Monitoring conditions of living organisms

Abstract

A method for monitoring a condition of a living organism, the method comprising: (a) receiving an image set comprising at least two reflected images generated at different time points, where the reflected images are generated when the living organism is irradiated by patterned coherent electromagnetic radiation, and an indication of at least one interval between the at least two different time points, where the reflected images are generated when the living organism is irradiated by patterned coherent electromagnetic radiation; the reflected image comprises at least one pattern feature, and wherein the reflected image shows at least one pattern feature formed by irradiating at least a portion of the living organism by the patterned coherent electromagnetic radiation, and (b) determining a feature contrast of the at least two pattern features, (c) determining a condition measure of the living organism based on the characteristic contrast degree and the indication of the at least one interval, (d) outputting the condition measure.

Description

[0001] The present invention belongs to the field of monitoring the condition of a living organism. In particular, the present invention relates to a method for monitoring the condition of a living organism, a non-transitory computer-readable data medium storing a computer program comprising instructions for performing the steps of the method, a system for monitoring the condition of a living organism, the use of a signal indicating a condition-based action (the condition-based action controls a condition monitoring system), and the use of a condition measure for operating a condition monitoring system. Background Art

[0002] Technologies for remotely monitoring heart rate, pressure or blood pressure indicating the condition of a living organism are well known, such as photoplethysmography (PPG) and ballistocardiography (BCG).

[0003] PPG can be used to detect changes in blood volume. Visible light is usually used to observe intensity changes caused by light absorption. Information about the cardiovascular system can be obtained from these changes. PPG can be used in a wide range of medical applications, such as monitoring heart rate and cardiac cycle, monitoring respiration, monitoring depth of anesthesia, monitoring hypovolemia and hypervolemia, monitoring blood pressure, see for example en.wikipedia.org / wiki / Photoplethysmogram. Ballistocardiography (BCG) is a noninvasive method based on the measurement of the body movement caused by the ejection of blood with each cardiac cycle, see L. Giovangrandi et al. "Ballistocardiography-A Method Worth Revisiting" Conf Proc IEEE Eng Med Biol Soc. [IEEE Medical and Biological Engineering Society Conference Proceedings], 2011;2011:4279-4282, doi:10.1109 / IEMBS.2011.6091062.

[0004] However, due to the use of visible light, these techniques usually require proximity to a living organism. Furthermore, the area needs to be evenly illuminated. This can lead to serious disadvantages when imaging non-skin areas (e.g., background due to movement) and is costly.

[0005] The object of the present invention is therefore to overcome these drawbacks. In particular, the object is a method for monitoring the condition of a living organism which provides sufficient mobility for the living organism without causing any distraction. Such a method should be fast, require inexpensive hardware, and should not involve physical contact with the living organism. Summary of the invention

[0006] These objects are achieved by the present invention. In one aspect, the present invention relates to a method for monitoring the condition of a living organism, the method comprising:

[0007] (a) receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between the at least two different points in time, wherein the reflection images show at least a portion of a living organism when the living organism is illuminated by coherent electromagnetic radiation and include a pattern having at least one pattern feature, and

[0008] (b) determining a feature contrast of the at least two pattern features,

[0009] (c) determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval,

[0010] (d) Output the status metric.

[0011] In another aspect, the present invention relates to a method for monitoring a condition of a living organism, the method comprising:

[0012] (a) receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between the at least two different points in time, wherein the reflection images are generated when a living organism is irradiated with patterned coherent electromagnetic radiation, and wherein the reflection images show at least one pattern feature formed by irradiating at least a portion of the living organism with the patterned coherent electromagnetic radiation, and

[0013] (b) determining a feature contrast of the at least two pattern features,

[0014] (c) determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval,

[0015] (d) Output the status metric.

[0016] The present invention is based on the recognition that moving body fluids or moving particles (such as blood cells (especially red blood cells), tissue fluid, intercellular fluid, lymph fluid, ions, proteins and nutrients) in a living body may cause motion blur of reflected light, while the rest of the body is stationary and therefore does not cause motion blur. Therefore, if coherent electromagnetic radiation is reflected by moving scattering particles (such as red blood cells), the pattern will fluctuate and the pattern features will become blurred. Due to this blurring, the feature contrast will be reduced. Therefore, the pattern and the feature contrast obtained from the pattern contain information about whether the irradiated object is in motion. The feature contrast may include at least one feature contrast value. The feature contrast values ​​are generally distributed between 0 and 1. Determining the feature contrast may include determining the feature contrast value. In particular, determining the feature contrast of at least two pattern features may refer to determining a first feature contrast associated with a first pattern feature of the at least two pattern features, and determining a second feature contrast associated with a second pattern feature of the at least two pattern features. Preferably, determining the feature contrast of the at least two pattern features may refer to determining a first feature contrast value associated with a first pattern feature of the at least two pattern features, and determining a second feature contrast value associated with a second pattern feature of the at least two pattern features.

[0017] When irradiating an object, a value of 1 may represent no motion, while a value of 0 may represent the fastest motion of particles, which results in the most noticeably blurred pattern features. Coherent electromagnetic radiation refers to electromagnetic radiation that is capable of exhibiting interference effects. Coherent electromagnetic radiation may also include partial coherence, i.e., non-complete correlation between phase values. Preferably, the coherent electromagnetic radiation may be in the infrared range, most preferably in the near infrared range. In particular, the coherent electromagnetic radiation is patterned coherent electromagnetic radiation. Irradiating at least a portion of a living organism with patterned coherent electromagnetic radiation may cause at least one pattern feature to be projected onto the living organism. The reflected image generated when irradiating the living organism with patterned coherent electromagnetic radiation may show at least one pattern feature. Irradiating at least a portion of a living organism with patterned coherent electromagnetic radiation may allow the patterned coherent electromagnetic radiation to interact with the living organism, in particular with the skin of the living organism. The interaction of the patterned coherent electromagnetic radiation with the living organism, in particular with the skin of the living organism, may cause the formation of speckles. The speckle may be a contrast variation, in particular a local contrast variation of the patterned coherent electromagnetic radiation, in particular a local contrast variation of an area illuminated by the patterned coherent electromagnetic radiation. Subsequently, the pattern feature may comprise a plurality of speckles and / or contrast variations. Determining a feature contrast may mean determining a contrast variation about a pattern feature associated with different points in time. Determining a contrast variation may mean determining at least two feature contrast values, in particular a first feature contrast value associated with the first reflected image and a second feature contrast value associated with the second reflected image.

[0018] The present invention provides an apparatus that provides an efficient and robust way to monitor the condition of a living organism. The feature contrast of multiple reflected images can be used to determine condition measures such as heart rate, blood pressure, inspiration level, etc. For example, the heart rate is a sensitive measure of the condition of the living organism, especially the stress level or similar factors indicated by the condition measure. Therefore, monitoring the living organism allows the identification of situations in which the living organism is, for example, stressed, and corresponding actions can be taken based on the determined condition measure. In situations where critical condition measures provide health or safety risks, it is particularly important to identify these situations. Examples of these situations can be a driver controlling a vehicle, a user using a virtual reality head-mounted device, or a person having to make a far-reaching decision. By identifying such situations, the safety risks and health risks in these situations are reduced.

[0019] Furthermore, the invention presented herein utilizes inexpensive hardware to monitor a living organism. In addition to this, the invention is easy to integrate and implement and does not require direct contact with the living organism while providing reliable results. Therefore, the living organism is not restricted in any way by the monitoring, and by deploying light in the IR range, the living organism may not be aware of the monitoring being performed. Therefore, by deploying the methods, systems, computer readable storage media, and use of signals disclosed herein, the living organism will not be distracted by the light and will not feel monitored.

[0020] A living organism is an individual life form, preferably a human or an animal. A living organism may be characterized by a bodily fluid flowing through the body of the living organism.

[0021] In another aspect, the present invention relates to a non-transitory computer-readable data medium storing a computer program comprising instructions for executing the steps of the method according to any one of the preceding claims.

[0022] In another aspect, the invention relates to a system for monitoring a condition of a living organism, the system comprising:

[0023] (a) an input for receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between the at least two different points in time, wherein the reflection images show at least a portion of the living organism when the living organism is illuminated by coherent electromagnetic radiation and include a pattern having at least one pattern feature, and

[0024] (b) a processor for

[0025] - determining feature contrast of at least two pattern features, and

[0026] - determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval,

[0027] (c) an output terminal, which is used to output the status metric.

[0028] In another aspect, the invention relates to the use of a condition measure obtained by a method as claimed in any one of the preceding method claims in a condition control system.

[0029] In another aspect, the present invention relates to a condition control system comprising:

[0030] (a) an input for receiving a condition measure obtained by a method according to any one of the preceding method claims and a threshold value associated with a critical condition measure,

[0031] (b) a processor for generating a signal indicative of a condition-based action based on a comparison of the condition measure with a threshold value associated with the critical condition measure,

[0032] (c) An output terminal for outputting a signal indicating an action based on the condition.

[0033] In another aspect, the present invention relates to a method for monitoring a condition of a living organism, the method comprising:

[0034] (a) receiving at least a first reflected image, a second reflected image, and an indication of an interval between a time when the first reflected image was generated and a time when the second reflected image was generated, wherein the first reflected image and the second reflected image show at least a portion of the living organism when the living organism is illuminated by coherent electromagnetic radiation, the coherent electromagnetic radiation comprising a pattern having at least one pattern feature, and

[0035] (b) determining a first feature contrast ratio of a pattern feature in the first reflected image and a second feature contrast ratio of a pattern feature in the second reflected image,

[0036] (c) determining a condition measure of the living organism based on the first feature contrast, the second feature contrast, and the indication of the interval,

[0037] (a) Output the status metric.

[0038] In another aspect, the present invention relates to a method for monitoring a condition of a living organism, the method comprising:

[0039] (a) receiving at least a first reflected image, a second reflected image, and an indication of an interval between a time when the first reflected image was generated and a time when the second reflected image was generated, wherein the first reflected image and the second reflected image show at least a portion of the living organism when the living organism is irradiated with patterned coherent electromagnetic radiation, and wherein the first reflected image and the second reflected image show at least one pattern feature formed by irradiating at least a portion of the living organism with the patterned coherent electromagnetic radiation, and

[0040] (b) determining a first feature contrast ratio of a pattern feature in the first reflected image and a second feature contrast ratio of a pattern feature in the second reflected image,

[0041] (c) determining a condition measure of the living organism based on the first feature contrast, the second feature contrast, and the indication of the interval,

[0042] (d) Output the status metric.

[0043] In another aspect, the present invention relates to a method for monitoring a condition of a living organism, the method comprising:

[0044] (a) receiving at least a first reflected image, a second reflected image, and an indication of an interval between a time when the first reflected image was generated and a time when the second reflected image was generated, wherein the first reflected image and the second reflected image were generated when at least a portion of a living organism was irradiated with patterned coherent electromagnetic radiation, and wherein the first reflected image and the second reflected image show at least one pattern feature formed by irradiating at least a portion of the living organism with the patterned coherent electromagnetic radiation, and

[0045] (b) determining a first feature contrast ratio of a pattern feature in the first reflected image and a second feature contrast ratio of a pattern feature in the second reflected image,

[0046] (c) determining a condition measure of the living organism based on the first feature contrast, the second feature contrast, and the indication of the interval,

[0047] (d) Output the status metric.

[0048] Any disclosure and embodiments described herein are related to the above-listed methods, systems, uses, computer program elements, and vice versa. Advantageously, the benefits provided by any embodiment and example are also applicable to all other embodiments and examples, and vice versa.

[0049] The coherent electromagnetic radiation is patterned coherent electromagnetic radiation. The patterned coherent electromagnetic radiation may include a plurality of beams, such as at least one beam, preferably at least two beams. A pattern feature may be associated with one beam of the patterned coherent electromagnetic radiation. In particular, one pattern feature may be associated with one beam of the patterned coherent electromagnetic radiation. Thus, projecting a beam of patterned coherent electromagnetic radiation onto a surface may produce a pattern feature. The pattern feature may refer to a light spot. In particular, the pattern feature may be produced by projecting the coherent electromagnetic radiation onto a living organism.

[0050] Projecting patterned coherent electromagnetic radiation onto a regular surface may cause the pattern features projected onto the regular surface to be speckle-independent. Projecting patterned coherent electromagnetic radiation onto a regular surface may cause the pattern features projected onto an irregular surface to include at least one speckle, preferably a plurality of speckles. Subsequently, the pattern features may include zero, one or more speckles, depending on the surface onto which the patterned coherent electromagnetic radiation is projected. The skin may have an irregular surface. Therefore, projecting patterned coherent electromagnetic radiation may result in the formation of speckles within one or more pattern features. The pattern features may be the result of projecting a beam associated with the patterned coherent electromagnetic radiation. A pattern feature refers to a coherent electromagnetic radiation spot of arbitrary shape. A pattern feature may refer to a continuous area irradiated with coherent electromagnetic radiation. Projecting coherent electromagnetic radiation onto an irregular surface may result in the formation of speckles. Subsequently, the pattern features may include one or more speckles. The pattern features may have an additional diameter between 0.5 mm and 5 cm, preferably between 0.6 mm and 4 cm, more preferably between 0.7 mm and 3 cm, and most preferably between 0.4 cm and 2 cm.

[0051] For example, patterned coherent electromagnetic radiation can be generated by multiple light emitters (such as a VCSEL array). One of the multiple light emitters can emit a light beam. Therefore, one of the multiple light emitters can be associated with a pattern feature, the formation of a pattern feature, and / or the projection of a pattern feature. Additionally or alternatively, patterned coherent electromagnetic radiation can be generated by one or more light emitters and an optical element (such as a DOE or a metasurface element). The optical element can replicate a certain number of light beams associated with one or more light emitters and / or can be suitable for replicating a certain number of light beams associated with one or more light emitters. For example, the light emitter can be a laser.

[0052] The reflection image shows at least a portion of the living organism when the living organism is irradiated by the patterned coherent electromagnetic radiation. Thus, the reflection image shows the projection of the patterned electromagnetic radiation on the living organism. The projection of the patterned coherent electromagnetic radiation can show at least one pattern feature, preferably at least two pattern features. Subsequently, the reflection image shows at least one pattern feature formed by irradiating at least a portion of the living organism with the patterned coherent electromagnetic radiation.

[0053] A pattern refers to a regular and / or irregular arrangement of pattern features. Subsequently, a pattern may refer to a regular and / or irregular arrangement of one or more coherent electromagnetic radiation spots of arbitrary shape. A pattern may refer to at least one continuous area, preferably at least two, more preferably at least five, most preferably at least ten continuous areas irradiated with coherent electromagnetic radiation. A pattern may not refer to a speckle arrangement. Further, the concept of a pattern may be independent of the concept of a contrast map and / or the concept of laying out an image for determining local contrast. A pattern may refer to a regular and / or irregular arrangement of one or more light beams. A pattern and / or pattern features may be formed before the interaction of the patterned coherent electromagnetic radiation with the surface of a living organism and / or may be independent of the interaction of the patterned coherent electromagnetic radiation with the surface of a living organism.

[0054] In an embodiment, the reflected image may show at least two pattern features. Preferably, the reflected image may show five or more pattern features.

[0055] Patterned coherent electromagnetic radiation refers to coherent electromagnetic radiation with spatially varying intensity. In particular, the patterned coherent electromagnetic radiation can form a pattern including at least one pattern feature when irradiating a living organism. Further, the intensity of the patterned coherent electromagnetic radiation can vary over time, and / or the coherent electromagnetic radiation can be a continuous wave.

[0056] The use of coherent electromagnetic radiation to assess the condition of a living organism is advantageous because the living organism is illuminated with different intensities (e.g., light spots). Compared to flood illumination, the use of patterned coherent electromagnetic radiation enables photosensitive areas such as eyes to be protected from damage. In addition, feature contrast can be determined for specific pattern features, and the average value can improve the accuracy of the estimate of the condition of the living organism. In short, the brightness of the pattern features can be better improved, thereby improving the quality of the reflected image. Ultimately, the improvement in the quality of the reflected image enables a more accurate estimate of the condition of the living organism.

[0057] The term "reflection image" as used herein is not limited to an actual visual representation of an object. Instead, the reflection image includes data generated based on light reflected by an object illuminated by light. The reflection image may include at least one pattern. The reflection image includes at least one pattern feature. The reflection image may be included in a larger reflection image. The larger reflection image may be a reflection image that includes more pixels than the reflection image included therein. Dividing the reflection image into at least two parts may produce at least two reflection images. The at least two reflection images may include different data generated based on light reflected by an object illuminated by light, for example, one of the at least two reflection images may represent a nose of a living organism, and the other of the at least two reflection images may represent a forehead of a living organism. The reflection image may be suitable for determining a feature contrast of at least one pattern feature. The reflection image may include a plurality of pixels. The plurality of pixels may include at least two pixels, preferably more than two pixels. In order to determine the feature contrast, at least one pixel associated with the reflection feature and at least one pixel not associated with the reflection feature may be suitable, wherein the reflection feature refers to a pattern feature.

[0058] In particular, the term "reflected image" as used herein may refer to any data based on which an actual visual representation of the imaged object may be constructed. For example, the data may correspond to an assignment of color or grayscale values ​​to image positions, wherein each image position may correspond to a position in or on the imaged object. For example, the reflected image or data referred to herein may be two-dimensional, three-dimensional, or four-dimensional, wherein a four-dimensional image is understood as a three-dimensional image evolving over time, and similarly, a two-dimensional image evolving over time may be considered a three-dimensional image. If the data is digital data, the reflected image may be considered as a digital image, wherein the image positions may then correspond to pixels or voxels of an image and / or image sensor. When generating the reflected image, the living organism may be illuminated with light (ultimately RGB light or preferably IR flood and / or patterned light). The patterned light may include at least one pattern. The patterned light may be projected onto the living organism. The patterned light may include patterned coherent electromagnetic radiation.

[0059] In this context, "generating" also includes capturing and / or recording images.

[0060] The term "pattern" as used herein refers to, but is not limited to, any known or predetermined arrangement comprising at least one pattern feature of arbitrary shape. The pattern may include at least one pattern feature. The pattern may include an arrangement of periodic or non-periodic pattern features. The pattern may be at least one of the following: at least one quasi-random pattern; at least one Sobol pattern; at least one quasi-periodic pattern; at least one dot pattern, in particular a pseudo-random dot pattern; at least one line pattern; at least one stripe pattern; at least one checkerboard pattern; at least one triangular pattern; at least one rectangular pattern; at least one hexagonal pattern or a pattern comprising further convex tessellations. The pattern feature is at least part of the pattern. The pattern feature may at least partially include symbols of arbitrary shape. The symbol may be any of the following: at least one dot; at least one line; at least two lines, such as parallel lines or crossing lines; at least one dot and one line; at least one arrangement of periodic pattern features; at least one pattern of arbitrary shape.

[0061] The feature contrast may represent a measure of the contrast of the intensity distribution within a pattern region, in particular within a pattern feature region. Additionally or alternatively, the feature contrast may refer to a measure of the contrast associated with a pattern feature. The feature contrast of at least two pattern features may be determined. The feature contrast may indicate at least two feature contrast values, wherein a first feature contrast value of the at least two feature contrast values ​​may be associated with a first reflected image in at least two reflected images and / or a first pattern feature in at least two pattern features, and a second feature contrast value of the at least two feature contrast values ​​may be associated with a second reflected image in at least two reflected images and / or a second pattern feature in at least two pattern features. In particular, a first pattern feature of at least two pattern features, in particular at least one first pattern feature, may be associated with a first reflected image in at least two reflected images, in particular at least one first reflected image. In particular, a second pattern feature of at least two pattern features, in particular at least one second pattern feature, may be associated with a second reflected image in at least two reflected images, in particular at least one second reflected image. The feature contrast of the first pattern feature in the at least two reflected images and the second pattern feature in the at least two reflected images may be determined. The feature contrast may be determined for the at least two pattern features of the at least two reflected images, respectively.

[0062] The feature contrast may indicate and / or may include determining at least two feature contrast values ​​associated with at least two pattern features. Determining the feature contrast of at least two pattern features may include determining a first feature contrast value associated with a first pattern feature, and determining a second feature contrast value associated with a second pattern feature. The feature contrast values ​​may be determined by determining a ratio of a standard deviation of pattern feature intensities to an average of pattern feature intensities. The pattern feature intensities may include intensity values ​​associated with corresponding pattern features.

[0063] In particular, the feature contrast value K on the pattern area can be expressed as the standard deviation σ and the average pattern feature intensity The ratio, that is,

[0064]

[0065] The feature contrast value is usually distributed between 0 and 1. The feature contrast may be determined based on at least one pattern feature. Subsequently, at least two feature contrast values ​​may be determined based on at least two pattern features.

[0066] In some embodiments, to determine feature contrast, the entire pattern of the reflected image may be used. Alternatively, to determine feature contrast, a portion of the pattern may be used. Preferably, a portion of the pattern represents a pattern area that is smaller than the pattern area. The area may have any shape. A portion of the pattern may be obtained by cropping the reflected image.

[0067] For different parts of the living organism, the feature contrast may be different. Different parts of the living organism may correspond to different parts of the reflected image. Subsequently, for different parts of the reflected image, the feature contrast may be different.

[0068] The image set may include a set of reflected images. Preferably, the image set includes at least two reflected images. The set of images may be generated at different time points.

[0069] In some embodiments, at least one or more than one feature contrast value may be determined for at least one pattern feature.The feature contrast value may correspond to a numerical value of the feature contrast.

[0070] A condition measure is a measure suitable for determining the condition of a living organism. The condition of a living organism may be a physical condition and / or a mental condition. A physical condition may be associated with a level of physical stress, fatigue, excitement, suitability for performing a specific task of a living organism, etc. A mental condition may be associated with a level of mental stress, attention, concentration, excitement, suitability for performing a specific task of a living organism, etc. Such a specific task may require the living organism to be focused, attentive, awake, calm, or similar. Examples of such tasks may be: controlling machinery, vehicles, mobile devices, etc.; operating other living species; sports-related activities; playing games; tasks in emergency situations; making decisions, etc. The condition measure indicates the condition of a living organism. The condition measure may be one or more of the following: heart rate, blood pressure, inspiration level, etc. In some embodiments, the condition of a living organism may be a critical condition corresponding to a high value of the condition measure, and the condition of a living organism may be a non-critical condition corresponding to a low value of the condition measure. Subsequently, according to these embodiments, the critical condition measure may be equal to or below a threshold value, and the non-critical condition measure may be below a threshold value. In other embodiments, the condition of the living organism may be a critical condition corresponding to a low value of the condition measure, and the condition of the living organism may be a non-critical condition corresponding to a high value of the condition measure. Subsequently, according to these embodiments, the critical condition measure may be equal to or above a threshold value, and the non-critical condition measure may be below a threshold value. The critical condition measure may be associated with a high stress level, low attention, low concentration, high fatigue, high excitement, low fitness for performing a specific task of the living organism, etc. The non-critical condition measure may be associated with a low stress level, high attention, high concentration, low fatigue, low excitement, high fitness for performing a specific task of the living organism, etc.

[0071] The condition measure of the living organism can be determined based on the movement of body fluid, preferably blood, most preferably red blood cells. The movement of body fluid is not constant over time, but may change due to the activity of a part of the living organism (e.g., the heart). This change in movement can be determined based on the change in feature contrast over time. A large difference between feature contrast values ​​at different time points can be associated with a rapid change in movement. A small difference between feature contrast values ​​at different time points can be associated with a slow change in movement. The change in movement of body fluid, preferably blood, can be periodically associated with a corresponding movement frequency. Therefore, the feature contrast can change periodically with the corresponding movement frequency. The movement frequency can correspond to a period length associated with the periodic change of the feature contrast. In some embodiments, half a period can be included in at least two reflection images. In other embodiments, one or several periods can be included in at least two reflection images. Preferably, pattern features associated with the same part of the living organism can be used to determine the condition of the living organism. This is advantageous because different parts of the body have different blood perfusions and therefore different feature contrasts. In some embodiments, at least one condition measure may be determined based on feature contrast.

[0072] The image set may include a time series. The time series may include reflection images separated by constant time intervals associated with the imaging frequency or by varying time intervals. Preferably, the time series is constructed so that the imaging frequency is at least twice the motion frequency. This is known as the Nyquist theorem. For higher resolutions, the received reflection images may be at least more than the amount required by the Nyquist theorem.

[0073] To determine the condition measure, an indication of an interval between different points in time at which at least two reflection images were generated is received. The indication of the interval comprises measure(s) suitable for determining the time between different points in time at which at least two reflection images were generated.

[0074] Frequency is the reciprocal value of cycle length. Cycle length can be determined by the interval between two reflection images (including a heart beat cycle or a part of a heart beat cycle). In a normal person at rest, the heart beats 60 to 80 times per minute, corresponding to a resting heart rate of 60 to 80 beats per minute (bpm). The resting heart rate can be lower, for example, if the person is exercising or suffering from bradycardia. In the case of human activity, the heart rate may increase to 230bpm. The heart rate range of animals can be 6 to 1000bpm. Reflection images can be generated according to the expected heart rate of the living organism being examined. The interval between reflection images can be selected to be up to 10 seconds. In the case of a person, the interval can be selected to be up to 2 seconds. Subsequently, the imaging frequency can be selected to be at least 12 reflection images per minute, or at least 60 reflection images in the case of a person. In an example, the method can be used to determine the heart rate of a person. For this purpose, an imaging frequency of 60 images per minute can be selected. When determining feature contrast, it may be realized that the imaging frequency may be too low. In this case, the imaging frequency may be increased so that the condition measure may be determined. Alternatively, the imaging frequency for imaging the person may be selected to be a high frequency, such as 460 images per minute. The heart rate may be determined based on at least two reflection images and an indication of an interval between at least two different time points, which indicates an interval of 0.13 seconds. In this example, the heart rate of the person may be in the range of 60 bpm to 80 bpm. Subsequently, the imaging frequency may be adjusted according to the expected and / or predetermined condition measure.

[0075] In an example, the interval may include half, all, twice, etc. of the period length. The interval may be between at least two reflection images. Subsequently, at least two reflection images may be separated by half, all, twice, etc. of the period length. In the exemplary case of three reflection images, an indication of one or two different intervals may be received. If more than two reflection images are received, the indication of the interval may include an indication of an interval between a first reflection image and a second reflection image and / or an interval between a first reflection image and a third reflection image (or each other reflection image, if more than three reflection images may be received) and / or an indication of an interval between a second reflection image and a third reflection image (or each other reflection image, if more than three reflection images may be received). As the skilled person will appreciate, this is correspondingly applicable to other scenarios with different numbers of reflection images. The measure for the indication of the interval may be at least two time points corresponding to different time points at which the at least two reflection images are generated, and / or the time elapsed between different time points, and / or an imaging frequency associated with generating the reflection images. The at least two time points may be determined based on the timestamps of the at least two reflection images. The imaging frequency may include a selected value. The imaging frequency can be selected based on an expected condition measure (e.g., an expected heart rate). Alternatively, the imaging frequency can be determined using the image frequency of the video. The expected heart rate can include a heart rate associated with the monitored living organism. In some embodiments, the imaging frequency can be selected using an estimate of the condition measure. The estimate of the condition measure can take into account the living species and its surroundings.

[0076] In an embodiment, the image data set may include at least one first reflection image and at least one second reflection image. The at least one first reflection image and the at least one second reflection image may be generated at different points in time, in particular at least two different points in time. The at least one first reflection image and the at least one second reflection image may be generated when the living organism is irradiated with patterned coherent electromagnetic radiation. The at least one first reflection image may show at least one first pattern feature, preferably at least two first pattern features, formed by irradiating at least a portion of the living organism with patterned coherent electromagnetic radiation. The at least one second reflection image may show at least one second pattern feature, preferably at least two second pattern features, formed by irradiating at least a portion of the living organism with patterned coherent electromagnetic radiation. In particular, the at least one first pattern feature and the at least one second pattern feature may be associated with the same body part of the living organism.

[0077] A feature contrast of at least one first pattern feature and at least one second pattern feature may be determined. The feature contrast may indicate a first feature contrast value associated with the at least one first pattern feature and a second feature contrast value associated with the at least one second pattern feature. A condition measure may be determined based on the feature contrast and an indication of at least one interval by providing a data driven model with the feature contrast of the at least one first pattern feature and the at least one second pattern feature and an indication of at least one interval between the generation of at least one first reflected image and at least one second reflected image, wherein the data driven model is parameterized on a training data set including historical feature contrasts indicating a plurality of first feature contrast values ​​associated with a plurality of first pattern features and a plurality of second feature contrast values ​​associated with a plurality of second pattern features, a plurality of historical indications of at least one interval, and a plurality of historical condition measures.

[0078] In an embodiment, a condition measure of a living organism may be determined based on feature contrast and an indication of at least one interval by providing a data driven model with feature contrast of at least two pattern features and an indication of at least one interval between generation of the at least two pattern images, wherein the data driven model is parameterized on a training data set comprising historical feature contrast, historical indications of at least one interval, and historical condition measures. The feature contrast may indicate at least two feature contrast values ​​associated with the at least two pattern features.

[0079] In an embodiment, a condition measure of a living organism based on a first feature contrast, a second feature contrast, and an indication of an interval can be determined by providing the first feature contrast, the second feature contrast, and an indication of an interval to a data-driven model, wherein the data-driven model can be parameterized based on a training data set comprising historical first feature contrast, historical second feature contrast, historical indications of at least one interval, and historical condition measures.

[0080] Providing feature contrast may include providing a first feature contrast value and a second feature contrast value. The data driven model may be parameterized based on a training data set to provide and / or output a condition measure based on the provided feature contrast of at least two pattern features and an indication of at least one interval between the generation of the at least two pattern images. The data driven model may be trained based on a training data set to provide and / or output a condition measure based on the provided feature contrast of at least two pattern features and an indication of at least one interval between the generation of the at least two pattern images, the training data set comprising historical feature contrast, historical indication of at least one interval, and historical condition measures. The condition measure of the living organism may be determined based on the feature contrast and the indication of at least one interval by providing the feature contrast of at least two pattern features and an indication of at least one interval between the generation of the at least two pattern images to the data driven model, wherein the data driven model is trained on a training data set comprising historical feature contrast, historical indication of at least one interval, and historical condition measures. The data driven model may receive a feature contrast at the input layer and an indication of at least one interval and / or may provide a condition measure based on the received feature contrast at the input layer and an indication of at least one interval. The data driven model may include at least one machine learning architecture, in particular a deep learning architecture. For example, the data driven model may be a neural network, such as a CNN (in particular a 3D CNN) or a transformer. Further, the data driven model may be a transformer network.

[0081] In an embodiment, at least two pattern features may be associated with at least two reflected images. Preferably, a first pattern feature of the at least two pattern features may be shown in a first reflected image of the at least two reflected images, and a second pattern feature of the at least two pattern features may be shown in a second reflected image of the at least two reflected images. A feature contrast of at least two pattern features including the first pattern feature and the second pattern feature may be determined. In particular, determining the feature contrast of at least two pattern features including the first pattern feature and the second pattern feature may include determining a first feature contrast, the first feature contrast including a first feature contrast value of a first pattern feature of a first reflected image of the at least two reflected images and a second feature contrast value of a second pattern feature of a second first reflected image of the at least two reflected images. Determining the condition measure of the living organism based on the feature contrast and the indication of the at least one interval may include determining the condition measure based on a first feature contrast value of the first pattern feature of the first reflected image of the at least two reflected images and a second feature contrast value of the second pattern feature of the second first reflected image of the at least two reflected images.

[0082] In some embodiments, the condition measure is determined using an algorithm that can implement a mechanism model or a data-driven model. Preferably, the mechanism model reflects the physical phenomenon in mathematical form, for example, including a first principle model. The mechanism model may include a set of differential equations that describe the interaction between the object and the coherent electromagnetic radiation, thereby generating a specific condition measure. In particular, the flow of the fluid and / or the geometry of the object can be represented by the mechanism model. The mechanism model may include a relationship between at least two reflection images, an indication of a time point, and a condition measure. For this purpose, these relationships may be suitable for determining a time interval between at least two reflection images and determining a motion frequency corresponding to a number of beats per unit time (heart rate). Based on the input required for the mechanism model to generate a feature contrast as determined from the pattern of at least two reflection images, the mechanism model may be used to determine the associated condition measure. Including pulse wave analysis in the mechanism model may be suitable for determining blood pressure as another condition measure. In this way, the speed of the pulse wave may be determined. This information may be included in the at least two reflection images and the indication of the interval. The absorption and reflection behavior of a portion of a living organism may indicate an aspiration level included in at least one pattern feature of the reflectance image. Oxygen-rich blood absorbs light and thus reflects light differently than oxygen-poor blood. Other condition measures may be determined by deploying a relationship between the pattern feature and the condition measure.

[0083] Preferably, the data-driven model is a parameterized classification model. The classification model may include at least one machine learning architecture, and model parameters. For example, the machine learning architecture may be or may include one or more of the following: linear regression, logistic regression, random forest, piecewise linear, nonlinear classifier, support vector machine, naive Bayes classification, nearest neighbor, neural network, convolutional neural network, generative adversarial network, support vector machine or gradient boosting algorithm, etc. In the case of a neural network, the model may be a multi-scale neural network or a recurrent neural network (RNN), such as but not limited to a gated recurrent unit (GRU) recurrent neural network or a long short-term memory (LSTM) recurrent neural network. As used herein, the term "training", also referred to as learning, is a broad term, and will be given its common and conventional meaning for those of ordinary skill in the art and is not limited to a special or custom meaning. The term may specifically refer to but is not limited to the process of building a classification model, particularly determining and / or updating the parameters of the classification model. The classification model may be at least partially data-driven. For example, the classification model may be based on experimental data, such as data determined by irradiating a plurality of living organisms (such as humans) and recording reflected images. For example, training may include using at least one training data set, wherein the training data set includes reflectance images, such as reflectance images of multiple people with known condition measurements. For example, if the neural network is a feedforward neural network (such as a CNN), a back propagation algorithm may be applied to train the neural network. In the case of an RNN, a gradient descent algorithm or a back propagation over time algorithm may be employed to achieve training purposes.

[0084] In another aspect, the present invention relates to a non-transitory computer-readable data medium storing a computer program comprising instructions for executing the steps of the method according to any one of the preceding claims.

[0085] Any disclosure and embodiments described herein are related to the methods, systems, and computer-readable media listed above, and vice versa. Advantageously, the benefits provided by any embodiment and example are also applicable to all other embodiments and examples, and vice versa. The system can be adapted to perform the steps of the method.

[0086] "Computer-readable data medium" means any suitable data storage device or computer-readable memory having stored thereon one or more sets of instructions (e.g., software) that embody any one or more of the methods or functions described herein. The instructions may also reside completely or at least partially in the main memory and / or processor during their execution by the computer, main memory, and processing device that may constitute the computer-readable storage medium. The instructions may further be sent or received over a network via a network interface device. Computer-readable data media include, for example, a hard drive on a server, a USB storage device, a CD, a DVD, or a Blu-ray disc. The computer program may contain all the functions and data required to perform the method according to the invention, or an interface may be provided to enable parts of the method to be processed on a remote system (e.g., a cloud system). The term "non-transitory" means that the purpose of the data storage medium is to permanently store the computer program, in particular without the need for permanent power supply.

[0087] The input end includes one or more of the following: a serial or parallel interface or port, USB, Centronics port, FireWire, HDMI, Ethernet, Bluetooth, RFID, Wi-Fi, USART or SPI, or an analog interface or port (such as one or more of ADC or DAC), or a standardized interface or port to other devices.

[0088] A "processor" is a local processor, including a central processing unit (CPU) and / or a graphics processing unit (GPU) and / or an application-specific integrated circuit (ASIC) and / or a tensor processing unit (TPU) and / or a field-programmable gate array (FPGA). The processor may also be an interface with a remote computer system such as a cloud service. The processor may include or may be a secure enclave processor (SEP). The SEP may be a secure circuit configured to process reflected images. A "secure circuit" is a circuit that protects isolated internal resources from direct access by external circuits. The processor may be an image signal processor (ISP) and may include circuits suitable for processing reflected images, particularly reflected images received by an input terminal or generated by a camera. In some embodiments, the processor may be configured to receive a reflected image from a camera.

[0089] The output is one or more of a serial or parallel interface or port, USB, Centronics port, FireWire, HDMI, Ethernet, Bluetooth, RFID, Wi-Fi, USART or SPI, or an analog interface or port (such as one or more of an ADC or DAC), or a standardized interface or port to other devices. In some embodiments, the output can be configured to output a signal indicating an action based on the condition.

[0090] A system for monitoring the condition of a living organism comprises an input, a processor and an output. The system for monitoring the condition of a living organism may be adapted to perform the steps of the method described herein. The system may be or may be integrated into a monitoring device. Examples of monitoring devices may be mobile devices, such as mobile phones (e.g., smartphones), smart watches, tablet computers, laptop computers, head-mounted devices (especially virtual reality head-mounted devices), glasses (especially smart glasses). The monitoring device may be integrated into machinery, vehicles, access points, walls, furniture, etc.

[0091] A condition control system may be suitable for receiving a condition measurement obtained by any of the foregoing embodiments of the method described herein, and a threshold value. Such a condition control system may be operated without contacting a living organism. Therefore, the condition of a living organism may be monitored without being perceived by the living organism and without interfering with the living organism. In addition, the hardware required for implementing the system or method as described herein is low-cost standard hardware that is easily available without major adjustments. The condition control system includes an input, a processor, and an output, the input being used to receive a condition measurement and a threshold value, the processor being used to generate a signal indicating a condition-based action based on a comparison of the condition measurement with the threshold value, and the output being used to output a signal indicating a condition-based action. The condition control system may be suitable for initiating a condition-based action. The condition control system may be or may be integrated into a monitoring device. Examples of monitoring devices may be mobile devices, such as mobile phones (such as smartphones), smart watches, tablet computers, laptop computers, head-mounted devices (especially virtual reality head-mounted devices), glasses (especially smart glasses), or any device suitable for performing the steps of the method described herein. The monitoring device may be integrated into machinery, vehicles, access points, walls, furniture, and the like. The connection may be established via a wired or wireless connection such as one of Ethernet, USB, LAN, WLAN, etc. Examples of control devices may be mobile devices such as smartphones, smart watches, portable computers, tablet computers, tablet computers, etc. The condition control system may be deployed in scenarios where a critical condition of a living organism may cause safety issues for the living organism and / or the surrounding environment. An example of such a scenario may be one of the following: a living organism controls a device such as a vehicle or a machine.

[0092] The action based on the condition is the result of the comparison of the condition measure with the threshold value. The condition measure can be determined as described herein. The signal indicating the action based on the condition can be generated based on the comparison. The signal can be received by the condition control system. The action based on the condition can be a suggested action and / or an intervention action. In some embodiments, the signal can indicate no action. If the condition measure of the living organism is below the threshold, the living organism should not be restricted. The suggested action provides suggestions to the living organism. In some embodiments, the suggested action can provide suggestions to another living organism different from the living organism that generates the reflection image. In exemplary scenarios such as monitoring children, animals, people with health problems, the elderly, the disabled, etc., the person responsible for caring for the living organism can be notified. The person responsible for caring can be a parent, a caregiver, a doctor, a veterinarian, etc. The suggested action can include any form of providing suggestions in a form suitable for identification by the living organism. Such suggestions can be, for example, suggesting that the living organism rest, drink water and / or eat, change the condition and / or surrounding environment (e.g., audio input, temperature, visual input, air circulation, etc.) of the living organism. Examples of suggested action forms may be visible through a display function, audible through sound generation (e.g., a warning signal), or tactile through vibration. Intervention actions may include any form of external regulation. This is advantageous when the condition of the living organism is critical and can be improved by performing intervention actions. Such regulation may be, for example, regulating the condition and / or surroundings of the living organism (e.g., audio input, temperature, visual input, air circulation, etc.), limiting the time when the living organism can operate and / or control a mobile device and / or perform a specific task. In some scenarios, previous suggested actions may be ignored by the living organism, requiring intervention actions. In addition, a condition measurement that is very close to critical may have a higher risk, and it may be more appropriate to take intervention actions to deal with it. Only when the condition measurement is slightly critical can the suggested action be taken to fully deal with it.

[0093] In one embodiment, a signal indicative of a condition-based action may be generated based on a comparison of the condition measure to a threshold value, and the signal may be output.

[0094] The threshold is a predetermined value indicating that the minimum value of the condition measurement reaches a critical value. In some embodiments, the threshold can be stored locally in a memory. In other embodiments, the threshold can be provided from an external source (such as a cloud server). The threshold can be received by an input terminal. The threshold can be selected based on the degree of certainty required for the condition of a living organism of a certain living organism to reach a critical value, thereby minimizing the false positive rate. The cost of doing so is that too many situations will be identified as critical, that is, a higher false negative rate is generated. Therefore, the threshold is usually a compromise between minimizing the false positive rate and keeping the false negative rate at a moderate level. The threshold can be selected to obtain equal or nearly equal false negative rates and false negative rates. The threshold can be suitable for comparison with the condition measurement. The threshold can be a numerical value. In some embodiments, the threshold can be a value on a certain scale. The range of this scale can be between two arbitrary values, preferably between 0 and an arbitrary value, and most preferably between 0 and 1. In other embodiments, the threshold can be a predetermined value of the condition measurement. The threshold can be associated with a critical condition measurement. The threshold can include a critical condition measurement.

[0095] In one embodiment, the threshold value may be specific to a living organism. The threshold value may be a predetermined value indicating the minimum value of a condition measure required to trigger a condition-based action. To ensure safety, the threshold value needs to be properly defined. Depending on factors such as age, gender, weight, body shape, genetics, etc., heart rate, blood pressure, inspiration level, etc. may differ from the median value. Therefore, the threshold value may be adjusted based on factors specific to the living organism. The threshold value may be selected from predetermined values ​​depending on these factors. In some embodiments, the threshold value may be determined based on data generated during monitoring of the living organism. For these purposes, algorithms such as data-driven algorithms or mechanism algorithms may be deployed.

[0096] In another embodiment, at least two reflected images can be generated. The at least two reflected images can be received from a camera. In particular, an image data set including at least two reflected images can be received from a camera. The image data set including at least two reflected images can be generated with a camera. The term "camera" specifically can refer to, but is not limited to, a device having at least one imaging element, the at least one imaging element being configured to record or record spatially resolved one-dimensional, two-dimensional or even three-dimensional optical data or information. The camera can be a digital camera. As an example, the camera can include at least one camera chip, such as at least one CCD chip and / or at least one CMOS chip configured to record images. The camera can be or can include at least one near-infrared camera and / or an RGB camera. In addition, in addition to at least one camera chip or imaging chip, the camera can include additional elements, such as one or more optical elements, for example one or more lenses.

[0097] In another embodiment, the living organism can be irradiated with coherent electromagnetic radiation, which includes a pattern having at least one pattern feature. The living organism can be irradiated with an irradiation source. The irradiation can be achieved by using a projector or an irradiation source that emits a light pattern onto a portion of the living organism. The irradiation source may include at least one light source. The irradiation source may include a plurality of light sources. The irradiation source is suitable for irradiating the object. The irradiation source may include an artificial irradiation source, in particular at least one laser source and / or at least one incandescent lamp and / or at least one semiconductor light source, such as at least one light emitting diode, in particular an organic and / or inorganic light emitting diode. As an example, the light emitted by the irradiation source may have a wavelength of 300 to 1100 nm, in particular 500 to 1100 nm. Additionally or alternatively, light in the infrared spectrum range, such as light in the range of 780 nm to 3.0 μm, may be used. In particular, light in a portion of the near-infrared region for which silicon photodiodes are applicable (in particular in the range of 700 nm to 1100 nm) may be used. Using light in the near infrared region allows the light to be undetectable or only weakly detectable by the human eye and still detectable by a silicon sensor, in particular a standard silicon sensor. The illumination source may be adapted to emit light of a single wavelength. In other embodiments, the illumination may be adapted to emit light having multiple wavelengths, thereby allowing additional measurements to be made in other wavelength channels. The light source may be or may include at least one multi-beam light source. For example, the light source may include at least one laser source and one or more diffractive optical elements (DOEs). The illumination source may include at least one line laser. A line laser may be adapted to send a laser line, such as a horizontal or vertical laser line, to an object. The illumination source may include multiple line lasers. For example, the illumination source may include at least two line lasers, which may be arranged so that the illumination pattern includes at least two parallel lines or crossed lines. The illumination source may include at least one light projector, which is adapted to generate a point cloud, so that the illumination pattern may include multiple point patterns. The illumination source may include at least one mask, which is adapted to generate an illumination pattern from at least one light beam generated by the illumination source.

[0098] In an embodiment, the reflected image may be generated with a camera. Preferably, the camera may include a polarizer. The polarizer may be adapted to select the polarization of the patterned coherent electromagnetic radiation reflected from the living organism. Depending on the penetration depth, the patterned coherent electromagnetic radiation may be polarized differently. The use of a polarizer eliminates light reflected directly from the surface that would interfere with the blood flow analysis. Subsequently, when the polarizer is used while obtaining the reflected image, the analysis will be more robust.

[0099] In one embodiment, the reflection images may be separated by constant time intervals associated with the imaging frequency or by varying time intervals.

[0100] In another embodiment, the imaging frequency may be at least twice the motion frequency associated with the expected periodic motion of the body fluid. The expected periodic motion of the body fluid may be associated with the expected periodic motion of the body fluid. The motion of the body fluid may be estimated based on the living organism and its surroundings.

[0101] In another embodiment, a data driven model may be used to determine a condition measure of a living organism based on feature contrast.

[0102] In some embodiments, the living organism may move relative to the camera, wherein the movement may not correspond to the movement of blood. In this case, the image may be motion corrected based on motion tracking data associated with the movement of the living organism. Motion tracking data may be received. The motion tracking data may include at least two contour images generated when at least two reflection images may be generated and / or a video generated when at least two reflection images may be generated. A contour image may refer to an image showing the contour of at least a portion of the living organism. The contour image may be suitable for identifying at least a portion of the living organism. For example, the contour image of at least a portion of the living organism may show the shape of a body part such as a hand, a face, etc. Additionally or alternatively, the motion tracking data may include an indication of an area associated with at least a portion of the living organism irradiated with patterned coherent electromagnetic radiation, an indication of an offset between at least two different time points of an area associated with at least a portion of the living organism irradiated with patterned coherent electromagnetic radiation. The motion tracking data may be used to identify movement associated with the living organism when at least two reflection images may be generated. A measure of the speed of movement of the living organism may be determined based on an indication of at least one interval between the generation of the at least two reflection images and a distance estimate associated with the motion tracking data. For example, the distance between the two eyes in the image can be estimated. This estimation can be used to obtain the distance associated with the movement of the living organism. The correction factor can be determined based on a measure of the speed of movement of the living organism. The correction factor can be a number indicating the effect of the movement of the living organism on the feature contrast of at least two reflected images. By correcting the feature contrast according to the correction factor, the corrected feature contrast can be determined. The condition measure can be determined based on the corrected feature contrast. Correcting the feature contrast according to the correction factor to determine the corrected feature contrast can include associating the correction factor and the feature contrast by mathematical operations such as multiplication, division, summation, and division. More details about motion correction and / or motion tracking can be found in US2022039679A1.

[0103] The purpose of this correction is to correct the reflected image for movements that are not related to blood perfusion. This can be achieved by tracking the movement of the living organism. By tracking the movement, a compensation factor suitable for subtracting from the feature contrast of the reflected image can be determined. The greater the movement of the living organism that is not related to blood perfusion, the greater the correction factor used for subtraction. The correction factor can be different for different parts of the reflected image. By doing so, the correction factor takes into account the uneven movement on the reflected image. This is advantageous because it enables the use of the reflected image of the living organism when it is in motion, and therefore the reflected image does not need to be discarded and the generation of more reflected images can be avoided. Subsequently, less data needs to be generated, processed and ultimately stored, which ultimately saves energy consumption.

[0104] In an embodiment, the patterned coherent electromagnetic radiation may be associated with a wavelength between 900nm and 1000nm and / or between 1100nm and 1200nm. Preferably, the wavelength of the patterned coherent electromagnetic radiation may be between 920nm and 980nm and / or between 1110nm and 1190nm. More preferably, the wavelength of the patterned coherent electromagnetic radiation may be between 930nm and 950nm and / or between 1120nm and 1180nm. Within this range, sunlight has a band gap and the light is invisible to humans. Subsequently, when the selected wavelength range is used, people are not distracted, and the methods and systems are insensitive to sunlight. This enables the application of the present invention as described herein in the presence of ambient light and outside of a closed room. Therefore, the present invention provides a mobile and universal measurement of the condition of a living organism.

[0105] In an embodiment, patterned coherent electromagnetic radiation may be emitted from an illumination source. Further, a reflected image may be generated using a camera. The distance between the living organism and the camera and / or the distance between the living organism and the illumination source may be between 5 cm and 150 cm. Preferably, the distance between the living organism and the camera and / or the distance between the living organism and the illumination source may be between 15 cm and 100 cm. More preferably, the distance between the living organism and the camera and / or the distance between the living organism and the illumination source may be between 20 cm and 80 cm. Projecting light onto the living organism and / or generating an image of the living organism enables contactless generation of data for assessing the condition of the living organism. Further, the distance determined above enables the use of low-cost and readily available hardware, such as hardware that can be easily integrated into a mobile device (such as a phone, a laptop computer, or a watch). Therefore, the present invention provides a low-cost, mobile, and widely applicable assessment of the condition of a living organism.

[0106] In some embodiments, at least a portion of the living organism preferably shown in the at least two reflected images may correspond to the same portion of the living organism. The portion of the living organism may be suitable for reflecting at least one pattern feature. In particular, the portion of the living organism may include at least a portion of the skin of the living organism.

[0107] In some embodiments, the image set may include at least three reflectance images, and wherein the at least three reflectance images may have been generated in a time series having a constant time interval associated with an imaging frequency or a varying time interval associated with an average imaging frequency.

[0108] In some embodiments, the step of outputting the status metric may be replaced by the following operations:

[0109] - generating a signal indicative of a condition-based action based on a comparison of the condition measure with a threshold value associated with a critical condition measure,

[0110] - outputting a signal indicative of the condition-based action.

[0111] In particular, the threshold value may be specific to a living organism. Depending on the individual life form, the threshold value may be different, similar to how the condition measure depends on the individual life form. The individual life form may be, for example, a human, a dog, a cat, an elephant, a pig, a cow, a fish, a frog, etc. Due to their different anatomical structures, the condition measure (such as heart rate) will also be in different ranges. Typically, a small animal (such as a cat) will have a faster heart rate than a large living organism (such as a human or a whale). By taking the individual life form into account, the determination of condition-based actions may be improved. This provides a more accurate determination of appropriate condition-based actions.

[0112] In some embodiments, at least a portion of the living organism may be shown in the at least two reflected images and may correspond to the same portion of the living organism.

[0113] In some embodiments, the system may further include an illumination source.

[0114] In some embodiments, the system may further include a camera.

[0115] In an embodiment, the at least two reflection images may show the same part of the living organism and / or the at least two reflection images may be generated when irradiating the same part of the living organism with the patterned coherent electromagnetic radiation.

[0116] In some embodiments, signals indicative of condition-based actions may be used in a condition control system.

[0117] Due to the benefits provided by the present invention, the use of these methods, systems, computer-readable storage media and signals can be applied to different fields. Exemplary fields of application can be automotive backgrounds, medical backgrounds, entertainment industry, sports backgrounds, or any other field where critical conditions of living organisms may cause safety problems for living organisms and / or the surrounding environment. In the automotive context, the driver can be the living organism. The driver can be accurately monitored and safe control of the vehicle can be ensured. Thus, the risk of accidents is reduced. In the medical field, these methods are particularly suitable for living organisms in cases where watches, clippers or any other devices may cause problems when they are in direct contact with living organisms. Examples are (newborn) babies, living organisms with more serious injuries or living organisms in sleep. Applying the present invention to the entertainment industry and sports fields can prevent living species from being overly nervous and / or overly stressed. In this case, the living organism can be a person. These methods, signals, condition measurements and / or systems can be included and / or can be used in VR technology, especially VR head-mounted devices.

[0118] Further possible implementations or alternative solutions of the present invention also encompass combinations of features described above or below with respect to these embodiments (not explicitly mentioned herein). A person skilled in the art may also add separate or isolated aspects and features to the most basic form of the present invention.

[0119] It shall be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or the above-described embodiments with the respective independent claim.

[0120] Other embodiments, features and advantages of the present invention will become apparent from the subsequent description and dependent claims, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1 A flow chart of an example embodiment of a method for monitoring a living organism is presented.

[0122] Figure 2 A block diagram of an exemplary system for monitoring a living organism is shown, the system including an input, a processor, and an output.

[0123] Figure 3 A representation of feature contrast determined from reflectance images at different points in time is shown.

[0124] Figure 4 A flow chart of an example embodiment of a method for monitoring a living organism is presented, the method further comprising generating a signal indicative of a condition-based action.

[0125] Figure 5 An example implementation of an embodiment of the invention in a vehicle is shown.

[0126] Figure 6 Example embodiments of reflected images (620, 640, 660, 680) are shown.

[0127] Figure 7 Example embodiments of methods and systems as described herein are presented. DETAILED DESCRIPTION

[0128] Figure 1 A flow chart of an example embodiment of a method for monitoring a living organism (100) is presented.

[0129] In a first step, at least two reflection images are received (110). A first reflection image may be generated at a time point t. A second reflection image may be generated at a point t+p, where p is the period length of a cardiac cycle associated with a heart rate. In addition, an indication of an interval between different time points at which the reflection images may be generated is received (110). In the case where there is a reflection image at point t and another reflection image at point t+p, the indication of the interval is suitable for determining the interval length of p. The feature contrast at point t and at point t+p may be equal, wherein the term equal is to be understood in terms of limitations of measurement uncertainty and / or biological variation. Since blood perfusion may deviate according to various criteria, a condition measure is based on biological variation. A data-driven model may be trained to compensate for uncertainty and / or biological variation. A mechanistic model may be suitable for compensating for uncertainty and / or biological variation. More than two reflection images may be received for determining the condition measure. In a time series, due to the periodic heartbeat, imaging the time evolution of blood perfusion is also periodic. The third reflected image between the two images separated by a period length in time can be advantageously used to ensure the change of motion during the period length. For example, the reflected image can be generated or received by a virtual reality head mounted device or a vehicle. In both scenarios, it is necessary to monitor the living organism in order to ensure the safe use of virtual reality (VR) technology and the safe control of the vehicle in which the living organism is located, especially when the driver is monitored, the safety aspect will be extended to the surrounding environment of the living organism. In this way, there is no need to establish direct contact with the living organism, and the living organism (preferably a person) will not be restricted in any movement.

[0130] Based on at least two pattern features, feature contrast is determined (120). At least one pattern feature may be included in one of the at least two reflected images. Feature contrast of two of the at least two pattern features may be determined by dividing the standard deviation of the intensity of one pattern feature by the average intensity of one pattern feature, thereby determining at least two feature contrast values ​​based on the at least two pattern features. In this way, the image may be divided into several parts. These parts may have any shape, preferably a shape that can cover the entire image without overlapping. In an exemplary scenario, the reflected image may be divided into squares of size 7×7 pixels. Then, for each pixel, the feature contrast may be determined by moving the square on the reflected image, thereby determining the feature contrast of each pixel. This is called spatial laser speckle contrast imaging (LSCI). Another method for determining feature contrast is dynamic LSCI. In dynamic LSCI, a shape may be moved on a time axis by determining the standard deviation and average intensity of the intensity of at least two images from different time points. By doing so, for example, the average intensity of pixels of at least a portion of the first image and pixels of at least a portion of the second image is determined.

[0131] Since the feature contrast depends on the movement of the object, blood perfusion can be observed in one reflection image. Rapid motion refers to low contrast caused by blurring of the pattern features reflected by the moving object. The premise of detecting only the motion caused by blood perfusion is that the living organism generating the reflection image does not move relative to the camera. If the motion unrelated to body fluids cannot be avoided, the reflection image can be corrected. The feature contrast determination as described is performed for each image. In an exemplary scenario of VR technology, motion correction may not be required because the movement of the living organism relative to the VR headset is only related to the movement inside the body (such as blood perfusion). Regarding the movement of the living organism controlling the vehicle, motion correction can be deployed to ensure that all generated reflection images only include feature contrast caused by moving body fluids (such as blood perfusion). In some embodiments, motion correction is not required because the driver is not constantly moving. Depending on the amount of movement of the driver, the reflection image can be generated without the driver moving, because a single reflection image can be captured in a few milliseconds (e.g., 1 to 5ms), so only a fraction of a second is sufficient to determine a condition metric, such as heart rate.

[0132] In the next step, a condition measure is determined based on the feature contrast (130). By determining a regular change in the feature contrast, the cycle length for the heartbeat can be identified. In the case where a first reflection image has been generated at point t and a second reflection image has been generated at point t+p, the associated interval is p. Thus, the cycle length for the heartbeat can be determined. The change in blood perfusion may refer to the change in blood perfusion of at least a portion of the reflection image. A portion of the reflection image may refer to at least one portion of the body of the living organism at different time points. The portions of at least two reflection images may include the same portion of the body of the living organism. In some embodiments, at least one or more condition measures (such as heart rate, blood pressure and / or inspiration level) may be determined to provide a more meaningful result based on a combination of all different factors related to the condition of the living organism. Another condition measure may be the presence of sweat, preferably the amount of sweat. When generating a reflection image of a living organism with sweat on its skin, the sweat may be more reflective than normal skin and may not allow as much coherent electromagnetic radiation to penetrate the skin. Therefore, more radiation may be reflected from particles that do not move. This can result in an increase in the intensity of specular reflections, which are direct reflections of pattern features that do not penetrate the skin. The amount of sweat can then be determined by analyzing the ratio of specular reflections to reflections caused by scattering at moving particles, such as blood.

[0133] In a final step, the condition measure is output (140). The condition measure may be output so that it can be provided to an external system, another part of a system for monitoring a living organism, or an external device. In other embodiments, the condition measure may be transmitted to another part of the device via an interface. The condition measure may be transmitted to a condition control system.

[0134] In some embodiments, these steps starting with generating the reflected image can be performed by an external device (such as a cloud infrastructure). After completing at least some of these steps, the resulting feature contrast and / or condition metric can be output to a device for use.

[0135] Figure 2 A block diagram of an exemplary system (200) for monitoring a living organism is shown, the system comprising an input (210), a processor (220), and an output (230).

[0136] When the system is integrated into a device, the input (210) and / or output (230) can be connected to another device or other part. This connection can be wired or wireless. The connection can be established via a wired or wireless connection (such as one of Ethernet, USB, LAN, WLAN, etc.).

[0137] Preferably, the processor (220) is configured to execute as shown in reference Figure 1 or Figure 4 The method steps are described. The input end (210) can be configured as an input end of the processor (220) for receiving the reflected image. The output end (230) can be configured as an output end of the processor (220). In some embodiments, the reflected image can be generated by a camera.

[0138] Preferably, the processor (220) comprises logic circuitry for processing the reflected image or a signal associated with the reflected image. The processor (220) is configured to determine a feature contrast of a pattern shown in the reflected image. To determine the feature contrast of the pattern, the processor (220) is configured to calculate a standard deviation of the illumination divided by a mean intensity. The resulting feature contrast value is typically in a range between 0 and 1. A feature contrast value of 1 indicates that the pattern feature is unblurred, i.e., there is no motion in the illuminated volume of the object, while a feature contrast value of 0 indicates that the pattern feature is maximally blurred due to the detection of motion of particles (e.g., red blood cells) in the illuminated volume of the object. The processor (220) is further configured to determine a condition measure based on the determined feature contrast. The processor (220) may provide the condition measure to the providing unit (230).

[0139] To this end, the processor (220) has a neural network module including a trained neural network. The neural network is trained to predict a condition measure of the reflectance image based on the determined feature contrast. Thus, the neural network is trained to use the feature contrast determined by the processor as input and to provide the condition measure as output. The trained neural network may be, for example, a multi-scale neural network or a recurrent neural network (RNN), such as a gated recurrent unit (GRU) recurrent neural network or a long short-term memory (LSTM) recurrent neural network. Alternatively, the neural network may be a convolutional neural network (CNN).

[0140] As an alternative or in addition to a trained neural network, the processor (220) may include an algorithm that may implement a mechanistic model. The mechanistic model is configured to determine a condition measure based on first principles assumptions.

[0141] In some embodiments, the system may further include an illumination source for illuminating the object.When generating the plurality of reflected images, the object may be illuminated with light, preferably patterned IR light.

[0142] In some embodiments, the system may further include a memory for storing at least one threshold value.

[0143] Figure 3 A representation (300) of feature contrast values ​​determined from a reflectance image at different time points is shown. In particular, a plurality of feature contrast values ​​associated with a hand of a living organism at different time points may be represented. The representation may be suitable for representing blood perfusion at different time points. The representation corresponds to a contrast map. As shown in Figure 1 As mentioned in the context of , due to contrast, a single image can be evaluated to produce a measure of blood perfusion. This can be done by Figure 3 The blood perfusion map (300) shown is shown. The map (300) can be colored according to feature contrast, wherein higher feature contrast values ​​are represented by dark colors and lower feature contrast values ​​are represented by light colors. The spatial orientation of each pixel of the image corresponds to the spatial orientation of the pixel of the reflected image having the corresponding feature contrast determined.

[0144] As an example, in Figure 3 In FIG. 3 , the time evolution of blood perfusion in the hand can be seen ( 300 ). By generating several reflection images at different time points, blood flow and heart rate can be visualized. Figure 3 Part (310) is a representation of a contrast map at time point t, showing a hand with low blood perfusion. Due to the cardiac activity responsible for pumping blood into the blood vessels, blood perfusion increases to a maximum value (320) in the hand at time point t+p / 2, at which point half of the cardiac cycle has passed. After reaching the maximum value, blood perfusion decreases until an initial blood perfusion (330) is reached. The change in blood perfusion is visualized by a change in color, where light colors correspond to high blood perfusion due to low characteristic contrast values, and dark colors correspond to low blood perfusion due to high characteristic contrast values. The time elapsed between t and t+p / 2 (340) is equal to the time between t+p / 2 and t+p (350), where p corresponds to the period length. The length of both intervals is p / 2. This is in Figure 3 denoted by arrows between time points. Another interval that provides a sufficient indication of the time that has elapsed between at least two reflection images is the interval (360) of length p between the first reflection image (310) and the last reflection image (330). By identifying the contrast change from minimum blood perfusion to maximum blood perfusion, the interval of length p / 2 can be used to determine the frequency of the heart rate. Alternatively, the interval of a complete cycle of length p can also be used to determine the frequency of the heart rate, also called the motion frequency.

[0145] Figure 4 A flow chart of an example embodiment of a method for monitoring a living organism is presented, the method further comprising generating a signal indicative of a condition-based action.

[0146] In the first step, if Figure 1 As described in the context of, receiving at least two reflected images.

[0147] In the next step, if Figure 1 In the context of , determining a feature contrast of at least one pattern feature.

[0148] In the next step, if Figure 1 As described in the context of , the condition measure is determined based on feature contrast.

[0149] In some embodiments, the status metric may not be output. Instead, the status metric may be used to compare with a threshold. In other embodiments, the status metric may be output to an external system, such as a cloud infrastructure. In such an infrastructure, the following steps may be performed.

[0150] In the next step, a signal indicating a condition-based action is generated based on a comparison of the condition measure with a threshold. Such condition-based actions can be used to ensure safe application or control of devices such as VR headsets or vehicles. In the VR example, a suggestion to take a break or have a drink can be deployed, and to end the current application used with the VR headset after a certain time limit, or to change (or suggest changing) the application. Similarly, in a vehicle scenario, a suggestion to take a break, have a drink, or change the driver, or change the route to a less demanding route can be deployed, and to limit the maximum speed, turn on or off the lights or music, adjust the temperature, etc.

[0151] In a final step, a signal indicative of a condition-based action is output based on the comparison of the condition measure with the threshold value. The signal may be an input to a condition control system. The signal may be transmitted back to the reflected image generator. There, the signal may be used to operate the condition control system.

[0152] Figure 5 An example implementation of an embodiment of the present invention in a vehicle (500) is shown. For this purpose, an embodiment of the system can be integrated in a vehicle (such as a car) and is suitable for performing the steps of the method as described above. Figure 5 In the specific example shown, a person (510), specifically a driver, may be controlling the vehicle, for example by pressing the accelerator to increase speed (as can be seen on the speedometer (520)), or the driver may be steering with the steering wheel (530). To ensure safe driving, the vehicle may be equipped with a Figure 2 A system for monitoring the condition of a living organism as described above. The system for monitoring the condition of a living organism comprises an input, a processor and an output. The input and the output can be connected to the processor via a wired or wireless connection. The input can be an interface to a camera, for example, for generating at least two reflected images. The output can be an interface to another processor, for example, for further processing of a condition metric. The condition metric can be used in a condition control system. In some embodiments, the condition metric can be generated in a condition control system. A system for monitoring the condition of a living organism can be included in a condition control system. A signal indicating a condition-based action is generated based on a comparison of the condition metric with a threshold value. For this purpose, the condition control system comprises: an input, a processor (570) and an output. The processor (570) of the condition control system is suitable for generating a signal indicating a condition-based action. The processor can be further suitable for determining the condition metric based on feature contrast. The signal indicating a condition-based action can be suitable for operating a device, a device part or a device unit (such as a speaker) to generate a warning signal. In some embodiments, a condition control system may be integrated into a dashboard (540) and may include an illumination source (550), a camera (560), a processor (570), and a device, device portion, or device unit adapted to perform a condition-based action in the form of limiting a maximum speed (580a) and / or generating a warning signal (580b) with a speaker. To determine the suitability of the driver, the system may monitor a condition measure indicative of a condition of a living organism, such as a heartbeat. To determine the heart rate, at least two reflection images and an indication of an interval may be received. When the driver is illuminated with NIR light from the illumination source (550), a reflection image may be generated with the camera (560). In an exemplary scenario, the driver may be illuminated for several seconds to generate a time series of reflection images and obtain at least one indication of an interval between different time points at which the reflection images are generated. The time series may be generated at a specific imaging frequency, such as 5 frames per second. The frequency is large enough to meet the requirements of the Nyquist theorem. The driver may be illuminated with patterned coherent light, such as a point cloud in a facial area or another portion of exposed skin that may be illuminated. In this way, the illumination source (550) can be placed at a distance between 10 and 200 cm from the driver, for example, the illumination source (550) can be integrated in a speedometer, a steering wheel or a dashboard. The illumination light can be reflected by the driver's skin and recorded as a pattern feature in the reflected image. For this purpose, a camera (560) can be used, which can also be integrated in the speedometer, the steering wheel or the dashboard. The camera (560) can be equipped with an image sensor that is sensitive to IR light or NIR light. These image sensors can sense the reflected light, thereby generating a reflected image showing at least one pattern feature. This reflected image can be received by an input terminal and transmitted to a processor (570).The processor (570) may be adapted to calculate the mean pattern feature intensity by dividing the standard deviation σ of the mean intensity. To determine a characteristic contrast. The characteristic contrast so determined contains information about the motion of the irradiated object. In the case of the skin, motion may occur due to blood perfusion. Low contrast may correspond to fast motion. In some scenarios, the driver may turn his head or make other movements while driving. Therefore, motion correction can be used to compensate for motion that is not related to blood perfusion. For this purpose, a variety of methods and devices are known in the prior art. By determining the characteristic contrast of at least two reflection images, the time evolution of blood perfusion can be tracked. Using an indication of at least one interval between at least two time points when the reflection images were generated, the frequency of the heart rate can be determined, because the frequency of the heart pumping cycle can be determined using at least two reflection images and at least one indication of the interval. Other condition measures (such as blood pressure or inspiration level) can be associated with information about how the coherent electromagnetic radiation interacts with the skin (which is included in the reflection image) and at least one interval. In order to obtain better results, in the case of blood pressure, reference measurements or reference values ​​can be used to adjust these values, thereby improving accuracy. In order to determine blood pressure and inspiration level, data-driven models or mechanism models can be deployed. Mechanism models are known in the prior art. The data driven model can be trained according to a training data set. Such a training data set may include a historical data set, which includes a reflection image and / or a feature contrast value, an indication of at least one interval, and a condition measurement corresponding to the reflection image. Such a model can be used for a processor, and (570) can be used to determine the condition measurement. Since the condition measurement is an indication of the condition of a living organism, the condition measurement can be suitable for monitoring the driver. If the condition measurement exceeds a threshold value that is considered to be a critical value of the condition measurement, the risk of unsafe driving may be high. In an example, a stressed driver (510) can be identified via a high heart rate and / or high blood pressure. This may be particularly dangerous during a vehicle traveling at a higher speed. In an example, a threshold value of 110 can be selected as a critical value for the driver's heart rate, and the driver's condition measurement can be 120 beats per minute, thereby triggering an action based on the condition. In this example, the driver (510) may be driving at an increased speed of 180km / h. The situation control system can generate a signal indicating a situation-based action, such as limiting the maximum speed to 130 km / h (580a) or generating a warning signal (580b) using a loudspeaker. This prevents the driver (510) from increasing the speed to a value greater than 130 km / h as long as the situation measure, in particular the heart rate, exceeds a threshold value selected for the driver's (510) heart rate. In this way, the vehicle can be braked until the speed is equal to or less than 130 km / h and acceleration to a speed greater than 130 km / h is prohibited. The threshold value is selected so that the driver (510) has a chance to calm down by reducing the stressful effects of the high speed on him.

[0153] Figure 6 Example embodiments (620, 640, 660, 680) of a reflection image associated with a contour of a living organism are shown. The reflection image shows at least a portion of the living organism under illumination by patterned coherent electromagnetic radiation. For this purpose, the living organism may be illuminated with patterned coherent electromagnetic radiation. The patterned coherent electromagnetic radiation may include at least one light beam. The at least one light beam may be projected onto the living organism. Projecting at least one light beam onto the living organism may cause the coherent electromagnetic radiation to illuminate at least one continuous area. The reflection image may be generated by light reflected from the living organism illuminated by the at least one light beam. At least one continuous area of ​​coherent electromagnetic radiation may be seen in the reflection image, for example, in the form of a light spot. The light spot in the reflection image may be referred to as a pattern feature. In the embodiment of FIG. Figure 6 In , four examples (620, 640, 660, 680) of reflection images associated with the outline of a living organism are shown. 620 may refer to a reflection image generated when the hand of a living organism is irradiated with patterned coherent electromagnetic radiation. In example 620, the pattern features may be star-shaped and may be arranged irregularly. The hand in 620 may be drawn to outline a reflection image that may be generated when the hand of a living organism is irradiated with patterned electromagnetic radiation. The reflection image may be independent of the hand outline of the living organism. Similarly, 640 shows six circular spots reflected by the hand of a living organism. Subsequently, the pattern associated with the patterned electromagnetic radiation may be a regular dotted pattern with rectangular symmetry. In 660, the face of a living organism is irradiated with patterned coherent electromagnetic radiation. Projecting patterned coherent electromagnetic radiation may cause the pattern features to be arranged regularly, such as a hexagonal arrangement. The pattern associated with the patterned coherent electromagnetic radiation may be a hexagonal pattern. In 680, the face of a living organism is irradiated with patterned coherent electromagnetic radiation. The patterned coherent electromagnetic radiation may be projected so that the pattern features are arranged irregularly. This may be achieved by randomly arranging the beam emitters in the illumination source and / or using different optical elements associated with the beams that are associated with the patterned coherent electromagnetic radiation.

[0154] Figure 7 An example application of the methods and systems described herein is shown. Figure 2 The system described in the context of Figure 1 The method described in the context of can be used for patient monitoring. In such an embodiment, a patient 720 can be irradiated by patterned coherent electromagnetic radiation emitted from an irradiation source 740. Preferably, the patient can be in a resting state, for example lying on a bed. The irradiation source can include a VCSEL array and a DOE for replicating the light beams emitted by the VCSEL array. Therefore, the irradiation source can emit multiple coherent light beams. The multiple coherent light beams (also referred to as patterned coherent electromagnetic radiation) can be projected onto the patient 720. Subsequently, the patient 720 can be irradiated with a light pattern. The light can interact with the skin of the patient 720. The interaction between coherent light and the skin of a living organism (such as the patient 720) can result in the formation of speckles. Two reflection images showing a patient under irradiation with coherent electromagnetic radiation can be generated at two different time points using a camera 760. The reflection image can show the projection of multiple light beams onto the skin of the patient 720, thereby generating multiple pattern features. Preferably, the number of pattern features can be equal to the number of light beams associated with the patterned coherent electromagnetic radiation. The reflection image can be associated with a timestamp. Based on the time stamp associated with the reflection image, an indication of at least one interval between at least two different time points can be obtained. The indication of the interval and the reflection image can be provided to the processor 780 via the input. The processor and the input can refer to Figure 2 The processor may execute as in Figure 1 The condition measure can be as follows: Figure 1 , for example via Figure 2 The output terminal of the system described in the context of . In an example, the condition measure can be provided to a display 790. The display 790 can be suitable for displaying the condition measure, in particular the value and / or unit associated with the condition measure. The display 790 can be used to visually track the condition of the patient 720.

[0155] As used herein, "determining" also includes "initiating or causing determination", "generating" also includes "initiating or causing generation", and "providing" also includes "initiating or causing determination, generation, selecting, sending, or receiving". "Initiating or causing performance of an action" includes any processing signal that triggers a computing device to perform a corresponding action.

[0156] Any disclosure and embodiments described herein are related to the above-listed methods, systems, apparatuses, computer program elements, and vice versa. Advantageously, the benefits provided by any embodiment and example are also applicable to all other embodiments and examples, and vice versa.

[0157] In the claims and the description, the word "comprising" does not exclude other elements or steps. The indefinite article "a" or "an" and the definite article "the" do not exclude a plurality. In particular, the indefinite article "a" or "an" may be replaced by one or more and the definite article "the" may be replaced by one or more. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to an advantageous embodiment.

Claims

1. A method for monitoring the condition of a living organism, the method include: (a) receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between the at least two different points in time, wherein the reflection images are generated when a living organism is irradiated with patterned coherent electromagnetic radiation, and wherein the reflection images show at least one pattern feature formed by irradiating at least a portion of the living organism with the patterned coherent electromagnetic radiation, and (b) determining a feature contrast of the at least two pattern features, (c) determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval, (d) Output the status metric.

2. The method according to claim 1, in, The image set comprises at least three reflectance images, and wherein the at least three reflectance images have been generated in a time series having a constant time interval associated with an imaging frequency or a varying time interval associated with an average imaging frequency.

3. The method according to claim 2, in, The imaging frequency is at least twice the motion frequency associated with the expected periodic motion of the body fluid.

4. The method according to any one of the preceding claims, in, The condition measure of the living organism based on the feature contrast and the indication of the at least one interval is determined by providing the feature contrast of the at least two pattern features and the indication of at least one interval between the generation of the at least two pattern images to a data-driven model, wherein the data-driven model is parameterized on a training data set comprising historical feature contrast, historical indications of the at least one interval, and historical condition measures.

5. The method according to any one of the preceding claims, in, The reflected images are motion corrected based on motion tracking data associated with the movement of the living organism.

6. The method according to any one of the preceding claims, in, The coherent electromagnetic radiation has a wavelength between 900 nm and 1000 nm.

7. The method according to any one of the preceding claims, in, The patterned coherent electromagnetic radiation is emitted from an illumination source, and wherein a set of images comprising at least two reflected images is received from a camera, and wherein a distance between the living organism and the camera and / or a distance between the living organism and the illumination source is between 5 cm and 150 cm.

8. The method according to any one of the preceding claims, in, The condition measure includes at least one of heart rate, blood pressure and / or inspiration level.

9. The method according to any one of the preceding claims, in, Outputting this status metric is replaced by the following operations: - generating a signal indicative of a condition-based action based on a comparison of the condition measure with a threshold value associated with a critical condition measure, - outputting a signal indicative of the condition-based action.

10. The method according to claim 9, in, The threshold is specific to the living organism.

11. A non-transitory computer-readable data medium storing a computer program comprising instructions for executing the steps of the method according to any one of the preceding claims.

12. A system for monitoring the condition of a living organism, the system include: (a) an input for receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between the at least two different points in time, wherein the reflections are generated when a living organism is illuminated by patterned coherent electromagnetic radiation, and wherein the reflection images show at least one pattern feature formed by illuminating at least a portion of the living organism with the patterned coherent electromagnetic radiation, and (b) a processor for - determining a feature contrast of the at least two pattern features, and - determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval, and (c) an output terminal, which is used to output the status metric.

13. The system according to claim 12, in, The system further comprises a camera for generating the at least two reflected images and / or an illumination source for illuminating the living organism with the patterned coherent electromagnetic radiation.

14. Use of a condition measure obtained by a method as claimed in any one of the preceding method claims in a condition control system.

15. A condition control system, include: (a) an input for receiving a condition measure obtained by a method according to any one of the preceding method claims and a threshold value associated with a critical condition measure, (b) a processor for generating a signal indicative of a condition-based action based on a comparison of the condition measure with the threshold value associated with the critical condition measure, (c) An output terminal for outputting a signal indicating an action based on the condition.

Images