time from the obtained continuous values ​​for the person by using a prediction model associated with a group of individuals, where predicting (36) includes associating at least some of the continuous values ​​for the person with the predicted evolution of the person's vision-related parameter over time, where associating includes jointly processing continuous values ​​associated with the same parameter of the first predetermined type, where the predicted evolution depends differently on each of the jointly processed values.

Description

[Technical field]

[0001] The present invention relates to a method and device for predicting the evolution over time of at least one vision-related parameter of at least one person. [Background technology]

[0002] While some factors that affect a person's vision, such as genetic factors, cannot be changed by the individual, some other factors, such as lifestyle, behavior, and / or environmental factors, can be changed by anyone. For example, the amount of time spent outdoors, the amount of time spent performing close-up tasks, or nutrition can affect vision, for example, by causing the onset, progression, or reduction of myopia.

[0003] Wearable devices are known and can, for example, correct a person's reading and / or writing posture and collect myopia-related parameters.

[0004] However, known devices are often standardized and therefore assume that they are the same for everyone, i.e. that all people have a similar risk of developing and progressing myopia, which is not the case in reality.

[0005] Additionally, for many existing devices, the predicted myopia progression profile is calculated once and is not subsequently updated.

[0006] Thus, if a person's lifestyle, behavior, and / or environment change after their predicted profile has been calculated, the unchanged predicted profile will be inconsistent and erroneous.

[0007] Therefore, when predicting the evolution of one or more vision-related parameters of a person over time, it is necessary to take into account changes in modifiable parameters that affect the person's vision. Summary of the Invention [Problem to be solved by the invention]

[0008] SUMMARY OF THE PRESENT EMBODIMENT An object of the present invention is to overcome the above-mentioned drawbacks of the prior art. [Means for solving the problem]

[0009] Therefore, the present invention relates to a method for predicting the evolution over time of at least one vision-related parameter of at least one person, comprising: obtaining successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; predicting, by the at least one processor, from the obtained successive values ​​for the at least one person, the evolution over time of at least one vision-related parameter of the at least one person by using a predictive model associated with the group of individuals, The predicting by using the predictive model comprises associating at least a portion of the continuous values ​​for the at least one person with a predicted evolution over time of the at least one vision-related parameter of the at least one person, the associating comprising jointly processing at least a portion of the continuous values ​​associated with the same one of the at least one parameter of a first predetermined type; It provides a method where the predicted progress depends differently on each of the jointly processed values.

[0010] Thus, the predictive model used in the prediction method is constructed by collecting data from a group of individuals, i.e. from a whole panel of individuals, and taking into account the possible modifications over time of the parameters measured for those individuals. By making the prediction dependent differently on each of these jointly processed values, i.e. taking into account both these successive values ​​themselves and the results of the joint processing of successive values ​​of the parameters, it is possible to obtain a very accurate and consistent dynamic prediction. That is, exchanging the inputs corresponding to those successive jointly processed values, for example by exchanging values ​​obtained at different dates and times, may affect the predicted evolution.

[0011] The enhanced predictive power potentially provided by the above method for predicted progression can be attributed in particular to the time-dependent personal visual sensitivity of the person under consideration, which is a specific expression of the individual chronotype.

[0012] In general, chronotype is a human attribute that reflects the time of day during which their bodily functions (hormonal levels, body temperature, cognitive performance, diet, and sleep) are active, changing, or reaching a certain level. It is considered an important predictor of sleep timing, sleep stability, sleep duration, sleep need, sleep quality, morning sleepiness, and adaptability to shift work.

[0013] The enhanced predictive capabilities may alternatively or additionally result from the implicit consideration of time-dependent environmental parameters that are not explicitly entered as inputs but whose continuous values ​​depend on the time at which they are obtained. These may include in particular the light spectral distribution, light ray direction, light radiance and / or light coherence and / or diffusion properties, whether associated with natural lighting, artificial lighting or both.

[0014] Furthermore, the fact that the prediction depends differently on each of the jointly processed values ​​makes it possible to identify and / or have better knowledge of parameters that affect the prediction without being explicitly entered. Chronobiology (in relation to recording of sleep cycles and their characteristics) and light beam orientation can be examples of such parameters.

[0015] In addition, the method allows for communicating current and predicted progress to a person in a wide variety of forms of interaction with the person.

[0016] The present invention also relates to a device for predicting the evolution over time of at least one vision-related parameter of at least one person, comprising: at least one input adapted to receive successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; at least one processor configured to predict, from the successive values ​​obtained for the at least one person, the evolution over time of at least one vision-related parameter of the at least one person by using a predictive model associated with a group of individuals, The predicting by using the predictive model comprises associating at least a portion of the continuous values ​​for the at least one person with a predicted evolution over time of the at least one vision-related parameter of the at least one person, the associating comprising jointly processing at least a portion of the continuous values ​​associated with the same one of the at least one parameter of a first predetermined type; It provides a device whose predicted progress depends differently on each of the jointly processed values.

[0017] The present invention further provides a computer program product for predicting the evolution over time of at least one vision-related parameter of at least one person, said computer program product being accessible to a processor and, when executed by the processor, causing the processor to: obtaining successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; one or more instruction sequences for predicting, from the successive values ​​obtained for the at least one person, the evolution over time of at least one vision-related parameter of the at least one person by using a predictive model associated with a group of individuals, The predicting by using the predictive model comprises associating at least a portion of the continuous values ​​for the at least one person with a predicted evolution over time of the at least one vision-related parameter of the at least one person, the associating comprising jointly processing at least a portion of the continuous values ​​associated with the same one of the at least one parameter of a first predetermined type; A computer program product is provided in which the predicted progress depends differently on each of the jointly processed values.

[0018] The present invention further relates to a non-transitory computer readable storage medium accessible to a processor and which, when executed by the processor, causes the processor to: obtaining successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; storing one or more sequences of instructions for predicting, from the successive values ​​obtained for the at least one person, the evolution over time of at least one vision-related parameter for the at least one person by using a predictive model associated with the group of individuals; The predicting by using the predictive model comprises associating at least a portion of the continuous values ​​for the at least one person with a predicted evolution over time of the at least one vision-related parameter of the at least one person, the associating comprising jointly processing at least a portion of the continuous values ​​associated with the same one of the at least one parameter of a first predetermined type; The predicted progression provides a non-transitory computer readable storage medium that is differently dependent on each of the jointly processed values.

[0019] The advantages of the prediction apparatus, the computer program product, and the computer-readable storage medium are similar to those of the prediction method, and therefore will not be repeated here.

[0020] The prediction device, the computer program and the computer readable storage medium are advantageously configured to carry out the prediction method in any of its modes of execution.

[0021] For a more detailed understanding of the description provided herein and the advantages thereof, reference should now be made to the following brief description taken in conjunction with the accompanying drawings and detailed description, in which like reference numerals represent like parts. [Brief description of the drawings]

[0022] [Figure 1] FIG. 2 is a flow diagram illustrating method steps for constructing a predictive model for use in a predictive method according to the invention in certain embodiments. [Diagram 2] 1 is a graph showing the myopia progression risk profile obtained by the prediction method according to the present invention in certain embodiments. [Diagram 3] FIG. 2 is a flow diagram illustrating steps of a prediction method according to the present invention in a particular embodiment. [Figure 4] 3 is the graph of FIG. 2 with added monitoring indicators. [Diagram 5] 1 is a set of two graphs showing examples of multiple risk profiles including predicted evolution over time obtained by implementing the prediction method according to the present invention in a particular embodiment. [Figure 6] 1 is a graph showing two myopia development risk profiles obtained by the prediction method according to the present invention in a specific embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] In the following description, the drawings are not necessarily drawn to scale, and certain features may be shown in a generalized or schematic form for clarity and conciseness or for informational purposes. In addition, while the making and using of various embodiments are discussed in detail below, it should be understood that as described herein, many inventive concepts are provided that may be embodied in a variety of contexts. The embodiments discussed herein are merely representative and do not limit the scope of the invention. It will also be apparent to those skilled in the art that all technical features defined in relation to a process can be substituted into an apparatus, either individually or in combination, and conversely, all technical functions related to an apparatus can be substituted into a process, either individually or in combination.

[0024] The terms "comprise" (and its grammatical variations such as "comprises" and "comprising"), "have" (and its grammatical variations such as "has" and "having"), "contain" (and its grammatical variations such as "contains" and "containing") and "include" (and its grammatical variations such as "includes" and "including") are open-ended linking verbs. They are used to specify the presence of a stated feature, integer, step or component or group thereof, but do not preclude the presence or addition of one or more other features, integers, steps or components or groups thereof. As a result, a method or step within a method that "comprises", "has", "contains" or "includes" one or more steps or elements is not limited to having only those one or more steps or elements, but those one or more steps or elements.

[0025] As shown in Figure 1, a method for constructing a predictive model for predicting the evolution over time of at least one vision-related parameter of at least one person, to be used in a predictive method according to the invention, comprises a step 10 of obtaining, for at least one member of a group of individuals, successive values ​​each corresponding to repeated measurements over time of at least one parameter of a first predetermined type.

[0026] As a non-limiting example, the vision-related parameter considered may be the myopia level of a person, expressed in diopters for the left and / or right eye. It may be visual aptitude or any other parameter related to any visual defect of the person, such as hyperopia, astigmatism, presbyopia, or any visual disease, such as eye diseases that may result in visual problems, including myopic macular degeneration, retinal detachment, and glaucoma. In addition to refractive error (expressed in diopters), eye biometric measurements such as axial length (mm), vitreous chamber depth (mm), choroidal thickness (expressed in μm), and corneal properties are other examples of vision-related parameters.

[0027] A group of individuals may include any number of individuals who may have no characteristics in common with each other or who may share one or more common characteristics, such as, by way of non-limiting examples, gender and / or date of birth and / or country of birth and / or past family history and / or ethnicity.

[0028] In any case, such fixed parameters of at least one member of the group of individuals may be input into the predictive model either in the preliminary step 8 of initialization, or at any later stage of the method. Input of such fixed parameters is optional. The fixed parameters may be available individually to members of the group of individuals, or collectively to subgroups of the group of individuals.

[0029] The continuous values ​​are not necessarily continuous in time.

[0030] The first type of parameters taken into account relate, for example, to the lifestyle or activity or behaviour of the individual or person taken into account.

[0031] As non-limiting examples, parameters of the first type may include the duration spent outdoors or indoors, the distance between the eyes and the text being read or written, the duration of reading or writing, the light intensity or spectrum, the duration of a sleep cycle, or the frequency or duration of wearing the visual device.

[0032] More generally, parameters of the first type are any parameters that are likely to influence the evolution of the selected vision-related parameter and that can be repeatedly measured at different time points.

[0033] The measurements may be taken by different kinds of sensors adapted to detect the parameters considered, possibly together with a timestamp.

[0034] For example, a light sensor, which may be included in a smart eyewear device or a smartphone, may be used to measure the intensity or spectrum of ambient light. An inertial motion unit (IMU), for example located in a head accessory, may be used to detect posture. The IMU may also be used to measure the time spent performing outdoor activities. A GPS may be used to detect outdoor activities or whether the individual is in a rural or urban environment. A camera or frame sensor may be used to detect the frequency and / or duration of wearing the glasses. A memory may be used to register the date of the current visual device, given the fact that older visual devices may affect visual aptitude.

[0035] After step 10, a step 12 of obtaining the evolution over time of the selected vision-related parameter is carried out for the same individuals of the group of individuals for which the continuous values ​​were obtained.

[0036] Such progress over time may be obtained by repeatedly measuring selected vision-related parameters for those individuals over time and / or by collecting information regarding the values ​​of the vision-related parameters provided by the individuals via any suitable interface to a processor that builds the predictive model.

[0037] The measurement frequency may be different for the various parameters measured in step 10 and may be independent of the measurement frequency of step 12.

[0038] For example, the first type of parameters may be measured at least once a day. Alternatively, the smart frame may be used to measure the first type of parameters at a frequency higher than 1 Hz.

[0039] Then, as an optional feature, an additional step 14 may be performed of obtaining information regarding the changed values ​​of one or more parameters of a second predetermined type for at least one of those individuals for which continuous values ​​were obtained.

[0040] The second type of parameter is any periodic or accidental event that is likely to affect the evolution of the selected vision-related parameter and that can be obtained at least once.

[0041] As non-limiting examples, the second type of parameter may be moving from an urban area to a rural area, a change in correction type, a change in corrective lens power, or pregnancy.

[0042] During a next step 16, executed by at least one processor, at least a portion of the successive values ​​obtained in step 10 is associated with the evolution over time obtained in step 12. Said portion of the successive values ​​is a selected sequence of values ​​taken from among the previously obtained values. The selected values ​​are not necessarily consecutive in time. In a particular embodiment, the selected sequence comprises at least three consecutive values.

[0043] Furthermore, at least some of the fixed parameters mentioned above may also be taken into account in the association process.

[0044] If optional step 14 is omitted, the association performed in step 16 comprises jointly processing said portions of consecutive values ​​obtained for the same parameter of the first type. As a non-limiting example, such joint processing may comprise calculating the mean and / or standard deviation values ​​over a predefined period of a predefined number of consecutive values ​​of the same parameter of the first type. It may also comprise a set of consecutive values ​​over a predefined period, which may then be averaged over the predefined period.

[0045] If optional step 14 has been performed, the association performed in step 16 comprises associating the modified value of the second type of parameter together with the above-mentioned portion of the continuous values ​​to the obtained evolution over time of the selected vision-related parameter.

[0046] Therefore, regardless of whether optional step 14 is performed or not, a correlation table or any other database means may be constructed and stored in a non-transitory computer readable storage medium such as a read only memory (ROM) and / or a random access memory (RAM), with the obtained parameter values ​​corresponding to the determined evolution over time of the selected vision-related parameter.

[0047] According to the present disclosure, in addition to the jointly processed values, the correlation table or other database means takes into account each of those individually obtained continuous values, or at least some of them, i.e. at least two, preferably at least three. In other words, the predictive model differs as a function of each of those continuous values, i.e. the predictive model depends differently on each of those jointly processed values, as well as on the result of the joint processing.

[0048] The prediction model may depend differently on each of the values ​​jointly processed through the joint processing. For example, the average may depend on characteristic weights associated with each different continuous value, e.g., a greater weight for 12pm than for 9pm. In an alternative implementation that can be combined with the previous one, the joint processing and difference consideration of the continuous values ​​is performed separately. For example, a set of continuous values ​​forms one prediction input and some of those values ​​form additional prediction inputs.

[0049] The order in which steps 8, 10, 12, 14 and 16 have been described is a non-limiting example. They may be performed in any other order. For example, the association step 16 may be started as soon as a part of the continuous values ​​and a part of the evolution over time of the visual acuity-related parameters have been obtained, and steps 10, 12 and 14 may be performed simultaneously as step 16 continues.

[0050] The predictive model building method and / or the prediction method may be implemented in a server.

[0051] In a particular embodiment of the prediction method, the group of individuals may also include a person for whom the evolution over time of one or more vision-related parameters is to be predicted by the prediction method using a prediction model constructed according to the construction method described herein, in other words for whom steps 10, 12, 16 and possibly also step 14 are performed.

[0052] In certain implementations, the processor used in step 16 may implement a machine learning algorithm, i.e., one or more neural networks may be trained by inputting a series of continuous values ​​for a large number of individuals and constructing a correlation table or any other database means containing a large number of data for better accuracy of the prediction method.

[0053] In such implementations, the association of step 16 may be performed by assigning weights to node connections in the neural network.

[0054] Self-reported parameters provided by individuals in the group may also be taken into account by the predictive model.

[0055] By way of non-limiting example, self-reported parameters may be input into the machine learning algorithm, such as, by way of non-limiting example, the respective gender, ethnic group, number of myopic parents, school grades, IQ test results, data from social networks, refractive values ​​of visual instruments, or genetic risk scores related to visual defects or diseases. Such self-reported parameters then modify the predictive model. Other fixed parameters and parameters of the first and / or second type, as well as the evolution over time of the vision-related parameters of the group of individuals may also be self-reported.

[0056] For inputting the self-reported parameters or the second type of parameters, the prediction device may include a display means already used to obtain the first type of parameter measurements and / or a smart phone or smart tablet, or any other type of user interface, including an audio interface.

[0057] The predictive model constructed by the predictive model construction method described above may be utilized in a number of ways to provide an individual with information regarding the predicted evolution over time of one or more vision-related parameters of that individual.

[0058] If the selected vision-related parameter is, for example, the risk of development or progression of a given visual defect, the predictive model may be used to show the evolution of that risk over time in the form of a profile graph.

[0059] 2 and 6 show such graphs in one embodiment where the vision impairment is myopia.

[0060] In FIG. 2 the evolution of the monitored person's myopia level is depicted as a function of time.

[0061] In Figure 6, the risk of developing myopia is plotted as a function of time.

[0062] In Fig. 2, the solid curve shows the actual measured myopia progression profile, the dashed curve shows the predicted myopia risk profile updating as a function of the dynamic predicted progression correction, and the dotted curve shows the predicted myopia risk profile before inputting the correction values ​​of the input parameters.

[0063] As a first type of parameter, the time spent on near vision tasks is measured. From time T1, with increasing time spent on such tasks, the risk of myopia progression increases, which is reflected by a sharp rise in the predicted myopia risk profile (dotted curve). At time T2, the monitored person moves from an urban area to a rural area, which is reflected by a gradual plateau in the predicted myopia risk profile.

[0064] It can be seen that the predicted profile corresponds closely to the actual measured evolution profile, in contrast to the predicted profile which has not been updated to take into account parameter modifications from T1 and T2.

[0065] In Fig. 6, at an initial time point, two scenarios are considered in predicting myopia development risk. In the first scenario, the monitored person continues to live in an urban area while maintaining near screen work habits, which induces myopia at future time T3, and then the predicted myopia level increases relatively rapidly over time. In the second scenario, the monitored person moves to live in a rural area and adopts a modified near screen work habit that leads to a greater myopia induction at future time T4 than T3, and a slightly lower myopia progression. This quantifies the lower myopia progression risk in the second scenario compared to the first scenario.

[0066] More generally, as shown in Figure 3, the proposed method for predicting the evolution over time of at least one vision-related parameter of at least one person comprises a step 30 of obtaining continuous values ​​for the person, each corresponding to repeated measurements over time of at least one parameter of a first type, and a step 36 of predicting by at least one processor the evolution over time of the vision-related parameter of the person from the continuous values ​​obtained in step 30 by using the above-described predictive model associated with a group of individuals.

[0067] Step 30 is performed for the person in a similar manner as step 10 for the individuals in the group.

[0068] Similar to optional initialization step 8 in Figure 1, optional initialization step 28 may collect fixed parameters for a person such as gender and / or date of birth and / or country of birth and / or family history and / or ethnicity. Step 28 may be performed either in a preliminary step of initialization or later at any stage of the prediction method.

[0069] In certain embodiments, the prediction step 36 may be preceded by an optional step 34 of obtaining information regarding a modified value of at least one parameter of a second type for the person.

[0070] The prediction step 36 uses a predictive model.

[0071] If optional step 34 is omitted, the prediction step 36 comprises associating at least a portion of the continuous values ​​for the person with the predicted evolution over time of the selected vision-related parameter of the person, the association operation comprising jointly processing said portion of continuous values ​​associated with the same parameter of the first type.

[0072] Such a portion of the series of values ​​is a selected series of values ​​taken from among previously obtained values. The selected values ​​are not necessarily contiguous in time. In a particular embodiment, the selected series comprises at least three consecutive values.

[0073] If optional step 34 has been performed, prediction step 36 further includes correlating the predicted evolution over time of the selected vision-related parameter for the person with the above-mentioned portion of the continuous values ​​of the first type of parameter for the person, as well as the changed values ​​of the second type of parameter.

[0074] According to the present disclosure, irrespective of whether optional step 34 is performed or not, with respect to the predictive model, the predictive evolution takes into account not only the result of joint processing of those successive values ​​or at least some of them, i.e. at least two, preferably at least three, but also each of those successive values ​​or at least some of them, so that the predictive evolution differs as a function of each of those successive values, i.e. the predictive model depends differently with respect to each of those jointly processed values.

[0075] The prediction device according to the invention comprises at least one input adapted to receive continuous values ​​for at least one person as described above. The device also comprises at least one processor configured to predict the evolution over time of the considered vision-related parameter of the person as described above.

[0076] Such a device may comprise a display unit and / or a smart phone or a smart tablet or a smart eyewear or a server, which may be the same as the display unit and / or a smart phone or a smart tablet or a smart eyewear or a server provided in the predictive model construction device. In case the predictive method is implemented in a remote centralized manner in a server, the output from the server is communicated to a user via a communication network, possibly via a wireless or cellular communication link.

[0077] In a particular embodiment of the prediction method, the group of individuals to which the predictive model is associated may also include a person for whom the evolution over time of one or more vision-related parameters should be predicted by a predictive model constructed according to the construction method described herein, in other words for whom steps 10, 12, 16 and possibly also step 14 are performed.

[0078] If self-reported parameters are provided by individuals in the group, the same self-reported parameters for the individual may also be input into the predictive model, such as the individual's gender, ethnicity, number of myopic parents, school grades, IQ test results, data from social networks, refractive values ​​of visual instruments, or genetic risk scores related to visual defects or diseases.

[0079] Other advantageous aspects of the prediction method according to the present disclosure relate to the numerous possibilities of interacting with the person, in particular by providing the person (and / or other people, such as the person's parents, if the person is a child) with feedback regarding the predicted evolution over time of at least one vision-related parameter of the person.

[0080] As a first possibility of interacting with the person, the predicted evolution over time of selected vision-related parameters of the person may be made available in the form of a graph of the type shown in Figure 2, which may be visualized, for example, on the screen of a smartphone or smart tablet through a mobile application.

[0081] As another possibility for interacting with the person, the prediction method may comprise triggering the sending of one or more warning messages to the person based on the predicted evolution over time of the vision-related parameter considered by the person, in which respect the content and / or frequency of the warning messages may vary according to the level of risk with respect to the vision-related parameter considered by the person.

[0082] For example, if the vision-related parameter considered for a person is the risk of developing or progressing myopia, then a person with a high risk of myopia may be warned about reading too close at a trigger threshold of less than 30 cm, whereas a person with a low risk of myopia may be warned at a trigger threshold of less than 20 cm.

[0083] Such trigger thresholds may vary for a given person over time, depending on the evolution of the predicted myopia risk for that person over time.

[0084] The frequency of the warning messages may vary as well.

[0085] The warning message may, for example, prompt, recommend or remind the person in time to adopt or maintain healthy eye habits, which helps the person to maintain visual fitness. Thus, the person can change his / her behavior from such timely reminders or prompts. A very simple visualization allows the person to know whether his / her behavior is beneficial or harmful to eye health.

[0086] Where the vision-related parameter considered is myopia level, the reminder or prompt discourages activities that pose a risk of myopia development or progression and / or encourages activities that have a protective effect against myopia development or progression.

[0087] The table below gives examples of activities and corresponding actions carried out by a smartphone or smart tablet included in a prediction device according to the invention, in the example of myopia.

[0088] [Table 1]

[0089] As another possibility for interacting with the person, as shown in FIG. 4, the prediction method may include providing the person with a monitoring indicator having a first state when the predicted progression of the person's vision-related parameter over time is less favorable than the actually measured progression of the person's vision-related parameter over time, or having a second state when the predicted progression of the person's vision-related parameter over time is more favorable than the actually measured progression of the person's vision-related parameter over time.

[0090] Thus, in the graph of Figure 4, which shows the same curve as Figure 2, the monitoring indicator having a hand shape has its thumbs up in both areas referenced by "A" to reflect the fact that in those areas the predicted evolution of the person's myopia level over time is less favorable than the actual measured evolution of their myopia level over time, and has its thumbs down in the area referenced by "B" to reflect the fact that the predicted evolution of the person's myopia level over time is more favorable than the actual measured evolution of their myopia level over time.

[0091] As another possibility of interacting with the person, multiple optimized targets or graphs showing the risk profile can be provided to the person and / or the person's parents based on several scenarios showing both good and bad eye use habits to recommend behavioral changes, such as playing outdoors, and to encourage healthy habits, such as habits that help prevent the onset of myopia or slow down the progression of myopia, when the vision-related parameter is myopia level or risk. For example, the predictive model calculates and presents an ideal myopia risk profile graph, which is optimized based on the recommended activity, when the person performs the recommended activity, such as going outdoors and spending more time outdoors.

[0092] FIG. 5 shows an example of such multiple risk profiles where the vision-related parameter is myopia level.

[0093] The graph on the left of FIG. 5 shows the evolution of a person's myopia level over time when the person has a low risk of myopia progression.

[0094] The graph on the right hand side of FIG. 5 shows the evolution of a person's myopia level over time when the person is at high risk of myopia progression.

[0095] In both graphs, the solid curve portion of each graph shows the actual measured myopia progression profile up to the current time, and the dashed curve shows the predicted myopia risk profile beyond the current time, which are updated as a function of the dynamic prediction model's modifications in response to changes in the person's eye use habits and / or behavior. The two dotted curves on each graph show the myopia risk profile in scenarios where the person does or does not follow the recommendations to change their eye use habits and / or behavior. The upper dotted curve corresponds to the scenario where the person does not follow the recommendations, and the lower dotted curve corresponds to the scenario where the person follows the recommendations.

[0096] The dotted curves may be accompanied by an explanatory message such as, for example, "Continuing close-up work for long periods of time increases the risk of myopia" for the upper dotted curve, and "Going outside to play reduces the risk of myopia" for the lower dotted curve.

[0097] As another possibility for interacting with the person, the prediction method may comprise providing the person with a maximum reduction or slowing of the progression of the person's visual impairment as a function of a change in value of at least one parameter of a first and / or second predetermined type of the person.

[0098] For example, if a person's myopia progression is initially estimated to be about 1 diopter per year, if the person adopts the healthiest behavior and / or activity and / or environment, the person may be able to achieve the maximum reduction in myopia progression.For example, the maximum time spent in outdoor activities and long reading distances may reduce myopia progression to 0.4 diopters per year, resulting in a maximum reduction in myopia progression of 0.6 diopters per year.On the other hand, if the person's behavior and / or activity and / or environment are not optimal, it may lead to a reduction in myopia progression of only 0.3 diopters per year, which corresponds to a 50% rate of the maximum possible reduction.

[0099] In a particular embodiment, the method according to the invention is computer-implemented, i.e. a computer program product accessible to a processor and comprising one or more instruction sequences which, when executed by the processor, cause the processor to carry out the method steps for building a predictive model and / or the method steps for predicting the evolution over time of at least one vision-related parameter as discussed above.

[0100] The predictive models may be used remotely, for example in the cloud, or locally in the smart frame. Updates and recalculations of the models may be advantageously performed in the cloud.

[0101] The sequences of instructions may be stored on one or several computer readable storage media, including a location in the cloud.

[0102] To construct the predictive model used by the predictive method, the processor may receive successive values ​​from various sensors, e.g. via a wireless or cellular communications link, each corresponding to repeated measurements over time of a first predetermined type of parameter for a member of the group of individuals and / or a person.

[0103] Although representative methods and apparatus have been described in detail herein, those skilled in the art will recognize that various substitutions and modifications may be made thereto without departing from the scope of what is described and defined by the appended claims.

Claims

1. 1. A computer-implemented method for predicting the evolution over time of at least one vision-related parameter of at least one person, comprising: obtaining successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; predicting, by at least one processor, from the obtained continuous values ​​for the at least one person, the evolution over time of the at least one vision-related parameter of the at least one person by using a predictive model associated to a group of individuals, said predicting by using said prediction model comprises associating at least a portion of said continuous values ​​for said at least one person with said predicted evolution over time of said at least one vision-related parameter of said at least one person, said associating comprising jointly processing said at least a portion of said continuous values ​​associated with a same one of said at least one parameter of said first predetermined type, the at least one parameter of the first predetermined type is a lifestyle, activity or behavioral parameter; when the continuous values ​​processed jointly are different results of a change in lifestyle, activity or behavior, the predicted evolution depends differently on each of the continuous values ​​processed jointly by assigning a characteristic weight to each of the different continuous values ​​processed jointly to predict the evolution over time of the at least one vision-related parameter by the prediction model that differs as a function of each of at least some of the continuous values ​​associated with the same parameter, said joint processing being to calculate an average value and / or a standard deviation value over a predetermined time period of a predetermined number of consecutive values ​​of the same parameter of said first predetermined type; method.

2. The method further comprises, prior to the predicting, obtaining information regarding a changed value of at least one parameter of a second predetermined type for the at least one person; said predicting by using said prediction model further comprises associating said modified value with said at least a portion of said continuous values ​​for said at least one person with said predicted evolution over time of said at least one vision-related parameter for said at least one person, The method of claim 1.

3. The method of claim 1 or 2, wherein the at least some of the continuous values ​​comprises at least three of the continuous values.

4. The method according to any one of claims 1 to 3, wherein said at least one person belongs to said group of persons.

5. The method according to any one of claims 1 to 4, further comprising providing feedback to the at least one person regarding the predicted evolution over time of the at least one vision-related parameter of the at least one person.

6. The method according to any one of claims 1 to 5, further comprising triggering the sending of at least one warning message to the at least one person based on the predicted evolution over time of the at least vision-related parameter of the at least one person.

7. The method of any one of claims 1 to 6, further comprising varying the content and / or frequency of the at least one warning message according to a level of risk associated with the at least one vision-related parameter of the at least one person.

8. 2. The method of claim 1, wherein the at least one parameter of the first predetermined type is a period of time spent outdoors or indoors, a distance between the eyes and a text to be read or written, a duration of reading or writing, a light intensity or spectrum, or a frequency or duration of wearing a visual device.

9. The method of any one of claims 1 to 8, wherein the method comprises obtaining a self-reported parameter, and wherein the predicting takes into account the self-reported parameter.

10. 10. The method of claim 1, further comprising providing the at least one person with an indicator having a first state when the predicted evolution over time of the at least one vision-related parameter of the at least one person is less favorable than the actually measured evolution over time of the at least one vision-related parameter of the at least one person, or having a second state when the predicted evolution over time of the at least one vision-related parameter of the at least one person is more favorable than the actually measured evolution over time of the at least one vision-related parameter of the at least one person.

11. 11. The method according to claim 1, further comprising providing said at least one person with a maximum value of a reduction in a progression of a visual impairment of said at least one person as a function of a change in value of said at least one parameter of said first and / or second predetermined type for said at least one person.

12. 1. An apparatus for predicting the evolution over time of at least one vision-related parameter of at least one person, comprising: at least one input adapted to receive successive values ​​for the at least one person, each corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; and at least one processor configured to predict, from the successive values ​​obtained for said at least one person, the evolution over time of said at least one vision-related parameter of said at least one person by using a predictive model associated to a group of individuals, said predicting by using said prediction model comprises associating at least a portion of said continuous values ​​for said at least one person with said predicted evolution over time of said at least one vision-related parameter of said at least one person, said associating comprising jointly processing said at least a portion of said continuous values ​​associated with a same one of said at least one parameter of said first predetermined type, the at least one parameter of the first predetermined type is a lifestyle, activity or behavioral parameter; when the continuous values ​​processed jointly are different results of a change in lifestyle, activity or behavior, the predicted evolution depends differently on each of the continuous values ​​processed jointly by assigning a characteristic weight to each of the different continuous values ​​processed jointly to predict the evolution over time of the at least one vision-related parameter by the prediction model that differs as a function of each of at least some of the continuous values ​​associated with the same parameter, said joint processing being to calculate an average value and / or a standard deviation value over a predetermined time period of a predetermined number of consecutive values ​​of the same parameter of said first predetermined type; Device.

13. 13. The device according to claim 12, comprising a display means and / or a smartphone or a smart tablet or smart eyewear for visualising the predicted progress.

14. 1. A computer program for predicting the evolution over time of at least one vision-related parameter of at least one person, the computer program being accessible to a processor and, when executed by the processor, causing the processor to: obtaining successive values ​​for the at least one person, the successive values ​​corresponding to repeated measurements over time of at least one parameter of a first predetermined type for the at least one person; one or more instruction sequences for predicting, from the obtained continuous values ​​for the at least one person, the evolution over time of the at least one vision-related parameter of the at least one person by using a predictive model associated with a group of individuals, said predicting by using said prediction model comprises associating at least a portion of said continuous values ​​for said at least one person with said predicted evolution over time of said at least one vision-related parameter of said at least one person, said associating comprising jointly processing said at least a portion of said continuous values ​​associated with a same one of said at least one parameter of said first predetermined type, the at least one parameter of the first predetermined type is a lifestyle, activity or behavioral parameter; when the continuous values ​​processed jointly are different results of a change in lifestyle, activity or behavior, the predicted evolution depends differently on each of the continuous values ​​processed jointly by assigning a characteristic weight to each of the different continuous values ​​processed jointly to predict the evolution over time of the at least one vision-related parameter by the prediction model that differs as a function of each of at least some of the continuous values ​​associated with the same parameter, said joint processing being to calculate an average value and / or a standard deviation value over a predetermined time period of a predetermined number of consecutive values ​​of the same parameter of said first predetermined type; Computer program.

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