Analysis of bodily emissions

Abstract

Apparatuses and methods for use with urine and feces discharged by a subject into a toilet bowl are described. One or more sensors (24) are coupled to the toilet bowl and configured to detect one or more urine-related parameters related to the subject's urine and one or more feces-related parameters related to the subject's feces without requiring anyone to perform any action after the urine or feces is discharged into the toilet bowl. A computer processor (28) determines whether the subject has dehydration based at least in part on the one or more urine-related parameters and classifies the dehydration as a given type of dehydration based at least in part on the one or more feces-related parameters. Other applications are also described.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 006,130, entitled “Detection and Classification Dehydration”, filed April 7, 2020, which is incorporated herein by reference. Technical Field

[0003] Some applications of this invention generally relate to the analysis of bodily waste products. Specifically, some applications of this invention relate to apparatus and methods for detecting and classifying dehydration by analyzing bodily waste products such as urine and feces. Background Technology

[0004] Dehydration is prevalent among middle-aged and older adults, imposing significant costs on individuals and the healthcare system. Older adults are particularly vulnerable for a variety of reasons, including age-related changes in total body water, impaired thirst sensation, reduced kidney concentrating ability, decreased vasopressin effectiveness, and medication-related hypohydration. Functional limitations, urinary frequency, and incontinence are common in older patients and can further increase their vulnerability to dehydration. The effects of dehydration include confusion, disorientation, nausea, dizziness, malaise, weakness, infection, coronary artery disease, impaired or delayed wound healing, and even death. Because dehydration symptoms are often diverse and sometimes nonspecific, diagnosis in a hospital requires valuable and critical time. This delay often exacerbates the condition.

[0005] Dehydration can be defined as a clinically relevant reduction in an individual’s optimal total body water (TBW) and may or may not involve electrolyte loss. (See, for example, R.F. Kushner, D.A. Choeller, “Estimation of Total Body Water by Bioelectrical Impedance Analysis,” American Journal of Clinical Nutrition, Vol. 44, No. 3, pp. 417-424, September 1986; and Weinberg, A.D., Minaker, K.L. J.A., “Dehydration: Assessment and Management in Middle-Aged and Older Adults,” JAMA Scientific Affairs Committee. 15;274(19):1552-6, November 1995.) There are three distinct forms of dehydration. Isotonic dehydration is caused by a loss of balance between water and sodium. For example, fasting for an extended period can cause isotonic dehydration. Similarly, vomiting and diarrhea can also cause isotonic dehydration due to the large amounts of water and electrolytes in the stomach contents. Hypertonic dehydration is the most common type of dehydration, caused by a loss of water greater than a loss of sodium. Hypertonic dehydration is characterized by high blood sodium levels (e.g., serum sodium levels exceeding 145 mmol / L) and hypertonicity (e.g., serum osmolality exceeding 300 mmol / kg). Fever is perhaps the most common cause of hypertonic dehydration, as it tends to increase water loss through the lungs and skin, while limiting the ability to increase oral fluid intake. Hypotonic dehydration occurs when sodium loss exceeds water loss. Serum sodium is decreased (e.g., less than 135 mmol / L) and serum osmolality is low (e.g., less than 280 mmol / kg). This type of dehydration primarily occurs in cases of excessive sodium loss due to overuse of diuretics. Summary of the Invention

[0006] According to some applications of the invention, one or more sensors coupled to a toilet bowl detect one or more urine-related parameters associated with a subject's urine, without requiring any action from anyone after the urine is flushed into the toilet bowl. Typically, one or more sensors coupled to the toilet bowl are also configured to detect one or more fecal-related parameters associated with a subject's feces, without requiring any action from anyone after the feces are flushed into the toilet bowl. A computer processor receives one or more urine-related parameters and one or more fecal-related parameters. Based at least in part on the one or more urine-related parameters, the computer processor determines whether the subject is dehydrated. Based at least in part on the one or more fecal-related parameters, the computer processor classifies the dehydration into a given type of dehydration, such as isotonic dehydration, hypertonic dehydration, and hypotonic dehydration. In response to this, an output is typically generated.

[0007] In some applications, the computer processor is configured to obtain the specific gravity of a subject's urine and determine whether the subject is dehydrated, at least in part, based on the specific gravity of the subject's urine. In some such applications, one or more sensors are configured to detect a signal indicating that the subject's urine absorbs cyan-green light, and the computer processor is configured to obtain the specific gravity of the subject's urine, at least in part, based on that signal. In some applications, these sensors are configured to detect a signal indicating that the subject's urine absorbs light in the 480nm-520nm wavelength band, and the computer processor is configured to obtain the specific gravity of the subject's urine, at least in part, based on that signal.

[0008] For some applications, the computer processor is configured to receive one or more urine-related parameters and obtain parameters indicating the foaming and / or turbidity of the subject's urine. The computer processor estimates the albumin concentration in the subject's urine based at least in part on the obtained parameters and generates output on an output device in response to the estimated albumin concentration in the subject's urine.

[0009] For some applications, a computer processor is configured to receive one or more urine-related parameters and, based at least in part on these parameters, obtain the concentration of at least one hemoglobin derivative molecule in the urine of a subject. Typically, this at least one hemoglobin derivative molecule includes bilirubin, pro-urobilinogen, and / or urobilin. The computer processor typically generates an output at least in part in response to the obtained concentration of the at least one hemoglobin derivative molecule. For some such applications, an illumination component illuminates the subject's urine with light in the wavelength range of 440 nm to 480 nm, and a sensor detects a signal indicating that the subject's urine emits light in the wavelength range of 505 nm to 535 nm in response to the illumination of the subject's urine. Typically, the computer processor determines the concentration of pro-urobilinogen in the subject's urine at least in part in response to this signal. Alternatively or additionally, a sensor is configured to detect a signal indicating that the subject's urine absorbs light in the wavelength range of 480 nm to 520 nm, and the computer processor determines the concentration of pro-urobilinogen in the subject's urine at least in part in response to this signal. Further, alternatively, or additionally, the sensor detects signals indicating that the subject's urine absorbs light within a first wavelength band of 390 nm–420 nm and a second wavelength band of 445 nm–475 nm. Typically, a computer processor determines the bilirubin concentration in the subject's urine in at least part of the response to these signals.

[0010] In some applications, one or more lighting components connected to the toilet bowl are configured to illuminate a subject's urine with light in the wavelength range of 300 nm to 520 nm. Sensors are configured to detect the intensity of light emitted by the urine in the wavelength range of 530 nm to 560 nm in response to the illumination. A computer processor obtains the fiber level in the subject's urine based at least in part on the detected light intensity and generates an output in response to the obtained fiber level in the subject's urine. In some such applications, the computer processor is configured to detect whether a subject has hematuria based on signals detected by one or more sensors, and the computer processor is configured to drive the lighting components to illuminate the subject's urine with light in the wavelength range of 300 nm to 520 nm in response to the detection of hematuria. Typically, the computer processor determines, based on the obtained fiber level in the subject's urine, that the suspected cause of the subject's hematuria is a biofilm-based pathogen in the subject's urinary tract.

[0011] Therefore, according to some applications of the present invention, a device is provided for use with urine and feces discharged into a toilet bowl by a subject and an output device, the device comprising:

[0012] One or more sensors are connected to the toilet bowl and configured to:

[0013] Detect one or more urine-related parameters associated with a subject's urine without requiring any action from the subject after urine is flushed down the toilet.

[0014] Detecting one or more fecal-related parameters associated with a subject's feces without requiring any action from the subject after the feces are flushed down the toilet; and

[0015] At least one computer processor, the at least one computer processor being configured to:

[0016] Receive one or more urine-related parameters.

[0017] Receive one or more fecal-related parameters.

[0018] Dehydration in a subject is determined, at least in part, based on one or more urine-related parameters.

[0019] Dehydration is classified into a given type of dehydration selected from a group consisting of at least one or more fecal-related parameters: isotonic dehydration, hypertonic dehydration, and hypotonic dehydration; and

[0020] In response to this, at least in part, output is generated on the output device.

[0021] In some applications, at least one computer processor is configured to detect one or more urine-related parameters associated with a subject's urine by detecting urine color. In some applications, at least one computer processor is configured to detect one or more urine-related parameters associated with a subject's urine by detecting urine volume. In some applications, at least one computer processor is configured to detect one or more urine-related parameters associated with a subject's urine by detecting the duration of urine excretion.

[0022] In some applications, at least one computer processor is configured to detect one or more fecal-related parameters associated with a subject’s feces by detecting one or more fecal-related parameters selected from a group consisting of: shape, size, texture, and color.

[0023] In some applications, at least one computer processor is configured to detect one or more urine-related parameters associated with the subject's urine by detecting the subject's urine excretion frequency. In some applications, at least one computer processor is configured to detect the subject's urine excretion frequency by automatically detecting individuals urinating at a given time.

[0024] In some applications, at least one computer processor is configured to detect one or more fecal-related parameters associated with a subject's feces by performing computer vision analysis on an image of feces. In some applications, at least one computer processor is configured to perform computer vision analysis on an image of feces by performing one or more steps selected from the group consisting of: masking, contrast enhancement, image edge detection, region of interest detection, applying morphological changes, and performing segmentation.

[0025] In some applications, at least one computer processor is configured to obtain the specific gravity of a subject's urine and determine whether the subject is dehydrated, at least in part, based on the specific gravity of the subject's urine. In some applications, one or more sensors are configured to detect a signal indicating that the subject's urine absorbs blue-green light, and at least one computer processor is configured to obtain the specific gravity of the subject's urine, at least in part, based on that signal. In some applications, one or more sensors are configured to detect a signal indicating that the subject's urine absorbs light in the 480nm-520nm wavelength band, and at least one computer processor is configured to obtain the specific gravity of the subject's urine, at least in part, based on that signal.

[0026] According to some applications of the present invention, a method for use with urine and feces discharged by a subject into a toilet bowl is further provided, the method comprising:

[0027] Using one or more sensors connected to the toilet bowl, one or more urine-related parameters are detected in relation to the subject's urine without requiring anyone to perform any action after the urine is discharged into the toilet bowl;

[0028] Using one or more sensors connected to the toilet bowl, one or more fecal-related parameters associated with the subject's feces are detected without requiring any action from anyone after the feces are discharged into the toilet bowl; and

[0029] Using a computer processor:

[0030] Receive one or more urine-related parameters;

[0031] Receive one or more fecal-related parameters;

[0032] Dehydration in a subject is determined, at least in part, based on one or more urine-related parameters.

[0033] The dehydration is classified into a given type of dehydration selected from the group consisting of at least one or more fecal-related parameters: isotonic dehydration, hypertonic dehydration, and hypotonic dehydration; and

[0034] In response to this, at least in part, output is generated on the output device.

[0035] In some applications, detecting one or more urine-related parameters associated with a subject's urine includes detecting urine color. In some applications, detecting one or more urine-related parameters associated with a subject's urine includes detecting urine volume. In some applications, detecting urine volume includes detecting the duration of urine excretion.

[0036] In some applications, detecting one or more fecal-related parameters associated with a subject's stool includes detecting one or more fecal-related parameters selected from the group consisting of: shape, size, texture, and color.

[0037] In some applications, detecting one or more urine-related parameters associated with a subject's urine includes detecting the frequency of the subject's urine excretion. In some applications, detecting the frequency of a subject's urine excretion includes automatically detecting individuals who urinate at a given time.

[0038] In some applications, detecting one or more fecal-related parameters associated with a subject's feces involves performing computer vision analysis on images of feces. In some applications, performing computer vision analysis on images of feces includes performing one or more steps selected from the group consisting of: masking, contrast enhancement, image edge detection, region of interest detection, applying morphological changes, and performing segmentation.

[0039] In some applications, determining whether a subject is dehydrated involves obtaining the specific gravity of the subject's urine, and the determination is based at least in part on the specific gravity of the subject's urine. In some applications, obtaining the specific gravity of the subject's urine involves detecting a signal indicating that the subject's urine absorbs blue-green light, and the specific gravity is based at least in part on this signal. In some applications, obtaining the specific gravity of the subject's urine involves detecting a signal indicating that the subject's urine absorbs light in the 480nm-520nm wavelength band, and the specific gravity is based at least in part on this signal.

[0040] According to some applications of the invention, a means for use with urine discharged by a subject into a toilet bowl and with an output device is further provided, the means comprising:

[0041] One or more sensors, coupled to the toilet bowl and configured to detect one or more urine-related parameters associated with the subject's urine, without requiring any action from anyone after urine is flushed into the toilet bowl.

[0042] At least one computer processor, the at least one computer processor being configured to:

[0043] Receive one or more urine-related parameters, and

[0044] Based at least in part on urine-related parameters, the concentration of at least one hemoglobin derivative molecule in the subject's urine is obtained, the at least one hemoglobin derivative molecule being selected from the group consisting of bilirubin, oxyurobilinogen, and urobilin.

[0045] An output is generated on the output device in at least a partial response to the concentration of at least one hemoglobin derivative molecule obtained.

[0046] In some applications, the device further includes an illumination component configured to illuminate the subject's urine with light in the wavelength range of 440 nm to 480 nm.

[0047] One or more sensors configured to detect, in response to irradiation of a subject's urine, a signal indicating light emitted by the subject's urine in the wavelength range of 505 nm to 535 nm, and

[0048] At least one computer processor configured to determine the concentration of urobilinogen in the urine of a subject, at least in part, in response to the signal.

[0049] In some applications, the one or more sensors are configured to detect a signal indicating that the subject's urine absorbs light in a wavelength band of 480 nm to 520 nm, and at least one computer processor is configured to determine the concentration of prourourobilinogen in the subject's urine in at least part of response to the signal.

[0050] In some applications, the one or more sensors are configured to detect signals indicating that the subject's urine absorbs light in a first wavelength band of 390 nm–420 nm and a second wavelength band of 445 nm–475 nm, and at least one computer processor is configured to determine the bilirubin concentration in the subject's urine, at least in part, in response to these signals. In some applications, the at least one computer processor is configured to determine the light absorption ratio of the subject's urine in each of the first and second wavelength bands relative to each other, and to determine the bilirubin concentration in the subject's urine based on the ratio.

[0051] According to some applications of the invention, a means for use with urine discharged by a subject into a toilet bowl and with an output device is further provided, the means comprising:

[0052] One or more sensors, coupled to the toilet bowl and configured to detect one or more urine-related parameters associated with the subject's urine, without requiring any action from anyone after urine is flushed into the toilet bowl.

[0053] At least one computer processor, the at least one computer processor being configured to:

[0054] Receive one or more urine-related parameters, and

[0055] Obtain parameters indicating the foaming and / or turbidity of the subject's urine.

[0056] The concentration of albumin in the subjects' urine was estimated, at least in part, based on the obtained parameters.

[0057] Output is generated on the output device in response to the estimated concentration of albumin in the subject's urine.

[0058] According to some applications of the invention, a means for use with urine discharged by a subject into a toilet bowl and with an output device is further provided, the means comprising:

[0059] One or more lighting components are connected to a toilet bowl and configured to illuminate the urine of a subject with light in the wavelength range of 300nm-520nm;

[0060] One or more sensors, coupled to the toilet bowl, are configured to detect the intensity of light emitted by the urine in the 530nm-560nm wavelength band in response to irradiation of the urine; and

[0061] At least one computer processor, the at least one computer processor being configured to:

[0062] The level of cellulose in the subject's urine was obtained at least in part based on the detected light intensity, and

[0063] Output is generated on the output device in response to the obtained cellulose level in the subject's urine.

[0064] In some applications, the computer processor is configured to detect whether a subject has hematuria based on signals detected by one or more sensors, and the computer processor is configured to, in response to detecting whether the subject has hematuria, drive an illumination component to illuminate the subject's urine with light in the wavelength range of 300 nm to 520 nm. In some applications, the computer processor is configured to determine, based on the obtained cellulose level in the subject's urine, that the suspected cause of the subject's hematuria is a biofilm-based pathogen in the subject's urinary tract.

[0065] The invention will be more fully understood from the following specific embodiments, taken in conjunction with the accompanying drawings, in which: Attached Figure Description

[0066] Figure 1 This is a schematic diagram of a device for analyzing bodily emissions according to some applications of the present invention;

[0067] Figure 2A , Figure 2B , Figure 2C , Figure 2D and Figure 2E Representative images of some computer vision analysis steps performed on images of feces in a toilet bowl according to some applications of the present invention are shown.

[0068] Figure 3 The present invention provides receiver operating characteristic (ROC) curves for some applications, illustrating the sensitivity and specificity of a classifier for detecting urine specific gravity based on blue light absorption; and

[0069] Figure 4 The ROC curves for some applications of the present invention illustrate the sensitivity and specificity of a classifier that uses the sensor module described herein to detect the presence of blood in the urine of a subject. Detailed Implementation

[0070] Now for reference Figure 1This figure is a schematic diagram of an apparatus 20 for analyzing bodily waste according to some applications of the present invention. As shown, the apparatus 20 typically includes a sensor module 22 disposed within a toilet bowl 23. For some applications (not shown), the sensor module (and / or additional components of the apparatus) is integrated into the toilet bowl. The sensor module includes one or more sensors 24. Typically, the one or more sensors are configured to detect one or more urine-related parameters (e.g., frequency of excretion, urine volume, urine color, and / or urine concentration, etc.) and one or more fecal-related parameters (e.g., fecal shape, size, texture, etc.). For some applications, the one or more sensors detect at least some of the above parameters while the subject is excreting bodily waste into the toilet bowl. Alternatively or additionally, the one or more sensors detect at least some of the above parameters after the subject has excreted and while the bodily waste is disposed of in the toilet bowl. For some applications, the sensors include imaging components, such as an RGB camera, a spectral camera, and / or a hyperspectral camera. Alternatively or additionally, the one or more sensors may include one or more light sensors configured to receive light from the bodily waste. For some applications, the sensor module includes one or more illumination components 25. Depending on the application, such illumination components include those configured to illuminate body emissions with a given spectral band (e.g., LED and / or laser), and / or broadband light sources. For some applications, the broadband light source is used in conjunction with one or more bandpass filters (which can be used to filter emitted light and / or detected light).

[0071] The computer processor typically receives one or more urine-related parameters from one or more sensors, and one or more fecal-related parameters from one or more sensors. Depending on the application, the computer processor performing the analysis described herein is either a computer processor 28 disposed within a housing 30 (which typically also houses the sensor module) or a different computer processor communicating with the sensor module. Typically, the computer processor determines whether a subject is dehydrated based at least in part on one or more urine-related parameters. For example, one or more urine-related parameters detected by one or more sensors may include urine color, urine volume (which can be measured by detecting the height h of urine in the toilet bowl), duration of excretion (which can be measured by detecting the time period during which the height h of urine in the toilet bowl rises), and / or frequency of excretion. Typically, to detect the excretion frequency of a particular subject, the sensor module is configured to receive input from the subject indicating that they are currently using the toilet, or the sensor module is configured to automatically detect subjects urinating into the toilet bowl at a given time.

[0072] For some applications, based at least in part on one or more fecal-related parameters (e.g., fecal shape, size, texture, color, etc.), a computer processor classifies dehydration into a given type of dehydration, such as isotonic dehydration, hypertonic dehydration, or hypotonic dehydration. For example, one or more sensors may include one or more cameras. For such applications, the computer processor typically receives one or more images of feces and analyzes these images to determine spatial parameters, and / or texture parameters, and / or color parameters of the feces. For some applications, images captured at multiple different wavelengths are analyzed. For example, feces may be illuminated with light at multiple different wavelengths at corresponding times, and images may be acquired simultaneously with the feces being illuminated at the corresponding wavelengths. Alternatively or additionally, filters, cameras, and / or other imaging devices or photodetectors configured to acquire images at different wavelengths (e.g., spectrometers, spectral cameras, hyperspectral cameras, etc.) may be used. For some applications, fecal image analysis is performed using computer vision analysis algorithms that perform segmentation and / or texture classification. Alternatively or additionally, deep neural networks trained for this task are used to perform image analysis.

[0073] As examples, and not limitations, some representative examples of steps that computer vision analysis algorithms may include are as follows:

[0074] • To conceal, for example, to eliminate areas far from feces;

[0075] • Contrast enhancement, used to enhance the areas of the image where feces are arranged;

[0076] • Image edge detection, used to detect the edges of feces;

[0077] • Detect regions of interest, for example, using a Gaussian mixture model;

[0078] • Perform morphological changes to remove noise;

[0079] • Use GrabCut to perform splitting.

[0080] refer to Figure 2A , Figure 2B , Figure 2C , Figure 2D and Figure 2E These figures illustrate representative images of some of the steps described above in the computer vision analysis performed on images of feces in a toilet bowl according to some applications of the present invention.

[0081] Figure 2A This is an example of contrast enhancement performed on an image of a subject's feces, where the left frame shows the feces image before enhancement and the right frame shows the enhanced image.

[0082] Figure 2B This is an example of edge detection performed on an image of a subject's feces, where the left frame shows the feces image before the edge detection algorithm was applied and the right frame shows the output of the edge detection algorithm.

[0083] Figure 2C This is an example of region of interest detection performed on images of a subject's stool using a Gaussian mixture model. The image sequence shows the corresponding clusters within the image that were identified as regions of interest. Typically, each region of interest is then further analyzed to detect parameters related to the subject's stool.

[0084] Figure 2D The diagram illustrates the corresponding stages of applying a noise removal algorithm to an image of a subject's feces, thereby applying morphological changes to the image to remove noise.

[0085] Figure 2E This is an example of applying the GrabCut image segmentation algorithm to images of a subject's feces, where the left frame shows the feces image before the GrabCut image segmentation algorithm was applied and the right frame shows the output of the GrabCut image segmentation algorithm.

[0086] Typically, computer processor 28 generates output on an output device (e.g., user interface device 32, as described below) based on the detection and classification of dehydration. For example, the computer processor may generate output indicating that a subject has a given type of dehydration, and / or may generate output indicating recommended course of action for the subject based on the type of dehydration. In this regard, it should be noted that different course of action is typically recommended for patients suffering from dehydration based on the type of dehydration.

[0087] It should be noted that, although Figure 1 The illustration shows urine 27 and feces 26 of a subject placed simultaneously in a toilet bowl, but the scope of this application includes analyzing a subject's feces at a time separate from the analysis of the subject's urine. For example, by analyzing a subject's urine, it can be determined first whether the subject is dehydrated. Subsequently, when the subject defecates, the subject's feces can be analyzed to classify the subject's dehydration into a given type of dehydration, as described above.

[0088] For some applications, the apparatus and methods described herein are performed in conjunction with the apparatus and methods described in Attar in US 10,575,830 and / or Attar in US 2019 / 0195802, both of which are incorporated herein by reference.

[0089] For some applications, device 20 includes a power source (e.g., a battery pack) disposed outside the toilet bowl and inside housing 30. Alternatively or additionally, the sensor module is connected to a main power source (not shown). Typically, the power source and sensor module 22 are wired (as shown) or wirelessly connected (not shown). Depending on the application, a computer processor performing the analysis described above is disposed inside the toilet bowl (e.g., a computer processor 28 disposed within housing 30, which typically also houses the sensor module) or remotely disposed. For example, as shown, the sensor module can wirelessly communicate with a user interface device 32 that includes a computer processor. Such a user interface device can include, but is not limited to, a telephone 34, a tablet computer 36, a laptop computer 38, or various types of personal computing devices. The user interface device typically serves as both an input device and an output device for user interaction with the sensor module 22. The sensor module can transmit data to the user interface device, and the user interface device's computer processor can run a program configured to analyze the received data.

[0090] For some applications, sensor module 22 and / or the user interface device communicate with a remote server. For example, the device can communicate with a doctor or insurance company via a communication network without the subject's intervention. The doctor or insurance company can evaluate the results and determine whether further testing or intervention is appropriate for the subject. For some applications, data associated with the received sensor signals is stored in a memory. For example, the memory can be located inside a toilet bowl (e.g., within the sensor unit), within housing 30, or remotely. The subject can periodically submit the stored data to an institution, such as a medical facility (e.g., a doctor's office, or a pharmacy) or an insurance company, where a computer processor can then perform the analysis described above on a batch of data acquired over a period of time related to multiple bodily emissions from the subject.

[0091] It should be noted that the apparatus and methods described herein include screening tests in which subjects do not need to physically touch bodily waste. Furthermore, subjects typically only need to periodically touch any part of the dedicated sensing device, for example, to install the device, or to replace or charge the device battery. (It should be noted that subjects may operate user interface devices, but this is typically a device that subjects can operate even when not using the sensing device (e.g., a telephone)). Additionally, the apparatus and methods described herein typically do not require the addition of any substance to the toilet bowl after the subject has emptied bodily waste to facilitate analysis of the waste and / or determination and classification of dehydration. For some applications, subjects do not need to perform any action after the device is installed in the toilet bowl. The test is automated and operated by the device, and monitoring of the subject's waste is seamless and requires no subject compliance, provided no abnormalities are detected.

[0092] Typically, after a subject empties bodily waste into the toilet bowl (and typically after the subject has finished emptying the waste and the waste is at least partially disposed of in the water in the toilet bowl), the bodily waste is imaged by receiving reflected and / or transmitted light from the toilet bowl, without requiring any action from the subject after emptying. For some applications, the bodily waste is analyzed during the process of being emptied into the toilet bowl.

[0093] In some applications, in the presence of a positive signal, the device reports the discovery of each emission by the subject to the subject via an output device, such as via a user interface device 32. In some applications, the output device includes an output component (such as a lamp (e.g., an LED) or a screen) built into the device 20.

[0094] Sensor module 22 is typically disposed within the toilet bowl. Furthermore, the sensor module typically includes an imaging component, which in turn includes one or more light sensors configured to receive light emitted from bodily excrement emitted by the subject, and is disposed within the toilet bowl. Typically, the sensor module is housed in a waterproof housing. Furthermore, typically, the side of the sensor module with the imaging component mounted thereunder is covered with a transparent waterproof cover. It should be noted that... Figure 1 A sensor module is shown positioned above the water level in the toilet bowl. However, for some applications, at least a portion (e.g., the entire sensor module) of the sensor module is submerged in the water within the toilet bowl.

[0095] For some applications, the sensor module includes a subject sensor. The subject sensor is configured to detect when a subject is on or near the toilet, and / or whether the subject has defecated and / or urinated into the toilet bowl. For example, the subject sensor may include a motion sensor configured to sense movement of feces, urine, the subject, or water in the toilet bowl. Alternatively or additionally, the subject sensor may include a light sensor configured to detect when a bathroom light is turned on, or when the subject is sitting on the toilet. For some applications, a light sensor for detecting light from bodily excretions is also used for the above functions. For some such applications, the sensor module is configured to be in standby mode most of the time (so that the power consumption of the sensor module is reduced). The sensor module is turned on in response to detecting that a subject is on or near the toilet, and / or that the subject has defecated and / or urinated into the toilet bowl. Typically, the imaging component of the sensor module acquires an image in response to detecting that a subject is on or near the toilet, and / or that the subject has defecated and / or urinated into the toilet bowl. For some applications, the subject manually turns on the sensor module.

[0096] In some applications, the computer processor 28 is configured to detect one or more additional components and / or parameters of a subject's urine based on data acquired by the sensor module 22. Some such applications are described below.

[0097] urine specific gravity

[0098] Urine specific gravity is a measure of the concentration of solutes in urine. It measures the ratio of urine density to water density and provides information about the kidneys' ability to concentrate urine. Typically, the specific gravity of urine in healthy adults is between 1.010 and 1.030. Increased specific gravity (called hypertonic urine) may be associated with dehydration, diarrhea, vomiting, excessive sweating, urinary tract / bladder infection, diabetes, renal artery stenosis, hepatorenal syndrome, reduced renal blood flow (especially due to heart failure), and / or excessive antidiuretic hormone. Decreased specific gravity (also called hypotonic urine) may be associated with kidney failure, pyelonephritis, diabetes insipidus, acute tubular necrosis, interstitial nephritis, and excessive fluid intake (e.g., psychogenic polydipsia).

[0099] For some applications, the sensor module is configured to generate a signal indicating that the subject's urine absorbs light, and the computer processor obtains the specific gravity of the subject's urine based on this signal. Typically, the computer processor obtains the specific gravity of the subject's urine based on the sensor's signal indicating that the subject's urine absorbs cyan-green light (e.g., light with a wavelength band in the 480nm-520nm range). For some applications, the computer processor generates an output in response to the obtained specific gravity. For example, the computer processor may generate an output on the user interface device 32 indicating whether the subject is dehydrated, and / or may generate an output on the user interface device 32 indicating a recommended course of action for the subject. For some applications, the computer processor is configured to detect whether the subject is dehydrated based on the specific gravity of the subject's urine, and is configured to classify dehydration using the techniques described above.

[0100] Now for reference Figure 3 The figure shows the ROC curves for some applications of the present invention, illustrating the sensitivity and specificity of a classifier for detecting the specific gravity of urine based on the absorption of blue light by urine. Figure 3 The results shown are for a classifier using a specific gravity of 1.025 (i.e., a specific gravity less than 1.025 is considered an indication of isotonic or hypotonic urine, and a specific gravity greater than 1.025 is considered an indication of hypertonic urine) as the classification point. The results of the classifier are compared with those using a Siemens (Germany) [system / device / etc.] The results obtained from the 10SG test strips were compared. The classifier produced a sensitivity of 81%, a specificity of 80.0%, and an area under the curve (“AUC”) of 82%, indicating that the classifier is a reliable classifier for determining proportions.

[0101] hematuria

[0102] Blood in urine (called hematuria) is a warning sign of various serious urinary tract diseases, some of which can be life-threatening. For example, hematuria can be caused by urinary tract infections, kidney infections, bladder or kidney stones, enlarged prostate, kidney disease, viral or streptococcal infections, or bladder and kidney cancer. In some applications, a computer processor analyzes signals from a sensor module to determine if a subject has hematuria. In some such applications, the computer processor uses techniques described by Attar in US 10,575,830, which is incorporated herein by reference.

[0103] Now for reference Figure 4The figure shows ROC curves for some applications according to the present invention, illustrating the sensitivity and specificity of a classifier used to detect the presence of blood in a subject's urine using the sensor module described herein. When compared to the results of a hemoglobin immunoassay performed using a QuikRead go wrCRP+Hb instrument manufactured by Aidian (Espo, Finland), the classifier produced a sensitivity of 80.5%, a specificity of 80.8%, and an AUC exceeding 88%, indicating that the sensor described herein can be used reliably to detect the presence of blood in a subject's urine.

[0104] Bilirubin, oxyurobilinogen and urobilin

[0105] Bilirubin, urobilin, and prourobilinogen are all derivative molecules of hemoglobin metabolism in the liver. Prourobilinogen is a colorless byproduct of bilirubin reduction. In liver diseases (such as hepatitis), prourobilinogen levels increase. When prourobilinogen is exposed to air, it is oxidized to urobilin, which gives urine its yellow color.

[0106] Bilirubin has a distinct absorption spectrum, with troughs in the wavelength range of 390 nm–420 nm (e.g., about 400 nm–410 nm) and peaks in the range of 445 nm–475 nm (e.g., about 455 nm–465 nm). Therefore, for some applications of the invention, the sensor module is configured to detect light absorption at each of the aforementioned wavelength bands, and the computer processor is configured to determine the bilirubin concentration in the subject's urine in response. For some such applications, the computer processor determines the light absorption ratio of each of the aforementioned wavelength bands relative to each other, and determines the bilirubin concentration in the subject's urine based on this ratio. For some applications, the computer processor is configured to generate an output in response to the detected bilirubin concentration. For example, in response to a high bilirubin concentration, the computer processor may generate an output on a user interface device instructing the subject to seek medical attention.

[0107] Oxidized urobilinogen has a distinct absorption spectrum with peaks in the wavelength range of 480 nm to 520 nm, for example, approximately 500 nm to 515 nm. Therefore, for some applications of the present invention, the sensor module is configured to detect light absorption in the aforementioned wavelength range, and the computer processor is configured to determine the concentration of oxidized urobilinogen in the subject's urine in response. For some applications, the computer processor is configured to generate an output in response to the detected concentration of oxidized urobilinogen. For example, in response to a high concentration of oxidized urobilinogen, the computer processor may generate an output on a user interface device instructing the subject to seek medical attention.

[0108] Furthermore, it has been demonstrated that pre-irradiation of urobilin at a wavelength of approximately 460 nm (e.g., between 440 nm and 480 nm) causes urobilin to emit light at a wavelength of approximately 520 nm (due to autofluorescence). Therefore, according to some applications of the invention, irradiating the urine of a subject with illumination in the wavelength range of 440 nm–480 nm (e.g., using an illumination element 25, such as…) Figure 1 (As shown). The computer processor then analyzes the sensor signal to determine the intensity of light emitted by the urine at a wavelength of approximately 520 nm (e.g., in the 505 nm–535 nm wavelength band, or in the 515 nm–525 nm wavelength band) in response to irradiation of the urine. Typically, the computer processor is configured to determine the concentration of oxyurobilin in the subject's urine in response to this.

[0109] Proteins (e.g., albumin)

[0110] Proteins (e.g., albumin) are typically abundant in the blood. If there is a problem with the kidneys, proteins can leak into the urine. A high protein content in urine may indicate kidney disease. Although proteins in urine tend to lack an optical signature, it has been observed that the presence of proteins in a subject's urine can cause foaming, as proteins have a soap-like effect of reducing the surface tension of urine. Urine expulsion causes electrostatic interactions between molecules within the liquid and its surface, resulting in the formation of bubbles in the urine due to air dispersion. For some applications, a computer processor is configured to detect sensor signals indicating the foaming and / or turbidity of a subject's urine, and / or the amount of foam and / or turbidity of the urine, and is configured to determine, at least in part, in response to this, that the subject has an excess of protein in her / his urine. Alternatively or additionally, the computer processor is configured to estimate the protein concentration in the subject's urine, and / or the amount of foam and / or turbidity of the urine, in response to the foaming and / or turbidity of the subject's urine. For some such applications, the computer processor is configured to estimate the turbidity of the urine by detecting its opacity. For some applications, the computer processor is configured to measure the height of the foam layer above the urine and, in response, determine that the subject has excessive protein in her / his urine, and / or estimate the protein concentration in the subject's urine. For some applications, the computer processor is configured to generate output in response to detecting whether there is excessive protein in the subject's urine, and / or based on the estimated protein concentration in the subject's urine. For example, in response to detecting whether there is excessive protein in the subject's urine, and / or based on the estimated protein concentration in the subject's urine, the computer processor may generate output on a user interface device instructing the subject to seek medical attention.

[0111] Biofilm-based pathogens

[0112] It has been proposed that the cause of recurrent urinary tract infections (UTIs) is the formation of pathogens (such as *Escherichia coli* (UPEC)) in the biofilm within the urinary tract. Typically, in such cases, cellulose is present in the extracellular matrix of the biofilm within the urinary tract. Furthermore, it has been demonstrated that pre-irradiation of cellulose with broadband illumination in the wavelength range of 300 nm–520 nm results in the cellulose emitting light at a wavelength of approximately 545 nm (due to autofluorescence). Therefore, according to some applications of the invention, irradiating the urine of a subject with broadband illumination in the wavelength range of 300 nm–520 nm (e.g., using an illumination element 25, such as…) Figure 1 (As shown). The computer processor then analyzes the sensor signal to determine the intensity of light emitted by the urine at a wavelength of approximately 545 nm (e.g., in the 530 nm–560 nm wavelength band, or in the 540 nm–550 nm wavelength band) in response to irradiation of the urine.

[0113] For some applications, the computer processor is configured to obtain the level of cellulose in the subject's urine based at least in part on the detected light intensity. As described above, for some applications, the computer processor is configured to detect hematuria by analyzing sensor signals. For some applications, in response to the detection of hematuria (e.g., using the techniques described above), the computer processor is configured to (a) drive the illumination component 25 to illuminate the subject's urine with broadband illumination in the wavelength range of 300 nm to 520 nm and (b) in response to the irradiation of the urine to determine the intensity of light emitted by the urine at a wavelength of approximately 545 nm (e.g., between 530 nm and 560 nm, or between 540 nm and 550 nm). Based on the detected light intensity (and the obtained cellulose concentration in the urine), the computer processor is configured to perform a differential diagnosis and determine whether the suspected cause of hematuria is a biofilm-based pathogen in the subject's urinary tract. For some such applications, the computer processor generates output in response to the differential diagnosis. For example, the computer processor may generate output on a user interface device instructing the subject to seek medical attention.

[0114] The invention described herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non-transitory computer-readable medium) that provides program code used or associated with a computer or any instruction execution system, such as a computer processor of user interface device 32, a computer processor 28 disposed within housing 30, or a cloud-based remote computer processor. For the purposes of this description, a computer-usable or computer-readable medium can be any means that may include, store, communicate, propagate, or transmit programs used or associated with an instruction execution system, apparatus, or device. This medium can be an electronic system, magnetic system, optical system, electromagnetic system, infrared system, or semiconductor system (or apparatus or device) or a propagation medium. Typically, the computer-usable or computer-readable medium is a non-transitory computer-usable or computer-readable medium.

[0115] Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer disks, random access memory (RAM), read-only memory (ROM), rigid disks, and optical discs. Current examples of optical discs include CD-ROM, CD-R / W, and DVD. For some applications, cloud storage is used.

[0116] A data processing system suitable for storing and / or executing program code will include at least one processor (e.g., the computer processor of the user interface device 32, a computer processor 28 disposed within the housing 30, or a cloud-based remote computer processor) directly or indirectly coupled to memory elements (e.g., the memory of the user interface device 32) via a system bus. These memory elements may include local memory, mass storage, and cache memory used during actual execution of the program code, providing temporary storage for at least some of the program code to reduce the number of times code must be retrieved from the mass storage during execution. The system can read inventive instructions from a program storage device and execute the methods of embodiments of the invention according to those instructions.

[0117] Network adapters can be attached to a processor, enabling the processor to connect to other processors or remote printers or storage devices via private or public networks. Modems, cable modems, and Ethernet cards are just a few of the types of network adapters currently available.

[0118] The computer program code used to perform the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar programming languages.

[0119] It is understood that the algorithms described herein can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer processor, special-purpose computer processor, or other programmable data processing apparatus used in production machines, such that instructions executed via the computer processor (e.g., the computer processor of user interface device 32, computer processor 28 disposed within housing 30, or cloud-based remote computer processor) or other programmable data processing apparatus create means for implementing the functions / behaviors specified in the algorithms described herein. These computer program instructions can also be stored in a computer-readable medium (e.g., a non-transitory computer-readable medium) that can direct a computer or other programmable data processing apparatus to operate in a particular manner, causing the instructions stored in the computer-readable medium to produce an article of art, including means of instruction for implementing the functions / behaviors specified in the algorithm. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide a process for implementing the functions / behaviors specified in the algorithms described herein.

[0120] The computer processor described herein is typically a hardware device programmed with computer program instructions to produce a special-purpose computer. For example, when programmed to execute the algorithms described herein, the computer processor is typically used as a special-purpose computer processor for analyzing bodily emissions. Typically, the operations performed by the computer processor described herein convert the physical state of the memory (which is a real physical artifact) into different magnetic polarities, charges, etc., based on the memory technology used.

[0121] Those skilled in the art will understand that this invention is not limited to what has been specifically shown and described above. Rather, the scope of this invention includes various combinations and sub-combinations of the different features described above, as well as any changes and modifications made by those skilled in the art after reading the above description that are not in the prior art.

Claims

1. An apparatus for use with urine discharged into a toilet bowl by a subject and in conjunction with an output device, the apparatus comprising: One or more sensors, coupled to a toilet bowl and configured to detect one or more urine-related parameters associated with the subject's urine, without requiring any action by anyone after the urine is flushed into the toilet bowl, the one or more urine-related parameters including signals indicating light absorption or emission of the subject's urine within a certain wavelength range, and At least one computer processor, said at least one computer processor being configured to: Receive one or more urine-related parameters, and Based at least in part on the urine-related parameters, the concentration of at least one hemoglobin derivative molecule in the subject's urine is obtained, wherein the at least one hemoglobin derivative molecule is selected from the group consisting of bilirubin, prourobilinogen, and urobilin. The output is generated on the output device in at least a partial response to the concentration of at least one hemoglobin derivative molecule obtained. The one or more sensors are configured to detect signals indicating light absorption in the subject's urine within a first wavelength band of 390 nm to 420 nm and a second wavelength band of 445 nm to 475 nm, and the at least one computer processor is configured to determine the light absorption ratio of the subject's urine in each of the first and second wavelength bands relative to each other, and to determine the bilirubin concentration in the subject's urine based on the ratio.

2. The apparatus of claim 1, further comprising an illumination component configured to irradiate the urine of the subject with light in the wavelength range of 440 nm to 480 nm. in, The one or more sensors are further configured to detect signals indicating light emitted by the subject's urine in the wavelength range of 505 nm to 535 nm in response to irradiation of the subject's urine, and The at least one computer processor is further configured to determine the concentration of urobilinogen in the subject's urine, at least in part, in response to the signal.

3. The apparatus according to claim 1, wherein, The one or more sensors are further configured to detect a signal indicating light absorption in the wavelength band of the subject's urine in the range of 480 nm to 520 nm, and wherein the at least one computer processor is further configured to determine the concentration of prourourobilinogen in the subject's urine in at least part of response to the signal.

Images