Wearable non-invasive bladder monitoring technologies

Abstract

An exemplary system and method are disclosed for (i) a set of bioimpedance electrodes attached to a body part proximal to or at an abdominal region to generate bioimpedance measurements of the person and (ii) a controller that can estimate urine volume in the person's bladder using the bioimpedance measurements.

Description

WEARABLE NON-INVASIVE BLADDER MONITORING TECHNOLOGIESRelated Application

[0001] This PCT application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 63 / 634,352, filed April 15, 2024, entitled “Wearable Non-Invasive Bladder Monitoring Technologies,” which is incorporated by reference herein in its entirety.Background

[0002] Medical practitioners often utilize diuretics to aid in the fluid management of the patient. The goal of diuretics is to promote excrement of excessive salts and water in the form of urine. To monitor the patient’s response and find the right medication dosage, fluid intake and urine output may be measured through methods such as an indwelling urinary catheter. Catheter-associated urinary tract infections make up to a large portion of the number of complicated urinary track infections in the United States.

[0003] A confounding condition that may impact the success of noninvasive bladder volume monitoring, e.g., in congestive heart failure (CHF), are ascites. Ascites refers to a buildup of fluid within the peritoneal membrane space, which encompasses the liver, spleen, stomach, and various parts of the gastrointestinal tract. Ascites in CHF patients can form due to a physiological feedback cycle initiated by a decompensated right ventricular failure.

[0004] The current gold standard for non-invasive bladder volume estimation is ultrasound. Desktop and handheld ultrasound devices have been utilized in clinics to inform patient management decisions. Ultrasound are (i) manually intensive, often requiring a specialized technician, and (ii) expensive, requiring specialized ultrasound scanners.

[0005] Other modalities of non-invasive bladder volume estimation that are more suited for wearable size restrictions exist but have yet to be clinically utilized, e.g., near-infrared spectroscopy (NIRS), which consists of a light source, typically from an LED and a detector, ft is known that adipose tissues attenuate NIRS signals but the implications within the context of bladder volume estimation have yet to be explored. Thus, while NIRS can accomplish bladder state sensing in laboratory settings, volume estimation has yet to be realized.

[0006] Bioimpedance analysis (BIA) can inject a known or measured current via a current-carrying electrode into the tissue and measure and the resulting voltage with a pickup electrode to determine a ratio of voltage to cunent. Some systems will combine the cunent carrying and pick-up electrodes into a singular bipolar pair. While bipolar systems measure impedance directly, they are often sensitive to asymmetry proximal to the pick-up electrode. A tetrapolar system is less sensitive to tissue proximal to the cunent-carrying electrodes andmeasures the transfer impedance between the current-carrying and pick-up electrode pairs rather than measures bioimpedance. While BIA shows promise, a singular tetrapolar measurement cannot localize the fluid of interest.

[0007] Electrical impedance tomography (EIT) can solve the problem by combining multiple BIA pattern measurements via a reconstruction algorithm based on the neighboring pair method to produce a medical image of the area. EIT has been observed to provide comparable estimation errors seen in ultrasound. Hundreds of measurements are made using EIT and reconstructed into an image / conductivity map. The map then undergoes additional processing to estimate bladder volume. The process is often computationally intensive.

[0008] There is a benefit to improving the non-invasive measurement of bladder fluids.Summary

[0009] An exemplary’ bioimpedance system and method are disclosed employing at least two bioimpedance electrode pairs optimized and configured to provide clinically meaningful analysis of the bladder condition. A study7was conducted that illustrated the use of digital twin models, and other models, can be used to maximize sensor sensitivity7for bioimpedance bladder measurement bladder. The exemplary7bioimpedance system may employ a bioimpedance algorithm (rule-based or Al-based algorithms) that operates on the tissues' frequency response via the bioimpedance measurement to isolate the bladder for its condition determination. The exemplary7system and method can be readily integrated into a wearable device capable of all-day use, optimized for battery life using efficient computation approaches, the wearable can employ a minimal set of bioimpedance sensor pairs that can be easy to position by the patient, user, or caretaker and that employs a straightforward interface to control. The exemplary7bioimpedance system and method can employ multiple excitation frequencies to provide multiple-frequency bioimpedance analysis (MFBIA). MFBIA allows volume predictions to have better accuracy and reduces the impact of ascites.

[0010] In some embodiments, a predefined set of bioimpedance sensor pairs are implemented for a specific analysis, e.g., to identify a condition of interest, e g., bladder being not empty7, at least 25% full, at least 50% full, or close to 100% full. Analysis performed by the study appears to show that bladder conditions consistently product a bioimpedance measurement pattern whereby a predefined set of bioimpedance sensor pairs may be used.

[0011] In other embodiments, rather than a pre-defined set of bioimpedance sensor pairs, the bioimpedance system includes an array of bioimpedance electrodes that are configured tobe interrogated in pairs to determine a subset to provide a measure of bladder volume and / or urine volume. A subset of the array is thus utilized in the bladder estimation analysis, but more than a subset may be initially analyzed and interrogated to determine the sensor pair to be used for the subsequent analysis. The array, while using the bioimpedance-based measurements, does not perform tomography analysis but sensor analysis that is facilitated by a pre-operation to determine the sensor pairing to be used for the subsequent analysis. To this end, computationally efficient rule-based controls (as Al equivalent) can be employed.

[0012] In some embodiments, the exemplary bioimpedance system additionally includes mechanical-based sensors and associated controls configured to provide movement, motion, or orientation measurements that can be employed with the bioimpedance measurement to reduce the effects of movement or motion. In some embodiments, the movement, motion, or orientation measurements allow the system to avoid acquiring bioimpedance measurement when movement is detected. In other implementations, the mechanical-based measurements and bioimpedance-based measurements are merged or fused in which the degree of movement, motion, or orientation (or changes thereof) are employed to correct or adjust the bioimpedance measurement. In some embodiments, the mechanical-based measurement is used both to avoid measurement during movement and to correct for bladder estimation based on the orientation of the sensors.

[0013] Patients with CHF can have ascites in their abdominal regions, which can cause a confounding effect (i.e., ascites effect) on the estimated urine volume. In some embodiments, the controller is configured to resolve the confounding effect by (i) measuring non-urine fluid (e.g., ascites) volume in the abdominal region and (ii) removing the measured non-urine fluid volume from the estimated urine volume. In other embodiments, the controller is configured to correct urine volume estimate errors caused by movements (e.g., run, walk) of the person detected by an additional set of mechanical sensors (e.g., inertial measurement unit (IMU), gyroscope, magnetometer, accelerometer) of the exemplary system.

[0014] The exemplary system is portable and self-containable as its bioimpedance electrodes and mechanical sensors are configured to measure the person's bioimpedance signals (e.g., voltage, current) and movements (e.g.. directions, velocities, accelerations), when in contact with the person’s skin, without any complicated mechanism (e.g., finger clipping) or cable connection to external devices. In addition to portability, the exemplary system is (i) power-efficient as it can operate for a long period of time without battery placement and (ii) memory-efficient as it does not need a large memory storage for processor instructions or measurement data.

[0015] A study was conducted that developed a digital twin model, and other models, that may be used to determine the optimized bioimpedance sensor pairing. The model shows that bioimpedance sensor pairing may indeed be optimized for a particular rule-based determination of condition of the bladder, e.g., the bladder being not empty, at least 25% full, at least 50% full, close to 100% full. The exemplar}' system and method can be used for a number of application domains, e.g., fluid management to adjust fluid intake provided to the patient, patient care to alert a nurse practitioner, caretaker, or patient when certain bladder conditions exist and action should be taken. The exemplar}’ system and method may be employed as a wearable device in hospital, clinic, or home-care settings, for continuous or temporary monitoring. In some embodiments, the exemplary system and method are employed as measurement instruments for short-term patient bladder monitoring or long-term patient bladder monitoring.

[0016] In an aspect, a system is disclosed for monitoring the volume of urine in the bladder of a person comprising: a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person; and a controller comprising: a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to: receive, via the processor, the bioimpedance measurements; determine, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and output, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

[0017] In some embodiments, the system further comprises a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person.

[0018] In some embodiments, execution of the instructions causes the processor to: receive, via the processor, the movement measurements; in response to the person causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determine, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and output, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.

[0019] In some embodiments, the execution of the instructions further causes the processor to: determine, via the processor, an ascites volume in the abdominal region (e.g..separate from the bladder) using the set of bioimpedance electrodes; and adjust, via the processor, the urine volume estimate by removing the determined ascites volume from the determined first urine volume estimate, wherein the adjusted urine volume estimate is outputted.

[0020] In some embodiments, the controller is configured to output, via the processor, the determined ascites volume estimate, wherein the additional output is subsequently used for monitoring and diagnoses of a disease or condition.

[0021] In some embodiments, the set of bioimpedance electrodes is configured as a bioimpedance electrode array comprising two or more pairs of bioimpedance electrodes, including a first pair of bioimpedance electrodes and a second pair of bioimpedance electrodes, wherein each electrode is evenly spaced on the body part.

[0022] In some embodiments, each electrode is a dry electrode.

[0023] In some embodiments, each electrode is a gel electrode.

[0024] In some embodiments, the execution of the instructions further causes the processor to: receive first bioimpedance measurements using the first pair of bioimpedance electrodes; receive second bioimpedance measurements using the second pair of bioimpedance electrodes; and determine the bioimpedance measurements as a down-selection operation of at least one of the first bioimpedance measurements and the second bioimpedance measurements, wherein the down-selection operation satisfies a predefined measurement range.

[0025] In some embodiments, each mechanical sensor is selected from the group consisting of an inertial measurement unit, a gyroscope, an accelerometer, and a magnetometer.

[0026] In some embodiments, the bioimpedance array comprises between 4 and 16 bioimpedance electrodes.

[0027] In some embodiments, the controller is implemented in a mobile device comprising a network interface configured to communicatively operate with the set of bioimpedance electrodes through a network.

[0028] In some embodiments, the controller is a remote computing device located in a cloud infrastructure comprising a network interface configured to communicatively operate with the bioimpedance electrodes through a network.

[0029] In another aspect, a method is disclosed for monitoring volume of urine in a bladder of a person comprising: providing a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedancemeasurements of the person; receiving, via a processor, the bioimpedance measurements; determining, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and outputting, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

[0030] In some embodiments, the method further includes providing a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person; receiving, via the processor, the movement measurements; in response to the person causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determining, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and outputting, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.

[0031] In some embodiments, the method further includes measuring an ascites volume in the abdominal region using the set of bioimpedance electrodes; determining, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of a confounding effect of the ascites volume being removed from the urine volume estimate; and outputting, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring or diagnoses of the disease or condition.

[0032] In some embodiments, the set of bioimpedance electrodes is configured as a bioimpedance electrode array comprising two or more pairs of bioimpedance electrodes, including a first pair of bioimpedance electrodes and a second pair of bioimpedance electrodes.

[0033] In some embodiments, the method further includes receiving first bioimpedance measurements using the first pair of bioimpedance electrodes; receiving second bioimpedance measurements using the second pair of bioimpedance electrodes; and determining the bioimpedance measurements as a down-selection operation of at least one of the first bioimpedance measurements and the second bioimpedance measurements, wherein the down-selection operation satisfies a predefined measurement range.

[0034] In some embodiments, the bioimpedance array comprises between 4 and 16 bioimpedance electrodes.

[0035] In another aspect, a non-transitory computer-readable medium is disclosed for monitoring the volume of urine in the bladder of a person having instructions stored thereon.wherein execution of the instructions by a processor causes the processor to: provide a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person; receive, via a processor, the bioimpedance measurements; determine, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and output, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

[0036] In some embodiments, the computer-readable medium is employed for a system having a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person.

[0037] In some embodiments, the execution of the instructions causes the processor to: receive, via the processor, the movement measurements in response to the person, causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determine, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and output, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.Brief Description of Drawings

[0001] Figs. 1A - IE each shows an example system having (i) a set of bioimpedance electrodes configured to attach to a body part (of a person) proximal to or at an abdominal region and (ii) a controller configured to estimate urine volume in a bladder of the person using the bioimpedance measurements generated by the set of bioimpedance electrodes, in accordance with an illustrative embodiment.

[0002] Figs. 2A - 2F show an example urine volume estimation and down-selection operation performed by the exemplary system in accordance with an illustrative embodiment. Fig. 2A shows the example urine volume estimation when ascites are present in the person's abdomen. Fig. 2B show s example changes in sensitivity7(shown as heat map) to the urine volume of each electrode in a bioimpedance electrode array at different urine volumes in the person’s bladder. Figs. 2D - 2F each shows an example selection of bioimpedance electrodes in the bioimpedance electrode array as a result of the down-selection operation.

[0003] Figs. 3A - 3B show example operation flows of the exemplary system in accordance with an illustrative embodiment. Fig. 3A show s the operation flow for estimatingurine volume in the bladder of a person. Fig. 3B shows the operation flow for a downselection operation on a bioimpedance electrode array disposed on the person. Fig. 2C shows an example organization of bioimpedance electrodes in the bioimpedance electrode array.

[0004] Figs. 4A - 4L show an experiment flow for evaluating the exemplary system and evaluation results for the performance of the exemplary system. Fig. 4A shows the experiment flow for evaluating the exemplary method. Fig. 4B shows a digital twin model and electrode sensitivity to the bladder at 50 kHz. Fig. 4C shows an improved sensitivity cross-section and current paths through the digital twin. Fig. 4D shows the measurement frame voltage change ratio and real component trends with bladder volume. Fig. 4E shows the measurement frames for a bladder volume of 151 mL. 300 mL, 450 rnL, and 598 mL using a frequency of / = 5 kHz. Fig. 4F shows the measurement frames for a bladder volume of 151 mL, 300 mL, 450 mL, and 598 mL using a frequency of / = 10 kHz. Fig. 4G shows the measurement frames for a bladder volume of 151 mL, 300 mL, 450 rnL, and 598 mL using a frequency of / = 20 kHz. Fig. 4H shows the measurement frames for a bladder volume of 151 mL, 300 mL, 450 mL, and 598 mL using a frequency of / = 100 kHz. Fig. 41 shows the measurement frames for a bladder volume of 151 rnL, 300 mL, 450 mL. and 598 rnL using a frequency of / = 200 kHz. Fig. 4J shows the bladder volume estimation results. Fig. 4K shows the absolute error of bladder volume prediction from the LASSO method for bladder volume greater than 100 mL. Fig. 4L shows the ascites simulation setup and results.

[0005] Fig. 5 shows a current state-of-the-art electrical impedance tomography (EIT) method for estimating bladder volume. The exemplary system and method does not need to perform image reconstruction as shown in such estimation.

[0006] Figs. 6A - 6H show a simulation illustrating the operation of the exemplary system to correct, via a correction algorithm, the urine volume estimate when a patient moves. Fig. 6A shows a simulation model that demonstrate bioimpedance signal change change due to patient movements. Fig. 6B shows a transparent w ire view' of an anatomically invalid COMSOL geometry of the bladder w here the bladder intersected with the fat layer. Fig. 6C shows placements of the bioimpedance electrodes on the patient in the simulation. Fig. 6D shows the mean absolute change in VCRs across the six simulated bioimpedance electrodes. Fig. 6E show's the operation flow of the exemplary system without the correction algorithm and with the correction algorithm. Fig. 6F show's the mean absolute change in VCR between the bladder's shifted position and its original position. Fig. 6G shows the motion filtering errors of the correction algorithm in the simulation. Fig. 6H shows the Bland-Altmanplot comparing the volume prediction relative error to the actual bladder volume on the COMSOL-generated data.Detailed Description

[0007] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and / or discussion of such references is provided merely to clarify the description of the disclosed technology and is not an admission that any such reference is “prior art” to any aspects of the disclosed technology described herein. In terms of notation, “[n]” corresponds to the nth reference in the list. For example, [1] refers to the first reference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entirety and to the same extent as if each reference was individually incorporated by reference.

[0008] Example System

[0009] Figs. 1A - 1 C each shows an example system 100 (shown as 100a, 100b, 100c, lOOd, lOOe) having (i) a set of bioimpedance electrodes 102 attached to a person or patient to generate bioimpedance measurements of the patient and (ii) a controller 106 that can estimate urine volume 112 in the person’s bladder using the bioimpedance measurements. In the example shown in Fig. 1 A, the system 100a has a baseline system comprising (i) the set of bioimpedance electrodes 102 and the controller 104. Fig. IB further employs a set of mechanical sensors 103 configured to attach the same body part proximal to or at the abdominal region as the set of bioimpedance electrodes 102. Fig. 1C configures the set of bioimpedance electrodes 102 as a bioimpedance electrode array having two or more pairs of bioimpedance electrodes, wherein each electrode can be evenly spaced on the body part. Fig. ID implements the controller 106 in a mobile device comprising an input / output (I / O) interface 110 configured to communicatively operate with the set of bioimpedance electrodes 102 through a network 130. Fig. IE implements the controller 106 as a remote computing device located in a cloud infrastructure comprising the interface 1 10 configured to communicatively operate with the bioimpedance electrodes 102 through the network 130.

[0010] As used herein, the term “abdominal region” includes a body part proximal to or at a portion of the abdomen and pelvic having correspondence to the bladder, for example, including hypogastric, umbilical, inguinal (left and right) abdominal region, lumbar (left and right) abdominal region, or a combination thereof.

[0011] Bioimpedance electrodes. In the examples shown in each of Figs. 1A - 1C, the set of bioimpedance electrodes 102 can be attached to the abdominal region. Each bioimpedance electrode 102 (shown as '‘Bioimpedance electrode” #1, #2,... , #N) is configured to provide signals for bioimpedance measurements 104 (e.g., voltage change ratio, current change ratio, voltage magnitude, etc.) corresponding to changes in current density7or voltage caused by the presence of urine or other fluids in the abdomen region, and / or changes in the urine or fluid, e.g., the urine filling up the bladder and / or non-urine fluid (e.g.. ascites) filling up the abdomen. Each electrode in the set of electrodes 102 can be a dry electrode or a gel electrode.

[0012] Mechanical sensors. To correct for movement and orientation change by the person or patient during the bioimpedance measurement, in the example shown in Fig. IB, the system 100b includes an additional set of sensors 103 (e.g., motion or orientation sensors) (shown as ‘'Mechanical sensors” 103) that is attached to the user. The mechanical sensor 103 provides mechanical measurement 105 associated with motion or orientation. The mechanical sensors 104 may include one or more inertial measurement units, gy roscopes, accelerometers, magnetometers, or a combination thereof.

[0013] In some embodiments, the mechanical sensor 103 is co-located with the bioimpedance electrodes 102. In other embodiments, the mechanical sensor 103 is positioned at a location proximal to the bioimpedance electrodes 102, e.g., at a second sensor location, whereas the bioimpedance electrodes 102 is positioned at a first sensor location. In other embodiments, the mechanical sensor 103 is positioned distal to the bladder.

[0014] The mechanical sensors are attached to the same body part as the set of bioimpedance electrodes 104. Each mechanical sensor (shown as #1, #2,... ,#N) is configured to generate movement measurements 105 (e.g., acceleration value, velocity value, etc.) corresponding to changes in orientations, velocities, or directions of the body part of the person.

[0015] The mechanical-based sensors and associated controls are configured to provide movement, motion, or orientation measurements that can be employed with the bioimpedance measurement to reduce the effects of movement or motion.

[0016] In some embodiments, the movement, motion, or orientation measurements allow the system to avoid acquiring bioimpedance measurement when movement is detected. In such a configuration, the controller for determining or analyzing the bladder condition is configured to interrogate the mechanical sensor and determine if there are movements or certain orientations. If such conditions exist, the controller may be configured to wait or delay bioimpedance measurement acquisition (e.g., as described in relation to Figs. 2A and2B) until the movement or motion has passed or until the mechanical sensor readings are stable. That is. bladder condition or urine bladder estimation (per Figs. 2A or 2B) may be delayed until the mechanical sensor measurements or associated motion / orientation is stable.

[0017] In other implementations, the mechanical-based measurements and bioimpedancebased measurements are merged or fused in which the degree of movement, motion, or orientation (or changes thereof) are employed to correct or adjust the bioimpedance measurement. In some embodiments, the mechanical-based measurement is used for both avoiding measurement to during movement and correcting bladder estimation based on the orientation of the sensors. An example of processing is provided in Mizell, David. "Using gravity to estimate accelerometer orientation." Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings. IEEE Computer Society, 2003. which is incorporated by reference herein

[0018] Controller and associated instructions. In the example shown in Figs. 1 A - 1C, the controller 106 can operate in accordance with processor instructions 108 (e g., 108a - 108d) stored in an associated memory (not shown). In Fig. ID, the controller 106 is implemented in a mobile device comprising an interface 110 configured to communicatively operate with the set of bioimpedance electrodes 102 through a network 130. In Fig. IE, the controller 106 is implemented as a remote computing device located in a cloud infrastructure comprising the interface 110 configured to communicatively operate with the bioimpedance electrodes 102 through the network 130.

[0019] Specifically, in Figs. 1 A, I D, and IE, the controller 106 can receive, via the input / output (I / O) interface 110, the bioimpedance measurements 104. When the person is not moving and the person’s abdomen does not contain ascites, the controller 106 can determine, via a urine volume estimator 108a. a urine volume estimate 112 in the bladder using the bioimpedance measurements 104. The controller 106 can then output, via the I / O interface 110, the urine volume estimate 112 to a display component 114, wherein the display component 114 is used for monitoring, diagnoses, or biofeedback of a disease or condition (e.g., congestive heart failure (CHF) conditions).

[0020] When the person’s abdomen contains ascites, after determining the urine volume estimate 112, the controller 106 can measure, via a non-urine volume estimator 108b, an ascites volume in the abdomen using the set of bioimpedance electrodes 102. The ascites volume in the abdomen can cause a confounding effect on the urine volume estimate 112. The controller 106 can correct this confounding effect, via volume adjustment instruction 108c, by removing the measured ascites volume from the urine estimate 112. Then, thecontroller 106 can output the corrected urine estimate 112 to the display component 114 for subsequent monitoring, diagnoses, or biofeedback.

[0021] In Fig. IB, when the person is moving, causing changes in orientations, directions, or velocities of the mechanical sensors attached to the body part, the set of mechanical sensors 103 can generate movement measurements 105 (e.g., acceleration value, velocity value, etc.). The controller 106 can receive, via the I / O interface 110, both the bioimpedance measurements 104 and the movements measurements 105. The controller 106 can then determine, via the urine volume estimator 108a, the urine volume estimate 112 using the bioimpedance measurements 104. When the movement measurements 105 exceed a predefined threshold, the urine volume estimate 112 can have an estimate outlier 108d (e.g., estimate error), which can be detected by the controller 116. To correct the urine volume estimate 112, the controller 106 can remove the estimate outlier 108d from the urine estimate 112 via the volume adjustment instruction 108c. Then, the controller 106 can output, via the I / O interface 110, the corrected urine volume estimate 112 to the display component 114 for subsequent monitoring, diagnoses, or biofeedback.

[0022] Bioimpedance electrode array. In the example shown in Fig. 1C, the bioimpedance electrodes are configured as a bioimpedance electrode array 107 comprising two or more pairs of bioimpedance electrodes, including a first pair of bioimpedance electrodes and a second pair of impedance electrodes. The first pair of bioimpedance electrodes can measure an injected current value, and the second pair of bioimpedance electrodes can measure a voltage value corresponding to the changes in current density or voltage caused by the urine filling up the bladder and / or ascites filling up the abdomen. Each electrode in the bioimpedance array 107 can be evenly spaced with respect to edge distances or angles (shown in Fig. 2C). In some embodiments, the bioimpedance electrode array 107 can comprise between 4 and 16 bioimpedance electrodes, which can be divided into 2-8 pairs of bioimpedance electrodes.

[0023] In some embodiments, the first and second pair of bioimpedance electrodes can be configured to measure the injected current and resultant voltage method (denoted as BioZ method), such as quadrature demodulation. The quadrature demodulation method can output a current value (denoted as I) and measure the response voltage (denoted as V) using the first and second pair of bioimpedance electrodes to obtain absolute impedance (denoted as abs(Z)) and the phase impedance (denoted as Z) phase angle.

[0024] In Fig. 1C, the bioimpedance electrode array 107 can be placed on the same body part of the person and comprise two or more pairs of bioimpedance electrodes, including thefirst pair and second pair of bioimpedance electrodes, each being configured to generate a bioimpedance measurement (e.g., current change, voltage change, etc.). The controller can receive (i) first bioimpedance measurements using the first pair of bioimpedance electrodes and (ii) second bioimpedance measurements using the second pair of bioimpedance electrodes. After that, the controller can determine the bioimpedance measurements 104 as a down-selection operation 108e (further detailed in Fig. 2B) of at least one of the first bioimpedance measurements and the second bioimpedance measurements, wherein the downselection operation 108e satisfies a predefined measurement range (e.g., current change or voltage change measured by the first or second pair of bioimpedance electrodes satisfies a predefined current or voltage range). Using the determined bioimpedance measurements 104, the controller 106 can determine and output the urine volume estimate 112 to the display component 1 14 for subsequent monitoring, diagnoses, or biofeedback.

[0025] Indeed, a subset of the array is utilized in the bladder estimation analysis but more than a subset may be initially analyzed and interrogated to determine the sensor pair to be used for the subsequent analysis. The array, while using the bioimpedance-based measurements, do not perform tomography analysis but sensor analysis that is facilitated by a pre-operation to determine the sensor pairing to be used for the subsequent analysis. To this end, computationally efficient rule-based controls (as Al equivalent) can be employed.

[0026] Example Method

[0027] Urine estimation. Fig. 2A shows an exemplary method to determine urine bladder volume using bioimpedance sensor measurements. An optimized bioimpedance sensor or sensor pairing configuration may be determined, e.g., using the modeling and experimental data described herein, that are optimized for a particular condition determination, e.g., bladder not empty, bladder at least 25% full, bladder at least 50% full, bladder close to 100% full, the bladder is expanded from the original shape (over 100% full beyond normal baseline size). Of course, other like values can be determined, e.g,. 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 41%. 42%. 43%. 44%. 45%. 46%. 47%. 48%. 49%.50%. 51%. 52%. 53%. 54%. 55%. 56%. 57%. 58%. 59%. 60%. 61%. 62%. 63%. 64%. 65%.66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%,82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, 99%, 100%. The noted condition can be in terms of volume (e.g., 100 mL) or state (e.g., 25% potential bladder state). The controller may be able to detect when the bladder is smaller than its baseline or when it is larger than its baseline size (e.g., by determiningoutliers from the baseline, e.g., determine as an average of the bioimpedance measurements. The pairing may be distinct electrodes or common electrodes.

[0028] Ascites estimation. Fig. 2A shows a further example of urine volume estimation performed by the execution of instructions 108a - 108c when ascites are present in the person's abdomen. In Fig. 2A, the exemplary' system employs two pairs of bioimpedance electrodes, including (i) a first electrode pair 208 to measure the voltage or current of (i. e. , bioimpedance measurement) the ascites 202 (shown as 202?) in the abdomen 204 and (ii) a second electrode pair 210 to measure the voltage of the urine volume 206 in the bladder. In addition to measuring the currents of the ascites 202 and urine volume 206, the two pairs (e.g., 208, 210) of bioimpedance electrodes can also capture the current values of other tissues, including skin. fat. and muscle. These dynamic current changes are negligible and do not impact the current of the ascites 202 and the urine volume 206. The pairing may use (i) distinct electrodes for bladder urine estimation and for ascites estimation or (ii) common electrodes in which a pair of electrodes are used for bladder urine estimation and the same subset of electrodes are used for ascites estimation. The exemplary system can then (i) determine, via the urine volume estimator 108a. a urine volume estimate in the bladder and (ii) measure, via the non-urine volume estimator 108b, an ascites volume in the abdomen 204.

[0029] The buildup of ascites 202 in the abdomen 204 can cause a change in the current measurement of the first electrode pair 208 and voltage of the second electrode pair 210. affecting the determined urine volume estimate. The urine volume estimate affected by the ascites effect is inaccurate, so the exemplary system should correct the urine volume estimate, via the volume adjustment instruction 108c, by removing the ascites volume from the urine volume estimate. After removal, the urine volume estimate is free of the ascites effect and can be outputted to a display component (shown as 114) for subsequent monitoring, diagnosis, or biofeedback.

[0030] Down-selection for bioimpedance electrode array. Fig. 2B shows example changes in sensitivity’ (shown as a heat map) to the urine volume of each electrode in a bioimpedance electrode array 107. As shown, the sensitivities of the electrodes (e.g.. 1, 9, 10, 18, 19, 27, etc.) can change when the urine volume in the bladder changes from 598 mL to 144 mL. In some embodiments, the exemplary system can utilize these changes in sensitivity as a criterion to select specific bioimpedance electrodes in the array 107 for the downselection operation 108e.

[0031] Fig. 2C shows an example organization of bioimpedance electrodes in a bioimpedance electrode array (shown as 107 in Figs. 1A - 1C). In some embodiments, the electrodes can be evenly placed at ad-cm distance from each other, in an edge-to-edge organization across the person's abdominal region. In other embodiments, the electrodes can be evenly placed at a ^-degree separation from each other across the person’s abdominal region. The placement of the electrodes in the array can be configured to correspond to the size of the abdominal region (e.g.. waist size) of the person.

[0032] Fig. 2D shows an example selection of bioimpedance electrodes (highlighted) in a bioimpedance electrode array (show n as 117 in Figs. 1 A - 1C) as a result of the dow nselection operation, wherein electrode sensitivity to the urine volume in the bladder is the selection criterion for the down-selection operation (i.e., different electrodes are selected when the urine volume changes).

[0033] Fig. 2E show s an example selection of bioimpedance electrodes in the array as a result of the down-selection operation, wherein sensitivity to the voltage change ratio or the current change ratio of the urine volume is the selection criterion for the down-selection operation. As shown, different electrode pairs are selected, corresponding to different voltage change ratios or current change ratios caused by different urine volumes in the bladder.

[0034] Fig. 2F shows an example selection of bioimpedance electrodes in the array as a result of the down-selection operation, wherein sensitivity to the voltage magnitude or the current magnitude of the urine volume is the selection criterion for the down-selection operation. As shown, different electrode pairs are selected, corresponding to different voltage magnitude values or current magnitude values caused by different urine volumes in the bladder.

[0035] Overall operation flow. Fig. 3A shows an example operation flow 300a of the exemplary system for estimating urine volume in the bladder of a person. In the example shown in Fig. 3A, the operation is described in 4 steps. It is contemplated that less than 4 steps may be performed and more than 4 steps may be performed.

[0036] In Fig. 3 A, at step 302, a set of bioimpedance electrodes can be disposed on the person, wherein the set of bioimpedance electrodes can be configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person. At step 304, the exemplary system can receive, via a processor, the bioimpedance measurements. At step 306, the exemplary system can determine, via the processor, a urine volume estimate in the bladder using bioimpedance measurements. At step 308, theexemplary system can output, via the processor, the urine volume estimate, wherein the output can be subsequently used for monitoring and diagnoses of a disease or condition.

[0037] Down-selection flow. Fig. 3B shows an example operation flow 300b for a downselection operation on a bioimpedance electrode array disposed on the person, which can comprise four steps. At step 310, a set of bioimpedance electrodes configured as a bioimpedance electrodes array can be disposed on the person, wherein the bioimpedance electrode array can comprise two or more pairs of impedance electrodes, including a first pair of impedance electrodes and a second pair of bioimpedance electrodes. At step 312, the exemplary system can receive first bioimpedance measurements using the first pair of bioimpedance electrodes. At step 314, the exemplary system can receive second bioimpedance measurements using the second pair of bioimpedance electrodes. At step 316. the exemplary system can determine the bioimpedance measurements using the downselection operation of at least one of the first bioimpedance measurements and the second bioimpedance measurements.

[0038] Experimental Results and Additional Examples

[0039] A study was conducted to develop, simulate, and evaluate the exemplary system and method that can non-invasively and continuously monitor bladder volume, which required (i) an ideal electrode stimulation pattern and (ii) a reduction in computation resources during estimation.

[0040] The study evaluated the usage of the exemplary system for the monitoring of fluid intake and output for congestive heart failure (CHF) patients to prevent fluid overload, a principal cause of hospital admissions. The exemplary bladder volume measurement system utilizing bioimpedance and electrical impedance tomography was used to explore the continuous monitoring function within a wearable design. Advancing this format, the study- developed a conductivity- digital twin from radiological data, where the study performed simulations to optimize electrode sensitivity- on an individual basis. The optimized placement demonstrated an efficient volume estimation that required as few as seven measurement frames while maintaining low errors (CI 95% -1.11% to 1.00%) for volumes >100 mL. Additionally, the study quantified the impact of ascites, a common confounding condition in CHF, on the bioimpedance signal. By improving monitoring technology, the study aimed to reduce CHF mortality- by empowering patients and clinicians with a more thorough understanding of fluid status.

[0041] Fig. 4A shows an experiment flow for evaluating the exemplary method. In Fig. 4A, the study analyzed an optimal electrode geometry- that used a conductivity digital twinderived from radiological data. Once an optimized placement was selected, the study simulated 16 bladder volumes to understand trends in measurement frames with the largest voltage change and voltage change ratio (VCR) as the bladder fills. The study demonstrated that the number of frames required for volume estimation can be reduced, and computational complexity can be constrained to be computed on a mobile or wearable device. Finally , the study evaluated the impact of ascites, a confounding condition in CHF patients

[0022] , on the observed signals.

[0042] Experiment Protocol

[0043] Digital twin design. To engineer a conductivity7digital twin that remained closely related to in vivo conditions and minimized artifacts from abstracted anatomy geometry, the model geometry was segmented from radiological data using ITK-Snap

[0044] . facilitating an accurate replication of anatomical structures. The DICOM data was sourced from patient CMB-PCA-MSB-07483 in the Cancer Moonshot Biobank - Prostate Cancer Collection

[0045] , The study selected patients due to age and weight, aligning with the ty pical range expected within CHF patient populations. Although the data was in a cancer collection, the position of their nodules did not interfere with the anatomy of interest, making for an ideal foundation for the digital twin.

[0044] The segmentations were labeled on a per-voxel basis, resulting in the tissue having a topographical appearance with many facets. Additionally, semi-automated segmentation can include parts of tissues or create boundaries that may pose problems when meshing for finite element analysis (FEA). While some of these issues can be addressed in ITK-SNAP, others cannot; therefore, the tissue geometries were imported into Fusion360

[0046] for post-processing to remove elements that may cause issues in FEA & reduce the facet count. The resulting height of the digital twin was a 40 cm section of the torso. The skin itself was not segmented, but rather, the air-body interface in the radiological scan was utilized to create the outer boundary of the skin. The surface w as then thickened inward to 1.5 mm within the range of recorded dermis values

[0047] ,

[0045] The final digital twin mesh consisted of the electrodes, skin, fat, pelvic girdle, bladder (144 mL volume), and background tissue domains, resulting in 2,507,927 mesh elements. The average element quality7measured by7skewness was 0.666, with a minimum element quality7of 0.087. The few7elements of quality7<0.1 w ere not in regions of tissue of interest nor where high currents were expected to pass, thus not affecting the convergence of the solution

[0048] ,

[0046] Additional bladder volumes were sequentially developed by sculpting the previous bladder surface in Fusion360. The radiologically derived bladder served as the initial surface. The behavior of shape changed due to filling and voiding the biomechanics principles, where regions with expected large deformation fields experienced more manipulation of the mesh compared to regions with weaker deformation fields

[0049] , Twelve bladder volumes in approximately 50 mL steps were created, as well as an additional five random volumes. One of the bladders not forming a union in COMSOL, due to the use of partitions via work planes to assist in meshing, was excluded from the analysis. The remaining 16 volumes ranged from 10 mL to 598 mL, ensuring coverage of filling volumes and adult capacities

[0050] , Table 1 shows a summary of the domains’ geometry sources and dimensions.Table 1

[0047] Electric currents parameterization. Six simulation frequencies, 10, 20, 50, 100, and 200 kHz, were used, with a drive current of 1 mA for all simulations. The frequencydependent conductivities of tissues were based on the accepted dielectric dispersion models described by Gabriel (1996) and Peyman & Gabriel (2012). While these previous studies reported the dielectric properties for several tissues across a broad frequency range, there was an increased margin of error for low frequencies (<1 MHz). Few previous studies reported accurate sub-megahertz tissue properties, covering a limited number of tissues with high intra-specimen variability and differing methodologies for reporting dielectric values

[0051] , As a result, the study utilized the values from the gold-standard studies for the tissue domains. The complex permittivity ermodeled using a 4-pole Cole-Cole dispersion, can be definedper Equation 1

[0052] , The specific parameters for the Cole-Cole model were sourced from the tissue database compiled(Eq. 1)

[0048] In Equation 1, £rrepresents the complex relative permittivity, £OTIS the relative permittivity as co —>■ x and o denote the permittivity and conductivity at DC, respectively. The termmaccounts for the susceptibility of the with pole, which is characterized by the change in relative permittivity Aem, relaxation time r™, and fractional coefficient am, per Equation 2.(Eq. 2)

[0049] The muscle domain serves as the background tissue to simplify the numerous tissues in the abdominopelvic cavity, excluding the bladder, bone, and fat domains. The conductivity of tissues in this cavity ranged from 0.1 Sm1to 0.5 Sm1in the 10 kHz to 100 kHz frequency range

[0051] , Since much of the abdominopelvic cavity consisted of muscular tissue, which had conductivity values near the mean for these tissues, the abdominopelvic cavity was often modeled as muscle in other studies

[0016] ,

[0025] ,

[0030] , Thus, the dielectric properties of muscle tissue were utilized to represent the background tissue in the study.

[0050] The gel electrodes were modeled using a dielectric dispersion approach, with values derived from a study on the dielectric properties of physiotherapy, electrocardiography (EKG, ECG), and electromyography (EMG) gel electrodes within the 10 kHz to 10 MHz range

[0053] , The study did not report the Cole-Cole parameters; therefore, the study manually extracted relative permittivity (&, ) and conductivity (x) values from the published graphs. These values were then interpolated in COMSOL using a piecewise cubic method approximating the original data.

[0051] Table 2 shows the conductivities (denoted as Sm'1) of dielectrics used at 50 KHz. as shown in previous studies [12’] and [13’].Table 2

[0052] Electrode geometry placement and optimization. Three rows of EKG gel electrodes (2.5 cm / 2.5 cm) were arranged in a semicircular patern, with nine columns below the navel. The anterior abdominal placement was chosen based on previous studies, which demonstrated that electrode configurations in this region outperformed those with posterior placements

[0016] ,

[0020] ,

[0038] , Electrode sizing was chosen based on the available dimensions of EKG electrodes, ensuring ease of access and availability to researchers. The botom row was vertically centered 2.8 cm proximal to the inferior bladder wall and 5.8 cm below the anterior superior iliac spine (ASIS). The electrodes were spaced by grid projection of the electrode’s center point (6.0 cm horizontal, 3.5 cm vertical) from the coronal and sagital planes onto the skin’s surface. Electrodes were extruded 0.5 cm to the skin at the center point. The extruded electrodes were imported into COMSOL to allow its computer- aided design (CAD) kernel to handle the electrode skin interfacing through Boolean operations.

[0053] By allowing COMSOL to form the interface, a tight interface between the electrodes and skin was ensured. The naming convention of the electrodes started on the patient’s lower-right side, with electrode one located in row one, column one. The process resulted in 25 of the 27 targeted electrodes forming a union in the simulation software.

[0054] Electrodes were evaluated based on their sensitivity to the bladder. The sensitivity S was calculated using the reciprocal lead field method

[0019] ,

[0048] , selecting two electrode pairs from the stored simulation results. One pair was the current-carrying (CC) electrode, and the other w as the voltage-sensing electrode, referred to as the pick-up (PU) electrode. Although voltage-sensing electrodes did not drive a significant current, their reciprocal field was needed for this experiment, as shown in Equation 3.,, _ JCC -JPU 5 " I2(Eq. 3)

[0055] In Equation 3, Jc'cand JP'Urepresent the CC and PU cunent density fields, respectively. The current 7, the current driven between electrodes, normalized a unity current. By selecting electrodes with maximal sensitivity across all= 75,900 combinations, the signal of urine in the bladder w as subsequently globally maximized.

[0056] Measurement frame trends and volume estimation. All sixteen bladder volumes were simulated to understand the behavior of the bioimpedance signal across a broad range of fill volumes, providing insights into how the bioimpedance measurements changed with bladder expansion. The simulation aimed to capture the sensitivity of the bioimpedance technique to varying bladder states, from empty to full. The analysis of the measurement data was conducted using two metrics: the VCR, defined in Equation 4, and the absolute change in voltage magnitude

[0015] .|V - Vol VCR = .100 o(Eq. 4)

[0057] In Equation 4, V represents the measured voltage at an arbitrary bladder volume, and To is the baseline voltage, corresponding to an empty bladder with 10 ml of residual urine. The VCR expresses the change in voltage relative to the baseline, providing a normalized measure of how the bioimpedance signal fluctuated as filling occurred. By using the two metrics, the study can assess the benefits of each metric.

[0058] The study used the measurement frames as features in a bioimpedance analysis (BIA) volume estimation algorithm based on an optimized electrode geometry. The study chose the Least Absolute Shrinkage and Selection Operator (LASSO) for linear regression and feature selection. Feature selection aimed to reduce the C .= 8,190 measurement frames from all possible electrode addressing combinations to a manageable number that can be collected within seconds, assuming a 100 Hz output data rate. The study performed a hyperparameter / . search with 25 logarithmically spaced values from 105to 102. refining the search within the decade of minimal error.

[0059] Ascites simulation configuration. The simulations conducted thus far assumed that the patient was free from ascites; however, ascites can occur in patients who have congestive heart failure (CHF). Ascites is a condition where fluid builds up in the peritoneal cavity surrounding the liver, spleen, stomach, and parts of the gastrointestinal tract

[0022] ,

[0054] , In healthy individuals, the peritoneal cavity can contain from 25 mL to 50 mL of fluid. Grade I ascites are diagnosed via ultrasound when at least 100 mL of fluid is present. Grade II follows a similar diagnosis pathway where at least 1 L of fluid is present, distending the abdomen. Grade III is characterized by a severely distended abdomen due to the presence of several liters of fluid

[0022] ,

[0060] The study evaluated the impact of grade I ascites on the measurement frames by using two ascites volumes to establish the range of expected effects. These simulations wereconducted using the optimized electrode geometry for three bladder volumes: 10 mL, 300 mL, and 598 mL. The ascitic fluid was modeled by abstracting the fluid throughout the peritoneal space to a layer of fluid closest to the electrodes under the subcutaneous fat. The abstraction was performed as the contribution of the electrical impedance of tissues drops rapidly by an / •4factor as the distance from the electrode increases

[0019] , Therefore, the ascitic fluid with the largest impact on the measured bioimpedance signal was closest to the electrodes. The ascitic fluid layer abstraction was developed in CAD using a swept rectangular extrusion extending between the left and right ASIS, following a smooth contour of the subcutaneous fat boundary. The lowest point of the extrusion was 38 mm below the ASIS. Table 3 shows the ascites geometry parameters.Table 3

[0061] Well-characterized dielectric properties of ascitic fluids within the frequency range of interest were not found. As a result, the study used the dielectric properties of blood to approximate its value, as ascitic fluid was a byproduct of unabsorbed blood filtrate

[0022] .

[0052] ,

[0062] Experimental Results

[0063] Optimization of electrode geometry sensitivity to the bladder. Fig. 4B shows a digital twin model and electrode sensitivity to the bladder at 50 kHz. As shown, electrodes (e.g.. 1, 10.19, 27, 18, 9. etc.) in general change their sensitivities corresponding to the urine volume present in the bladder, and some pairs of bioimpedance electrodes can be chosen based on their sensitivity' values using a down -selection operation (shown in Fig. 2B).

[0064] Fig. 4B, subpanel (a) shows a diagram of the digital tw in model w ith domains labeled whose Cole-Cole parameters were specified. Fig. 4B, subpanel (b) shows the sensitivity electrode matrix overlaid with cumulative sensitivity’ to the bladder for simulated volumes; electrode IDs are included on edge columns, where column one is positioned on the right side of the twin. Fig. 4B, subpanel (c) show s the measurement frame absolute sensitivity distribution for all unique combinations of electrode pairings. Fig. 4B, subpanel (d) shows the frequency-dependent proportion of shared frames across bladder volumes at various percentiles; the Pearson correlation coefficient betw een the values at varying frequencies is r > 0.999. Fig. 4B, subpanel (e) show s the similarity’ matrix of shared measurement frames in the third quartile across injection current frequencies.

[0065] In Fig. 4B, subpanel (a), the study designed the digital twin frequency -dependent model using anatomically accurate geometry to assess and optimize bioimpedance electrode placement, which was fundamental to measuring tissue conductivity changes related to bladder volume. When the bladder fills with urine and expands, the transfer impedance changes, which can be correlated with the bladder volume. A pseudo-three-by-nine matrix electrode configuration was placed on the lower anterior abdomen to evaluate the optimal placement for detecting the transfer impedance change. In a tetrapolar bioimpedance system, two pairs of contact electrodes were used; one pair injected a safe, imperceptible AC signal into the tissue, while the other pair measured the resultant voltage across the tissue

[0019] ,

[0023] , The study performed exhaustive simulations, iterating over all electrode pairings. The simulations utilized the conductivity of five tissues and one material via a four-pole Cole- Cole relaxation model. The digital twin simulations were computed in COMSOL Multiphysics® v6.0 with the AC / DC physics module

[0024] ,

[0066] The electrode placement simulations utilized two bladder volumes: a lower volume segmented from radiological data measured at 144 mL and a larger volume 598 mL bladder derived from the original segmented bladder. The study computed sensitivity. S. a unitless measure of a given volume’s weight in the transfer impedance measurement

[0019] , In Fig. 4B, subpanel (b), the electrodes with the highest cumulative sensitivity to the volume of the bladder across all measurement frames were medial. When the lower bladder volume was simulated, the inferior row of electrodes was the most sensitive, with the medial electrode having the highest cumulative A As the bladder volume expanded laterally into the abdomen, the electrodes with the largest S tracked this movement, with the second row of electrodes providing the most sensitive measurements. For both volumes, there was clustering of high S in the electrode matrix: one central group around the medial electrode and two groups on the lateral sides of the patient. These patterns on the electrode geometry remained the same across stimulation frequencies injected by the current-carrying (CC) electrodes.

[0067] The study observed an initial steep decline in sensitivity when ranking measurement frames, as illustrated in Fig. 4B. subpanel (c). For a bladder volume of 144 mL, the maximum sensitivity value (|S|) was 2.00. w hich dropped to half its maximum value within the top 4.1% of the frames. The 598 mL bladder experienced a slow er decay of sensitivity, halving its maximum of 2.93 within the top 9.6% of frames. The difference in decay rates suggested that a given frame’s sensitivity rank was dependent on the accompanying bladder volume.

[0068] Additionally, in Fig. 4B, subpanel (d), the frames of the highest sensitivity were not shared across volumes. Frames in the 95th percentile & above were shared <53.4% of the time. The injection frequency in the range simulated had a minimal effect (r = 0.999). This observation was supported by the similarity matrix in Fig. 4B, subpanel (e), which showed a difference of <1% across all frequency permutations. An optimized electrode geometry may account for volume-dependent sensitivity, as measurement frames may not maintain their percentile ranking. Additionally, a single frequency within the range of 5 kHz to 200 kHz simulated was sufficient to characterize the geometry’s sensitivity.

[0069] Fig. 4C is a visualization of an improved sensitivity cross-section and current paths through the digital twin. As shown, two types of electrodes were used to generate bioimpedance measurements: (i) electrodes configured to measure current change ratio and (ii) electrodes configured to measure voltage change ratio corresponding to the change of urine volume in the bladder (also shown in Fig. 2A).

[0070] Fig. 4C, subpanel (a) shows a 3D view of the digital twin model, highlighting the sensitivity distribution through the internal tissues; the current injection electrodes are 410a - 410b, using a 50 kHz AC, and the voltage sensing electrodes are 410c - 410d. Fig. 4C. subpanel (b) shows a view of the digital twin visualizing current density, where faster arrow movement and warmer tones (shown as 412) indicate higher densities.

[0071] To select the optimized geometry from the sensitivity electrode matrix, the study considered the similarity of the electrodes’ sensitivity between volumes. The study selected 16 positions of the largest S for each volume, chosen to remain compatible with an electrical impedance tomography (EIT) system with 16 electrodes, resulting in 11 overlapping positions: 1, 2, 4. 5, 6, 7, 8, 9, 14, 15, 18 (Fig. 4B, subpanel (b)). In previous studies, the lower volume estimations have been prone to large reconstruction errors [6],

[0010] ,

[0025] , To address this issue, the study developed an optimal electrode geometry with a bias toward lower bladder volumes to mitigate the higher error rates associated with these estimations. The optimized configuration, consisting of 15 electrodes shown as 402 (e.g., 402a-402c) in Fig. 4A, was selected to improve the robustness of bladder volume assessments across a range of physiological conditions.

[0072] Indeed, the study validated the assumption that optimized pairs of bioimpedance sensor measurements, e.g., as described in relation to Figs. 1 A-1B and 2A - 2B can be used to determine a specific bladder condition, e.g., bladder not empty, bladder at least 25% full, bladder at least 50% full, bladder close to full, etc., as described herein, in terms of bladder volume.

[0073] Voltage magnitude and VCR behavior. The optimized electrode geometry was simulated across a wide range of bladder volumes. The simulations were configured to drive a constant current, facilitating impedance variations to be calculated from the measured voltage at the electrodes. Of course, a voltage may be alternatively applied to which a current can be measured. The study selected VCR and the voltage magnitude’s real component to assess measurement frame performance, facilitating the characterization of impedance signal changes and their ability to be sensed by an analog-to-digital converter (ADC). Due to the skewed nature of VCR and the presence of infinitesimal initial conditions for some frames leading to explosive growth, the study applied the interquartile range (IQR) outlier detection method with an IQR multiplier of 4.5. The filtering removed <3.1% of measurement frames across all bladder volumes.

[0074] Fig. 4D shows the measurement frame voltage change ratio and real component trends with bladder volume. For all subpanels, the VCR concerns a residual bladder volume of 10 mL with outliers removed. In Fig. 4D, subpanels (a) - (c) show absolute real voltage and VCR across a range of bladder volumes (injection current 50 kHz), and subpanel (d) shows the frequency response of absolute real voltage and VCR.

[0075] In Fig. 4D, subpanels (a) - (c), the measurement frames with the largest changes in voltage did not correspond to a larger VCR. As the bladder volume increased from 151 mL to 598 mL, the effect was more pronounced as Pearson's correlation coefficient, r, decreased from -0. 170 to -0.237. In Fig. 4D, subpanel (c), for the 598 mL bladder. IdVerl in the third quartile had a range of 1.04 mV to 2.46 mV and a mean I VC BBI of (5.85 ± 4.95)%. The I VC BBI had a third quartile range of 14.1% to 73.9%, with a mean I FBH of (351 ± 336) pV.

[0076] In Fig. 4D, subpanel (d), the maximal change in VCR and voltage remained nonoverlapping across the frequency spectrum simulated. However, as the injection frequency increased, the spread of values tended towards the origin. The same IQR-filtered frames appearing in both the 5 kHz and 200 kHz were compared. The study observed that the maximum \dVBV\ decreased from 2.59 mV to 2.11 mV, an 18.5% drop. The \ VCR B\ was less affected, with a decrease of 75. 1% to 74.8%, a 0.3% drop.

[0077] Figs. 4E - 41 each shows the measurement frame for a bladder volume using different frequencies. Fig. 4E shows the measurement frames for a bladder volume of 151 mL (subpanel (a)), 300 mL (subpanel (b)), 450 mL (subpanel (c)), and 598 mL (subpanel (d)) using a frequency of / = 5 kHz. Fig. 4F shows the measurement frames for a bladder volume of 151 mL (subpanel (a)), 300 mL (subpanel (b)), 450 mL (subpanel (c)), and 598 mL(subpanel (d)) using a frequency of / = 10 kHz. Fig. 4G shows the measurement frames for a bladder volume of 151 mL (subpanel (a)). 300 mL (subpanel (b)), 450 mL (subpanel (c)), and 598 mL (subpanel (d)) using a frequency of / = 20 kHz. Fig. 4H shows the measurement frames for a bladder volume of 151 mL (subpanel (a)), 300 mL (subpanel (b)), 450 mL (subpanel (c)), and 598 mL (subpanel (d)) using a frequency of / = 100 kHz. Fig. 41 shows the measurement frames for a bladder volume of 151 mL (subpanel (a)), 300 mL (subpanel (b)), 450 mL (subpanel (c)), and 598 mL (subpanel (d)) using a frequency of / = 200 kHz.

[0078] Bladder volume estimation using selected measurement frames. To determine the minimal number of measurement frames required for volume estimation, the study developed the BIA estimation algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) was selected for linear regression and feature selection

[0026] , where the features were the individual measurement frames from the optimized electrode geometry.

[0079] Fig. 4J shows a bladder volume estimation. Fig. 4J, subpanel (a) shows an assortment of the bladder meshes used in a simulation; the 144 mL bladder was segmented from radiological data and served as the base geometry from which other volumes were designed. Fig. 4J, subpanel (b) shows a modified Bland Altman plot demonstrating the relative volume errors for volumes >100 mL. Fig. 4J, subpanel (c) shows the measurement frames selected by LASSO voltage change from a 10 mL bladder volume baseline.

[0080] In Fig. 4J, subpanel (b), hyperparameter tuning for a 50 kHz stimulation yielded the best relative errors with = 1.62 * 104and used seven measurement frames for prediction. The relative error of predicted bladder volumes had limits of agreement of -0.06 ± 3.80%, (confidence interval (CI) 95% -1.11% to 1.00%) for volumes >100 mL and maximum absolute error (MAE) 6.09 mL for <100 mL. The frames selected by LASSO chose those with large voltage changes, as shown in Fig. 4J, subpanel (c).

[0081] Table 4 shows the actual bladder volumes and their predicted values using LASSO.Table 4

[0082] Fig. 4K shows the absolute error of bladder volume prediction from the LASSO method for bladder volume greater than 100 mL.

[0083] The study shows experiment data being consistent with the digital twin measurement and that the model described above can thus be used in determining during the development of the optimized sensor configurations for bioimpedance measurement (for bladder urine estimation or ascites estimation) or during runtime, the down selection of sensors from an array to which a subset can be used for bladder urine estimation or ascites estimation, and the application described herein.

[0084] Grade I ascites measurement frame impact. Fig. 4L shows the ascites simulation setup and results of the confounding effect caused by the ascites volume. For all subpanels, the bladder volume increased from 10 mL to 598 mL. All quartiles were calculated from the baseline condition of no ascites. Fig. 4L, subpanel (a) shows an overview of ascites simulations. Fig. 4L, subpanel (b) shows the digital twin model in a sliced view showing the placement of the ascitic fluid domain; the fluid was present along the anterior, below the subcutaneous fat. Fig. 4L, subpanel (c) shows the absolute voltage change from a bladder volume of 10 mL to 598 mL with vary ing levels of ascitic fluid; the measurement frame rank from the simulation without ascites is maintained across conditions to visualize drift from this state. Fig. 4L, subpanel (d) shows the comparisons of voltage changes due to ascites across the quartiles of \A FBH . Fig. 4L, subpanel (e) shows the change in VCR due to ascites broken dow n by the quartiles of VCR.

[0085] The study characterized the confounding effects of ascites, a build-up of fluid in the abdomen frequently occurring in clinical populations that require bladder volume monitoring, by simulating two low-grade ascites shown in Fig. 4L, subpanels (a) - (b). In Fig. 4L, subpanel (c), the I FBH across all conditions is presented, where the voltages had an average change of 3.13% and 4.09% due to the 95 mL and 200 mL ascites, respectively. This change was most pronounced for measurement frames that had lower I FBZI initially. Thefirst quartile of measurements had a mean change of 9.52%, whereas the third quartile had a mean change of 1.93%. Similarly, the VCR had more pronounced changes within the first quartile of measurements, with a mean difference of 5.05% from the no ascites baseline. The study observed values in the third quartile to change by 1.73% on average. A breakdown of these effects by ascites volume is shown in Fig. 4L, subpanels (d) - (e).

[0086] Demonstration of urine volume estimation correction. The study also developed simulations to show how the exemplary system corrected urin volume estimation when the patient moved (i.e., patient position shifts). Fig. 6A shows a simulation model engineered to demonstrate how bioimpedance signals (from the set of bioimpedance electrodes of the exemplary system) change due to patient movements. Subpanel (a) shows a visualization of perturbations to the bladder’s center of mass. When the patient changes position, gravity may pull the bladder to a new resting position. Subpanel (b) shows the simulation model (e.g., COMSOL) being developed using 1584 patient-driven values (e.g., BMI, bladder volume and position, and stimulus) in 213840 simulations.

[0087] Table 5 shows anatomically valid waist circumference values corresponding to various fat thickness values. Fig. 6B shows a transparent wire view of an anatomically invalid COMSOL geometry of the bladder 602 (shown as 602’) where the bladder 602 intersected with the fat layer 604 (at region 606). In the simulation, unrealistic geometries of the bladders (e.g., Fig. 6B) were excluded from the demonstration results. Specifically, the parameter sweep in the simulation resulted in 24 different BMI parameter values utilized in forming the simulation geometry. However, the study did not use all geometries as some geometries showed that the bladder intersected with the fat layer (Fig. 6B), which was not anatomically accurate. After removing the invalid geometries of the bladders, the study achieved 15 valid BMI configurations that were utilized for calculations.Table 5

[0088] In phase 1 of the simulation, the exemplary system employs a correction algorithm for urine volume estimation using voltage change ratios (VCRs) received from the (optimized) set of electrodes in the exemplary' system. Fig. 6C show s placements of the bioimpedance electrodes on the patient in the simulation. Subpanel (a) shows the cross-section of the patient's abdominal region. Subpanel (b) is a visualization of perturbations to the bladder's center of mass. Eight points of electrode placements on the transverse plane and five points of electrode placements on the coronal plane were evaluated. Due to the symmetric simulation setup, point of placement 610 can be evaluated from prior simulations.

[0089] To understand the impact of bladder position changes (of the patient’s bladder) on the bioimpedance signals, the study compared VCRs from the original position to the shifted position of the bladder. The study calculated VCR as the voltage differential between bioimpedance electrodes. The bioimpedance electrodes were selected after a current electrode pair was chosen as a result of the dow n-selection operation. In subpanel (a), the current electrodes were at points of placement 610 and 612. The bioimpedance electrode pairs can be any combination from positions 610, 612, 614, 616. or 618.

[0090] The study first considered all valid simulation geometries at 50 kHz and a voltage change ratio computed from 0 mL 460 mL. The study computed metrics for each bioimpedance electrode position utilized in a VCR measurement. The study also compared VCR from the bladder in the original position to the shifted position. These position changes mimicked the bladder settling after a patient goes from sitting to standing or lying on their side to sitting. Fig. 6D and Table 6 show the mean absolute change in VCRs across the six simulated bioimpedance electrodes. Two (i.e., a pair ol) bioimpedance electrodes w ere needed to create a VCR measurement, so a change in VCR (denoted as 4VCR) was provided for electrodes. The data in Fig. 6D and Table were collapsed into unique electrode drive and electrode combinations.Table 6

[0091] Fig. 6E shows the operation flow' of the exemplary' system without the correction algorithm (subpanel (a)) and w ith the correction algorithm (subpanel (b)). In phase 1 of the simulation, the study did not employ the correction algorithm in the exemplary system, which resulted in the operation flow shown in subpanel (a) where the controller received the bioimpedance measurements (shown as BioZ) from the bioimpedance electrodes and normalized the measurements in an analoig front end (AFE) as part of the VCR calculation.

[0092] In phase 2 of the simulation, the study placed the exemplary system on the patient and pressed the button to “zero” as a calibration operation, the correction algorithm after aninitial void. Then, the controller compared the bioimpedance measurements (from the electrodes) against the "zeroed" state to compute the voltage change ratio (VCR). An inertia measurement unit (IMU) (i.e., a mechanical sensor) measured and transmitted the patient's movements (e.g., orientation, tilt, etc.) to the controller to determine the scaling factor of the input (e.g., bioimpedance measurements) to the correction algorithm. In other words, the correction algorithm fused the measurements from the IMU to compensate for the patient's movements (e.g.. posture changes).

[0093] Fig. 6F shows the mean absolute change in VCR between the bladder's shifted position and its original position.

[0094] Fig. 6G shows the motion fdtering errors of the correction algorithm. In the simulation, the study moved the bladder from its origin position to augment patient position changes and movements. However, the study did not know how much shift occurred for positions other than lying down. The study approximated this shift based on the accuracy of the “present motion scaling factor and the future IMU to bladder position” mapping algorithm. The study iterated through the future mapping algorithm accuracies (50% to 100%) to generate the volume estimate errors shown in Fig. 6G. The best mean volume error was 4.45% when the accuracy of the mapping algorithm was 90%.

[0095] Fig. 6H shows the Bland-Altman plot comparing the volume prediction relative error to the actual bladder volume on the COMSOL-generated data. The x-axis shows the true bladder volume (mL). and the y-axis shows the difference of the prediction as a percentage of the true volume.

[0096] Discussion

[0097] Discussion #7. Congestive heart failure (CHF) affects approximately 6.5 million adults in the United States and is accompanied by fluid overload conditions, which account for most CHF hospitalizations [1], [2], In critical care and inpatient settings, clinicians monitor urine output to assess a patient’s fluid balance, renal function, and overall metabolic state to optimize the titration of diuretics and other treatments. In outpatient settings, patients are expected to weigh themselves simultaneously daily to monitor for any sudden weight changes indicative of fluid overload [3], However, in both environments, there are concerns about proper volume monitoring, whether it is infection risk from the monitoring method or patient adherence to a routine [4], [5],

[0098] The current gold standard of urine output monitoring in hospitals are indw elling urinary catheters. The catheter allows for urine to flow freely from the bladder into a drainage bag, the volume of which medical staff can then measure. The drawback of an invasiveapproach is the catheter introduces an infection vector to the patient, resulting in 1 million urinary tract infections annually in the United States. The additional healthcare costs to treat the infections is estimated to range from $115 million to $1.82 billion annually [4] . Moreover, use of a catheter may be a contraindication for the patient [6], Noninvasive bladder monitoring methods are needed to prevent infections, reduce costs, and address contraindications .

[0099] Non-invasive methods do not require the patient to matriculate to obtain a reading, therefore can monitor bladder volume directly. One method used to influence clinical decisions today is ultrasound, which requires a trained clinician to perform periodic scans manually to assess bladder volume. General-purpose ultrasound devices have errors of 21.8% and remain dependent on the technician operating them [7], The error of conventional ultrasound methods arises from the positioning of the scanner and volume estimation algorithm, which is dependent on the current bladder volume [7], Dedicated handheld ultrasound scanners (e.g., BVI 9400™ and Prime™ from BladderScan®) can have smaller errors [6], Nonetheless, both require a trained technician present to take measurements, resulting in noncontinuous monitoring. Recent advancements in the miniaturization of ultrasound transducers have enabled the development of wearable systems that could increase scan frequency. However, these systems have focused on detecting voiding [8], [9] or bladder state detection (full vs empty) to help patients manage incontinence rather than bladder volume estimation

[0010] ,

[0100] Continuous bladder volume estimation in a wearable form factor may allow additional populations, such as those who take diuretics, to manage their illness better. Other sensing modalities compatible with wearable size and power constraints are near-infrared spectroscopy (NIRS) and bioimpedance analysis (BIA). NIRS uses an LED and a detector flushed with the skin to measure light absorbed at specific wavelengths, typically at a high absorption coefficient for water in the urine [6], Previous studies have shown this technology can detect the bladder state, full vs empty7, in laboratory-controlled environments using a single subject

[0011] .

[0012] and a small group of subjects (N= 5)

[0013] , Adipose tissues may attenuate NIRS signals

[0014] , but the implications of bladder volume estimation have yet to be published. Thus, while NIRS can accomplish bladder fill state sensing in laboratory settings, volume estimation has yet to be realized. In contrast, BIA has shown promise for volume estimation.

[0101] Previous studies have found a change in voltage as bladder volume is perturbed in simulation and in vivo [15-18], While BIA has demonstrated promise, it cannot localize thefluid to the bladder. Electrical impedance tomography (EIT) can address this challenge by utilizing multiple electrodes to take impedance measurements from several positions and operating on a priori knowledge of the skin boundary, allowing for the reconstruction of conductivity maps

[0018] ,

[0019] , Fig. 5 shows a current state-of-the-art electrical impedance tomography (EIT) method for estimating bladder volume.

[0102] These EIT methods can have comparable estimation errors in ultrasound, which is used clinically [6].

[0010] , The development of these systems has focused on remaining compatible with existing hardware, such as the Goe MF II. These hardware systems use the neighboring method of electrode addressing

[0020] (Fig. 5, subpanel (b)), which is not optimized for monitoring the bladder. Prediction of volume using EIT requires reconstruction of a small image, 32 x 32 pixels

[0021] , rather than reconstruction in the 3D domain or larger image to avoid exponential grow th of operations and memory usage (Fig. 5, subpanel (c)).

[0103] Discussion #2. The instant study investigated the bioimpedance signal using a digital twin to aid the design of non-invasive systems for continuous bladder monitoring within a wearable form factor to achieve relevant volume estimations. The findings in the study included: (1) selecting measurement frames that maximize information in the region of interest can reduce the number of frames needed for accurate volume estimation; (2) the exemplary7method demonstrated low relative error across a wide range of bladder volumes; (3) the low error was achieved through optimizing electrode geometry to enhance bladder volume sensing at the individual electrode level; (4) maximizing the VCR involved a tradeoff with changes in voltage magnitude; and (5) the study established bounds on the impact of ascites on the bioimpedance signal. These insights can advance the potential of bladder volume monitoring technologies as they may evolve toward practical, wearable formats.

[0104] Electrical impedance tomography (EIT) addressed the electrodes by the neighboring method using 16 electrodes, which required N x (N~ 3) measurement frames resulting in 208 measurements required for reconstruction [6],

[0020] ,

[0021] ,

[0027] ,

[0028] , Using the standard estimation technique show n in Fig. 5, subpanel (a), the number of measurements required can be reduced as only half the measurements are linearly independent

[0027] but may impact error performance. The study demonstrated that direct estimation of bladder volume using optimized frames can reduce the number of measurements by up to 96.6 % without impacting estimation errors.

[0105] When comparing the exemplary system to other simulation-based studies, the algorithm employed by the exemplary system achieved similar or outperformed existing methods. For instance, a study by Schlebusch et al. demonstrated improved accuracy over theequivalent circular diameter method, which had a relative error of 16.0%, and the singular value difference method (4.7%). However, global impedance (0.1%) and neural networks (0.8%) achieved lower errors

[0027] . Another study by Sun et al. tested configurations with varying numbers of electrode rows, with the lowest error observed being 7.3% (CI 95% -5.6% to 20.1%) for vol >100 mL29. The errors of the exemplar}' method showed no consistent trend across volumes, contrasting with other reported algorithms where errors were higher for lower bladder volumes across sensing modalities [6],

[0025] . [27-30],

[0106] The effectiveness of a non-invasive bladder volume monitoring system as a replacement for catheterization in clinical applications can depend on its accuracy across the expected range of volumes. While the estimation errors of the exemplary system were compared against other computational models, it was important to consider the performance of devices currently used in clinical practice. For instance, evaluations of the BVI 9400™ overestimation by +17.5% (CI 95% 8.8% to 26.3%), whereas the Prime™ underestimated volumes by -4.1% (CI 95% -8.8% to 0.5%) without a pre-scan, and -6.3% (CI 95% -11.6% to -1.1%) across all observed volumes. Although these errors may suffice for certain clinical applications, they did not meet the stringent ±5% error margin required for postoperative care

[0031] , emphasizing a need for continued advancements of methodologies in simulation and the translation of these improvements into real-world systems to bridge this gap in performance.

[0107] Bioimpedance measurement technology can tackle challenges posed by clinical and at-home environments. Advancements in this field can enable the development of smaller, more power-efficient sensors capable of operating within a wearable form factor and providing runtime in the tens of hours before needing to charge

[0032] ,

[0033] , While previous studies focused on desktop bioimpedance acquisition systems [6], wearable EITs have emerged

[0034] , Reconstruction and volume estimation algorithms were designed for desktop systems, where computational power and memory resources were abundant and less constrained compared to wearable systems [6],

[0027] ,

[0035] ,

[0036] ,

[0108] The study demonstrated the feasibility of reducing computational complexity and memory requirements to provide continuous monitoring from a mobile or wearable device while maintaining acceptable estimation errors. The approach reduced the dependency on desktop systems and supported ambulatory use in settings beyond the clinic.

[0109] Wearable devices' restricted power and memory7resources required trade-offs between computational efficiency and battery life. Microcontrollers, used in wearable systems with battery life measured in days, can be more limited in memory than their desktop counterparts

[0032] ,

[0033] , For instance, performing reconstruction with Graz consensusreconstruction algorithm for electrical impedance tomography (GREIT) for a 32 by 32 pixel image using 16 electrodes and neighboring addressing utilized a reconstruction matrix of 828 kB. In contrast, direct estimation from selected frames was more memory-efficient, using only 0.027 kB for the column vector weights for the seven chosen frames from LASSO. A similar order of magnitude decrease in computation time of 142 ms for reconstruction versus 4.6 ps per estimation on microprocessors with limited resources. These optimizations made it feasible to process measurements directly on microprocessors (e.g.. Microchip or Nordic), eliminating the need for desktop systems and further enabling ambulatory use.

[0110] The placement of electrodes can play a crucial role in the performance of volume estimation methodologies. Bioimpedance and EIT techniques can be sensitive to electrode positioning, as the spatial arrangement can affect the accuracy of impedance measurements and subsequent image reconstructions

[0019] , To address this sensitivity, the exemplary method introduced an optimization approach to electrode placement by evaluating a subset of configurations from a matrix. By doing so, the study evaluated C f = 3.27 million placement configurations, more than previous studies, each tested only a handful (one to seven) of electrode placements

[0016] ,

[0020] ,

[0027] ,

[0028] ,

[0037] ,

[0038] , Furthermore, previous studies explored only a limited number of electrode geometries (=10) and remained limited in the exploration of electrode stimulation patterns within two-dimensional (2D) geometries.[OHl] These previous studies investigated electrode geometries using semicircular

[0030] .

[0038] and 2D geometries

[0020] ,

[0028] ,

[0037] , For one-dimensional geometries, semicircular arrangements can be more sensitive to the bladder volume than ring arrangements

[0029] ,

[0030] ,

[0038] , In 2D electrode geometries, including the superior dimension can enhance sensitivity and improve global impedance measurements

[0020] ,

[0028] , Based on these observations, the study focused on electrode placements on the lower anterior abdomen, as configurations in this region outperformed other tested arrangements.

[0112] Consistent with previous studies

[0020] ,

[0028] that multiple rows of electrodes improved sensitivity to the bladder, the results in the instant study visualized the shift in electrodes contributions to measurement frame sensitivity. By analyzing this dynamic, the study optimized electrode placement to enhance sensitivity to bladder volume, particularly for smaller bladder volumes, which were more challenging to estimate accurately [6],

[0025] , [27-30], The optimized configuration resulted in three-electrode groupings, one medial and two lateral. To improve reproducibility when placed, the medial electrode located in rowthree was aligned with the navel as a landmark. The optimized 15 -electrode geometry' in the study facilitated the current systems to take advantage of the increased sensitivity’.

[0113] The neighboring electrode pairs protocol was used in previous studies due to its proportional selectivity', making it useful for various applications. However, the neighboring electrode pairs protocol struggled with sensitives to tissues deeper in the body than other addressing methods

[0039] , In monitoring bladder volume, the signal of interest is from a given region of tissue, not the tissue as a whole. Therefore, the study sought to maximize the signal from the bladder. The study observed that patterns that maximized a change in voltage magnitude had CC electrodes that spanned rows and voltage-sensing electrodes that were not neighboring each other, as shown in Fig. 4C.

[0114] The study also found that the maximum of the VCR and the real voltage component changes were not from the same measurement frames. The frames with large VCRs often occurred due to a small initial voltage. When bladder volume was perturbed, even a small voltage change can produce large VCRs. For example, all VCRs larger than 50% had a change of <500 pV (Fig. 4D. subpanel (c)). In wearables, the analog front ends that took bioimpedance measurements had stringent power requirements compared to their desktop counterparts, resulting in ADCs that may have reduced resolution or sample rate

[0040] ,

[0041] , The step size resolution of the ADC may correspond to a substantial change in bladder volume, resulting in poor accuracy. Therefore, the VCR should not be optimized without consideration of the change in the resultant voltage magnitude. Optimization of the voltage magnitude can lead to frames where the ADC step size corresponded to considerably smaller steps in bladder volume.

[0115] The non-invasive bladder monitoring system design has been driven by providing clinicians and patients with a better understanding of bladder volume without the risks of infection from catheterization. Previous studies aimed to assist individuals with nerve damage or other forms of bladder dysfunction by alerting them when it was time to relieve themselves. Additionally, accurate urine output measurements can benefit the management of primary’ conditions unrelated to urologic function [6], including CHF. A common symptom of CHF, and a primary cause of emergency room admissions, can be fluid overload [2], Fluid overload can manifest as ascites in the peritoneal space located just above the bladder

[0022] , The study simulated low-volume grade I ascites in the digital twin to quantify the bounds of expected impact on the bioimpedance signal, offering a framework for other studies to interpolate their signals to approximate the impact ascites may have on their volume estimation algorithms.

[0116] A strength of utilizing digital twins in the exemplary system was exploring several electrode configurations and variable effects without confounding factors resource- efficiently. Previous studies found success using fewer conductivity domains than the instant study or in two dimensions to predict configurations that perform effectively in vivo

[0016] ,

[0020] ,

[0028] , While the study may replicate in vivo, some simplifications and complexities may not be captured. Limitations of the study included the reliance on the dielectric properties of blood to represent the ascitic fluid domain, the use of muscle as a simplification of the tissues in the abdominopelvic cavity that were not segmented, and the simplification of the dynamic range of urine conductivity due to hydration status

[0027] , The digital twin in the exem lary system utilized an EKG gel electrode skin interface for current injection and voltage sensing, which had different interface characteristics compared to other electrode types, such as dry electrodes

[0042] , The direct volume estimation using LASSO did not account for the impact of intersubject variability due to factors like waist circumference, subcutaneous fat thickness, or urine conductivity variation

[0016] ,

[0043] , In the ascites simulations, the fluid domain was modeled in the worst-case placement closest to the electrodes rather than distributed throughout the peritoneal membrane.

[0117] The instant study understood key components of bioimpedance-based bladder volume monitoring through simulations with an anatomically accurate digital twin. By analyzing electrode geometry at an individual level, the study considered millions of configurations to optimize sensitivity to the bladder, particularly at lower volumes. Using the assessed geometry, the study demonstrated that frame selection can enable computationally efficient volume estimation algorithms suitable for wearable devices and reduce the number of measurements required for estimation. The optimization of the signal from the bladder can be a trade-off between a large VCR or voltage magnitude response. Furthermore, the study evaluated the robustness of bioimpedance approaches w hen confronted with complications such as ascites, showing promising results. Future studies should investigate the influence of patient-specific characteristics on the bioimpedance signal to develop effective normalization methods. Furthermore, conducting in vivo measurements can provide insights into potential artifacts due to motion or postural position changes.

[0118] Discussion #3. In the restricted power environment of the w earables, the design became a trade-off of computational resources and batten- life. Microcontrollers, used in wearable systems with battery life measured in days, can be more limited in memory than their desktop counterparts [T], [2’]. For example, performing EIT reconstruction using 16 electrodes and neighboring addressing for a 32 by 32 pixel image can be done per Equation 5.x — Ry(Eq. 5)

[0119] In Equation 5, x is the reconstructed image, R is the reconstruction matrix computed from boundary conditions, and y is the measurements [3 ’] . Assuming 32-bit singleprecision floating-point values were used, the size of R alone can be approximately 828 KB. Of the tens of state-of-the-art microcontroller families from Microchip and Nordic, only one has sufficient SRAM to store R [4’], [5’].

[0120] If R were stored in FLASH instead of SRAM, the wearable may need to be reflashed for each new patient rather than simply loading R from an SD card or app via BLE or Wi-Fi into SRAM. In comparison, direct estimation from selected frames can be much more memory-efficient, requiring only a column vector for the weights. For example, in direct estimation with the parameters and seven frames selected from LASSO, the weights required just 0.027 KB.

[0121] Computation time also poses a challenge. In the EIT example above, reconstructing a single image can require approximately 213,000 multiplication operations. On microcontrollers without a floating-point unit (e.g., Cortex-M0+), these multiplications can be performed by software rather than hardware, with each multiplication taking up to 32 clock cycles [6’]. At a system clock speed of 48 MHz, a single reconstruction can take up to 142 ms, which does not account for the time needed for operations to estimate volume from the image, data acquisition, communication to enable the Internet of Things, and other system functionality. The computation time can be prohibitive for some real-time cases.

[0122] By comparison, direct estimation in the study required fewer weights, providing faster computation. From the LASSO selected parameters, direct estimation took up to 4.6 ps. The faster computation can be more suitable for additional processes such as averaging, normalization, and other operations, making it better suited for real-time monitoring.Processing measurements on a mobile device eliminated the need for a desktop, providing ambulatory use in environments outside the clinic.

[0123] Conclusion

[0124] The construction and arrangement of the systems and methods, as show n in the various implementations, are illustrative only. Although only a few implementations have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes, proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied, and the nature ornumber of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative implementations. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the implementations without departing from the scope of the present disclosure.

[0125] The present disclosure contemplates methods, systems, and program products on any machine-readable media for accomplishing various operations. The implementations of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Implementations within the scope of the present disclosure include program products, including machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine- readable media can be any available media that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machineexecutable instructions or data structures, and which can be accessed by a general purpose or special purpose computer or other machine with a processor.

[0126] When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardw ired or wireless) to a machine, the machine properly views the connection as a machine-readable medium; thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, special-purpose computer, or special-purpose processing machine to perform a certain function or group of functions.

[0127] Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also, two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on the designer's choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with programmingtechniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps.

[0128] Machine Learning. In addition to the machine learning features described above, the analysis system can be implemented using one or more artificial intelligence and machine learning operations. The term “artificial intelligence” can include any technique that enables one or more computing devices or computing systems (i. e. , a machine) to mimic human intelligence. Artificial intelligence (Al) includes but is not limited to knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is defined herein to be a subset of Al that enables a machine to acquire knowledge by extracting patterns from raw data. Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks. The term “representation learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders and embeddings. The term “deep learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc., using layers of processing. Deep learning techniques include but are not limited to artificial neural networks or multilayer perceptron (MLP).

[0129] An artificial neural network (ANN) is a computing system including a plurality of interconnected neurons (e.g., also referred to as “nodes”). This disclosure contemplates that the nodes can be implemented using a computing device (e.g., a processing unit and memory as described herein). The nodes can be arranged in a plurality of layers, such as an input layer, an output layer, and optionally one or more hidden layers with different activation functions. An ANN having hidden layers can be referred to as a deep neural network or multilayer perceptron (MLP). Each node is connected to one or more other nodes in the ANN. For example, each layer is made of a plurality' of nodes, where each node is connected to all nodes in the previous layer. The nodes in a given layer are not interconnected with one another, i.e., the nodes in a given layer function independently of one another. As used herein, nodes in the input layer receive data from outside of the ANN, nodes in the hidden layer(s) modify the data between the input and output layers, and nodes in the output layer provide the results. Each node is configured to receive an input, implement an activation function (e.g., binary step, linear, sigmoid, tanh. or rectified linear unit (ReLU) function), and provide an output in accordance with the activation function. Additionally, each node isassociated with a respective weight. ANNs are trained with a dataset to maximize or minimize an objective function. In some implementations, the objective function is a cost function, which is a measure of the ANN’s performance (e.g., error such as LI or L2 loss) during training, and the training algorithm tunes the node weights and / or bias to minimize the cost function. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used for training the ANN. Training algorithms for ANNs include but are not limited to backpropagation. It should be understood that an artificial neural network is provided only as an example machine learning model. This disclosure contemplates that the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model. Optionally, the machine learning model is a deep learning model. Machine learning models are known in the art and are therefore not described in further detail herein.

[0130] A convolutional neural network (CNN) is a type of deep neural network that has been applied, for example, to image analysis applications. Unlike traditional neural networks, each layer in a CNN has a plurality of nodes arranged in three dimensions (width, height, depth). CNNs can include different types of layers, e.g.. convolutional, pooling, and fully- connected (also referred to herein as "dense") layers. A convolutional layer includes a set of filters and performs the bulk of the computations. A pooling layer is optionally inserted between convolutional layers to reduce the computational power and / or control overfitting (e.g.. by downsampling). A fully-connected layer includes neurons, where each neuron is connected to all of the neurons in the previous layer. The layers are stacked similarly to traditional neural networks. GCNNs are CNNs that have been adapted to work on structured datasets such as graphs.

[0131] Other Supervised Learning Models . A logistic regression (LR) classifier is a supervised classification model that uses the logistic function to predict the probability of a target, which can be used for classification. LR classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize an obj ective function, for example, a measure of the LR classifier's performance (e.g., an error such as LI or L2 loss), during training. This disclosure contemplates that any algorithm that finds the minimum of the cost function can be used. LR classifiers are known in the art and are therefore not described in further detail herein.

[0132] A Naive Bayes’ (NB) classifier is a supervised classification model that is based on Bayes’ Theorem, which assumes independence among features (i.e., the presence of one feature in a class is unrelated to the presence of any other features). NB classifiers are trainedwith a data set by computing the conditional probability distribution of each feature given a label and applying Bayes’ Theorem to compute the conditional probability distribution of a label given an observation. NB classifiers are known in the art and are therefore not described in further detail herein.

[0133] A k-NN classifier is an unsupervised classification model that classifies new data points based on similarity measures (e.g., distance functions). The k-NN classifiers are trained with a data set (also referred to herein as a "‘dataset”) to maximize or minimize a measure of the k-NN classifier’s performance during training. This disclosure contemplates any algorithm that finds the maximum or minimum. The k-NN classifiers are known in the art and are therefore not described in further detail herein.

[0134] A majority voting ensemble is a meta-classifier that combines a plurality of machine learning classifiers for classification via majority voting. In other words, the majority voting ensemble’s final prediction (e.g., class label) is the one predicted most frequently by the member classification models. The majority voting ensembles are known in the art and are therefore not described in further detail herein.

[0135] It is to be understood that the methods and systems are not limited to specific synthetic methods, specific components, or to particular compositions. It is also to be understood that the terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting.

[0136] As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another implementation includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another implementation. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0137] “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur and that the description includes instances where said event or circumstance occurs and instances where it does not.

[0138] Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integersor steps. “Exemplary’" means “an example of’ and is not intended to convey an indication of a preferred or ideal implementation. “Such as” is not used in a restrictive sense but for explanatory purposes.

[0139] Disclosed are components that can be used to perform the disclosed methods and systems. These and other components are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these components are disclosed while specific reference of each various individual and collective combinations and permutation of these may not be explicitly disclosed, each is specifically contemplated and described herein, for all methods and systems. This applies to all aspects of this application, including, but not limited to, steps in disclosed methods. Thus, if there are a variety of additional steps that can be performed it is understood that each of these additional steps can be performed with any specific implementation or combination of implementations of the disclosed methods.

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Claims

What is claimed:

1. A system for monitoring volume of urine in a bladder of a person comprising: a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person; and a controller comprising: a processor; and a memory having instructions stored thereon, wherein execution of the instructions causes the processor to: receive, via the processor, the bioimpedance measurements; determine, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and output, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

2. The system of claim 1 further comprising: a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person.

3. The system of claim 2, wherein execution of the instructions causes the processor to: receive, via the processor, the movement measurements; in response to the person causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determine, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and output, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.

4. The system of claim 1, wherein the execution of the instructions further causes the processor to: determine, via the processor, an ascites volume in the abdominal region using the set of bioimpedance electrodes; and adjust, via the processor, the urine volume estimate by removing the determined ascites volume from the determined first urine volume estimate, wherein the adjusted urine volume estimate is outputted.

5. The system of claim 4, wherein the controller is configured to output, via the processor, the determined ascites volume estimate, wherein the additional output is subsequently used for monitoring and diagnoses of a disease or condition.

6. The system of claim 1, wherein the set of bioimpedance electrodes is configured as a bioimpedance electrode array comprising two or more pairs of bioimpedance electrodes, including a first pair of bioimpedance electrodes and a second pair of bioimpedance electrodes, wherein each electrode is evenly spaced on the body part.

7. The system of claim 1. wherein each electrode is a dry’ electrode.

8. The system of claim 1, wherein each electrode is a gel electrode.

9. The system of claim 6, wherein the execution of the instructions further causes the processor to: receive first bioimpedance measurements using the first pair of bioimpedance electrodes; receive second bioimpedance measurements using the second pair of bioimpedance electrodes; and determine the bioimpedance measurements as a down-selection operation of at least one of the first bioimpedance measurements and the second bioimpedance measurements, wherein the down-selection operation satisfies a predefined measurement range.

10. The system of claim 2, wherein each mechanical sensor is selected from the group consisting of an inertial measurement unit, a gyroscope, an accelerometer, and a magnetometer.

11. The system of claim 6. wherein the bioimpedance array comprises between 4 and 16 bioimpedance electrodes.

12. The system of claim 1, wherein the controller is implemented in a mobile device comprising a network interface configured to communicatively operate with the set of bioimpedance electrodes through a network.

13. The system of claim 1, wherein the controller is a remote computing device located in a cloud infrastructure comprising a network interface configured to communicatively operate with the electrode array assembly and the flexible photoplethysmographic circuit through a network.

14. A method for monitoring volume of urine in a bladder of a person comprising: providing a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person; receiving, via a processor, the bioimpedance measurements; determining, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and outputting, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

15. The method of claim 14, further comprising: providing a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person; receiving, via the processor, the movement measurements; in response to the person causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determining, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and outputting, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.

16. The method of claim 14, further comprising: measuring an ascites volume in the abdominal region using the set of bioimpedance electrodes; determining, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of a confounding effect of the ascites volume being removed from the urine volume estimate; and outputting, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring or diagnoses of the disease or condition.

17. The method of claim 14, wherein the set of bioimpedance electrodes is configured as a bioimpedance electrode array comprising two or more pairs of bioimpedance electrodes, including a first pair of bioimpedance electrodes and a second pair of bioimpedance electrodes.

18. The method of claim 17. further comprising: receiving first bioimpedance measurements using the first pair of bioimpedance electrodes; receiving second bioimpedance measurements using the second pair of bioimpedance electrodes; determining the bioimpedance measurements as a down-selection operation of at least one of the first bioimpedance measurements and the second bioimpedance measurements, wherein the down-selection operation satisfies a predefined measurement range.

19. The method of claim 17. wherein the bioimpedance array comprises between 4 and 16 bioimpedance electrodes.

20. A non-transitory computer-readable medium for monitoring volume of urine in a bladder of a person having instructions stored thereon, wherein execution of the instructions by a processor causes the processor to: provide a set of bioimpedance electrodes configured to (i) attach to a body part proximal to or at an abdominal region and (ii) generate bioimpedance measurements of the person; receive, via a processor, the bioimpedance measurements; and determine, via the processor, a urine volume estimate in the bladder using the bioimpedance measurements; and output, via the processor, the urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of a disease or condition.

21. The non-transitory computer-readable medium of claim 20 further comprising: a set of mechanical sensors configured to (i) attach to the body part proximal to or at the abdominal region and (ii) generate movement measurements of the person.

22. The non-transitory computer-readable medium of claim 21, wherein execution of the instructions causes the processor to: receive, via the processor, the movement measurements; in response to the person causing (i) the movement measurements to exceed a predefined threshold and (ii) an estimate outlier in the urine volume estimate, determine, via the processor, a second urine volume estimate, wherein the second urine volume estimate is a result of the estimate outlier being removed from the urine volume estimate; and output, via the processor, the second urine volume estimate, wherein the output is subsequently used for monitoring and diagnoses of the disease or condition.

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