Technique for determining risk indicators of myopia

Abstract

A system for determining a risk indicator for myopia is provided. The system includes a wearable device configured to be attached to a user's body. The wearable device includes at least one distance sensor configured to determine at least a first distance value indicative of a distance between the wearable device and an object located in a central visual area of the user, and a second distance value indicative of a distance between the wearable device and an object located in a peripheral visual area of the user. The system further includes a control unit configured to determine a risk indicator for myopia based on the first distance value and the second distance value. Additionally, a method and a computer program product are provided.

Description

Technical Field

[0001] The present disclosure relates to the field of ophthalmology. More precisely, the present disclosure relates to a technique for determining a risk indicator for myopia. Specifically, the risk indicator may indicate the risk of myopia onset and / or progression. This technique may be embodied in at least one system and / or at least one method. Background Art

[0002] As is well known, myopia (nearsightedness), especially in children, may be caused by eye growth. In this case, the growth of the eye causes the eye to become too large, so that the image is not formed on the retina (as it should be), but in front of the retina, i.e., inside the eye.

[0003] It is further known that a phenomenon called "hyperopic defocus" (where the image is formed behind the retina of the eye) may cause eye growth, leading to myopia. Further details regarding general myopia and regarding the above phenomenon are described, for example, in Flitcroft, D.I. (2012): "The complex interactions of retinal, optical and environmental factors in myopia aetiology", Progress in Retinal and Eye Research, 31(6), 622 - 660.

[0004] Accommodation error or accommodation lag is the mismatch between the focusing state (accommodation response) of the eye and the distance to the observed object (accommodation demand). Usually, the eye is insufficiently adapted to near objects, and the closer the distance, the greater the degree of insufficiency or "accommodation lag". This lag is the source of hyperopic defocus in the posterior part of the eye (macula). Hyperopic defocus can also be caused by the shape of the posterior part of the eye, since myopic eyes often exhibit relative peripheral hyperopia, such that when corrected vision and viewing of distant objects occur, the peripheral retina is exposed to hyperopic defocus. It may also be due to the structure of the visual environment, where the distances of objects in the peripheral visual field from the centrally viewed object are different, since the eye's accommodation system only adapts to the focus demands in the central visual field.

[0005] Details of these phenomena and the eye growth caused by these phenomena will be further described below.

[0006] Currently, several techniques are known for correcting refractive errors such as myopia and hyperopia in the human eye. These techniques include, for example, prescription glasses, contact lenses, and interventions that change the optical properties of the eye lens, such as refractive surgery, like photorefractive keratectomy (PRK) and laser - assisted in situ keratomileusis (LASIK).

[0007] However, a technique is needed to determine whether a particular person has an increased risk of developing myopia, especially myopia caused by eye growth. If such a "risk indicator" indicating the risk of developing myopia can be determined, measures can be taken as early as possible to prevent the development of myopia. SUMMARY OF THE INVENTION

[0008] Accordingly, an object of the present disclosure is to provide a technique for determining a risk indicator for myopia, especially myopia caused by eye growth. The risk indicator can be used to determine the risk of onset and / or progression of myopia.

[0009] According to a first aspect, there is provided a system for determining a risk indicator for myopia, the system comprising a wearable device configured to be attached to a user's body (specifically, attached to the user's head). The wearable device includes at least one distance sensor configured to determine at least a first distance value (which indicates the distance between the wearable device and an object located in the user's central visual field) and a second distance value (which indicates the distance between the wearable device and an object located in the user's peripheral visual field). The system further includes a control unit configured to determine a risk indicator for myopia based on the first distance value and the second distance value.

[0010] The wearable device can be attached to the user's body in the broadest possible sense. Specifically, the wearable device can be attached to the user's head. For example, at least one attachment member can be provided for attaching the wearable device to the head or other parts of the body. For example, the attachment member can be provided in the form of one or more earphones (configured to be placed on the user's ears, similar to the sidepieces of glasses and / or similar to a headset). The wearable device can be permanently incorporated into glasses worn by the user. In addition, the attachment member can be provided in the form of one or more clamping devices (configured to clamp onto the temple pieces of glasses worn by the user). Thus, the "user's head" can be understood to include the user's head wearing glasses (e.g., prescription glasses or sunglasses). Although attachment to the user's head is preferred, the wearable device or a part thereof can also be attached to any other location on the user's body. For example, the wearable device can be attached to the user's chest. In this case, the sensors of the device can point forward.

[0011] The distance sensor can operate according to known techniques for determining distance. For example, the distance sensor can include a laser distance sensor, an ultrasonic distance sensor, an infrared proximity sensor, radar, an imaging sensor, a camera, or any other suitable device for determining a distance value indicative of the distance between the wearable device and an object located in front of the user's head. The camera can be a standard two-dimensional (2D) imaging camera or a distance imaging camera that provides an image of the distance to an object. For example, by identifying an object with known geometric dimensions and calculating the distance from the dimensions on the image, the distance value can be estimated from a 2D image. The distance imaging camera can implement stereoscopic triangulation, sheet light triangulation, structured light illumination decoding, time-of-flight measurement, interferometric imaging, or coded aperture, etc. The camera can be a light field camera capable of detecting the direction and intensity of light. The distance value can be, for example, a length provided in meters, centimeters, or millimeters. The distance value can indicate, for example, the distance between the user's eyes and an object. In this case, the distance value indicates the distance between the wearable device and the object when the spatial relationship between the wearable device and the user's eyes is known. For example, in the absence of an object in front of the user's head, or when the next object in front of the user's head is farther than a predetermined threshold, the distance value can indicate an infinite value.

[0012] In one or more embodiments, the distance sensor can be configured to measure the user's viewing distance based on the accommodation effort of the eyes. The distance sensor can also be adapted to be an eye movement sensor capable of detecting the movement (and / or change in size) of the pupils. When the human eyes focus on an object, they coordinately adjust the divergence and shape of the lens to change the optical power, and accordingly change the focal length and pupil size. For example, monitoring the positions of both eyes can allow detection of vergence (convergence and divergence), which is the simultaneous movement of the two eyes in opposite directions to obtain or maintain binocular vision. When the eyes focus on a near object, the eyes move towards each other; when the eyes focus on a far object, the eyes move away from each other. The change in the shape of the lens can be monitored by tracking the reflection of the probing light from the lens surface (e.g., by analyzing Purkinje images P3 and P4). When focusing on a nearby object, the pupil constricts to minimize image blur. The pupil size can be measured by imaging or any other suitable method. The system can detect accommodation by detecting changes in pupil size. During the detection of accommodation, the system can compensate for the effect of brightness, which can be measured by an ambient light sensor, on pupil size.

[0013] Additionally, the distance to an object in the user's peripheral vision can be measured using different sensors aligned in different directions or a device capable of scanning multiple directions.

[0014] The control unit may include at least one processor and at least one memory for storing instructions to be executed by the processor. The control unit may be configured to receive distance values from at least one distance sensor. In the present disclosure, when it is said that a second value is "based on" a first value, this means that an algorithm or calculation rule using the first value as an input parameter is provided. In other words, the determined result, i.e., the second value, is affected by the first value. In the case of the risk metric and the first and second distance values, this means that the first and second distance values have an impact on the risk metric (e.g., on the value of the risk metric). However, the first and second distance values are not necessarily the only values or parameters that affect the risk metric.

[0015] The risk metric may be, for example, a numerical value, where a higher value indicates a higher risk of myopia. Alternatively, the risk metric may be a binary value ("0" or "1"), where "0" indicates that the risk of myopia (e.g., the risk of developing myopia within a predetermined time frame, e.g., 1 year out of 2 years) is lower than a predetermined threshold, and "1" indicates that the risk of myopia is higher than the predetermined threshold. Thus, "1" may indicate that measures for early prevention of myopia should be considered. Throughout the present disclosure, "risk of myopia" may indicate the risk of myopia progression and / or the risk of onset of myopia. For example, one and the same numerical value may be used to determine the risk of onset of myopia and the risk of myopia progression. According to other embodiments, one value may be output for the risk of onset of myopia, and a different value may be output for the risk of myopia progression. In other words, the risk metric may be a multi-dimensional (e.g., two-dimensional) risk metric.

[0016] At least one distance sensor may be configured to determine distance values that are temporally separated / resolved. In other words, each distance sensor of the system may be configured to record a time series d(t) of distance values. The time series may be recorded and / or stored in the memory in such a way that the time (e.g., date and time) at which the respective distance value was recorded is assigned to the respective distance value in the form of a time stamp.

[0017] Specifically, for a single distance sensor configuration (i.e., the system includes only one distance sensor), the distance sensor must provide measurements that are separated in time to provide a sequence of distance measurements (i.e., a time series of distance values). The frequency of the distance measurements should be sufficient to obtain multiple measurements during each visual activity to facilitate statistical analysis of the data. Nowadays, due to the use of mobile devices, the human attention span has significantly shortened. It is normal for users to switch from one activity to another several times per minute. Therefore, it is desirable to sample the distance sensor at a sub - second frequency. At the same time, due to the limited physical speed of human head and body movements, sampling above 100 Hz is not required frequently. Thus, the optimal range for the distance sensor sampling frequency can be between 1 Hz and 100 Hz. This can be applied to each distance sensor of the system, especially when the system has only one distance sensor.

[0018] In the case where a series of parameters are collected, the frequencies of good behavior patterns (i.e., reducing the risk of myopia) and bad behavior patterns (i.e., increasing the risk of myopia) can be analyzed to provide specific recommendations for behavior change to minimize the risk of myopia progression or onset.

[0019] The control unit of the wearable device and the system do not have to be set in the same physical location and / or the same housing. For example, the control unit can be part of the wearable device. The wearable device can be provided in the form of glasses. In this case, the wearable device can include an output unit configured to output a risk metric (e.g., in the form of a display unit).

[0020] Optionally, the control unit can be provided in the form of a separate device, configured to receive the output values of the wearable device, and specifically, configured to receive the distance values determined and recorded by the wearable device. For example, the wearable device can be configured to record distance values over a predetermined period and store the distance values in the memory of the wearable device. The wearable device can include an interface configured to output the recorded distance values to the control unit. The control unit can include an input unit for receiving the distance values from the control unit. Then, the control unit can be configured to determine and optionally output a risk metric.

[0021] In one or more embodiments, the control unit can be part of the cloud. In other words, the control unit can be located on one or more network servers accessible to the wearable device. The one or more network servers can be accessed via the Internet (e.g., via an encrypted connection). Thus, the evaluation of the measured distance values can be performed by the control unit acting as a central control unit located on one or more cloud computing devices. Once the control unit determines the risk metric, the risk metric can be signaled back to the user, e.g., back to the wearable device. Additionally, the risk metric can be exported from the control unit via a network interface (e.g., an Internet page protected by a login process) such that the user and / or doctor can access the risk metric. The risk metric can also be signaled exclusively to the doctor (e.g., the user's treating physician).

[0022] Below, details of the model for determining the risk metric by the control unit are described.

[0023] Accommodation errors can be aggregated to create a metric that reflects the risk of myopia onset and / or progression. Generally, a higher incidence of accommodation errors leads to a higher risk, so the simplest model can employ some statistical metric of the accommodation error distribution and relate it to the risk. The required metric can be calculated in real time from the accommodation error (i.e., from the mismatch between the first and second distance values), or the system can store a history of accommodation errors, so the metric can be calculated based on historical data. For example, the average accommodation error in a time window of interest can be used, where the time window can be one hour, one day, one week, one month, one year, etc. Storing the history of relevant parameters allows the user or healthcare provider to select different intervals for data analysis and explore periodicity in the data, e.g., by looking at specific times of day, days of the week, or quarterly statistics.

[0024] The metric can also be a median or any other percentile. As a simpler measurement, the absolute or relative time of accommodation errors above a predetermined (critical) threshold can be used. For example, the system can be configured to report the number of hours per week that the accommodation error is outside the normal range. In another example, the system can report the time related to the absolute time period or times of wearing / using the wearable device, e.g., the system can be configured to report the percentage of time with abnormal defocus / error per selected interval.

[0025] At the next approximation level, other factors affecting risk can be included in the model, such as ambient light, time spent indoors / outdoors, viewing / reading distance, time spent on different activities, eye geometry, demographics, and family history of myopia. These factors can be input into the model independently or attenuate the contribution of other factors, such as peripheral defocus. For example, due to the increased depth of field of the eye optics, exposure to high levels of ambient light is expected to reduce the effect of accommodation error, while dim light conditions maximize the effect of accommodation error.

[0026] In a similar manner, information about eye geometry obtained by another method allows for an explanation of the differences in eye shape. For example, in elongated eyes, peripheral hyperopic defocus is magnified, and conversely, in short eyes, peripheral defocus is reduced.

[0027] The effect of peripheral defocus may also vary with the circadian rhythm, especially the diurnal rhythm. For example, the periodic changes in axial length and choroidal thickness of the eye affect the effect of peripheral hyperopia, with a longer eye geometry amplifying the effect of peripheral defocus and a shorter eye suppressing it. Therefore, the eye is more sensitive to hyperopic defocus in the early morning and less so at night. By introducing a real-time clock into the device and introducing time information into the model, the circadian rhythm can be taken into account.

[0028] The cumulative effect model of myopia risk may include a reset mechanism. Animal studies have shown that the absence of hyperopic accommodation error (clear vision) in the short term can neutralize the cumulative effect of hyperopic defocus. This effect can be considered by introducing an integration window, for example, in the form of a leaky integrator that slowly charges in the case of hyperopic defocus and discharges relatively quickly in the absence of hyperopic defocus.

[0029] In one implementation, the risk score can be a non-negative integer-valued accumulator variable R that increments by a first value (such as 1) every full minute that the persistent hyperopic defocus (D) exceeds a first defined threshold (D1). At the same time, each minute of hyperopic defocus below a second defined threshold D2 (lower than the first threshold, D1 > D2) causes the accumulator variable R to decrement by a second value that is expected to be larger in absolute value than the first value (such as 5). This assumes that defocus is labeled with positive values corresponding to hyperopic defocus and negative values corresponding to myopia.

[0030] Since R is non-negative, decrementing can only bring it to a minimum value of zero, so during periods of persistent clear vision or myopic defocus, the accumulator R can only be maintained at the minimum value, which means a lack of preventive effect for clear vision or myopic defocus.

[0031] In the implementation of the risk integrator, the variable R is a real value and non-negative and is adjusted according to the following rules at each time step i:

[0032] R(i) = f(D(i)) + R(i - 1), where R > 0

[0033] R(i) is the risk accumulator variable at time step i, R(i - 1) is the same variable at the previous time step, D(i) is the real-valued hyperopic defocus, and f(D) is the response function.

[0034] The response function can have the shape of a step function:

[0035] f(D) = A, for D > D1 (hyperopic defocus charging) and

[0036] f(D) = -B, for D < D2 (clear vision and myopic defocus discharging),

[0037] f(D) = 0, for D2 ≤ D ≤ D1, (uncertainty / insensitive zone)

[0038] where

[0039] D2 < D1 are predefined thresholds, and

[0040] A, B > 0 (predefined values).

[0041] The response function can include linear dependence and saturation in more detail:

[0042] f(D) = A, for D1′ < D (hyperopic defocus charging saturation)

[0043] f(D) = α(D - D0), for D0 < D < D1' (linear hyperopic defocus charging)

[0044] f(x) = -β(D - D0), for D2′ < D < D0 (linear clear vision / myopic defocus discharging) f(x) = -B, for D < D2' (saturated clear vision / myopic defocus discharging), where

[0045] D2' < D0 < D1' are predefined thresholds, and

[0046] α, β, A, B > 0 and A = α(D1′ - D0) and B = -β(D2′ - D0).

[0047] The response function can include linear dependence, saturation, and an insensitive zone:

[0048] f(D) = A, for D1′ < D (hyperopic defocus charging saturation)

[0049] f(D) = α(D - D1), for D1 < D < D1' (linear hyperopic defocus charging)

[0050] f(D) = 0, for D1 ≤ D ≤ D2 (uncertainty / insensitive zone),

[0051] f(x) = -β(D - D2), for D2' < D < D2 (linear clear vision / myopic defocus discharge), f(x) = -B, for D < D2' (saturated clear vision / myopic defocus discharge),

[0052] where

[0053] D2' < D2 < D1 < D1' are thresholds, and

[0054] α, β, A, B > 0 and A = α(D1' - D1) and B = -β(D2' - D2).

[0055] The response function can be in the form of a Sigmoid / Logistic function, hyperbolic tangent, rectified linear unit, etc. or any combination.

[0056] The control unit can be configured to determine a risk metric such that a higher mismatch between the first distance value and the second distance value results in a risk metric indicating a higher myopia risk. In other words, the mathematical model used by the control unit can take into account the mismatch between the first distance value and the second distance value, and in the case where this mismatch is high (e.g., having a value higher than a predetermined threshold), the risk metric will indicate a high risk of myopia. It can be determined whether the mismatch is above the predetermined threshold for a minimum number of times and / or for a minimum amount of time, and in such cases, the risk metric can be increased.

[0057] The wearable device can include: a first distance sensor that points in a central direction to the central visual region of the user, wherein the first distance sensor is configured to determine a first distance value; and a second distance sensor that points in a peripheral direction to the peripheral visual region of the user, wherein the second distance sensor is configured to determine a second distance value.

[0058] For example, in the above case, the system is able to sample in multiple directions without relying on device movement (i.e., not having to rely on the sensor output of one or more sensors indicating the movement of the wearable device). This can be achieved by at least one additional distance sensor (i.e., the second distance sensor) with a different orientation (e.g., downward) from the first distance sensor. This can also be achieved by a single sensor having multiple spatially or angularly resolved detection zones, as a detector array or as a camera (see below). The single sensor can have multiple or probing signal sources, e.g., for the case of a time-of-flight sensor, lasers in multiple directions. The sensor can be designed to change the direction of the source and / or the detector to detect distances in different directions (active scanner). Sampling in different directions can be performed simultaneously as in a camera configuration, or sequentially (scanner configuration). The ability to obtain measurements using additional sensors can allow for an increase in the density of the sampled environment, especially outside the head movement range.

[0059] In addition to the distance to an object in space, the system may include other measurement parameters associated with the same direction and / or location. For example, by including the amplitude of the reflected signal, the data can be enhanced to improve the accuracy of object and activity classification. For example, the reflectivity of a computer screen surface may be higher than that of a table surface, so classification algorithms can be designed to take into account the requirements for object reflectivity. Additionally or alternatively, the wearable device may include a light intensity sensor as part of the distance sensor or a separate sensor that is co-directed with the distance sensor and used to detect the light intensity and spectral content in the viewing direction. For example, handheld mobile devices such as mobile phones or handheld computers / tablets, computers, terminals, televisions, etc. typically use active illumination displays. The light intensity sensor can be configured to identify the light emitted by these objects from the intensity, spectral content, flicker pattern (frequency and intensity), or other light properties. The light intensity sensor measurements can be aligned with the azimuth and / or position measurements and mapped to a representation of the environment to support the classification of activities performed by the user and the identification of the environment (e.g., indoor or outdoor). Combining the light property measurements with the distance measurements can further improve the classification. Generally, the output of the light intensity sensor can be used to determine a risk metric (i.e., it can have an impact on determining the risk metric).

[0060] The distance sensor may include a camera having a field of view including a central visual region and a peripheral visual region, wherein the distance sensor is configured to determine a first distance value and a second distance value based on one or more images captured by the camera. For example, the control unit may be configured to analyze a time series of images captured by the camera and determine the first distance value and the second distance value based on the time series. The camera may include a plurality of sub-cameras, each having a sub-field of view, wherein the combined field of view (i.e., the combination of the sub-fields of view) includes the central visual region and the peripheral visual region.

[0061] The distance sensor may include an eye activity sensor capable of measuring the viewing distance according to the accommodation effort of the eyes (e.g., according to convergent eye movement, pupil adjustment, and / or lens change, etc.). In this case, the first distance value and the second distance value can be determined from a time series of the viewing distance. For example, the viewing distance determined during a fixation period may correspond to the central visual region (the first distance value), while the viewing distance determined during a period outside of fixation may correspond to the peripheral visual region (the second distance value). Additionally or alternatively, the visual region can be identified from the eye direction. The gaze direction can be derived by combining the eye direction with the head azimuth estimated from the azimuth sensor. By combining the viewing distance measurements with the gaze direction, the geometry of the environment related to the user's head can be reconstructed and the estimated peripheral defocus can be utilized.

[0062] In the case where multiple distance sensors are provided, each distance sensor can simultaneously provide a distance value, for example, indicating the distance between the corresponding distance sensor and the object pointed to by the distance sensor. In the case where a single distance sensor determines multiple distance values, the distance sensor can be configured to scan a laser beam within a predetermined angular range, or the distance sensor can include a camera for providing a two-dimensional image, which has the ability to process the two-dimensional image to determine multiple distance values. Additionally, the distance sensor can include at least two cameras for determining two-dimensional images through each camera, where the two-dimensional images are processed to determine multiple distance values. The peripheral direction can have an angle of at least 5 degrees, at least 10 degrees, at least 20 degrees, at least 30 degrees, or at least 45 degrees relative to the central direction. For example, the peripheral direction can be downward and / or point to one side with respect to the central direction.

[0063] In the case where more than one second distance value is determined, more directions can be considered, which can increase the accuracy of the system. For example, at least one vertical second distance value can be determined for a direction vertically deviated from the first direction, and at least one horizontal second distance value can be determined for a direction horizontally deviated from the first direction.

[0064] By analyzing the first distance value and the second distance value, the chance of peripheral defocus occurring and / or the degree of peripheral defocus can be determined.

[0065] The control unit can be configured to identify the first distance value during the fixation period when the variability of the distance measurement of the distance sensor is below a first predetermined threshold for more than a second predetermined threshold during a time interval and a second distance value is identified outside the fixation period.

[0066] The variability can include or can correspond to at least one of the number of times the measured distance value changes from a value below the first predetermined threshold to a value above the second predetermined threshold within a predetermined period, the number of times the time derivative of the measured distance value changes its sign within a predetermined period, the difference between the maximum value and the minimum value of the measured distance value within a predetermined period, and the maximum value of the time derivative of the measured distance value within a predetermined period.

[0067] However, the temporal variability should not be limited to the above examples, which represent a list of mathematical and well-defined methods for determining temporal variability. There can be other methods for determining temporal variability, which are also covered by the present disclosure.

[0068] As described above, the control unit may be configured to identify fixation periods, i.e., when the user is focused on the main object of a visual activity, typically aligned in the central visual field, and to identify deviations from the main visual activity, which creates a distance to surrounding objects that would otherwise be located in the peripheral visual field. For example, when the user is watching TV, the user's fixation should be on the TV, and the distance sensor will primarily report the distance to the TV screen. However, due to natural head movements or distractions, the user may occasionally turn his or her head towards an object in the periphery (i.e., the peripheral visual field), e.g., an object in the user's hand, such as a snack or a remote control. In another example, a user working on a desktop personal computer will primarily focus on the computer monitor and occasionally turn the head towards the keyboard or other objects on the desk. The algorithm implemented by the control unit may be configured to statistically distinguish fixation periods as periods of low variability in distance measurements, e.g., identified by a standard deviation below a predetermined threshold, and correspondingly, to associate measurements falling outside the range as outliers associated with objects outside the main visual activity (i.e., objects in the peripheral visual field).

[0069] The distance sensor may be capable of providing additional metrics associated with the distance signal, e.g., the amplitude of the signal and / or other quality metrics. In this case, the additional metrics may also be used to distinguish measurements of the main object of the visual activity (i.e., the object in the central visual field) from objects in the visual environment (i.e., objects in the peripheral visual field). For example, the distance measurement may be based on detecting and characterizing pulses sent to and reflected from an object (such as ultrasonic sensors and laser time-of-flight sensors). In this case, the distance sensor may also be capable of measuring the amplitude of the reflected pulse. In the above example, the reflection from the computer screen may produce a pulse amplitude different from the reflection from the environment, which may be included in the signal analysis.

[0070] In addition, the wearable device may include a motion sensor, and the control unit may be configured to identify fixation periods as periods having a motion exceeding a second predetermined threshold below a first predetermined threshold during a time interval, and to identify a first distance value during one of the fixation periods and a second distance value outside the fixation periods. Thus, the output of the motion sensor may define which of the measured distance values of the distance sensor (e.g., of a time series d(t)) is the first distance value and which of the measured distance values is the second distance value.

[0071] The wearable device may include exactly one distance sensor for determining exactly one distance value at a given time, such that exactly one distance sensor is configured to determine the first distance value and the second distance value at different times.

[0072] For example, exactly one distance sensor may point in only one direction and thus may only determine the distance between the wearable device and an object located in that direction. The expression "exactly one distance value at a given time" means that no more than one distance value is determined simultaneously. However, different distance values may be determined at different times. For example, a wearable device may be provided that includes only one distance sensor pointing in the direction of the central axis of the wearable device. Alternatively, exactly one distance sensor may point in a direction away from the central axis of the wearable device, into the peripheral vision area.

[0073] The control unit may be configured to determine a first distance value and at least one second distance value based on the output of exactly one distance sensor.

[0074] The control unit may be configured to determine the cumulative duration during which the mismatch between the first distance value and the second distance value is higher than a predetermined threshold within a predetermined time period, and to determine a risk metric such that a higher cumulative duration results in a risk metric indicating a higher myopia risk.

[0075] In this way, the fraction of time during which there is peripheral defocus can be determined. The higher this fraction of time, the higher the risk of myopia may be.

[0076] The wearable device may include at least one additional sensor configured to output additional sensor data, wherein the control unit is configured to determine a first distance value and a second distance value based on the additional sensor data and on the output of at least one distance sensor. The additional sensor may include at least one of an orientation sensor for determining the orientation of the wearable device, a position sensor device for determining the position of the wearable device, and an acceleration sensor for determining the acceleration of the wearable device.

[0077] The acceleration sensor may be configured to detect the amount of movement of the user. Thus, the acceleration sensor may also be referred to as a motion measurement sensor. During a fixation period, typically the user reduces the amount of body and head movement to maintain the best quality of the image perceived by the eyes. On the other hand, a distraction event is characterized by a deviation from the original fixation direction, which is reflected in the motion signal of the acceleration sensor. Thus, the additional sensor data, in particular the motion signal, may be used as an additional or primary signal for identifying fixation and deviation periods. In other words, the control unit may be configured to determine a fixation period based on the additional sensor data.

[0078] It should also be understood that in the case of a moving visual target, for example when viewing a passing object, the user tends to move the head during fixation. This can be accounted for in signal processing by distinguishing tracking distance and / or head movement and deviation signals.

[0079] Additionally or optionally, the wearable device may include at least one of an orientation sensor and a position sensor. The combination of such sensors with distance measurements allows mapping the measured distances to the geometry of the environment. In the above example, when looking at the computer screen, the user will maintain a first orientation of the sensor, and during intermittent deviations to the desktop or other surrounding objects, the user's head will naturally tilt downwards or laterally, which can be detected by the orientation sensor. The control unit may be configured to detect the main orientation of the system (e.g., straight ahead during looking at the computer screen), and distinguish it from secondary orientations during deviations from the main visual activity (e.g., when looking down). Such detection can be performed by statistically processing the orientation signals and detecting events of stable orientation when the statistical dispersion / variation of the orientation is below a predetermined threshold. The statistical dispersion can be quantified using variance, standard deviation, interquartile range, interpercentile range, statistical range (spanning between the minimum and maximum values), mean absolute difference, or any other statistical measure of dispersion. The main orientation can be detected using a statistical measure of the central tendency of the orientation distribution, such as arithmetic mean, median, mode, or other measures. The distance measurements corresponding to the main orientation obtained on the system orientation are associated with the central visual area and are mainly responsible for regulating the response. For example, a deviation event can be detected as the period when the orientation significantly deviates from the center of the distribution (e.g., when the absolute difference between the current orientation and the main orientation is higher than a predefined limit). The distances measured during such an event correspond to the peripheral visual area and thus typically correspond to the accommodation requirements of the peripheral vision.

[0080] Due to the natural head and body movements of the user, there is a sufficient amount of sampling, and distance scans of the environment can be obtained from measurements made in different direction ranges relative to the head position. For example, measurements from an accelerometer allow associating the orientation of the device with the gravitational field (pitch angle). Using a magnetometer, the orientation can be related to the magnetic field (yaw angle and pitch angle), which may be related to the Earth's magnetic field if calibrated correctly. The combination of these sensors and an optional gyroscope sensor allows estimating the absolute orientation of the sensor in three-dimensional space. This combination of a three-axis accelerometer, a three-axis magnetometer, and a three-axis gyroscope is called an absolute orientation sensor.

[0081] In this way, similar to the above orientation detection, the wearable device may be equipped with at least one position sensor to detect the lateral displacement of the wearable device and associate them with the distance measurements in order to distinguish between looking and distraction periods. The position sensor in combination with the distance measurements allows scanning the environment due to the natural movement of the user. The position sensor can be implemented by measuring the acceleration detected by an accelerometer in order to estimate the relative displacement, or it can be implemented from distance measurements to nearby anchor nodes with known fixed positions, such as radio frequency emitting devices, to a mobile phone, an internet access point, or a Bluetooth beacon. The position sensor can be a geolocation sensor.

[0082] A position sensor can be combined with an orientation sensor to further improve the scanning of an environment represented as a point cloud, which is a set of data points in space. The point cloud can be used to identify objects in the space and / or identify the activities of the user, so as to separate the distance to an object in the central observation area from the distance to an object in the peripheral observation area, thereby calculating the peripheral defocus and risk metrics.

[0083] For example, the head movement of the user can be detected, for example, by means of a motion sensor (such as an accelerometer, a gyroscope, and / or a magnetometer). Based on the output of the motion sensor, the orientation to which the user's head is pointed can be derived. Based on this direction, it can be determined whether the currently detected distance value corresponds to a first distance value (such as pointing to the central orientation of the wearable device) or a second distance value (such as pointing to the peripheral orientation of the wearable device). Based on the motion sensor, it can be determined that the user has only turned the head without shifting the head to another position.

[0084] The control unit can be configured to determine a risk metric by using biometric information indicating the shape of the user's eyes, wherein the biometric information is used to determine the amount of peripheral defocus of the light beam from a second direction.

[0085] For example, longer eyes may suffer from higher peripheral defocus because the difference (i.e., distance) between the central and peripheral regions is greater in longer eyes compared to, for example, eyes having a substantially spherical shape. This knowledge can be appropriately considered during the process of determining the risk metric.

[0086] The first distance value can be measured along the central axis of the wearable device, and the second distance value can be measured along a peripheral orientation with respect to the central axis.

[0087] When the user looks straight ahead, the central axis of the wearable device can be aligned with the viewing orientation of the user's eyes. In other words, the central axis of the wearable device can be aligned with the viewing orientation of the user's eyes when the user looks at a point on the horizon directly in front of him / her. In the case where the wearable device includes temple arms, the central axis can be substantially parallel to the extending orientation of the temple arms. The peripheral orientation can be a direction that is inclined downward and / or laterally with respect to the central axis.

[0088] The wearable device can include an eye-tracking device for determining the viewing direction of the user's eyes. The control unit can be configured to determine a first distance value (for indicating the distance to an object located on the optical axis of the eyes) and a second distance value (for indicating the distance to an object located in a peripheral direction forming a predetermined angle greater than zero with respect to the optical axis of the eyes) based on the determined viewing orientation and based on the output of at least one distance sensor.

[0089] Thus, in the case of using an eye-tracking device, the control unit does not rely on the assumption that the first direction of the wearable device corresponds to the user's viewing direction, but can consider the user's true viewing direction to determine the first distance value and the second distance value.

[0090] The wearable device may further include a light sensor for determining light intensity and / or spectral content, and the control unit may be configured to determine a risk metric based on the light intensity and / or spectral content.

[0091] The wearable device may incorporate a light sensor and / or a color sensor co-directed with the distance sensor. The measurement of light can be used to improve the classification of objects and / or activities. For example, the screens of mobile devices, computer screens, and display panels are typically actively backlit and act as light sources. The ability to detect the light from these devices allows for increased sensitivity and specificity in classification. For example, including a light sensor co-directed with the distance sensor can help distinguish between reading a book or reading a tablet, as the latter will include a light source while the former does not. The ability to obtain periodic samples of the light intensity allows the system to adapt to detect the temporal component of the illumination, which can be used to distinguish the media types presented on the display. For example, dynamic media such as videos or games will have variable intensity and spectral content due to the frequent changes in the visual display of color and intensity content, and thus can be used to identify such content. In contrast, e-book readers or book applications will have a relatively stable visual presentation between page turns, as any dynamic changes would interfere with the reading activity. Therefore, the control unit can be configured to determine the activity based on the output of the light sensor.

[0092] Additionally or alternatively, the wearable device may include an ambient light sensor designed to measure the intensity and / or color content of ambient light. It can be aligned in the same direction as the distance sensor or in a different direction, such as upward. An ambient light sensor pointing upward will be able to measure the light from light sources that are typically located above the user's head, such as the sun, the sky, cellular lightning, streetlights, etc. The ambient light sensor may have channels designed to detect ultraviolet light. Ambient lighting conditions can have an important impact on the risk of myopia progression. High illumination conditions cause the user's pupils to constrict, increasing the depth of field of the image on the retina and reducing the effect of peripheral defocus. Conversely, low illumination causes the pupils to dilate and the image depth of field to decrease, thus maximizing the effect of peripheral defocus. Therefore, the lighting conditions can be included in the model for determining the risk metric such that in the case of a lower amount of illumination detected by the light sensor, the risk metric indicates a higher risk of myopia.

[0093] For example, a light sensor in the form of an ambient light sensor can be used to distinguish between indoor and outdoor settings. This distinction is an important factor in the risk of myopia, as it has been shown that the time spent outdoors has a protective effect on myopia progression.

[0094] Since the indoor lighting level rarely reaches that of outdoor daylight, the simplest detection can be carried out by comparing the ambient light level with a predetermined threshold and reporting an outdoor scene when the ambient light is above the threshold and an indoor scene otherwise. Since there is no artificial ultraviolet light source in a normal indoor environment, sensors sensitive to the ultraviolet spectral region should have higher specificity, while artificial ultraviolet light sources are inevitably present in the daily outdoor environment. The threshold can also be adjusted according to the time of day, season, and geographical location (longitude, latitude, and altitude) to account for the expected changes in outdoor lighting conditions. For example, at night, due to the absence of the sun as a light source, the logic based on the lighting threshold will not work, which will be explained by the information about the solar phase estimated from the location and date / time. Thus, it can be determined whether the output of the light sensor is above the predetermined threshold, and based on this, it can be determined whether the user is in an indoor or outdoor environment.

[0095] Another implementation can use the detection of light flicker. It is known that the light from screens and some modern artificial light sources is modulated. A capable device can detect the periodic fluctuations in intensity and thereby detect the presence of artificial light sources and thereby identify the indoor / outdoor scene.

[0096] Therefore, light sensors can be used to identify the type of activity of the user and / or the environment where the user is currently located.

[0097] Other sensors can be used to distinguish between outdoor / indoor settings, for example, distance sensors. Since the viewing distance range in a typical indoor environment is limited by the walls and ceiling, the indoor environment can be detected from the statistical data of the viewing distance, for example, by comparing with a threshold. The distance between walls may have great variability, while in a typical building, the distance to the ceiling is more consistent. Therefore, it is beneficial to consider including an orientation sensor to detect the presence of the ceiling and the distance to the ceiling when the system is upward. In an implementation, the wearable device can include a distance sensor with an upward tilt or a fully vertical upward orientation. Using this device, the presence of the ceiling can be reliably detected and attributed to the indoor environment.

[0098] In an implementation, the characteristics of peripheral defocus, i.e., the deviation from the center and the peripheral distance, can be used for the detection of the indoor environment. It is well known that the peripheral defocus caused by a typical indoor environment is much higher than that of the outdoor environment. Therefore, the measurement of peripheral defocus can be used alone or in combination with other signals as a signal for distinguishing indoor / outdoor settings.

[0099] In summary, for example, the hypothesis that higher lighting levels can cause the pupil size of a user's eyes to decrease, which reduces the degree of peripheral defocus caused by the increased depth of focus due to the smaller pupil size. Thus, a risk metric can be determined such that a higher lighting level over a greater amount of time results in a risk metric indicating a lower myopia risk. A more detailed implementation may include modeling the pupil size as a function of the measured scene brightness or overall lighting level. In an eye model using ray tracing, the pupil size can be included in the model for peripheral defocus calculation.

[0100] The control unit may also be configured to determine the risk metric based on the type of activity detected by the wearable device.

[0101] Possible types of activities may include exercise, reading a book or newspaper, looking at a computer monitor, looking at a smartphone monitor, etc. These activities can be determined by one or more suitable sensors (e.g., at least one distance sensor). Additionally or alternatively, one or more additional sensors may be provided to determine the type of activity, such as a camera and / or a motion sensor (e.g., an accelerometer) or a combination of multiple sensors.

[0102] The visual activities detected with the disclosed system can be used as a factor in myopia risk estimation. For example, the time spent working using an electronic display device ("screen time") can be directly input into the model as a factor increasing the risk of myopia progression. As another implementation, the peripheral defocus / accommodation error library / database during an activity can be used to estimate the cumulative value of peripheral defocus. For example, by detecting during work on a personal computer, the system can indirectly estimate peripheral defocus using a typical model of the visual environment for the personal computer. This can also be combined with the actual measurements of the environment to enrich the data. The viewing distance measured with a distance sensor can also be included in the risk model. For example, a short working distance, such as using a small mobile phone, or close reading is a recognized risk factor. Thus, the risk metric can be determined such that a statistical measure (average, median, momentum, time, etc.) of the working distance used by the user corresponding to a shorter working distance over a greater amount of time results in a risk metric indicating a higher risk of myopia progression.

[0103] The control unit may also be configured to evaluate the validity of the currently sampled environment / scene / activity. For example, when the user is working on a computer, the system may sufficiently sample the environment and be able to distinguish between primary and secondary orientations and / or positions. When the user switches to another activity, for example, turns to a colleague and engages in a discussion or stands up and walks towards the coffee machine, the environment completely changes and the algorithm representation may have to be reset. The system may be configured to evaluate the validity by comparing measurements during fixation and / or deviation. For example, when the user turns from the computer screen to a colleague, the primary distance will change from a standard computer screen distance (such as 0.6 - 0.8 m) to the distance to the counterpart, which is typically within the social distance range between colleagues, starting from 1.2 m. By detecting the sudden switch of the primary distance, the scene change can be detected and the reset of the collected statistical information can be initiated. The scene switch can also be detected with the help of additional sensors such as orientation and / or position sensors by detecting a significant change in the orientation or position of the wearable device. The motion sensor can act as a scene reset trigger by detecting a sudden significant movement of the system, for example, when the user stands up and walks away, which is associated with a significant change in the movement signature. As mentioned before, visual fixation is typically associated with a reduction in head and body movements because movements distort visual function.

[0104] The wearable device may include a control unit. In this case, the system may be implemented by the wearable device, where both the detection of the distance value and the further processing of the distance value are performed within one and the same device. In this case, the wearable device may include an output interface for outputting the determined risk metric. The risk metric may be output to, for example, the user and / or the user's treating physician. Based on the output risk metric, the physician may decide whether it is necessary to take specific measures to prevent the occurrence / development of myopia.

[0105] According to a second aspect, there is provided a method for determining a risk metric for myopia. The method includes determining at least a first distance value (indicating the distance between a wearable device attached to the user's body (in particular, attached to the user's head) and an object located in the user's central visual field) and a second distance value (indicating the distance between the wearable device and an object located in the user's peripheral visual field). The method further includes determining a risk metric for myopia based on the first distance value and the second distance value.

[0106] Each detail described above regarding the system of the first aspect can also be applied to the method of the second aspect. More precisely, the method of the second aspect may involve one or more of the additional steps / capabilities described above regarding the system of the first aspect.

[0107] According to a third aspect, there is provided a computer program product. The computer program product includes a program code portion that, when executed on one or more processing devices, performs the steps of the method of the second aspect.

[0108] A computer program product can be stored on a computer-readable recording medium. In other words, a computer-readable recording medium including the computer program product of the third aspect can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Embodiments of the technology described herein are described below with reference to the drawings, in which:

[0110] Figure 1 A schematic cross-section of a user's eye for explaining different potential causes of myopia with respect to three foci is shown;

[0111] Figure 2 A schematic cross-section of an eye for explaining the effect of peripheral defocus is shown;

[0112] Figure 3 A schematic cross-section of three eyes with different shapes and the effect of peripheral defocus on these eyes is shown;

[0113] Figure 4 A schematic cross-section of an eye with a reduced pupil size and the effect of the reduced pupil size on peripheral defocus is shown;

[0114] Figure 5 A first embodiment of a wearable device having a distance sensor for determining a risk metric for myopia is shown;

[0115] Figure 6 A second embodiment of a wearable device having a plurality of distance sensors for determining a risk metric for myopia is shown;

[0116] Figure 7 A logical structure of a control unit according to an embodiment of the present disclosure is shown;

[0117] Figure 8 Shows Figure 6 An example of the measurement results of two distance sensors shown in and the corresponding calculation mismatch;

[0118] Figure 9 The concept of a risk integrator that can be used by the control unit is shown; and

[0119] Figures 10 to 13 Different examples of response functions that the control unit can use to determine the risk metric are shown. DETAILED DESCRIPTION

[0120] Specific details are set forth below, but are not limited thereto, in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present invention can be used in other embodiments, which may be different from the details set forth below.

[0121] Figure 1 A schematic diagram showing a cross-section of the user's eye 2 is presented. Below, the possible causes of myopia will be discussed with reference to the eye 2 shown in Figure 1 . It should be noted that Figure 1 the representation does not necessarily show a specific point in time, but rather different situations are shown in the same figure for illustrative purposes.

[0122] As Figure 1 shown, light rays 4 enter the eye 2 from the left side. The light rays 4 pass through the pupil of the eye 2 and are focused by the lens of the eye 2 ( Figure 1 neither the pupil nor the lens is shown in Figure 1 ). In an ideal situation, that is, in order to obtain a clear image, the light rays 4 are focused onto the retina 6 of the eye 2. Regarding the light rays 4a that form a focal point 8 on the retina 6, this situation is shown in Figure 1 . In the case where the focal length of the lens of the eye 2 is too short (or the lens of the eye 2 is currently out of focus or focused on another object), the light rays 4 are focused in a region in front of the retina 6 and thus within the eye 2, as shown at the focal point 9 of the light rays 4b in Figure 1 . The focal point 9 is also referred to as myopic defocus, or can be regarded as the result of myopic defocus of the eye 2. In the case where the focal length of the lens of the eye 2 is too long (or the lens of the eye 2 is currently out of focus or focused on another object), the light rays 4 are focused in a region behind the retina 6 and thus outside the eye 2, as shown at the focal point 10 of the light rays 4c in

[0123] Regarding Figure 1 , the causes of myopia (nearsightedness) can be explained. The following discussion applies specifically to children and myopia caused by the growth of the eye 2. When the eye 2 is too large (i.e., has become too large), an image is formed in front of the retina 6, as discussed above regarding myopic defocus 8. However, the growth of the eye is triggered by the presence of hyperopic defocus 10 (i.e., when the image is formed behind the retina 6), see Figure 1 .

[0124] As explained below, there is a mechanism that triggers ongoing eye growth even when the eye 2 has grown too large and thus the eye 2 has become myopic. One influence that may cause this phenomenon is here referred to as the "spatial component".

[0125] Spatial component: The adjustment control mechanism is designed to focus the image on the central area around the fovea of the retina 6. Obviously, the image on the retina 6 is also formed in the peripheral area surrounding the central area. There is data showing that peripheral defocus 10 also contributes to eye growth. In a modern indoor environment, if a person (especially a child) is looking at a distant object, such as a TV screen, and there are other nearby objects (such as a table, a screen, a book, etc.) located in the peripheral orientation and projecting behind the retina 6 to form hyperopic defocus 10, then the likelihood is high because the eye 2 is not adapted to these objects. Moreover, this hyperopic defocus 10 can trigger eye growth, which may lead to myopia.

[0126] Hyperopic defocus 10 has been identified as one of the main risks for myopia development. As mentioned above, when the eye 2 focuses the image in the central area, the peripheral area (surrounding the central area) may not be in focus. This effect may be exaggerated as the eye grows because it elongates the eye 2. In this case, the peripheral area is even closer to the lens than the central area, so the image is in hyperopic defocus (or "peripheral defocus"). The eye 2 may respond to hyperopic defocus, which may first cause the choroid to thin and then the eye to grow (elongate), during which it causes a further mismatch between the central and peripheral areas of the image. This may create a vicious cycle of eye growth. Some researchers have pointed out that the peripheral area can trigger eye growth even more than the central area.

[0127] Environmental factors affecting peripheral defocus are the inevitable presence of objects in a person's visual periphery. When a person may be focusing on a distant or intermediate-distance object, there are usually other objects located closer to his head. Although these objects are not in the central visual area, they will be focused behind the retina 6, resulting in hyperopic defocus.

[0128] As Figure 1 shown, the above mechanism triggers eye growth as indicated by the thick arrow 12. As a result of this eye growth, the myopic refractive error increases, as indicated by the double-sided arrow 14 in Figure 1 .

[0129] In summary, hyperopic defocus (i.e., the image is formed behind the retina 6) may stimulate eye growth (especially in the eye growth of children).

[0130] Hyperopic defocus is typically caused by insufficient accommodation of the natural lens of the eye 2. A natural mechanism stimulates the growth of the eye, which moves the retina 6 backward and focuses the image on the retina 6. Ideally, when the eye 2 is already myopic, the defocus is myopic and thus does not trigger the growth of the eye. However, as discussed above, in some cases, this mechanism is triggered even in myopic eyes, which can lead to the adverse effect of further eye growth. As mentioned above, one effect targets defocus (spatially heterogeneous or spatial components) in the peripheral region of the eye 2.

[0131] Therefore, it may be important to understand the user's working and living environment to characterize the risk of myopia development and progression based on factors of surrounding (hyperopic) defocus. According to the present disclosure, "peripheral defocus" refers to hyperopic defocus in the peripheral region of the retina 6 of the eye 2.

[0132] Figure 2 A representation similar to Figure 1 is shown, in which the occurrence of hyperopic defocus 10 is shown. Figure 2 The representation of shows a cross-section through a vertical plane and thus shows a side view of the eye 2. The lens 16 of the eye 2 is focused on an object (not shown) set at a distance d c (the central distance) (which can also be, for example, infinity). As Figure 2 shown, an image of a distant object is formed at the regular focus 8 on the retina. However, there is another (nearby) object 18 at a distance d p (the peripheral distance) in front of the user's head. In the example shown in Figure 2 , it is a candle. Since the lens 16 of the eye 2 is not adapted to the nearby object 18, an image of the nearby object 18 is formed at the hyperopic defocus point 10, that is, in the region behind the retina. Thus, while another distant object is focused by the lens 16, the object 18 located close to the eye 2 may cause hyperopic defocus, leading to myopia (if this occurs frequently and / or over a long period).

[0133] Figure 3 A representation shows how the shape of the eye 2 affects the appearance of hyperopic defocus 10. In the Figure 3 left part of, an eye 2a with a shortened length along its optical axis is shown. In the Figure 3 middle part of, an eye 2b with a normal length is shown. Figure 3 The right part of shows an elongated eye 2c, which may be the result of excessive eye growth. As shown for the eye 2c, the central region 20 (i.e., the region where the optical axis of the eye 2c intersects the retina) is relatively far from the central axis ( Figure 3a peripheral region 22 of the retina that has a predetermined angular distance from a central horizontal axis passing through the middle of the lens 16. For example, the peripheral region 22 of the eye 2c can be in the region where the light beam 4c intersects the retina. As can be seen from the comparison between the eyes 2a and 2c, in the elongated eye 2c, the central region 20 and the peripheral region 22 are relatively far from each other, which enhances the effect of peripheral defocus.

[0134] In Figure 4 is shown the effect of a reduced pupil size on the occurrence of peripheral defocus. As Figure 4 shown, a large amount of ambient light causes the iris 24 of the eye 2 to reduce the pupil size. Due to the increased depth of focus, the degree of peripheral defocus is reduced.

[0135] Below, an example will be described of how to use the above observations to determine a risk metric for myopia through the techniques of the present disclosure.

[0136] Figure 5 Shown is a wearable device 50 according to a first embodiment of the present disclosure. The wearable device 50 is attached to a user's head 52. More precisely, the wearable device 50 is attached, for example, by means of a clamping device to a frame 58 of prescription glasses worn by the user, wherein the wearable device 50 is clamped to a temple of the frame 58 via the clamping device. However, according to the invention, the combination of the wearable device 50 and the frame 58 can also be considered as a wearable device, where, from this point of view, the wearable devices 50, 58 are attached to the user's head 52 by means of the temples of the frame 58. Instead of prescription glasses, the frame 58 can be a sunglasses frame, a frame with plano lenses without optical power, or an "empty" frame without lenses.

[0137] The wearable device 50 includes a distance sensor 54 for measuring a first distance value d(t) representing the distance between the wearable device 50 and an object 56 as a function of time. When the present disclosure states the measurement of a time-dependent distance value, this means that a plurality of individual values (d(t = t1), d(t = t2), d(t = t3), etc.) are measured one after another and optionally stored in association with a time stamp. Thus, appropriate sampling of the distance sensor 54 is achieved. The sampling frequency of the distance measurement should be sufficient to obtain a plurality of measurements during each visual activity event for statistical analysis of the data. Nowadays, due to the use of mobile devices, the attention span of humans has significantly shortened. It is normal for a user to switch from one activity to another several times per minute. Therefore, it is desirable for the distance sensor 54 to sample at a sub-second frequency. At the same time, due to the limited physical speed of the human head and body movements, there is little need to sample more frequently than 100 Hz. Therefore, the optimal range of the sampling frequency of the distance sensor can be between 1 Hz and 100 Hz. This can be applied to the distance sensor 54 of the present embodiment, but also to other distance sensors of the wearable devices described herein.

[0138] To measure the distance value d(t), the wearable device 50 may employ known techniques, such as a laser rangefinder, an ultrasonic rangefinder, etc. As Figure 5 shown, the distance sensor 54 points in a first direction, that is, it points in the first direction. In other words, the distance sensor 54 is configured to measure the distance value d(t) along the first direction (indicated by the Figure 5 line in) to the object 56 positioned along the first direction. The distance value d(t) is measured to indicate the distance between the wearable device 50 and the object 56, where the distance value between the object 56 and any reference point having a fixed spatial relationship with the wearable device 50 (e.g., a reference point where one of the user's eyes 2 is usually located) can also be measured.

[0139] As Figure 5 shown, the first direction in which the distance value d(t) is measured corresponds to the central direction along the central axis of the wearable device 50. When the wearable device 50 is worn on the user's head 52 and when the user looks straight ahead (e.g., at a point on the horizon), the central axis can be defined as the direction along the viewing direction of the user's eyes 2. The central direction substantially corresponds to the extending direction of the temple of the frame 58.

[0140] In Figure 5 the embodiment, it is assumed that the first direction corresponds to the viewing direction of the user. This assumption is a good approximation because it has been shown that users typically turn their heads 52 in the viewing direction such that the viewing direction and the central axis of the eyes generally correspond to each other.

[0141] The distance sensor 54 measures the time - related distance value d(t). The distance value d(t) represents the distance along the central axis of the user's eyes 2 towards the object 56. When the user turns his / her head during the measurement, more distance values are measured by the distance sensor 54, and more distance values can indicate the distances to different objects. For example, in the case where the user points his / her head in the direction of the object 60, it indicates the distance to the object 60 located in front of the user's head 52.

[0142] The wearable device 50 includes a memory for storing the measured distance values. In Figure 5In an embodiment, the wearable device 50 further includes a control unit for further processing the measured distance value. However, the wearable device 50 may also only measure and record the distance value, and perform further processing of the distance value at an external control unit. For example, the external control unit may be a general-purpose computer or any other suitable control unit configured to receive the distance value. The external control unit may be located in the cloud, i.e., in one or more network servers accessible via a network connection. For example, the wearable device 50 may include an interface (e.g., a wired interface or a wireless interface) for outputting the measured distance value d(t). The (external) control unit may include an interface (e.g., a wired interface or a wireless interface) for inputting the measured distance value output via the interface of the wearable device 50.

[0143] In Figure 5 an embodiment, the wearable device is a stand-alone device with an integrated control unit. However, as described above, further embodiments are possible and are also covered by the present disclosure, according to which the control unit is provided as an external device. The combination of the wearable device 50 and the control unit is also referred to as a system for determining a risk metric for myopia.

[0144] The control unit receives the measured distance value and performs further processing on the distance value in order to determine a risk metric for myopia.

[0145] More precisely, the control unit derives from the time series of the measured distance value d(t) at least a first distance value indicating the distance between the wearable device (50) and an object (e.g., Figure 5 the object 56 shown in Figure 5 in) located in the central visual field of the user and a second distance value indicating the distance between the wearable device (50) and an object (e.g.,

[0146] the object 60 shown in) located in the peripheral visual field of the user.

[0147] One way to distinguish the first distance value and the second distance value is to determine the temporal variability of the distance value d(t).

[0148] Based on the temporal variability, the control unit determines whether there is a fixation period. There is a fixation period when the temporal variability of the distance values measured during a time interval exceeding a second predetermined threshold is lower than a first predetermined threshold, and the distance values during this period are identified as first distance values. The distance values outside the fixation period are second distance values. These first and second distance values are then analyzed to determine a myopia risk metric. According to the present embodiment, the risk metric is determined such that a higher mismatch between the first distance value and the second distance value results in a risk metric indicating a higher myopia risk. In this case, it can be assumed that a peripheral defocus situation occurs.

[0149] For example, as Figure 5 shown, the user can turn his head 52 from the distant object 56 to the nearby object 60 in order to view the object 60. Therefore, the lens 16 of the user's eye 2 must change its focal length to a shorter focal length in order to focus the object 60. In the case where the user turns his head 52 back to the object 56, the focal length must change back to a longer focal length. In this case, it is assumed that the fixation period is the period during which the user directs his eye 2 towards the central region (along the central azimuth), i.e., towards the object 56. Outside the fixation period, the user's eye 2 wanders around in the peripheral region, e.g., towards the object 60.

[0150] According to one or more embodiments, the wearable device 50 may include motion sensors (e.g., accelerometers and / or gyroscopes) for detecting the user's head movement. Based on the output of the motion sensors, the direction in which the user's head 52 is pointing is derived. Based on this direction and based on the measured distance value d(t), a first distance value (in the direction in which the user's head 52 is turned) and a second (peripheral) distance value can be determined. Then, the first distance value and the second distance value can be processed by the control unit, similar to the processing of the first distance value d c (t) and the second distance value d p (t) described below with respect to the second embodiment.

[0151] Figure 6 Fig. shows a wearable device 50 according to a second embodiment of the present disclosure. The wearable device 50 of the second embodiment is similar to the wearable device 50 of the first embodiment. Therefore, in Figure 5 and Figure 6 the same reference signs are used to denote the same features. In the following, only the differences and additional features of the second embodiment will be explained, while the other features are the same as those described above with respect to the first embodiment.

[0152] The wearable device 50 of the second embodiment includes two distance sensors 54a and 54b. Similar to the distance sensor 54 of the first embodiment described above, the first distance sensor 54a points along the central axis. The first distance sensor 54a measures a first distance value d c (t) (central distance) indicating the distance between the wearable device 50 and an object 56 positioned along the central axis, in a time-dependent manner. The second distance sensor 54b measures a second distance value d p (t) (peripheral distance) in a direction different from the central axis, in a time-dependent manner. In other words, the direction along which the second distance value d p (t) is measured forms a predetermined angle with respect to the first direction. In Figure 6 an embodiment, the direction in which the second distance value d p (t) is measured (also referred to as the "peripheral direction") turns to one side with respect to the central axis such that the two directions are substantially in a horizontal plane. According to other embodiments, the second distance value d p (t) may also be measured in a direction downward with respect to the central axis (i.e., with respect to the first direction d c ) or in a direction pointing both downward and sideward with respect to the first direction d c . In each case, the second, peripheral direction is different from the first, central direction. Thus, two different distance values are measured and recorded (stored), namely the first distance value d c (t) (central distance) related to the first direction and the second distance value d p (t) (peripheral distance) related to the second direction.

[0153] More precisely, the wearable device 50 performs time-dependent measurements of the distance values d c (t) and d p (t). The control unit of the wearable device 50 receives and processes the first distance value d c (t) and the second distance value d p (t), and determines a risk index for myopia based on the first distance value d p (t) and the second distance value d c (t).

[0154] The control unit of the wearable device 50 calculates the discrepancy (i.e., mismatch) between the first distance value d c (t) and the second distance value d p (t) (more precisely, the time-dependent discrepancy value). The control unit determines the cumulative duration during which the difference (i.e., the time-dependent difference) is higher than a predetermined threshold within a predetermined period. The control unit also determines a risk index such that a higher cumulative duration results in a risk index indicating a higher risk of myopia.

[0155] If the geometry (dimensions) of the eye is given and it is assumed that the eye is oriented and focused on an object located at a distance d c (t), then the induced peripheral defocus of the image of the object located at a distance d p (t) can be calculated directly. This can be achieved by tracing light rays through the optical body of the eye. If the eye geometry is not provided, the calculation can assume a standard / default eye shape. The choice of default eye geometry can be based on user demographics such as age, gender, ethnicity or other physiological / anatomical measurements such as prescription, height, eye length, corneal curvature, pupil size, etc. If the patient / user is using refractive correction such as glasses, contact lenses, etc., this optical element can also be considered when calculating peripheral hyperopic defocus.

[0156] In another implementation, a mathematical model can be derived that relates the time-dependent distances d p (t) and d c (t) to hyperopic defocus. An approximate model of the defocus amount can be derived based on machine learning methods, whether or not an explicit calculation of the eye's optical system is performed.

[0157] In yet another embodiment, a mathematical model of the myopia progression risk can be derived from the time-dependent signals d c (t) and / or d p (t). The model may use an explicit physical calculation of the peripheral defocus. The model can use other signals collected by wearable devices such as time-dependent ambient light intensity and spectral content, amount of exercise, user's posture, etc. The model can use information about the user's eye geometry / shape / size. The model may use the user's demographics and physiological / anatomical measurements. The model can use the genetic history of eye diseases (family history of myopia). The model may include other known risk factors for myopia progression to improve prediction.

[0158] The model can be derived based on historical / follow-up data of myopia progression and measurements of the time-dependent signals d c (t) and / or d p (t). For example, the model may be able to identify the d c (t) and / or d p (t) or statistics of the derived defocus signal that typically lead to myopia progression.

[0159] The model can be derived based on a theoretical understanding of the myopia mechanism, based on statistics of observational data collected by other means, based on statistics of observational data collected by (public) wearable devices, or any combination.

[0160] A higher cumulative duration means that there is a first distance value d c(t) and a second distance value d p a longer time period in which the difference between (t) is greater than a predetermined threshold. During these time periods, the user is likely to view an object at a greater distance (e.g., Figure 6 the object 56 shown in), while at the same time, a different object at a shorter distance relative to the user's eyes (e.g., Figure 6 the object 60 shown in) causes peripheral defocus as described above. As further explained above, these situations may be a factor affecting the onset of myopia. Therefore, if such situations occur more frequently and / or for a longer time, the risk metric will be higher.

[0161] Under normal circumstances in the waking state, the human body and head are in a state of perpetual motion. Not all movements are associated with visual activity. For example, during walking, the movement of the head is not necessary to align the gaze with an object. In order to be able to study focus shifts more correctly, processing may be necessary, which will involve interpreting the origin and purpose of head movements. This processing can be based on the distance signals d c (t) and d p (t), or it can be based on signals from other sensors such as motion sensors (e.g., accelerometers, gyroscopes, magnetometers, etc.), position sensors (e.g., geolocation GPS, GLONASS, etc.) and other scene sensors, or in combination with signals from other sensors. Such scene sensors can be part of a wearable device.

[0162] For example, walking has a well-defined acceleration pattern that can be recognized by an accelerometer / gyroscope and thus compensated for in the d c (t) and d p (t) signals to estimate the actual focus deviation.

[0163] On the other hand, during tasks that require attention and vision, humans try to suppress unnecessary movements of the body and head. Therefore, attention / focus periods can be identified from the statistics of d c (t) and d p (t), for example, similar to a decrease in the change in distance within a specific time interval. Focus periods can also be identified from additional sensors (e.g., motion, rotation, position, etc.). For example, an accelerometer sensor can be used to detect focus periods as periods of reduced movement / acceleration.

[0164] The present disclosure is not limited to the above embodiments. Instead of one or two distance sensors, a camera or a three-dimensional distance scanner may be provided for determining a first (central) distance value and a plurality of different second distance values pointing in different peripheral directions in a time-related manner. Additionally, one or more sensors may be provided that simultaneously detect a plurality of distance sensors in different orientations without scanning by using spatially resolved sampling. Further, according to one or more embodiments, an eye tracking device is provided that determines the viewing direction of a user's eyes. In combination with a three-dimensional distance scanner, based on the output of the eye tracking device, it can be determined which of the plurality of measured distance values is the central distance value regarding the viewing direction and which distance values are the peripheral distance values regarding the viewing direction. Then, the control unit can use the central direction as the first direction and one or more of the peripheral directions as the second direction to determine a risk metric. The advantage of using an eye tracking device can be that the result of the risk metric is more accurate because the user's true viewing direction can be considered.

[0165] From the above description of the embodiments, it can be gleaned that the wearable device 50 of the embodiments may allow for the measurement of peripheral defocus by sampling one or more distances around the user. The wearable device 50 can be used to measure distances in the central region (first distance value) and distances in the peripheral region (second distance value). Although as described above, it may be relatively straightforward to equip the user with an eye tracker (eye tracking device) and map the distances of a three-dimensional measurement device (such as a camera or 3D-scanner) to the viewing direction from the eye tracker, it may be easier and cheaper to provide one or more distance sensors pointing in the direction of fixation, such as Figure 5 and Figure 6 as shown in the embodiment of.

[0166] Thus, a method depends on the orientation of the sensors relative to the wearable device and thus relative to the user's head's direction of fixation. It is well known that during prolonged visual activities or challenging visual tasks, people tend to align their heads with the direction of their gaze. Thus, the method depends on this alignment and algorithms can be used to be able to identify periods of alignment as well as periods of misalignment.

[0167] This can be done by analyzing signals from multiple sensors, such as inertial sensors provided in the wearable device. The distance in the central visual region (first distance value) is measured with a centrally directed sensor (first distance sensor), while the peripheral region can be probed with one or more lateral sensors (second distance value). In another embodiment, by taking advantage of the user's natural head movements, the peripheral region can be sampled with the same sensor.

[0168] The wearable device 50 of one or more embodiments measures the differences in near and far distances experienced by a user (wearer) at different directions (a part of the retina).

[0169] The processing may include estimating the optical power in the central region and the optical power on the periphery, and then determining the difference. The wearable device 50 can characterize the variability of distances in the user's environment.

[0170] Figure 7 The logical structure of a control unit 70 according to an embodiment of the present disclosure is shown. For example, the control unit 70 can be provided as a part of the wearable device 50 of an embodiment of Figure 5 or Figure 6 . Figure 7 A plurality of components 72, 74, 76, 78 are shown. Each of these components can be provided in the form of hardware or software.

[0171] The first component 72 is configured to perform geometric calculations of defocus. The input parameters for the first component 72 are a time-dependent first distance value t c (t) and a time-dependent second distance value t p (t). Optional input parameters for the first component 72 are parameters defining the eye geometry (e.g., the shape of eye 2) and parameters output by the scene sensor of the wearable device 50. These parameters may be related to the ambient light l(t) measured by the ambient light sensor of the wearable device 50. Based on the above input parameters, the first unit 72 determines the time-dependent defocus.

[0172] The time-dependent defocus is output to a second component 74 that performs defocus statistics. In other words, the second component 74 observes the time-dependent defocus and statistically analyzes the defocus. The output parameters of the second component 74 indicate defocus statistics.

[0173] A third component 76 is provided, which receives the defocus statistics and applies a model to the defocus statistics. Optional input parameters for the third component 73 are additional factors such as ambient light, working distance, genetics, etc. These factors may have an impact on the myopia risk. For example, a genetic factor may indicate an increased myopia risk for a particular user. This may result in a higher risk metric.

[0174] In a fourth component 78, a risk metric is determined based on the output of the third component 76. As Figure 7 shown, the risk metric is determined based on the first distance value t c (t) and the second distance value d p (t). In addition, the optional parameters discussed above may have an impact on the determination of the risk metric, such as eye geometry, output parameters of the scene sensor, and / or additional factors. Details regarding the determination of the risk metric are described above with respect to other embodiments.

[0175] Next, refer to Figures 8 to 13 the examples and details of the models used in the control unit.

[0176] Figure 8 It shows the regulation mismatch monitoring using a dual distance sensor system as described in the embodiment regarding Figure 6 . The first distance sensor 54a points forward and is aligned with the central vision, and the second distance sensor 54b points downward by 30 degrees and monitors the requirements of the peripheral vision. In other words, the first distance value d c (t) is also referred to as the "accommodation response", and the second distance value d p (t) is also referred to as the "accommodation demand". Figure 8 The example of

[0177] Figure 9 shows a desktop computer working event, and the distance from the monitor is about 0.8 meters - 1.0 meters. The second distance sensor 54b usually measures the distance to the same monitor, which results in a baseline accommodation error of 0.2D - 0.3D. However, it also detects the distance of an object in the user's hand or on the table, which results in an error of up to 10 diopters. These errors are integrated to produce an estimate of the risk of myopia progression. It should be noted that in this example, the accommodation error (or the mismatch between the first distance value and the second distance value) is expressed in diopters (1 / m). Figure 5 and / or Figure 6 The concept of a risk integrator that can be used by any control unit described herein (especially by the control unit of the device shown in Figure 9 . According to

[0178] , the model of the cumulative effect of myopia risk includes a reset mechanism. Animal studies have shown that in the short term, the absence of hyperopic accommodation error (clear vision) can neutralize the cumulative effect of hyperopic defocus. This effect can be considered by introducing an integration window, for example, in the form of a leaky integrator, which charges slowly in the case of hyperopic defocus and discharges relatively quickly in the absence of hyperopic defocus.

[0179] In one implementation, the risk score can be a non - negative integer accumulator variable R, which increments by a first value (e.g., 1) every complete minute when the continuous hyperopic defocus (D) exceeds a first defined threshold (D1). At the same time, every minute when the hyperopic defocus is below a second defined threshold D2 (lower than the first threshold, D1 > D2) causes the accumulator variable R to decrement by a second value, and it is expected that the absolute value of the second value is greater than the first value (e.g., 5). It is assumed that the defocus is marked with a positive value corresponding to hyperopic defocus and a negative value corresponding to myopia.

[0180] In another implementation of the risk integrator, the variable R is real-valued and non-negative and is adjusted at each time step i according to the following rule:

[0181] R(i) = f(D(i)) + R(i - 1), where R > 0

[0182] R(i) is the risk accumulator variable at time step i, R(i - 1) is the same variable at the previous time step, D(i) is a real-valued hyperopic defocus, and f(D) is a response function.

[0183] The response function can have the shape of a step function as Figure 10 shown:

[0184] For D > D1, f(D) = A (hyperopic defocus charging) and

[0185] For D < D2, f(D) = -B, (clear vision and myopic defocus discharging),

[0186] For D2 ≤ D ≤ D1, f(D) = 0, (uncertainty / insensitivity region),

[0187] where

[0188] D2 < D1 are predetermined thresholds, and

[0189] A, B > 0 (predetermined values).

[0190] The response function can be described in more detail as including linear dependence and saturation, as Figure 11 shown:

[0191] For D1′ < D, f(D) = A, (hyperopic defocus charging saturation)

[0192] For D0 < D < D1', f(D) = α(D - D0), (linear hyperopic defocus charging)

[0193] For D2′ < D < D0, f(x) = -β(D - D0), (linear clear vision / myopic defocus discharging),

[0194] For D < D2', f(x) = -B (saturated clear vision / myopic defocus discharging)

[0195] where

[0196] D2' < D0 < D1' are thresholds, and

[0197] α, β, A, B > 0 and A = α(D1′ - D0) and B = -β(D2′ - D0).

[0198] The response function can include linear dependence, saturation, and an insensitivity region, asFigure 12 As shown:

[0199] For D1′ < D, f(D) = A, (hyperopic defocus charging saturation)

[0200] For D1 < D < D1', f(D) = α(D - D1), (linear hyperopic defocus charging)

[0201] For D1 ≤ D ≤ D2, f(D) = 0, (uncertain / insensitive region),

[0202] For D2′ < D < D2, f(x) = -β(D - D2), (linear clear vision / myopic defocus discharging),

[0203] For D < D2', f(x) = -B, (saturated clear vision / myopic defocus discharging),

[0204] Where

[0205] D2' < D2 < D1 < D1' are thresholds, and

[0206] α, β, A, B > 0 and A = α(D1′ - D1) and B = -β(D2′ - D2).

[0207] The response function can be in the form of a sigmoid / logistic function, hyperbolic tangent, rectified linear unit, etc. or any combination thereof. For example, an example of a sigmoid function is shown in Figure 13 .

[0208] In the above description and figures, the same reference numerals are used for corresponding features or units of different embodiments. However, the details elaborated on one of these features or units also apply correspondingly to the features of other embodiments having the same reference signs. Furthermore, the present invention is not limited to the above embodiments, which are merely examples of how to implement the present invention. The technologies disclosed above, particularly the components of the control unit 70, can also be implemented in the form of a computer program product.

Claims

1. A system for determining a risk indicator of myopia, the system comprising: A wearable device (50) configured to be attached to a user's body, the wearable device (50) including at least one distance sensor (54; 54a, 54b), the distance sensor being configured to determine at least a first distance value and a second distance value, wherein the first distance value indicates the distance between the wearable device (50) and an object located in the user's central visual field, and the second distance value indicates the distance between the wearable device (50) and an object located in the user's peripheral visual field; A control unit (70) configured to determine a risk indicator of myopia based on the degree of mismatch between the first distance value and the second distance value.

2. The system according to claim 1, wherein, The control unit (70) is configured to Determine the risk indicator such that a higher mismatch between the first distance value and the second distance value results in a risk indicator indicating a higher risk of myopia.

3. The system according to claim 1 or 2, wherein The wearable device (50) includes: a first distance sensor pointing in the central direction to the user's central visual field, wherein the first distance sensor is configured to determine the first distance value; and a second distance sensor pointing in the peripheral direction to the user's peripheral visual field, wherein the second distance sensor is configured to determine the second distance value.

4. The system according to claim 1 or 2, wherein, The distance sensor includes a camera having a field of view that includes the central visual field and the peripheral visual field, wherein the distance sensor is configured to determine the first distance value and the second distance value based on one or more images captured by the camera.

5. The system according to claim 1 or 2, wherein The control unit (70) is configured to identify the first distance value during a fixation period and to identify the second distance value outside the fixation period, wherein during the fixation period, the variability of the distance measurements of the distance sensor (54; 54a) during a time interval is lower than a first predetermined threshold that exceeds a second predetermined threshold.

6. The system according to claim 1 or 2, wherein The wearable device (50) includes a motion sensor, and wherein the control unit (70) is configured to identify the fixation period as a period during which the motion is lower than a first predetermined threshold that exceeds a second predetermined threshold during a time interval, and to identify the first distance value during one of the fixation periods, and to identify the second distance value outside the fixation period.

7. The system according to claim 1 or 2, wherein The wearable device (50) includes exactly one distance sensor (54) for determining exactly one distance value at a given time, such that the exactly one distance sensor (54) is configured to determine the first distance value and the second distance value at different times.

8. The system according to claim 2, wherein The control unit (70) is configured to determine the cumulative duration during which the mismatch between the first distance value and the second distance value within a predetermined period is higher than a predetermined threshold, and to determine the risk indicator such that a higher cumulative duration results in a risk indicator indicating a higher risk of myopia.

9. The system according to claim 1 or 2, wherein, The wearable device (50) includes at least one additional sensor configured to output additional sensor data, wherein the control unit (70) is configured to determine the first distance value and the second distance value based on the additional sensor data and based on the output of the at least one distance sensor (54; 54a, 54b), and, wherein the additional sensor includes at least one of the following: an orientation sensor for determining the orientation of the wearable device (50), a position sensor device for determining the position of the wearable device (50), and an acceleration sensor for determining the acceleration of the wearable device (50).

10. The system according to claim 1 or 2, wherein, The wearable device (50) includes an eye tracking device for determining the viewing direction of the user's eyes (2), and, wherein the control unit (70) is configured to determine the first distance value and the second distance value based on the determined viewing direction and based on the output of the at least one distance sensor (54; 54a, 54b), wherein the first distance value is used to indicate the distance to an object located on the optical axis of the eyes (2), and the second distance value is used to indicate the distance to an object located in a peripheral direction forming a non-zero predetermined angle with respect to the optical axis of the eyes (2).

11. The system according to claim 1 or 2, wherein the wearable device (50) further includes a light sensor for determining the light intensity, and the control unit (70) is configured to determine the risk metric based on the light intensity.

12. The system according to claim 1 or 2, wherein The control unit (70) is further configured to determine the risk metric based on the type of activity of the wearer of the wearable device (50) detected by the wearable device (50), wherein the type of activity includes exercise, reading a book or newspaper, looking at a computer monitor, looking at the display of a smartphone.

13. The system according to claim 1 or 2, wherein, The wearable device (50) includes the control unit (70).

14. A computer program product including program code portions that, when the computer program product is executed on one or more processing devices, perform the following steps of a method for determining a risk metric for myopia: Determine at least a first distance value and a second distance value, wherein, The first distance value indicates the distance between the wearable device (50) attached to the user's body and an object located in the user's central visual field, and the second distance value indicates the distance between the wearable device (50) and an object located in the user's peripheral visual field; and Determining a risk metric for myopia based on the first distance value and the second distance value.

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