Laser speckle measurement with multiple exposure times

Abstract

Systems and methods for laser speckle sensing are disclosed. A laser speckle sensor device comprises an optical sensor configured to receive coherent light that has interacted with a target region, the coherent light defining speckles. The optical sensor comprises a plurality of pixel groups, each pixel group comprising one or more pixels. A first pixel group from the plurality of pixel groups has a first effective integration time, and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time. The laser speckle sensor device also comprises a processor, communicatively connected to the optical sensor. The processor is configured to receive a plurality of signals from the optical sensor, each signal from the plurality of signals representing a respective measurement from a pixel or pixel group, determine a speckle contrast based on the plurality of signals; and calculate a parameter of interest based on the plurality of signals, the parameter of interest being a function of the effective integration time and the speckle contrast.

Description

[0001] WO41435 / SV-TD

[0002] Laser speckle measurement with multiple exposure times

[0003] Technical field

[0004] This disclosure relates to laser speckle sensing, and in particular, though not exclusively, to devices and methods for laser speckle sensing. It also relates to a computer program product enabling a computer system to perform such methods.

[0005] Background

[0006] Standard laser speckle contrast methods are based on a correlation between speckle contrast and flow velocity. However, these methods can only determine qualitative relative flow velocities. Thus, they can distinguish between high and low flow regions, or detect changes in flow over time (e.g., pulsatile flow due to a heartbeat); however, they cannot be used for quantitative measurements. As a consequence, such measurements cannot be used (reliably) for comparing measurements that are not closely related, e.g., separated by a too large amount of time, different body parts, or different persons.

[0007] Laser speckle contrast is a function of, primarily, a ratio between the exposure time and the decorrelation time and a so-called motile fraction. The decorrelation time is inversely related to the sample motion. By combining information from images with different exposure times, so-called multi-exposure speckle imaging (MESI), a quantitative flow estimation may be obtained, under the assumption that the decorrelation time (and hence, the flow) remains substantially constant during the measurements. However, such an approach is unsuitable for dynamic conditions like pulsatile blood flow where flow speed varies rapidly.

[0008] To address some of these issues, synthetic methods may be used to estimate information with different exposure times from images with single (identical) exposure times. For example, US 2024 / 0188840 A1 combines blocks of pixels to simulate longer exposure times. However, this leads to a loss of spatial resolution. As another example, US 2018 / 0344176 A1 combines information from subsequent images. However, this method is relatively slow, as there can be substantial read-out overhead, especially for sensors that have limits on size and energy consumption (so that they are suitable for wearable devices). Moreover, these methods only yield approximations (of varying quality) of cumulative intensities over longer exposure times, which introduce various errors.

[0009] Consequently, there is a need for devices and methods that can determine laserspeckle derived parameters in targets with time-varying characteristics in its dynamics, in particular for biomarker measurements using a wearable device.Summary

[0010] It is an aim of embodiments in this disclosure to provide a system and method for laser speckle sensing that avoids, or at least reduces the drawbacks of the prior art.

[0011] In an aspect, this disclosure relates to a laser speckle sensor device. The laser speckle sensor device comprises an optical sensor configured to receive coherent light that has interacted with a target region, the coherent light defining speckles. The optical sensor comprises a plurality of pixel groups, each pixel group comprising one or more pixels. A first pixel group from the plurality of pixel groups has a first effective integration time, and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time. The laser speckle sensor device also comprises a processor, communicatively connected to the optical sensor. The processor is configured to receive a plurality of signals from the optical sensor, each signal from the plurality of signals representing a respective measurement from a pixel or pixel group, determine a speckle contrast based on the plurality of signals; and calculate a parameter of interest based on the plurality of signals, the parameter of interest being a function of the effective integration time and the speckle contrast.

[0012] The pixel groups exist simultaneously; i.e., at any given time, the sensor comprises pixels belonging to different pixel groups.

[0013] In particular, calculating the parameter of interest may comprise determining a first speckle contrast value for the first pixel group and a second speckle contrast value for the second pixel group based on the plurality of signals; determining one or more parameters of a parametrized laser speckle model based on the first speckle contrast value and the second speckle contrast value; and calculating a parameter of interest based on the determined one or more parameters of the parametrized laser speckle model.

[0014] By using different effective integration times, the laser speckle contrast can be sampled as a function of the effective integration time, allowing to determine the decorrelation time of the speckles created by interaction of the coherent light with the target region. This, in turn, allows for computation of a parameter that is directly proportional to flow speed, or in some cases even an absolute flow, as well as for a more accurate determination of a relative flow (or parameters derived therefrom).

[0015] Additionally or alternatively, properties of the electromagnetic field defined by the coherent light may be determined, such as a temporal correlation of the electromagnetic field, a spatial speckle size, and / or a degree of polarization.

[0016] There are several ways to separate the speckle parameters (such as speckle decorrelation time and motile fraction) based on variations in effective integration time. A first option is to use a plurality of different, predetermined exposure times in different pixel groups, and measure the accumulated (or integrated) intensity during each predetermined exposure time. A second option is to use one or more (distinct) predetermined accumulated intensity thresholds, and measure the exposure time needed to reach each predetermined accumulated intensity threshold. In that case, each pixel typically has a different effective integration time (resulting in pixel groups of typically one pixel each). Using multiple differentaccumulated intensity thresholds allows sampling over a wider range of exposure times, compared to using only a single accumulated intensity threshold. Combinations of these options (i.e., some pixels having a predetermined exposure time and some pixels having a predetermined accumulated intensity thresholds) may also be used.

[0017] As used herein, the effective integration time defines the amount of time during which a light signal is integrated by a light-sensitive element (typically a photodiode). In systems with continuous illumination, the effective integration time is the exposure time of the lightsensitive element, but in systems with pulsed illumination, the effective integration time is generally the illumination time. As continuous illumination is assumed, the term exposure time is used herein interchangeably with effective illumination time.

[0018] It is noted that by using different pixel groups, measurements with different effective integration times can be done substantially simultaneously (i.e., in parallel). Thus, the measurements associated with the different effective integration times can be assumed to provide information about the target region in the same state. By contrast, in case of sequential measurements (e.g., as in known MESI implementations using, for instance, pulsed lasers), it is not possible to separate changes due to different exposure times from changes (over time) in the target region.

[0019] Moreover, as each pixel group is read only at the end of the exposure time, read-out overhead is minimised. In embodiments based on accumulated intensity thresholds, read-out overhead is similarly minimised. By minimising the acquisition time for all different effective integration times combined, an optimal balance between coherent light intensity, signal-to-noise ratio, and effective integration times can be obtained.

[0020] The different effective integration times (and / or accumulated intensity thresholds) may be specifically selected to sample speckle decorrelation behaviour.

[0021] More in particular, the effective integration times and / or accumulated intensity thresholds may be selected such that the largest (expected) effective integration times is at most of the same order of magnitude as the (expected) decorrelation times. The effective integration times and / or accumulated intensity thresholds may be selected such that the (expected) effective integration times cover about one to two orders of magnitude.

[0022] In some embodiments, the device may be configured to select only measurements for which the effective integration time is shorter than the decorrelation time. This can be a selection in time (in particular if the behaviour of the target region is dynamic), and / or a selection out of a plurality of effective integration times. In general, at least three different effective integration times should be selected to obtain an estimate of the decorrelation time a quantity derived thereof, or another parameter of interest. These effective integration times should be sufficiently different given the noise and uncertainty of the system.

[0023] The laser speckle sensor device may comprise a light source for generating the coherent light. The light source may be arranged relative to the optical sensor such that optical sensor receives the coherent light that has passed through the target region (transmission mode), or coherent light that is reflected (or more properly, back-scattered by particles at various depths) by the target region (back-scatter mode).In some embodiments, the device may be configured to adjust an intensity of the coherent light. In general, a higher intensity may lead to a higher signal-to-noise ratio.

[0024] However, a (too) high intensity may negatively impact battery life, and may be harmful to the target region. In embodiments based on accumulated intensity thresholds, the intensity of the coherent light has a direct impact on the effective integration times.

[0025] The illumination of the measurement region with the coherent light may be substantially homogeneous. The measurement region may be selected to be substantially homogeneous.

[0026] In principle, a system with three pixel groups is sufficient to separate the exposure time and the decorrelation time of the speckles, but a larger number of pixel groups may give a more robust result.

[0027] Similarly, as long as the results from two pixel groups are statistically distinguishable, they may be used to separate the exposure time and the decorrelation time of the speckles. In practice, the different effective integration times may differ by about a factor of hundred.

[0028] The sensor device may be used to monitor vital signs in a target region comprising living tissue. However, the device may also be used in other applications, such as fuel monitoring, irrigation monitoring, vibration measurements, and so on. The device is particularly useful in environments where the target region exhibits dynamic behaviour whose dynamics change over time on a time scale similar to that of the effective integration time of the measurements.

[0029] In an embodiment, the effective integration time is a predetermined effective integration time and the respective measurement is indicative of an accumulated intensity of the coherent light during the predetermined effective integration time.

[0030] In an embodiment, the respective measurement is indicative of the effective integration time and wherein the effective integration time of a given pixel or a given pixel group is determined by the accumulated intensity of the coherent light accumulated by the given pixel or the given pixel group reaching a predetermined accumulated intensity threshold.

[0031] Thus, in general, each measurement comprises a pair (C, 7), wherein C represents an accumulated intensity (typically in the form of an accumulated photo-electric charge), and T represents the effective integration time. Depending on the implementation, either the accumulated intensity may be fixed (with the effective integration time being variable), or the effective integration time (or exposure time) may be fixed (with the accumulated intensity being variable).

[0032] In an embodiment, a maximum predetermined effective integration time is smaller than 50 ms, e.g., smaller than 20 ms, smaller than 10 ms, or smaller than 1 ms. Typically, the predetermined effective integration times are selected between 1 ps and 1 ms.

[0033] In an embodiment, the predetermined accumulated intensity threshold is selected such that an expected maximum effective integration time is smaller than 20 ms, smaller than 10 ms, or smaller than 1 ms. Typically, predetermined accumulated intensity threshold is selected such that the expected maximum effective integration time is between 1 ps and 1 ms.This range has been found suitable for many physiological applications, being sufficiently short that changes over time in a (physiological) signal of interest may be considered negligible, and sufficiently long that meaningful measurements may be obtained.

[0034] In particular, the effective integration times may be selected around the (expected) decorrelation times of, e.g., a flow that is being monitored. This may also be referred to as the ‘sensitive’ region, i.e., the region of the temporal ratio where change in the contrast is (most) significant and hence most robust to other noise sources when fitting to / reconstructing the underlying speckle parameters.

[0035] In an embodiment, the laser speckle sensor device further comprises a light source for generating the coherent light, e.g., a laser light source (e.g., based on a LED or VCSEL) or other coherent light source. The light source may be arranged relative to the optical sensor such that, when in use, the laser speckle device is configured in a transmission mode or in a backscatter mode.

[0036] In an embodiment, determining the parameter of interest comprises determining, for each pixel group having a respective effective integration time separately, a speckle contrast value based on the respective integrated amount of light, and determining the parameter of interest based on the plurality of respective speckle contrast values. Thus, several speckle contrast values may be obtained as a function of effective integration time, and the parameter of interest can be deduced from the obtained function.

[0037] In an embodiment, the plurality of pixel groups comprises at least three pixel groups, preferably at least four pixel groups, more preferably at least eight pixel groups, each of the plurality of groups having a different effective integration time. A larger number of pixel groups may provide more robust statistics to derive the parameter of interest.

[0038] In an embodiment, the first and second effective integration times differ by at least a factor of 1.1. In some embodiments, the first and second effective integration times may differ by at least a factor of two, at least a factor of five, at least a factor of ten, or at least a factor of fifty. Although a small range of effective integration times may be sufficient to determine the parameter of interest, a larger range of effective integration times may provide more robust statistics.

[0039] The parameter of interest may be a global parameter, i.e., a single parameter value may be determined for the entire sensor, or at least a non or barely spatially-resolved parameter.

[0040] In an embodiment, the parameter of interest is a vital sign, such as a blood parameter and / or a cardiovascular parameter, e.g., at least one of: a blood flow, a perfusion rate, a blood pressure, a heart rate, a heart rate variation, a respiration rate, a shear rate, a cardiac output, or a vascular stiffness.

[0041] In an embodiment, the optical sensor is a charge-coupled device (CCD) camera or a complementary metal oxide semiconductor (CMOS) camera or a charge-injection device (CID) camera. These are suitable sensor types, especially for implementation in a wearable device with the corresponding size and energy consumption constraints.In an embodiment, a group of pixels corresponds to one or more rows of the optical sensor. If a pixel group comprises multiple rows of pixels, the rows of pixels of each pixel group may be distributed approximately evenly over a sensor area of the optical sensor.

[0042] Alternatively, the pixels of a pixel group may be distributed approximately evenly over a sensor area of the optical sensor. More in general, the pixel groups may be spatially interleaved or otherwise distributed such that all pixel groups observe the same illuminated measurement region and the same underlying speckle dynamics.

[0043] In an aspect, embodiments of this disclosure relate to a wearable device comprising a laser speckle sensor device as described herein.

[0044] In an aspect, the embodiments described in this disclosure relate to a method for determining a parameter of interest. The method comprises receiving coherent light with an optical sensor, the coherent light having interacted with a target region, the coherent light defining speckles. The optical sensor comprises a plurality of pixel groups, each pixel group comprising one or more pixels, wherein a first pixel group from the plurality of pixel groups has a first effective integration time and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time. The method further comprises receiving a plurality of signals from the optical sensor, each signal representing a respective measurement from a pixel or pixel group, and determining the parameter of interest based on the plurality of signals, the parameter of interest being a function of an effective integration time and a decorrelation time of the speckles, in particular of a ratio between the effective integration time and the decorrelation time of the speckles.

[0045] As noted above, the pixel groups are simultaneously existing pixel groups.

[0046] The step of determining the parameter of interest based on the plurality of signals may comprise determining a first speckle contrast value for the first pixel group and a second speckle contrast value for the second pixel group based on the plurality of signals; determining a speckle decorrelation time (i.e., the decorrelation time of the speckles) based on at least the first speckle contrast value and the second speckle contrast value; and determining the parameter of interest based on the plurality of signals.

[0047] As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system”. Functions described in this disclosure may be implemented as an algorithm executed by a microprocessor of a computer. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied, e.g., stored, thereon.

[0048] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, butnot limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non- exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0049] A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0050] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fibre, cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including a functional or an object oriented programming language such as Java, Scala, C++, Python or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer, server or virtualized server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0051] Aspects of the present invention are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor, in particular a microprocessor or central processing unit (CPU), or graphics processing unit (GPU), of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer, other programmable data processing apparatus, or other devicescreate means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0052] These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0053] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0054] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0055] The embodiments will be further illustrated with reference to the attached drawings, which schematically will show embodiments according to the invention. It will be understood that the invention is not in any way restricted to these specific embodiments. Identical reference signs refer to identical, or at least similar elements.

[0056] Brief description of the drawings

[0057] The embodiments will be further illustrated with reference to the attached schematic drawings, in which:

[0058] Fig. 1 schematically depicts a device according to an embodiment;

[0059] Fig. 2A-C schematically represent optical sensors for use in embodiments of the invention;

[0060] Fig. 3A-C schematically represent further optical sensors for use in embodiments of the invention;

[0061] Fig. 4 schematically represents a further optical sensor for use in embodiments of the invention;Fig. 5A and 5B are flowcharts of methods according to various embodiments; and, Fig. 6 depicts a block diagram illustrating an exemplary data processing system configured to perform one or more method steps according to an embodiment.

[0062] Detailed description

[0063] The embodiments in this disclosure aim to provide methods and systems for multiexposure speckle sensing. Although most of the examples provided below relate to biosensing, i.e., sensing a property of a living being, such as a human, other embodiments are readily apparent to the skilled person and are similarly envisaged. For example, instead of studying properties of blood in a blood vessel, similar systems may be used to monitor (mixtures of) fluids or gases in, e.g., industrial applications in a context of quality control or safety control. In such an application, the system may be embodied as an Internet of things (loT) device, e.g., a mesh device.

[0064] Fig. 1 schematically depicts a cross-section of a system according to an embodiment. The system 100 comprises a coherent-light source 102, e.g., a laser source, and an optical sensor 106. The optical sensor can be, e.g., a CCD sensor or CMOS sensor. The system may be implemented as a wearable device, e.g., in a smart watch, fitness tracker, or a dedicated wearable sensor device. In the depicted example, the coherent-light source is driven by an analogue front end 116. In principle, the wavelength of the coherent light is not very restricted and may be selected based on the sensed target, but wavelengths in the visible, infrared, and / or ultraviolet part of the electromagnetic spectrum are mostly used, in particular wavelengths in the visible and near-infrared part of the spectrum, e.g., between about 0.4 pm and 2.0 pm.

[0065] Laser-speckle analysis may be used for a wide variety of applications, including both biological applications, such as agricultural, medical, and food-related applications, and non-biological applications. In both cases, laser speckle analysis may be used for non-invasive and non-destructive analysis and / or monitoring of a sample. Some examples include monitoring of fruit ripening, food degradation, or paint drying processes; detection and measurement of parasites, contaminations, or blood contents (including oxygenation); flow measurement such as blood flow measurement, et cetera. The suitable and optimal wavelengths for each application depend on the target substance and, where applicable, the medium comprising the target substance.

[0066] The coherent-light source 102 is arranged to illuminate a target region. The target region can be a two-dimensional region, e.g., an essentially opaque surface, or a three-dimensional region, e.g., an at least partially translucent volume. In some embodiments, the lateral dimensions of the volume are much larger than the depth, e.g., when the target region is a part of the (subcutaneous) microvasculature. In the depicted example, the target region comprises a conduit 120 through which two kinds of particles 122,124 flow. The conduit may be a blood vessel, and the particles can be, e.g., red blood cells and white blood cells, or oxygenated and unoxygenated haemoglobin molecules. The conduit can also be a pipe or other structure containing moving particles. In other applications, the conduit may compriseonly a single kind of particle, or more than two kinds of particles (such as in the blood example).

[0067] The coherent light is scattered one or more times by the particles 122,124 in the conduit 120. Because coherent light is used, the scattering results in an interference pattern, the speckle pattern (which is sometimes referred to as a pseudo-random pattern). In some embodiments, coherent light of two or more distinct wavelengths may be used. Light of two wavelengths are considered distinct when the peak wavelengths are further apart than the average the FWHM of the coherent-light peaks. In such cases, a speckle pattern is generated for each of the plurality of wavelengths. The plurality of wavelengths may be selected such that the different kinds of particles in the conduit interact differently with both wavelengths. For example, the different kinds of particles may have different absorption rates at the plurality of wavelengths. This way, the relative presence of each kind of particle may be determined, e.g., the ratio between Hb and HbC>2 during SpC>2 measurements. The plurality of wavelengths may also be used for other purposes, for example, to obtain information at different depths (longer wavelengths typically having larger characteristic path lengths, generally leading to larger penetration depths), or for error correction (certain artifacts may have different effects at different wavelengths).

[0068] The scattered light is detected by the optical sensor 106. Optionally, various optical elements may be positioned in the path of the light beam between the target region and the optical sensor. In the depicted example, these optical elements comprise a lens 112 and a filter 114, but fewer, more, or different optical elements are readily envisaged. The filter can be a narrow-band filter, e.g., an interference filter. Additionally or alternatively, the filter can be a polarisation filter, e.g. a linear polarisation filter or circular polarisation filter. In the depicted example, the device further comprises a protective cover 118. Other embodiments may, additionally or alternatively, use an optical direction filter.

[0069] In the depicted example, the optical axes of the coherent-light source 102 and the optical sensor 106 are essentially parallel. This configuration can make efficient use of a limited space. In other embodiments, the coherent-light source and the optical sensor may make an angle, e.g., such that the optical axes intersect within the target region, for instance close to a centre of the target region. In the depicted example, the coherent-light source 102 and the optical sensor 106 are positioned on a same side of the target region (also known as reflection mode). In other embodiments, the coherent-light source 102 and the optical sensor 106 may be positioned on opposite sides of the target region; such a configuration is also known as transmission mode.

[0070] In other examples, changes in the target region may not be due to motion but due to other processes, e.g., chemical and / or physical processes affecting a composition of a material in the target region.

[0071] The optical sensor 106 is configured to generate signals representing measurements with different effective integration times. A measurement is typically associated with an effective integration time and an accumulated charge (accumulated during the effective integration time), which is representative of an integrated light intensity (integrated over the effective integration time). Usually, one of effective integration time and accumulated chargeis fixed while the other is variable, the signal representing the variable quantity. However, other arrangements are also possible, e.g., a pixel may be associated with both an integration time threshold and a charge threshold, and a signal may be sent when the first threshold is met.

[0072] For example, the effective integration time may be a predetermined effective integration time and the measurement signal may be indicative of an accumulated intensity of the coherent light during the predetermined effective integration time; or the measurement signal may be indicative of the effective integration time, and the effective integration time may be determined by the accumulated intensity of the coherent light reaching a predetermined accumulated intensity threshold.

[0073] Thus, the optical sensor 106 comprises a plurality of pixel groups, each pixel group comprising one or more pixels, wherein a first pixel group from the plurality of pixel groups has a first effective integration time and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time. Examples of such optical sensors are described in more detail with reference to Fig. 2-4 .

[0074] The system 100 further comprises a processor 109, communicatively connected to the optical sensor 106. The processor is configured to receive a plurality of signals from the optical sensor, each signal from the plurality of signals representing a respective measurement from a pixel or pixel group; to determine a speckle variance of light received by the first and second pixel groups; and to calculate a parameter of interest based on the plurality of signals. The speckle variance is typically a function of the effective integration time and the parameter of interest; and hence, the parameter of interest may be deduced by determining the speckle variance for several different effective integration times.

[0075] Fig. 2A-C schematically represent optical sensors for use in embodiments of the invention. In particular, Fig. 2A represents an optical sensor 200 wherein each pixel group 204i-,n is formed by one or more lines (e.g., rows or columns) of pixels 202i_nhaving a predetermined effective integration time. The predetermined effective integration time can be different for each line of pixels. Alternatively, several lines of pixels can have the same predetermined effective integration time; in that case, lines of pixels with the same predetermined effective integration time may be grouped together, distributed (substantially) evenly over all lines of pixels, or distributed in another manner. Pixel groups with different predetermined effective integration times may comprise different numbers of lines of pixels. Such sensors are commercially available, e.g., from Teledyne Vision Solutions. For example, the lines of pixels may be read out using a rolling shutter, and the lines may alternately be read from top to bottom and from bottom to top.

[0076] Fig. 2B represents an optical sensor 200 comprising rectangular sets of pixels, each set of pixels comprising a pixel from each of the pixel groups 214i-™. Each pixel group comprises one or more pixels 212i_n. In the depicted example, there are nine pixel groups, some of which may have identical predetermined effective integration times. In another example, the sensor may have four pixel groups with respective predetermined effective integration times {T, 2T, AT, 8T}, and each set of pixels may comprise one pixel from the firstpixel group, one pixel from the second pixel group, one pixel from the third pixel group, and one pixel from the fourth pixel group. Such sensors are commercially available; an example is described in US8390691B2, which is hereby incorporated by reference in its entirety.

[0077] Fig. 2C represents an optical sensor comprising rectangular (in this case, square) pixel groups 224i-m, each pixel group comprising one or more pixels 212i_n. Other arrangements are readily apparent to the skilled person.

[0078] It is noted that, regardless of the distribution of the pixel groups over the optical sensor, there is no need for the pixel groups to have equal amounts of pixels. It is furthermore noted that a single pixel does not need to have the same exposure time in each measurement; for example, a pixel may cycle through a plurality of exposure times. In general, pixels with a shorter exposure time can provide measurements more frequently than pixels with a longer exposure time. Hence, a balance may be struck between a desired amount of data points for a given exposure time, and a desired data rate.

[0079] Fig. 2A-C only depict a limited number of pixels; more realistic embodiments comprise hundreds to thousands of rows, and hundreds to thousands of columns.

[0080] Instead of a predetermined exposure time, the optical sensor may be configured to provide a signal for a pixel once the accumulated charge in that pixel exceeds a predetermined charge threshold, the signal representing the effective integration time. The predetermined charge threshold may be equal for all pixels, or different predetermined charge thresholds may be defined for different groups of one or more pixels. Such an optical sensor may be arranged in various ways.

[0081] Fig. 3A schematically depicts a pixel of an optical sensor according to an embodiment. The pixel 300 comprises a photodiode 302 which is reverse-biased so that the photocurrent is at least approximately linear to the incident light during normal operation. Thus, the total pixel current Ipis given by

[0082] >

[0083]

[0084] where IDrepresents the dark current,

[0085]

[0086] t) represents the photon flux, the surface integral is over the active area A of the photo diode, and Q is a conversion constant (typically dependent on bias voltage, wavelength of the incident light, et cetera). In the following, it will be assumed that the dark current is negligible or corrected for, so that the output current of the photodiode will be referred to as the photocurrent.

[0087] The photocurrent may be integrated (possibly after or in combination with amplification) and converted to a voltage to using an integrator 304 known in the art. Such an integrator typically comprises a capacitor on which the accumulated charge (i.e., the time-integrated current) is stored. This results in a voltage Vp(over the capacitor), referred to as the pixelvoltage, that is proportional to the stored (accumulated) charge, and hence to the integrated current. Typically, the pixelvoltage is given by

[0088]

[0089] >

[0090] wherein Cfrepresents the capacitance of the capacitor and T represents the integration time (since the last reset).This pixelvoltage is compared to a reference voltage 7refusing a comparator 306, typically a unipolar comparator with hysteresis. The reference voltage corresponds to a predetermined accumulated charge (on the pixel), for which the pixelvoltage equals the reference voltage.

[0091] When the pixelvoltage is equal to or larger than the reference voltage, a signal is sent to a sample-and-hold circuit 308, as is known in the art. The sample-and-hold circuit samples a time-varying signal, typically a time-varying voltage 7tjme,e9-> by briefly opening a connection between a voltage line carrying the time-varying voltage and a grounded capacitor. The time-varying signal varies sufficiently slowly that different effective integration times can be uniquely determined for the whole expected range. The time-varying signal may be shared with multiple pixels. Thus, the sampled and held signal, which may also be referred to as the output signal, e.g., output voltage 70Ut, can be uniquely linked to an effective integration time during which the photodiode 302 in the pixel 300 accumulated the charge corresponding to the reference voltage.

[0092] The output voltage can be read out using a read-out circuit 310. Such read-out circuits are known in the art. The read-out circuit 310 may comprise, e.g., a holding capacitor, and one or more switches for selecting a pixel to be read out.

[0093] After the output voltage has been read out, the pixel is reset, typically by removing the charge from the integrator (e.g., by temporarily closing a reset switch) and by removing the charge from the sample-and-hold circuit. Such reset switches are known in the art.

[0094] The analogue output voltage is typically converted to a digital signal using an analog-to-digital converter (ADC) 312. Such ADCs are well-known in the art. The pixel may have a dedicated ADC (e.g., provided on the pixel), or the ADC may be shared between several pixels on an optical sensor (e.g., a line of pixels, a block of pixels, or all pixels). In embodiments wherein each pixel has a dedicated ADC, the order of the read-out circuit and the ADC can be inverted, so that the digital signal is read out instead of the analogue signal.

[0095] The output voltage may be read out in one of various known ways. Typically, the output voltage is read out periodically, e.g., per pixel, or more commonly, using an architecture resembling a global or rolling shutter, reading out and resetting the pixels in a line or block of pixels, or for the entire sensor. At the same time, the time-varying voltage may be reset, so that each cycle has the same relationship between sampled and held voltage, and time since last reset. In some embodiments, the same time-varying voltage line is shared by all pixels that are read-out and reset simultaneously.

[0096] An advantage of using such an architecture, is that the read-out and data processing can be very similar to more conventional optical sensors using fixed exposure times.

[0097] The pixel may furthermore comprise further elements (not shown), e.g., for signal stability, signal amplification, noise suppression, pixel selection, and so on.

[0098] Fig. 3B schematically depicts an optical sensor according to an embodiment. The optical sensor 320 comprises a plurality of pixels configured to provide a signal representative of a length of a time period during which the pixel received an accumulated light intensity equal to a predetermined accumulated intensity threshold, e.g., a pixel as described above with reference to Fig. 3A. The plurality of pixels 322i_nmay be arranged ina two-dimensional grid, e.g. similar to the pixel arrangements of Fig. 2A-C. In the current example, only two lines are shown, but typically, a sensor comprises multiple such lines in a parallel configuration, with hundreds of pixels per line. In the depicted example, each line of pixels (e.g., a row or column) shares a line 324I,2 for the reference voltage 7ref, and a line 326I,2 for the time-varying voltage 7time. Thus, in this example, all pixels in a single line have the same reference voltage, and hence, the same predetermined accumulated charge, and hence, the same predetermined accumulated intensity. The line of pixels furthermore shares a signal line 328I,2 or read-out line, which is connected to each pixel in the line via an individually addressable pixel selection switch. Finally, the line of pixels shares a reset signal line 330I,2, which is synchronized with the time-varying signal; e.g., the same signal may reset the pixels and the time-varying signal, or both may be controlled by a common external signal. However, in the current example, the time-varying voltages (and hence also the readout signals and reset signals) are different for different pixel lines. Assuming equal hardware components, a longer read-out and reset period will typically be combined with a higher reference voltage, as a longer maximum integration time allows for larger integrated photocurrent amounts.

[0099] In the depicted embodiment, each line is controlled individually, analogous to Fig. 2A.

[0100] In other embodiments, all lines share the same reference voltage and the same time-varying signal. In yet other embodiments, pixels may be organized in blocks, or other configurations, analogous to what was discussed previously with reference to Fig. 2A-C.

[0101] Fig. 3C schematically depicts an optical sensor according to another embodiment. In this embodiment, the optical sensor 340 comprises a plurality of pixels configured to provide a signal representative of a length of a time period during which the pixel received an accumulated light intensity equal to a predetermined accumulated intensity threshold, e.g., a pixel as described above with reference to Fig. 3A. The plurality of pixels 342i:t-m,nmay be arranged in a two-dimensional grid. The pixels may be connected to a read-out circuit via row signal lines 350 and column signal lines 351 (only shown here for the first row and column). In this example, when the pixel voltage exceeds the reference voltage, not only is the sample-and-hold circuit activated, but a trigger signal is also sent to activate a read-out circuit. The read-out circuit determines which pixel sent the trigger signal, reads out the pixel’s output signal (typically, 70Ut), and then resets the pixel. In some embodiments, the resetting of the pixel is handled by a different (sub)circuit. In such an embodiment, the readout circuit may acknowledge successful read-out, after which the pixel is reset by a reset circuit.

[0102] An advantage of such an embodiment is that, in principle, data output is maximal as there is no dead time: each pixel is read-out and reset practically immediately. However, in general, this leads to an asynchronous data stream; whereas a synchronous data stream as supplied by the embodiments of Fig. 3A and 3B may be easier to process.

[0103] In the depicted example, a trigger signal is translated into a column identifier and a row identifier by an X-arbiter 346 and a Y-arbiter 348. Thus, the pixel sends a row request 352 and a column request 356, and after successful read-out, the pixel receives a row acknowledgement 354 and a column acknowledgement 358, which initiates the resetprocedure of the pixel sending the trigger signal. The use of request and acknowledgement signals ensures each signal is properly processed also in cases where many pixels send a request signal more or less simultaneously. Such an architecture is known, e.g., from so-called event-based cameras, as described, for instance, in M. Bouvier, Study and design of an energy efficient perception module combining event-based image sensors and spiking neural network with 3D integration technologies, Micro and nanotechnologies / Microelectronics, Universite Grenoble Alpes (2021), available on-line at https: / / theses.hal.science / tel-03405455v1 , which is hereby incorporated by reference in its entirety. An example of a read-out protocol is given in section II. C.3 “Event-Based Pixel Grid Readout Working Principle”, pages 19-20.

[0104] In some embodiments, each pixel may have a dedicated time-varying signal which is reset when the pixel is reset. In other embodiments, a global (or local) time-varying signal may be used. In such embodiments, the reset time of each pixel may be stored in addition to the read-out time, and the effective integration time may be determined based on a difference between the reset time and the read-out time. In yet other embodiments, the pixel may only send the trigger signal without activating a sample-and-hold circuit (which indeed need not be present); in such embodiments, registering the read-out time may be done (solely) by the read-out circuit in response to receiving the trigger signal. The time-varying signal can be, e.g., a local clock signal. In general, these various designs offer a trade-off between complexity of the hardware design, and temporal resolution and / or data rates.

[0105] Of course, combinations are also possible, e.g., the read-out method based on a handshake as described with reference to Fig. 3C, in combination with a local or global reset of the pixels and / or the time-varying signal, as described with reference to Fig. 3B.

[0106] Fig. 4 schematically represents a further optical sensor for use in embodiments of the invention. In particular, Fig. 4 shows an example of a pixel with a so-called non-destructive pixel read-out mode. An advantage of such non-destructive read-out is that, effectively, overlapping measurements with different effective integration times may be obtained. Such non-destructive read-out implementations are described in more detailed, e.g., in C.P.

[0107] Welsch et al., ‘Alternative techniques for beam halo measurements’, Measurement Science and Technology 17 :7 (2006) 2035-2040 (in particular with reference to Fig. 4, from which the current Fig. 4 and the accompanying description are derived), and in Space Telescope Science Institute, Near Infrared Spectrograph. User documentation for Cycle 4, in: JWST User Documentation (JDox), (2016- ; pdf version 22 August 2024), which are both hereby incorporated by reference in their entirety.

[0108] In one embodiment, charge accumulated by the photodiode is repeatedly transferred to and stored in a (pixel-specific) charge storage. The charge may be transferred, e.g., periodically with a fixed time interval, and / or based on the pixel sending a signal when a certain amount of charge has been accumulated. In both cases, the intermediate charge amount and / or intermediate integration time may be stored and processed.

[0109] In another embodiment, the charge is not transferred at discrete times, but, e.g., a voltage over a storage capacitor is sampled repeatedly in between pixel resets.As an example, Fig. 4 depicts a single pixel of a charge injection device. The “charge injection device (CID)” derives its name from its ability to clear individual pixel sites of photon-generated charge by injecting the charge directly into the substrate. The main features of this imager technology are its distinctive readout capabilities including inherent resistance to ionizing radiation, inherent resistance to charge blooming, true random pixel addressability, non-destructive pixel readout (NDRO), and on-sensor collective pixel readout and clear.

[0110] The on-sensor collective read feature allows the data acquisition routines to select contiguous pixel regions (e.g., a 3 by 3 pixel region) and interrogate those pixels with a single reading that is the electronic average of the signals on those pixels, thereby improving both readout speed and signal-to-noise ratio. This collective read feature is analogous to the ‘binning’ that can be performed with certain CCD camera systems. However, unlike the CCD where the charge packets from the individual pixels are physically combined into a single larger charge packet, the CID collective read feature preserves the spatial integrity of the photon-generated charge in the pixels and the read process is non-destructive to that charge. The CID architecture also allows for the clearing of photon-generated charge from contiguous pixel regions with a single ‘inject’ pulse.

[0111] In general, each pixel on the CID imager is individually addressable and allows for random access non-destructive pixel readout. In the depicted example, a pixel starts in an initial state 402 (e.g., after a reset), substantially empty of accumulated charge (stage 0).

[0112] During image acquisition, in a state 404, the photon-generated charge is typically stored under the drive (e.g., row) electrode (stage 1 ). In order to determine the level of accumulated charge in state 406, the sense (e.g., column) electrode is allowed to float, and a 1stvoltage sample is taken on the electrode (stage 2). Next, the drive electrode voltage is collapsed, thereby causing the photon-generated charge to transfer to the sense electrode, as shown in state 408. At this point, a 2ndvoltage sample is acquired (see stage 3).

[0113] The voltage difference between the 2ndvoltage sample and the 1stvoltage sample is proportional to the amount of photon-generated charge at the pixel site. At this point, the column and row electrodes may be returned to their original bias conditions allowing for the continued integration of photon-generated charge (stage 4, which is identical to stage 1, except for the transferred charge). Alternatively, the voltages on both electrodes can be collapsed thereby causing the pixel to be cleared of charge (stage 0), returning the pixel to the initial state 402.

[0114] Thus, photon-generated charge may be transferred within each individually addressable pixel for readout without actually destroying the charge. This allows, essentially, overlapping effective integration times in a single pixel.

[0115] Regardless of the hardware implementation, which may be implemented, for instance using a sensor as described with reference to Figs. 2-4, the signals may be processed as described in the following.

[0116] In general, the sample region may be assumed to be spatially homogeneous; consequently, measurements from all pixels with the same predetermined effective integration time or the same predetermined charge threshold may be considered samplesfrom a single distribution. Moreover, temporal changes in the sample region may be assumed to be relatively slow compared to the maximum effective integration time.

[0117] It is assumed the sensor is operated in a linear regime; this may involve avoiding underexposure or overexposure. Then in general, the accumulated charge Cpof a pixel p is proportional to the product of the average light intensity and the effective integration time TpCp<x TpTp= f^pIp(t) dt, where / =

[0118]

[0119] dt is the average light intensity averaged over the effective integration time T (and the active pixel area / I). In the following, the bar denoting the temporal average over the effective integration time and the spatial average over the active pixel area will be dropped.

[0120] The speckle contrast K2can be determined from the measurements via

[0121] {C2)S- {C)2S{I2T2)S— {I T)l2

[0122] K (C ) = - - = - - - - —

[0123] <C)l {IT}2 2

[0124] wherein {x}s= ^p=1xpdenotes an ensemble mean over a group of N pixels, with Ip= C IT is the average light intensity of pixel p where only pixels with an equal effective integration time T are used to compute the respective means. Here, for the spatial ensemble averaging, pixels with different pixel coordinates from a single moment in time (or at least a single relatively short time interval) are combined. For example, in embodiments with an asynchronous data stream, time windows (either rolling or successive) may be used to group input data belonging to substantially the same moment in time. The width of such a time window may depend on the speed with which changes in the sample region occur.

[0125] For an exponentially decaying coherence time, the speckle contrast K can be modelled by:

[0126]

[0127] wherein x = T / TCis a dimensionless scale parameter, with T the effective integration time andCthe decorrelation time, p is a motile fraction, and ? is a coherence parameter which accounts for a loss of coherence, polarisation, speckle-to-pixel mismatch, et cetera. In some cases, ft may be determined by calibration of the laser speckle device.

[0128] Other models may provide different relations between the speckle contrast and the speckle parameters, notably the decorrelation time and the motile fraction.

[0129] Fig. 5A is a flowchart of a method according to a first embodiment. In this embodiment, a plurality of pixels groups is associated with respective mutually different predetermined effective integration times. The method may be performed, for instance, by processor 109 of a system 100 as described above with reference to Fig. 1.

[0130] A step 502 comprises receiving a plurality of signals from an optical sensor. The optical sensor is configured to receive coherent light that has interacted with a target region, the coherent light defining speckles. The optical sensor comprises a plurality of pixel groups, each pixel group comprising one or more pixels. The plurality of signals represent accumulated charges Cpper pixel p, for each pixel group Gnwith an associated exposure time Tn. It is noted that for each pixel group, the measurement may arrive at a different moment in time, especially if the cycle times are not integer multiples. In particular, pixel groups with a smaller exposure time may provide measurements much more often thanpixels with a longer exposure time. For the signal analysis, measurements may be associated with one ore more discrete moments in time.

[0131] A step 504 comprises estimating, for each predetermined exposure time Tn, a speckle contrast for instance by estimating an expectation value and a variance of a probability distribution underlying the speckle formation and computing a ratio thereof. For example, the expectation value may be estimated by computing an arithmetic mean p of the measurements E[Cn] « / z = {Cn)s, and the variance may be estimated by computing a square of a standard deviation o of the measurements Var[Cn] « a2= (C^)s- (Cn)2. The speckle contrast may then be estimated by K2(Tn) " . Other methods to estimate the

[0132]

[0133] expectation value and variance, and / or the coefficient of variation, are known in the art.

[0134] In other embodiments, estimating the speckle contrast may comprise fitting a speckle model to the observed data, possibly after normalising the data. Normalising the data may comprise dividing measurements having a same (or sufficiently similar) effective integration time through an average of these measurements.

[0135] A step 506 comprises determining a parameter of interest, based on the speckle contrast values and a speckle model. For example, using the speckle model

[0136]

[0137] (with again x = T / TC), the decorrelation timeCand the motile fraction psmay be fitted; these parameters may also be referred to as speckle parameters. The decorrelation time is at least approximately inversely proportional to the flow speed.

[0138] It is noted that the specifics of the speckle model may depend on the (assumed) characteristics of the motion (e.g., random, ordered, or a mixture). The model can be an analytical model, an experimental model, or a mixture of both.

[0139] The parameter of interest can be a flow speed, a (voluminous) flow rate, or a parameter derived therefrom, e.g., by analysing a speckle parameter over time. In particular, when the target area comprises living tissue, the parameter of interest can be a vital sign, such as a blood parameter and / or a cardiovascular parameter. Typical examples of parameters of interest include: a blood flow, a perfusion rate, a blood pressure, a heart rate, a heart rate variation, a respiration rate, a shear rate, a cardiac output, or a vascular stiffness. It is known in the art how to derive these parameters from the speckle parameters; for instance, several examples are described in A. Khalil et al., ‘Laser speckle contrast imaging: age-related changes in microvascular blood flow and correlation with pulse-wave velocity in healthy subjects’, Journal of Biomedical Optics, Vol. 20, Issue 5 (November 2014) 051010.

[0140] Fig. 5B is a flowchart of a method according to a second embodiment. In this embodiment, a plurality of pixels groups is associated with respective mutually different predetermined accumulated charge threshold. The method may be performed, for instance, by processor 109 of a system 100 as described above with reference to Fig. 1.

[0141] A step 522 comprises receiving a plurality of signals from an optical sensor. The optical sensor is configured to receive coherent light that has interacted with a target region, the coherent light defining speckles. The optical sensor comprises a plurality of pixel groups,each pixel group comprising one or more pixels. The plurality of signals represent effective integration times Tpper pixel p, for each pixel group Gnwith an associated accumulated charge threshold Cn.

[0142] A step 524 comprises estimating a speckle contrast K(Cn), for instance by estimating an expectation value and a variance of a probability distribution underlying the speckle formation and computing a ratio thereof. This may comprise first determining a mean intensity ( / ), e.g., by first computing Ip= Cp / Tpfor several pixels and averaging over pixels that can be assumed to be correlated in their underlying probability density function; this can be done, e.g., using a (normalised) kernel function based on a uniform kernel, a binomial kernel, a Gaussian kernel, or another suitable kernel. Subsequently, a normalised accumulated charge Cpmay be computed for each pixel p as Cp=

[0143]

[0144] Tp. The speckle (c2)-(c )2

[0145] contrast may then be estimated as K =p, using similar consideration as put forth

[0146]

[0147] above.

[0148] More in general, the distribution of the effective integration times can be shown to follow the following (unnormalized) joint probability distribution:

[0149] wherein Mt(xp,ps) =

[0150]

[0151] dimensionless scale parameter, psis the motile fraction, Ip= CPITPis the time-averaged light intensity of pixel p averaged over the effective integration time Tp,

[0152]

[0153] is an estimate of the expectation value of fp, and Cpis the accumulated charge (i.e., the integrated light intensity at pixel p over the effective integration time Tp). Hence, the function Mt(xp,ps) can be fitted directly to the measurements, without making many assumptions about the speckle model; in practice, fitting a parametric model (possibly based on physical considerations) may provide a more robust result. It is noted that for embodiments with a fixed predetermined exposure time, the joint probability distribution corresponds to a gamma distribution.

[0154] A step 526 comprises determining a parameter of interest, based on the speckle contrast values and a speckle model, e.g., as described above for step 506.

[0155] Thus, in brief, the method for determining a parameter of interest may comprise, in reaction to irradiating a target area with coherent light:

[0156] - receiving measurement signals representing a speckle signal, the measurement signals typically being indicative of an accumulated light intensity of the coherent light during a predetermined effective integration time, or of an effective integration time during which a predetermined accumulated intensity of the coherent light is received;

[0157] - normalising the measurement signals for global intensity variations, so that the measurements may be considered independent and identically distributed random variables;

[0158] - fitting one or more speckle parameters of a parametrized laser speckle model to the normalised measurement signals, using known (single- or multi-parametric)optimisation algorithms, e.g., a least-squares algorithm; generally, the one or more speckle parameters comprise a motile fraction p and a decoherence time TC; - using the one or more speckle parameters (and, optionally, their temporal behaviour) to determine the parameter of interest, e.g., a biomarker such as blood flow, heart beat (e.g., by detecting peaks in a time series of blood flow speed data, e.g., using a Fourier transform), skin properties, and so on.

[0159] The method may be used as part of a larger measurement system, e.g., a system measuring in multiple (possibly correlated) locations, e.g., along a blood vessel, or in a multi-spectral setting. In such cases, additional biomarkers may be determined, such as SpC>2, SpHb, pulse wave analysis, blood indices (such as determining haematocrit values, or detecting sickle cell anaemia), and so on.

[0160] Fig. 6 depicts a block diagram illustrating an exemplary data processing system that may perform the method as described with reference to Fig. 9.

[0161] As shown in Fig. 6, the data processing system 600 may include at least one processor 602 coupled to memory elements 604 through a system bus 606. As such, the data processing system may store program code within memory elements 604. Further, the processor 602 may execute the program code accessed from the memory elements 604 via a system bus 606. In one aspect, the data processing system may be implemented as a computer that is suitable for storing and / or executing program code. It should be appreciated, however, that the data processing system 600 may be implemented in the form of any system including a processor and a memory that is capable of performing the functions described within this specification. The data processing system may be an Internet / cloud server, for example.

[0162] The memory elements 604 may include one or more physical memory devices such as, for example, local memory 608 and one or more bulk storage devices 610. The local memory may refer to random access memory or other non-persistent memory device(s) generally used during actual execution of the program code. A bulk storage device may be implemented as a hard drive or other persistent data storage device. The processing system 600 may also include one or more cache memories (not shown) that provide temporary storage of at least some program code in order to reduce the quantity of times program code must be retrieved from the bulk storage device 610 during execution. The processing system 600 may also be able to use memory elements of another processing system, e.g. if the processing system 600 is part of a cloud-computing platform.

[0163] Input / output (I / O) devices depicted as an input device 612 and an output device 614 optionally can be coupled to the data processing system. Examples of input devices may include, but are not limited to, a keyboard, a pointing device such as a mouse, a microphone (e.g. for voice and / or speech recognition), or the like. Examples of output devices may include, but are not limited to, a monitor or a display, speakers, or the like. Input and / or output devices may be coupled to the data processing system either directly or through intervening I / O controllers.

[0164] In an embodiment, the input and the output devices may be implemented as a combined input / output device (illustrated in Fig. 6 with a dashed line surrounding the inputdevice 612 and the output device 614). An example of such a combined device is a touch sensitive display, also sometimes referred to as a “touch screen display” or simply “touch screen”. In such an embodiment, input to the device may be provided by a movement of a physical object, such as e.g. a stylus or a finger of a user, on or near the touch screen display.

[0165] A network adapter 616 may also be coupled to the data processing system to enable it to become coupled to other systems, computer systems, remote network devices, and / or remote storage devices through intervening private or public networks. The network adapter may comprise a data receiver for receiving data that is transmitted by said systems, devices and / or networks to the data processing system 600, and a data transmitter for transmitting data from the data processing system 600 to said systems, devices and / or networks.

[0166] Modems, cable modems, and Ethernet cards are examples of different types of network adapter that may be used with the data processing system 600.

[0167] As pictured in Fig. 6, the memory elements 604 may store an application 618. In various embodiments, the application 618 may be stored in the local memory 608, the one or more bulk storage devices 610, or separate from the local memory and the bulk storage devices. It should be appreciated that the data processing system 600 may further execute an operating system (not shown in Fig. 6) that can facilitate execution of the application 618. The application 618, being implemented in the form of executable program code, can be executed by the data processing system 600, e.g., by the processor 602. Responsive to executing the application, the data processing system 600 may be configured to perform one or more operations or method steps described herein.

[0168] Various embodiments of the invention may be implemented as a program product for use with a computer system, where the program(s) of the program product define functions of the embodiments (including the methods described herein). In one embodiment, the program(s) can be contained on a variety of non-transitory computer-readable storage media, where, as used herein, the expression “non-transitory computer readable storage media” comprises all computer-readable media, with the sole exception being a transitory, propagating signal. In another embodiment, the program(s) can be contained on a variety of transitory computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., flash memory, floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access semiconductor memory) on which alterable information is stored. The computer program may be run on the processor 602 described herein.

[0169] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features,integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0170] The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiments were chosen and described in order to best explain the principles and the practical application, and to enable others of ordinary skill in the art to understand the various embodiments with various modifications as are suited to the particular use contemplated.

Claims

CLAIMS1. A laser speckle sensor device comprising:an optical sensor configured to receive coherent light that has interacted with a target region, the coherent light defining speckles, the optical sensor comprising a plurality of simultaneously existing pixel groups, each pixel group comprising one or more pixels, wherein:- a first pixel group from the plurality of pixel groups has a first effective integration time and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time; and a processor, communicatively connected to the optical sensor, the processor being configured to:- receive a plurality of signals from the optical sensor, each signal from the plurality of signals representing a respective measurement from a pixel or pixel group; - determine a first speckle contrast value for the first pixel group and a second speckle contrast value for the second pixel group based on the plurality of signals; - determine one or more parameters of a parametrized laser speckle model based on the first speckle contrast value and the second speckle contrast value; and - calculate a parameter of interest based on the determined one or more parameters of the parametrized laser speckle model.

2. The laser speckle sensor device as claimed in claim 1 , wherein the effective integration time is a predetermined effective integration time and the respective measurement is indicative of an accumulated intensity of the coherent light during the predetermined effective integration time; orwherein the respective measurement is indicative of the effective integration time and wherein the effective integration time of a given pixel or a given pixel group is determined by the accumulated intensity of the coherent light accumulated by the given pixel or the given pixel group reaching a predetermined accumulated intensity threshold.

3. The laser speckle sensor device as claimed in 2, where a maximum predetermined effective integration time is smaller than 1 ms, preferably the predetermined effective integration times being selected between 1 ps and 1 ms; orwherein the predetermined accumulated intensity threshold is selected such that an expected maximum effective integration time is smaller than 50 ms, preferably such that the expected maximum effective integration time is smaller than 10 ms, more preferably such that the expected maximum effective integration time is between 1 ps and 1 ms.

4. The laser speckle sensor device as claimed in any one of the preceding claims, further comprising a light source for generating the coherent light.

5. The laser speckle sensor device as claimed in any one of the preceding claims, wherein determining the parameter of interest comprises determining, for each pixel group having a respective effective integration time separately, a speckle contrast value based on the respective integrated amount of light, and determining the parameter of interest based on the plurality of respective speckle contrast values.

6. The laser speckle sensor device as claimed in any one of the preceding claims, wherein the plurality of pixel groups comprises at least three pixel groups, preferably at least four pixel groups, more preferably at least eight pixel groups, each of the plurality of groups having a different effective integration time.

7. The laser speckle sensor device as claimed in any one of the preceding claims, wherein the first and second effective integration times differ by at least a factor of 1.1 , preferably at least a factor of two, more preferably at least a factor of five, even more preferably at least a factor of ten, most preferably at least a factor of fifty.

8. The laser speckle sensor device as claimed in any one of the preceding claims, wherein the parameter of interest is a vital sign, preferably a blood parameter and / or a cardiovascular parameter, preferably at least one of: a blood flow, a perfusion rate, a blood pressure, a heart rate, a heart rate variation, a respiration rate, a shear rate, a cardiac output, or a vascular stiffness.

9. The laser speckle sensor device as claimed in any one of the preceding claims, wherein the optical sensor is a CCD camera or a CMOS camera or a CID camera.

10. The laser speckle sensor device as claimed in any one of the preceding claims, wherein a group of pixels corresponds to one or more rows of the optical sensor.

11. A wearable device comprising a laser speckle sensor device as claimed in any one of the preceding claims.

12. A method for determining a parameter of interest, the method comprising: receiving coherent light with an optical sensor, the coherent light having interacted with a target region, the coherent light defining speckles, the optical sensor comprising a plurality of simultaneously existing pixel groups, each pixel group comprising one or more pixels, wherein a first pixel group from the plurality of pixel groups has a first effective integration time and a second pixel group from the plurality of pixel groups has a second effective integration time, different from the first effective integration time;receiving a plurality of signals from the optical sensor, each signal representing a respective measurement from a pixel or pixel group; anddetermining a first speckle contrast value for the first pixel group and a second speckle contrast value for the second pixel group based on the plurality of signals;determining a speckle decorrelation time based on at least the first speckle contrast value and the second speckle contrast value; anddetermining the parameter of interest based on the plurality of signals, the parameter of interest being a function of a ratio between an effective integration time and the speckle decorrelation time.

13. A computer program product comprising software code portions configured for, when run by a processor of laser speckle sensor device as claimed in any one of claims 1-10, causing the laser speckle sensor device to perform the method of claim 12.

14. A computer-readable signal medium for transmitting a computer program as claimed in claim 13 or a non-transient storage medium storing a computer program product according to claim 13.

Images