Measuring effective circulating volume and estimating changes in total body water during dehydration and rehydration

Abstract

The present invention provides a system and method for measuring the effective circulating volume (ECV), and for estimating changes in total body water (TBW) during the dehydration and rehydration phases. This invention is applicable in clinical and non-clinical settings, including but not limited to sports performance monitoring, medical diagnostics, and managing fluid balance in patients with cardiovascular or renal conditions.

Description

System and Method for Measuring Effective Circulating Volume and Estimating Changes in Total Body Water During Dehydration and Rehydration

[0001] Technical field

[0002] The present invention relates to the field of physiological monitoring systems, specifically to methods and devices for assessing hydration status in human subjects. The system provides a system and method for measuring the effective circulating volume (ECV), and for estimating changes in total body water (TBW) during the dehydration and rehydration phases. This invention is applicable in clinical and non-clinical settings, including but not limited to sports performance monitoring, medical diagnostics, and managing fluid balance in patients with cardiovascular or renal conditions.

[0003] Background

[0004] Current methods for assessing hydration status encompass a variety of techniques, including changes in body weight, urine osmolality, plasma electrolyte concentrations, and bioelectrical impedance analysis to estimate total body water (TBW). These methods typically assume the body functions as a single fluid compartment, thereby lacking the sensitivity to distinguish between the specific changes occurring within the intracellular, interstitial, and plasma compartments. While sweat sensors have been developed to measure fluid loss during physical activity, they are inherently limited as they do not account for fluid intake or the dynamic redistribution of fluids within the body during rehydration. Moreover, traditional approaches fall short in accurately assessing the effective circulating volume (ECV), the most critical fluid compartment responsible for maintaining perfusion to vital organs. The absence of precise, compartment-specific measurements in existing technologies highlights the need for an advanced system capable of directly computing an ECV observable proxy and reliably estimating TBW changes relative to an initial baseline, particularly during both dehydration and rehydration phases.

[0005] Plasma, Interstitial, and Intracellular Fluid Assessment Methods

[0006] The accurate assessment of human hydration status remains one of the most persistent challenges in clinical medicine, sports science, and occupational health. While maintaining optimal fluid balance is critical for physiological function, our current methodological arsenal suffers from significant limitations that compromise our ability to make precise, clinically meaningful assessments. This inadequacy stems from body water distribution's complex, multicompartmental nature and the technical limitations inherent in existing measurement approaches.

[0007] The most widely employed method, monitoring changes in total body mass, exemplifies the oversimplified approach that characterizes much of current practice. This approach assumes that acute weight changes reflect fluid losses or gains, with practitioners commonly interpreting a 1-2% reduction in body mass as indicating mild dehydration. However, this methodology is fundamentally flawed because it provides no information about which fluidcompartments are affected, treats all individuals as having identical body water percentages, and ignores substantial inter-individual variability in total body water (TBW) content. The reliance on body mass changes becomes particularly problematic when considering that different tissues have vastly different water contents, and individuals vary dramatically in their body composition. A muscular athlete and an elderly individual with higher adipose tissue may show identical weight losses, yet experience completely different degrees of physiological stress from fluid depletion.

[0008] Recognition of the limitations inherent in simple mass-based assessments has led to increased interest in total body water measurements as a more sophisticated but still limited approach. This approach acknowledges that body water content varies substantially between individuals based on age, sex, body composition, and fitness level. Young, muscular individuals typically maintain approximately 60% of their body mass as water, while older adults with higher adipose tissue content may have TBW values in the 40-45% range. Consequently, a one-liter fluid loss represents a 2.8% reduction in TBW for a muscular individual but a 4.4% reduction for someone with lower baseline water content, a difference that has physiological implications.

[0009] Despite representing a clear improvement over crude mass-based methods, TBW assessment still suffers from significant limitations. The most employed techniques — bioelectrical impedance analysis (BIA) and dilution methods using deuterium oxide or tritiated water — are expensive, technically demanding, and provide only snapshot measurements rather than dynamic assessment capabilities. More critically, TBW measurements still fail to provide information about the distribution of water between the body's three primary fluid compartments: plasma, interstitial fluid, and intracellular fluid. Since dehydration affects these compartments differently and at different rates, TBW alone cannot fully characterize hydration status or predict physiological consequences.

[0010] The measurement of plasma volume represents perhaps the most technically sophisticated approach for hydration assessment but has practical limitations. These approaches employ methods such as Evans Blue dye dilution, indocyanine green tracers, radioactive albumin tracers, or carbon monoxide rebreathing protocols and can provide reasonably accurate estimates of plasma volume changes under steady-state conditions. This is physiologically significant given that plasma volume maintenance is critical for cardiovascular function and thermoregulation — a kilogram of fluid loss from intravascular plasma can directly impair perfusion and blood pressure.

[0011] However, plasma volume measurement methods are severely constrained by their complexity, cost, invasiveness, and fundamental inability to track rapid changes. The gold standard Evans Blue technique requires intravenous injection of the tracer, multiple blood sampling over extended periods (typically 30-60 minutes for equilibration), and sophisticated laboratory analysis — making it completely impractical for routine clinical use, field applications, or real-time monitoring. Alternative indirect methods using hemoglobin / hematocrit shifts areconfounded by capillary fluid shifts, protein losses, and acute vascular tone changes, making them unreliable during dynamic states such as exercise, illness, or acute fluid therapy. Most critically, all plasma measurement techniques fail during periods of rapid fluid flux — precisely when accurate assessment is most needed.

[0012] Interstitial fluid, which comprises approximately 75% of extracellular fluid volume, presents the unmeasurable majority of fluid compartment assessment challenges. Current approaches include segmental bioelectrical impedance analysis, tissue dielectric constant devices, and advanced imaging modalities, but each suffers from fundamental limitations that become particularly problematic during dynamic hydration changes. Bioimpedance techniques are highly sensitive to skin temperature, electrode placement, and local perfusion, making them unreliable when these parameters change rapidly during dehydration or exercise. Imaging modalities such as MRI and ultrasound can provide high-resolution data about tissue water content, but these approaches are costly, require specialized training, and are completely impractical for real-time monitoring or field applications.

[0013] This represents a critical gap in our assessment capabilities, particularly during periods of rapid fluid shifts. Interstitial fluid serves as the intermediary compartment between plasma and intracellular spaces, and changes in volume and composition directly impact cellular function. Yet we remain essentially blind to this compartment during the dynamic phases of hydration assessment when accurate measurement is most crucial. Current practice typically infers interstitial fluid changes from plasma measurements or relies on clinical signs like skin turgor and mucous membrane appearance — approaches that are subjective, imprecise, and often detect changes only after significant dehydration has already occurred.

[0014] Intracellular fluid assessment is an equally formidable obstacle despite representing approximately 65% of total body water and having different consequences for performance and metabolism compared to extracellular losses. Multi-frequency bioelectrical impedance analysis claims to distinguish between intracellular and extracellular fluid volumes, but the accuracy of these methods remains questionable, particularly during dynamic hydration changes when tissue electrical properties fluctuate rapidly. Nuclear magnetic resonance spectroscopy and certain tracer methods can quantify intracellular volume in research contexts, providing precise measurements under controlled conditions. However, these techniques are completely inaccessible in clinical or field environments due to their cost, complexity, and time requirements.

[0015] The temporal limitations of intracellular fluid assessment are particularly problematic because intracellular dehydration may occur at different rates than extracellular losses, and cellular-level fluid deficits may emerge well before systemic signs of dehydration become apparent. Current methods that attempt to calculate intracellular water by subtracting extracellular water estimates from total body water compound errors from both measurements and provide no information about the rate or distribution of intracellular changes.

[0016] The inadequate state of current practice reflects the harsh reality that despite decades of research and technological advancement, we still lack practical, accurate methods for comprehensive hydration assessment that can function during the dynamic conditions when such assessment is most critical. Current approaches force clinicians, athletes, and researchers to choose between simple but meaningless measurements (body mass changes) or sophisticated but impractical techniques that provide incomplete information and fail entirely during periods of rapid fluid flux. This methodological inadequacy creates a diagnostic gap that has profound implications for clinical fluid management decisions, athletic performance optimization, and occupational safety in environments where heat stress is a concern.

[0017] The temporal limitations are particularly problematic because the most critical periods for hydration assessment — during acute illness, intense exercise, heat exposure, or therapeutic interventions — are precisely when current methods become unreliable or completely nonfunctional. Traditional steady-state measurement techniques cannot track the minute-to-minute or even hour-to-hour changes that characterize these dynamic situations. Real-time monitoring capabilities are essential for effective fluid management, yet none of the current compartmentspecific measurement approaches can provide continuous, real-time data about fluid distribution and movement between compartments.

[0018] An accurate hydration measurement is paramount across a wide range of applications. For general wellness, proper hydration is essential for maintaining overall health and preventing dehydration-related conditions. In athletic performance, precise hydration monitoring can optimize physical output and recovery, reducing the risk of injury and enhancing endurance. For the elderly, who are more susceptible to dehydration and its complications, effective hydration assessment is crucial for maintaining their health and quality of life and preventing falls. In workplace environments where individuals must wear protective clothing, such as in industrial settings or during hazardous material handling, monitoring hydration is vital for ensuring safety and preventing heat-related illnesses. Additionally, in military settings, where physical exertion and environmental conditions can drastically affect hydration levels, an accurate measurement of fluid balance is critical for maintaining soldier readiness and effectiveness. The ability to monitor hydration in these diverse contexts underscores the broad utility and significance of the invention and its broad applicability.

[0019] Disclosure of the Invention

[0020] The invention presents a groundbreaking approach to measuring hydration status by utilizing left ventricular ejection time (LVET) and heart rate to determine the effective circulating volume (ECV), or plasma volume, in the body. The present invention can use measurements such as those in US17 / 869,093 and US18 / 421 , 071 , each of which is incorporated herein by reference. The human body has three fluid compartments — intracellular, interstitial, and plasma — but the ECV, or plasma compartment, can be the most important compartment because it contains the fluid delivering oxygen and nutrients to the brain, organs, and muscles.Therefore, a hydration sensor specifically targeting this vital compartment represents a significant advancement in the science of hydration.

[0021] Within the framework of managing dehydration, understanding the plasma compartment is essential for grasping how dehydration impacts cardiovascular function. However, a secondary objective is understanding the volume requirements required to return all fluid compartments to a normovolemic or euvolemic (properly hydrated) state. The return to a euvolemic state can be determined by examining total body water changes (TBW).

[0022] An important inventive component of this system is its ability to measure the ECV and estimate changes in total body water from an initial normovolemic baseline. The dual capability of computing an ECV observable proxy and estimating TBW allows for a precise assessment of hydration status, ensuring that both the critical ECV is maintained and that overall fluid balance across all compartments is properly managed. This level of detail offers a valuable tool in both clinical and performance settings, providing insights that traditional methods and existing technologies fail to deliver.

[0023] Time series analysis provides a powerful framework for understanding and predicting phenomena that evolve over time, offering valuable insights that drive effective decision-making across various fields.

[0024] Brief Description of Drawings

[0025] FIG. 1 is an illustration of the impact of dehydration on power per heartbeat.

[0026] FIG. 2 is an illustration of the body's fluid compartments.

[0027] FIG. 3 is an illustration of the Starling principle during euvolemia.

[0028] FIG. 4 is an illustration of the Starling principle during exercise.

[0029] FIG. 5 is an illustration of the Starling principle during deprivation.

[0030] FIG. 6 is an illustration of fluid redistribution during a dehydration study

[0031] FIG. 7 is an illustration of ECV is the control point for physiological response

[0032] FIG. 8 is a simple overview of the Guyton model.

[0033] FIG. 9 is an illustration of the LVET and HR changes during dehydration.

[0034] FIG. 10 is an illustration of LVET and HR changes due to positional changes

[0035] FIG. 11 is the violin plot of LVET and HR measure values

[0036] FIG. 12 is an illustration of EVC proxy changes due to positional changes

[0037] FIG. 13 is an illustration of the ECV-Centric Hydration Model (ECHM).

[0038] FIG. 14 is a demonstration of the ECV-Centric Hydration Model (ECHM) ability to model endogenous fluid movement.

[0039] FIG. 15 is an illustration of a time series modeling where each estimate depends on prior values

[0040] FIG. 16 is a demonstration of the ECV-Centric Hydration Model (ECHM) ability to determine exogenous fluid movement.

[0041] FIG. 17 is a plot showing the relationship of estimated TBW versus modeled TBW.

[0042] FIG. 18 is a system diagram communicating the use of additional observable parameters

[0043] FIG. 19 is an example embodiment of a hydration assessment system using a ring

[0044] FIG. 20 is an example embodiment of a hydration assessment system using eyewear

[0045] FIG. 21 is an example embodiment of a hydration assessment system using nonwearable point measurement system

[0046] Industrial Applicability and Modes of Carrying Out the Invention

[0047] Definitions.

[0048] For the purposes of this invention, hydration or dehydration are defined broadly as a measure of the amount of water present in the body. Changes in hydration status occur when water intake is inconsistent with changes in free water lost due to normal physiologic processes, including breathing, urination, and perspiration, or other causes, including diarrhea and vomiting.

[0049] As used herein, the time course of aortic valve opening and closing refers to any data representation that contains the relationship between the status of the aortic value and some measurement of time.

[0050] As used herein, the left ventricular ejection time (LVET) is the time interval between the aortic valve opening (AVO) and the aortic valve closure (AVC). Blood is ejected from the left ventricle during this interval.

[0051] As used herein, the interbeat interval (IBI) refers to the time interval between similar points in the cardiac cycle. For example, the IBI can be defined as the time between aortic valve openings (AVOs) in successive cycles. Interbeat interval and heart rate are related terms and represent the same information.

[0052] As used herein, the terms “body posture”, “body position”, or “body pose” refer to the different physical configurations that the human body can assume. Common body postures include supine (lying on the back), seated, and standing positions.

[0053] The term “physiological model” as used herein is broadly defined as a model that predicts one or more physiological parameters. For example, the prediction of effective circulation volume can be done by a physiological model. The model can use a determination model to create the result.

[0054] The term “determination model” as used herein is broadly defined as any process that takes defined inputs and applies calculations or a designated set of steps to determine a desired output. Prediction models are constructed by determining a relationship between data or data features and desired output; once the relationship is determined the model can be applied to novel data with no reliance on the training or reference data. These models are distinct from matching models which rely on pre-existing library of training or reference data. A matching model determines the proximity of novel data to reference data to produce the desired output. Examples of prediction models include regression models, where features are mapped to outputs through linear or non-linear relationships, as well as some machine learning models,in which more complex data representations are mapped to the desired output. In these approaches, often referred to as “deep learning models”, the useful features and representations are essentially learned by the model in training, along with the function that maps the inputs to the desired outputs. Because the relationship between input and outputs is often quite complex (involving thousands of weights in multiple hierarchical layers) the engineer or architect of the model might be unaware of the features or information that the model has extracted, or how and why that information is combined to form the output. In some embodiments of this invention, the determination model can use as inputs features extracted from a data representation that contains the relationship between the status of the aortic value and some measurement of time. In other embodiments, the determination model can use as inputs a raw or conditioned data representation that contains the relationship between the status of the aortic value and some measurement of time. A determination model can also include additional inputs, such as body position or information about the user. The output of a given determination model is the desired parameter, such as hydration status.

[0055] As used herein, an ECV-Informed physiological model is a model that uses as an input an estimation of effective circulating volume.

[0056] The term “noninvasive” refers to a method or apparatus that does not create a break in the skin and makes no contact with an internal body cavity beyond a natural body orifice. PPG and EKG sensors are examples of noninvasive sensors that can make measurements without breaking the skin. Likewise, PPG is an example of noninvasive sampling, wherein measurements are acquired optically from the skin surface without introducing instruments into the body.

[0057] As used herein, “observable physiological parameters” refers to all physiological parameters that can be observed noninvasively and can impact or influence osmotic or Starling forces. Example observable physiological parameters include but are not limited to skin temperature, heart rate, respiratory rate, activity level and body position. Although these parameters are not direct measures of Starling forces or osmotic forces, they can act as valuable proxies for inferring changes in osmotic, hydrostatic, and oncotic pressures within the capillaries.

[0058] As used herein, "sensible losses" refers to measurable fluid losses from the body that can be quantified, including urine production and water content in fecal matter, as distinguished from insensible losses such as evaporation through skin and respiration.

[0059] As used herein, "total body water" refers to the sum of all water contained within the body's fluid compartments, including intracellular fluid, interstitial fluid, and plasma volume, typically expressed as a percentage of total body weight or in absolute volume units.

[0060] As used herein, "total body mass" refers to the complete weight of an individual, including all body tissues, fluids, and contents, commonly measured to assess changes in overall hydration status through weight variations.

[0061] As used herein, "endogenous fluid shifts" refers to internal redistributions of fluid between the body's compartments (plasma, interstitial, and intracellular spaces) that occur without any change in total body water, driven by physiological mechanisms such as Starling forces, hormonal regulation, and postural influences.

[0062] As used herein, "exogenous fluid" refers to fluid movements that cross the boundary between the body's internal circulation and the external environment, including fluid intake through ingestion and fluid losses through sweating, respiration, urination, and other excretory processes.

[0063] As used herein, effective circulating volume (ECV) denotes a physiological state of circulatory adequacy — specifically, the functional sufficiency of venous return and cardiac filling pressure to sustain organ perfusion. ECV is a functional quantity inferred from cardiovascular behavior and is distinct from anatomical compartment volumes such as plasma volume or total blood volume. Unless stated otherwise, references to “ECV” in this disclosure refer to this target physiological state and not to a directly measured physical volume.

[0064] As used herein, an ECV measurement with posture sensitivity — also referred to as an ECV observable proxy — is a calculated, noninvasive metric derived from systolic time intervals (e.g., left ventricular ejection time and interbeat interval / heart rate) that correlates with the ECV state and exhibits a reproducible, directionally consistent change in response to a gravitational fluid shift induced by a posture change (e.g., supine-to-stand, stand-to-supine, passive leg raise) within a defined time window and magnitude threshold. This posture responsiveness demonstrates that the metric reflects changes in cardiac preload and circulatory adequacy rather than a static compartment volume; no direct measurement of physical volume is performed. The metric can be computed during a fixed posture; a maneuver is not required for each reading, but its posture sensitivity provides a functional signature that validates and characterizes the proxy.

[0065] As used herein, an observable proxy is a measurable or computable parameter that indirectly indicates a target physiological state or quantity that is impractical to measure directly in real time by noninvasive means, and that maintains a predictable, quantifiable relationship to that target. In this disclosure, the ECV observable proxy (the posture-sensitive ECV measurement defined in

[0047] ) serves as the observable proxy for the target physiological state ECV; it is correlated with — but distinct from — anatomical compartment volumes such as plasma volume or total blood volume, and enables noninvasive, real-time assessment of circulatory adequacy and hydration status.

[0066] As used herein, the terms “body posture”, “body position”, or “body pose” refer to the different physical configurations that the human body can assume. The most common body postures include supine (lying on the back), seated, and standing positions.

[0067] ECV observable proxy (short form: “ECV proxy”) — a noninvasive, posture-sensitive metric derived from systolic time intervals (e.g., LVET and heart rate) that correlates with thephysiological state of effective circulating volume (ECV) and serves as the information-bearing input to the ECV-centric hydration model; it is not a direct physical volume measurement. An effective circulating volume (ECV) observable proxy is determined from heart rate (HR) and left- ventricular ejection time (LVET) as a joint deviation from a reference condition (e.g., a euvolemic baseline or a matched reference). This value is expressly a proxy index, not a physical volume: LVET has units of time (e.g., milliseconds) and HR has units of inverse time (e.g., beats per min). Any distance or ratio formed from these timings therefore carries mixed or unitless dimensions — not liters or milliliters — and thus cannot be interpreted as a direct compartment volume

[0068] As used herein, “positional changes”, “posture changes”, or “changes in pose” are terms that refer to any process that alters body position in a manner that changes the venous return to the heart. For example, one simple way to manipulate venous return via positional changes is to move between supine, seated, and standing positions.

[0069] Speckle plethysmography (SPG), as used herein, is a noninvasive optical measurement system that measures blood flow in the body. The system uses a laser, coherent light sources, or other light source to illuminate the skin and tissue, and then analyzes the scattered light patterns, or speckles, which are produced. The system can operate in reflection sampling mode and transmission and transmission sampling mode. The system can be used to measure blood flow in various parts of the body, such as the hand, finger, wrist, foot, or brain, and can provide important information about the function of the circulatory system and the health of tissues and organs. A speckle sensor system creates a plethysmogram representing changes in blood flow over the cardiac cycle and has a signal that is related to the cardiac cycle and contains aortic value opening and closure information.

[0070] Photoplethysmography (PPG), as used herein, is an optical measurement system that measures changes in blood volume using changes in light absorption and can be used to measure blood volume in a transmission sampling mode and reflection sampling mode. The measured signals, a plethysmogram, can be used to calculate both physiological and cardiometric parameters for both physiological assessments and the determination of cardiac fitness. A PPG system creates a photo plethysmogram representing changes in blood volume over the cardiac cycle and has a signal that is related to the cardiac cycle and contains aortic value opening and closure information.

[0071] Optical sensor system, as used herein, comprises at least one light emitter and at least one photodetector arranged to deliver photons into tissue and receive a resulting signal so that aortic valve opening and closing events can be resolved.

[0072] As used herein, plethysmographic data refers to time-series physiological measurements obtained through plethysmographic techniques, including photoplethysmography (PPG) and spectral plethysmography (SPG), that detect cardiovascular parameter changes by measuring variations in optical, electromagnetic, or other physicalproperties of tissue. From this data, cardiovascular parameters, including systolic time intervals, to include heart rate and left ventricular ejection time, are extracted and used for determining effective circulating volume.

[0073] As used herein, systolic time intervals refer broadly to measured durations between any two physiologically definable events of the cardiac cycle, including electrical, mechanical, or hemodynamic markers, that delineate phases of ventricular activation, contraction, and ejection as illustrated in a Wiggers diagram or equivalent hemodynamic representation. Such events include, but are not limited to, the onset of ventricular depolarization, atrioventricular valve closure, isovolemic contraction onset, aortic valve opening, peak ventricular pressure, aortic valve closure, and the onset of ventricular relaxation. The intervals may encompass traditional measures such as heart rate, pre-ejection period (PEP), left ventricular ejection time (LVET), and electromechanical systole (QS2), as well as any derivative, composite, or transformed timing parameter that reflects the temporal relationship of cardiac cycle phases. These measurements can be obtained directly from or inferred by analysis of noninvasive physiological signals, including photoplethysmography, seismocardiography, ballistocardiography, phonocardiography, impedance cardiography, and electrocardiography, alone or in combination.

[0074] As used herein, an information-driven forcing function refers to a measured or calculated parameter that provides essential information content to drive the solution of an inverse problem, where a desired target parameter cannot be directly measured but must be inferred from readily available input measurements. The forcing function supplies physiologically meaningful data that constrains the solution space and enables determination of the target parameter. In the context of this invention, the effective circulating volume (ECV) measurement serves as the information-driven forcing function that contains sufficient cardiovascular information to drive the inverse determination of total body water and hydration status, enabling non-invasive assessment of parameters that would otherwise require direct measurement through other procedures.

[0075] Detailed Description

[0076] The Value of ECV in Hydration Monitoring

[0077] The present invention is an advanced system and method designed to accurately assess hydration status by measuring the effective circulating volume (ECV), a fundamental physiological concept that represents the functional adequacy of venous return and cardiac filling pressure rather than a static, measurable compartment like plasma volume. ECV reflects the sufficiency of blood volume and circulatory dynamics to maintain optimal cardiac preload and support adequate organ perfusion and tissue delivery of oxygen and nutrients. Unlike structural volume measurements, ECV captures the body's functional circulatory capacity by directly influencing venous filling pressure and cardiac performance. When ECV is adequate, it ensures sufficient venous return to create optimal cardiac preload, enabling the heart to maintain effective stroke volume and cardiac output through the Frank-Starling mechanism. Thisphysiological relationship guarantees adequate tissue perfusion, which represents the ultimate functional goal of the circulatory system. When ECV is compromised — whether due to dehydration, blood loss, or pathological conditions such as heart failure where venous return is impaired despite normal plasma volumes — these critical physiological processes are disrupted, resulting in measurable alterations in cardiac function that can be detected through systolic time intervals. By computing an ECV observable proxy through its direct influence on cardiac performance, this invention provides a more accurate and clinically relevant assessment of hydration status than traditional methods that rely on structural compartment measurements or treat the body as a single fluid reservoir, offering unprecedented insight into the functional parameter that the body's regulatory systems actually defend.

[0078] Unlike traditional hydration measurement techniques that treat the body as a single fluid reservoir or rely on structural compartment measurements through indicator-dilution methods, this invention specifically targets the ECV through functional assessment of cardiac performance. This functional-specific measurement approach fundamentally differs from conventional methods that quantify plasma volume, total body water, or bioimpedance-derived fluid estimates, none of which capture the physiological parameter that the body's regulatory systems actually defend. By measuring systolic time intervals to assess ECV, the invention provides direct insight into the adequacy of venous return and cardiac filling pressure — the functional capacity that determines circulatory effectiveness and drives homeostatic responses. This targeted approach significantly enhances both the accuracy and clinical relevance of hydration assessments because it measures what matters physiologically: whether the circulatory system has sufficient functional capacity to maintain optimal cardiac preload and tissue perfusion. Traditional methods may indicate normal fluid volumes while missing critical functional deficits, or conversely, may suggest dehydration when compensatory mechanisms have successfully maintained circulatory adequacy. The invention's focus on ECV ensures that hydration assessment reflects actual physiological function rather than static volume measurements, providing actionable information that directly correlates with the body's circulatory performance and regulatory responses.

[0079] The system achieves this functional specificity by measuring the direct influence of ECV changes on cardiac performance through monitoring of systolic time intervals, with particular emphasis on left ventricular ejection time (LVET) and heart rate (HR). Venous return and cardiac filling pressure directly influence cardiac preload and stroke volume through the Frank- Starling mechanism, creating measurable changes in systolic timing that reflect circulatory volume. By tracking real-time variations in LVET and HR, the system provides direct, non- invasive measurement of ECV, making it highly specific to the functional parameter that governs circulatory effectiveness and drives the body's fluid regulatory responses.

[0080] This real-time capability is especially valuable in dynamic settings where rapid changes in circulatory status can have critical implications, such as during intense physical exertion, inelderly patients at risk of dehydration, or in military operations where maintaining optimal hydration is essential for performance and survival. By focusing on the direct physiological relationship between ECV and cardiac function, the invention ensures that measurements reflect immediate circulatory adequacy rather than delayed or indirect indicators. This provides a significant advantage over existing methods that rely on structural volume measurements, bioimpedance estimates, or clinical surrogates that often lag behind actual physiological changes and fail to capture the functional parameter that determines circulatory performance and tissue perfusion.

[0081] The accurate real-time measurement of ECV enables precise estimation of total body water (TBW) changes by establishing a temporal baseline and tracking fluid redistribution or loss over time. Since ECV represents the body's defended functional setpoint, any deviation triggers measurable compensatory mechanisms including predictable fluid redistribution patterns between intracellular, interstitial, and intravascular compartments, alterations in urine production, and drive to drink if appropriate. The system's physiological model leverages ECV monitoring to distinguish between endogenous fluid shifts — representing the body's compensatory responses to maintain circulatory adequacy — and exogenous fluid perturbations such as losses through sweating, respiration, sensible losses, or other unmeasured routes. By quantifying these temporal changes in ECV and using established physiological principles governing fluid movement between compartments, the system calculates cumulative TBW changes from any known starting point, providing comprehensive fluid balance assessment through direct measurement of ECV and estimated changes in total body water.

[0082] This dual approach — combining functional-specific measurement of ECV with temporal modeling of TBW changes — provides unprecedented insight into the body's dynamic fluid status. Real-time assessment of the ECV state enables immediate optimization of circulatory function during dehydration and rehydration by monitoring the parameter the body actually defends, while the estimation of cumulative TBW changes supports comprehensive hydration management toward achieving optimal fluid balance. The integration of these complementary measurements allows clinicians and users to distinguish between functional circulatory adequacy and overall fluid status, providing the granular information needed to make precise, physiologically informed decisions about fluid replacement strategies and timing.

[0083] In summary, the invention represents a paradigm shift in hydration monitoring by measuring what the body's regulatory systems defend — effective circulating volume — and using this functional parameter to reconstruct comprehensive fluid balance changes over time. This approach transcends traditional structural volume measurements by providing both immediate insight into circulatory adequacy and cumulative assessment of total body water evolution from established baselines. The system's ability to deliver real-time functional assessment coupled with temporal fluid balance addresses critical needs across clinical medicine, athletic performance optimization, elderly care, and military operations, where understanding bothimmediate circulatory status and overall hydration trends is essential for maintaining optimal physiological function and preventing adverse outcomes.

[0084] The Critical Difference Between Plasma Volume and Effective Circulating Volume

[0085] While plasma volume has long been recognized as important for cardiovascular function, traditional measurement approaches have focused on structural volume quantification rather than functional capacity assessment. Plasma volume — the measurable intravascular plasma space — can be quantified using indicator-dilution methods such as radiolabeled albumin, dye dilution, or CO-rebreathing techniques that rely on concentration measurements to calculate total compartment volume. However, these structural measurements fail to capture what the body's regulatory systems actually defend: the effective circulating volume (ECV). The ECV represents a functional concept — the adequacy of venous return and cardiac filling pressure as sensed by the cardiovascular system's regulatory mechanisms and reflected in cardiac preload capacity.

[0086] To illustrate the distinction between a physical volume and a functional concept, consider a car engine. The engine block volume (or displacement) is a fixed, measurable quantity — for example, a 2.0-liter engine. This is analogous to plasma volume, which can be measured directly. However, the engine's horsepower is a functional concept; it's a measure of the engine's power output and its ability to do work. Just as a highly tuned 2.0-liter engine can produce significantly more horsepower than a less efficient one, a patient's plasma volume might be normal, but their effective circulating volume (ECV) could be low. This occurs when the filling pressure to the heart is low, which reduces cardiac preload — the stretch on the heart muscle before it contracts. This reduced preload impairs the heart's pumping efficiency and ultimately diminishes its functional capacity. Thus, like the car analogy, a direct volume measurement is related to but does not define the functional capability of the system.

[0087] Microgravity is an observable experiment demonstrating the difference between ECV and Plasma Volume. The physiological response to microgravity provides compelling evidence that the body defends effective circulating volume rather than plasma volume. In the weightless environment of space, the absence of gravitational forces causes an immediate headward redistribution of blood that was previously pooled in the lower extremities on Earth. This fluid shift dramatically improves venous return and cardiac filling pressure, effectively increasing ECV despite no change in total plasma volume. The cardiovascular system's response is immediate and decisive: within 24-48 hours, the body dumps 10-20% of its plasma volume through increased diuresis and reduced fluid retention, even as central venous pressure often decreases due to cardiovascular deconditioning. This paradoxical response — reducing plasma volume while experiencing improved central filling — demonstrates that the regulatory system prioritizes functional circulatory adequacy over absolute volume. If plasma volume were the defended parameter, the body would maintain its pre-flight plasma levels regardless of gravitational changes. Instead, the rapid volume reduction clearly shows that the systemresponds to improved ECV by reducing total plasma to maintain the defended functional setpoint.

[0088] Postural changes also reveal the ECV-Plasma Volume Dissociation. The transition from supine to standing position provides another clear demonstration of how ECV and plasma volume can diverge functionally. When an individual moves from lying down to standing upright, gravitational forces cause approximately 300-600ml of blood to pool in the venous capacitance vessels of the legs and lower body. This postural change does not alter total plasma volume — the same amount of plasma remains in the circulation — yet the effective circulating volume is dramatically reduced because venous return to the heart decreases substantially. This functional reduction in ECV triggers immediate compensatory responses: heart rate increases by 10-20 beats per minute, left ventricular ejection time decreases due to reduced preload, and peripheral vasoconstriction occurs to maintain blood pressure. From a hydration assessment perspective, the standing individual appears physiologically "dehydrated" despite having identical plasma volume to their supine state. Traditional volume-based measurements would show no change, yet the cardiovascular system responds as if significant volume depletion has occurred. This postural example illustrates why measuring functional circulatory adequacy through cardiac performance provides more relevant physiological information than structural volume quantification alone.

[0089] The Creation of the Plasma-Centric Hydration Model

[0090] The ability to compute an ECV observable proxy in real-time has enabled the inventors to invent an entirely novel physiological framework: the ECV-Centric Hydration Model (ECHM). Prior to this invention, no such model could be created because effective circulating volume — the actual regulated variable in human fluid homeostasis — was unmeasurable and could only be inferred from indirect clinical surrogates. The ECHM recognizes ECV as the body's primary fluid regulation target and defended setpoint, functioning as the hemodynamic node around which the body's circulatory control system is organized. This model represents a functional and physiologically based framework that can account for the adequacy of venous return and cardiac filling rather than relying solely on structural compartment measurements. The ECHM incorporates control theory principles to predict how perturbations from exogenous fluxes (fluid intake, sweating, urinary excretion) and endogenous shifts (transcapillary refill, cellular osmotic exchange) generate deviations from the defended ECV setpoint, and how the body's integrated sensor pathways and controllers respond to restore functional cardiac filling adequacy through coordinated adjustments in vascular function, cardiac output, and inter-compartmental fluid movement.

[0091] Real-Time Assessment of the ECV State Enables Comprehensive Fluid Balance Understanding

[0092] The ability to measure effective circulating volume changes over time via plethysmographic measurements enables the creation and practical application of the ECHM.Through continuous ECV monitoring, the ECHM can distinguish between endogenous compensatory responses — which represent modellable physiology based on the measured ECV changes — and exogenous fluid perturbations that would otherwise remain unmeasured and unaccounted for in traditional hydration assessment approaches. Since ECV represents the tightly regulated parameter that drives the body's fluid homeostatic responses through its influence on venous return and cardiac filling pressure, deviations from its defended setpoint trigger predictable and quantifiable compensatory mechanisms throughout the body's fluid compartments. The system leverages this principle by using real-time ECV observable proxies as inputs to the ECHM, which calculates the endogenous fluid shifts that have occurred based on established physiological principles and control theory. When the magnitude of these internal compensatory responses is determined through the model, the system can identify and quantify the exogenous influences — such as fluid losses through perspiration, respiration, or other unmeasured routes — that initially challenged the ECV setpoint. This transforms ECV monitoring from a simple status indicator into a comprehensive fluid balance assessment tool.

[0093] In some embodiments, hydration status is assessed directly from the ECV observable proxy without estimating total body water or solving an inverse problem. The proxy is compared to a reference baseline to generate a hydration index. The reference baseline may be (i) a subject-specific baseline acquired under euvolemic, physiologic-rest conditions; (ii) a matched- cohort baseline selected based on demographics and / or physiology (e.g., age, sex, height / weight or BMI, training status, heat acclimatization, device site, posture); (iii) a physiology-matched baseline selected using pulse characteristics (e.g., resting heart rate, LVET, HRV, perfusion index, plethysmographic morphology); (iv) a model-derived baseline predicted from observable parameters and context (e.g., posture, activity, circadian phase, ambient temperature / humidity / altitude); or (v) a combination thereof via weighting or blending. The hydration index can be posture-normalized and mapped to categories (e.g., euhydrated, mild, moderate, severe dehydration) and can run continuously to trigger user feedback or clinician alerts.

[0094] Direct hydration assessment from ECV proxy

[0095] In some user scenarios, hydration status is assessed directly from the ECV observable proxy without estimating total body water or solving an inverse problem. The proxy is compared to a reference baseline to generate a hydration index. The reference baseline may be (i) a subject-specific baseline acquired under euvolemic, physiologic-rest conditions; (ii) a matched- cohort baseline selected based on demographics and / or physiology (e.g., age, sex, height / weight or BMI, training status, heat acclimatization, device site, posture); (iii) a physiology-matched baseline selected using pulse characteristics (e.g., resting heart rate, LVET, HRV, perfusion index, plethysmographic morphology); (iv) a model-derived baseline predicted from observable parameters and context (e.g., posture, activity, circadian phase, ambient temperature / humidity / altitude); or (v) a combination thereof via weighting or blending. Thehydration index can be posture-normalized and mapped to categories (e.g., euhydrated, mild, moderate, severe dehydration).

[0096] Determination of Total Body Water Change

[0097] The invention's method for determining integrated changes in total body water provides a unique solution that combines real-time ECV observable proxy via plethysmographic assessment with the ECHM's ability to model endogenous physiological responses. By tracking ECV changes over specified time intervals and applying the model's predictions of compensatory fluid redistribution, the system calculates the exogenous perturbations that can be summed to determine cumulative total body water changes. This approach accounts for both measured functional changes and unmeasured fluid fluxes, providing a comprehensive assessment of hydration status evolution that has been historically impossible without direct ECV observable proxy capability. The integration of real-time functional measurement with physiological modeling enables unprecedented accuracy in total body water change determination, supporting optimal fluid management strategies across clinical and performance applications where precise hydration assessment is critical.

[0098] Clinical Application of Hemodynamic Node Monitoring

[0099] The effective circulating volume functions as the regulated variable and reference setpoint around which the body's entire circulatory control system is organized, responding to both exogenous fluxes — such as fluid intake, perspiration, and urinary losses — and endogenous shifts including transcapillary refill and cellular osmotic exchange. When perturbations generate deviations from the defended plasma volume setpoint, integrated sensor pathways produce error signals that autonomic and neurohormonal controllers use to implement coordinated feedback responses. These controllers modulate vascular resistance and capacitance, cardiac output, renal sodium-water handling, and inter-compartmental fluid movement to restore plasma volume and stabilize perfusion pressure. By quantifying the changes that have occurred in this central node over defined time periods, healthcare providers can estimate both the endogenous compensatory shifts that have taken place based on established physiological principles and calculate exogenous fluid losses that would otherwise remain unmeasured, providing a comprehensive assessment of total body water balance.

[0100] The Influence of Effective Circulating Volume on Cardiac Function

[0101] A measurable consequence of decreased ECV is decreased cardiac performance. FIG. 1 is an example of an exercise dehydration study where the impact of dehydration is clearly visible in terms of heart rate and power per heartbeat. The study design included two days of testing with the same environmental conditions and the same exercise test. The two test days differed in level of fluid consumption. During one study day, the subject was without fluid replenishment. During the other study day, the subject consumed fluid at a rate to maintain TBW.

[0102] The subject exercised on a bike, and a cycling ergometer dictated the power output. The power profile was the same on both days (11 ).

[0103] The heart rate response over the study (12) shows a similar starting heart rate but a higher heart rate when the subject was fluid-restricted, especially as the level of dehydration increased. At the maximal power output in the final block, the difference in heart rate (15) is 180 versus 150 beats per minute. For most people, a heart rate of 180 is both uncomfortable and approaching the maximum heart rate.

[0104] Power per beat was calculated for the two study days and across the study. As seen in the plot, the power per beat on both days was the same at the start of the study (13). However, at the end of the study (14) the power per beat decreased by 20%. The subject was approximately 2% dehydrated at the end of the study. Thus, a 2% change in TBW resulted in a 20% change in power per beat.

[0105] Power per beat measures cardiac efficiency, reflecting the heart's ability to pump blood effectively with each contraction. Dehydration reduces the ECV, leading to decreased filling pressure and reduced power per beat. The study showed the remarkable sensitivity of the heart to circulating volume and the cardiac implications of a decrease in circulating volume. Whether in athletic performance or managing medical conditions, monitoring and maintaining effective circulating volume is essential for sustaining health and performance.

[0106] The Physiology of Hydration

[0107] Fluid Compartments

[0108] The human body comprises several fluid compartments, each playing a vital role in maintaining physiological balance and overall health. These compartments include the intracellular fluid (ICF), which is the fluid within cells, and the extracellular fluid (ECF), which resides outside of cells. The ECF is further divided into two primary components: the interstitial fluid and the plasma. Together, these fluid compartments are crucial for processes such as nutrient transport, waste removal, and cellular function. The intracellular fluid is the largest compartment, making up about 40% of total body weight, or roughly two-thirds of the body's total water content. The extracellular fluid accounts for about 20% of total body weight, (33% of total body water), with the interstitial fluid constituting the majority and the plasma making up the remainder. FIG. 1 is an illustration of the impact of dehydration on power per heartbeat. FIG. 1 is an illustration of the impact of dehydration on power per heartbeat.

[0109] FIG. 2 shows the fluid compartments of the human body. It is also important to note the difficulty of measuring a 1 % change in ECV. In a 70 Kg person, a 1% change in ECV equates to 28 ml. This volume amount is less than a single shot glass of 44 ml.

[0110] Intracellular fluid (ICF) is the fluid contained within the cells and is essential for maintaining cell structure and function. This compartment holds around 63% of the body's total water, providing the environment for most of the body's metabolic processes. Interstitial fluid, a major component of the extracellular fluid, surrounds the cells and acts as a medium for theexchange of nutrients, gases, and waste products between cells and the bloodstream. The plasma compartment, also part of the extracellular fluid, constitutes the liquid portion of the blood, carrying essential substances such as electrolytes, oxygen, proteins, and hormones throughout the body. Together, the plasma and interstitial fluids make up the extracellular fluid, which accounts for approximately one-third of the body's total water content. Understanding these fluid compartments is important for assessing hydration status and managing fluid balance in various clinical and physiological scenarios.

[0111] Deterministic Fluid Movement

[0112] The human body's fluid management operates through a complex array of interconnected compartments that move fluid in a highly deterministic manner governed by well- established physiological principles. Unlike static volume measurements that treat the body as fixed containers, fluid distribution responds dynamically to physiological demands through predictable mechanisms that can be modeled and quantified. This deterministic behavior stems from the distinct properties of cellular and capillary membranes, each governed by different forces that create systematic patterns of fluid redistribution during various physiological states.

[0113] Cellular membranes operate under osmotic forces, where water moves rapidly across selectively permeable barriers in response to changes in osmolality to maintain cellular integrity. This immediate response is critical during activities that cause rapid shifts in fluid and solute levels. Capillary membranes, conversely, function under Starling forces — a balance between hydrostatic pressure pushing fluid out of capillaries and oncotic pressure pulling fluid back in. Unlike rapid osmotic adjustments, fluid movement across capillary membranes occurs more gradually, stabilizing over minutes to hours. This dual-membrane system creates a sophisticated regulatory network where fluid redistribution follows predictable patterns based on the prevailing physiological conditions.

[0114] The significance of this deterministic behavior cannot be overstated: during dehydration, exercise, heat exposure, or rehydration, the body's fluid compartments undergo systematic redistributions that can be mathematically modeled and predicted. These redistributions are not random but follow established physiological principles that prioritize maintaining effective circulating volume above all other considerations.

[0115] Fluid Movement Across the Capillary Membrane

[0116] The Starling principle describes how fluid movement across capillary membranes is governed by the balance between hydrostatic pressure, which pushes fluid out of the capillaries, and oncotic pressure, which pulls fluid back in. It provides a foundational framework for understanding water movement between plasma and interstitial spaces in maintaining fluid balance. The original model has some limitations, but the basic fluid movement parameters remain valid.

[0117] The Starling equation mathematically represents this fluid exchange:Jv = LpS[(Pc - Pi) - a(FIc - no]

[0118] Jv represents the net fluid movement across the capillary membrane. The variables include Lp, the hydraulic permeability coefficient, indicating the capillary wall's permeability to fluid, and S, the surface area of the capillary. The capillary hydrostatic pressures Pc and Pi are key drivers in this process; Pc is the pressure within the capillary, typically higher at the arteriolar end (around 32 mmHg), which pushes fluid out of the capillary and into the interstitial space. In contrast, Pi is the pressure within the interstitial fluid, usually slightly negative or close to zero, which can resist this outward movement.

[0119] The oncotic pressures lie and nt, generated by plasma proteins such as albumin, counterbalance the hydrostatic forces. lie, the capillary oncotic pressure, is generally around 25 mmHg and acts to draw fluid back into the capillaries from the interstitial space, n / , the interstitial oncotic pressure, is lower (approximately 5 mmHg), creating a gradient that opposes fluid reabsorption into the capillaries. The reflection coefficient a, which ranges from 0 to 1 , reflects the capillary wall's selectivity for proteins, with higher values indicating less permeability to proteins and thus greater retention of fluid within the capillary.

[0120] At the arteriolar end of the capillary, the hydrostatic pressure gradient (Pc - Pi ) is typically greater than the oncotic pressure gradient (lie - fit), resulting in a net flow of fluid out of the capillaries and into the interstitial space. As blood moves toward the venular end of the capillary, the hydrostatic pressure decreases, and the oncotic pressure becomes more influential, leading to fluid reabsorption. However, not all the filtered fluid is reabsorbed; the remaining fluid is returned to the circulation via the lymphatic system. This delicate balance between filtration and reabsorption is essential for maintaining fluid homeostasis.

[0121] The key factors influencing fluid movement across the capillary and the balance between filtration and reabsorption can be well communicated using the two graphs of FIG. 3. The top graph shows the pressure changes as one moves through the capillary. The bottom graph shows the net cumulative fluid flow as one moves from the arterial side to the venous side.

[0122] The capillary hydrostatic pressure (CHP) is 30 mmHg at the arterial end, but due to resistance to blood flow, it falls to 10 mmHg at the venous end of the capillary (11). CHP forces fluid out through the capillary wall. Interstitium hydrostatic pressure (IHP) is omitted from the figure since it is close to zero.

[0123] Capillary oncotic pressure (OPc) is 25 mmHg, and causes reabsorption of fluid from the interstitium, which is opposed by interstitium oncotic pressure (OPi) of 8 mmHg. Thus, when OPi (8 mmHg) is subtracted from OPc (25 mmHg), the reabsorption oncotic pressure (OPr) of 17 mmHg is obtained. OPr (12) is the net oncotic pressure present and does not change significantly over the capillary.

[0124] The second graph in FIG. 3 illustrates a cumulative estimation of the net fluid flow across the capillary from the arteriole to the venule. Under normal physiological conditions, filtration exceeds reabsorption by approximately 10%. The excess fluid moves into the interstitial spaceand allows the delivery of nutrients and oxygen to the tissues. The excess interstitial fluid is generally absorbed by the lymphatic system, returning it to the plasma space.

[0125] The normal physiological state of FIG. 3 is modified by any physiological changes that impact the terms of the Starling equation. Exercise alters Starling forces due to substantial changes in systolic blood pressure and vasodilation caused by heat generation. The magnitude of these changes is considerable, with systolic blood pressure often rising to between 160 and 220 mm Hg and even up to 250 mm Hg during high-intensity exercise. Diastolic pressure may also increase, sometimes up to 110 mm Hg.

[0126] The increased capillary hydrostatic pressure (CHP) can significantly impact fluid movement from the plasma volume to the interstitial spaces. As CHP rises, the force pushing fluid out of the capillaries into the interstitial space becomes stronger, leading to enhanced capillary filtration.

[0127] FIG. 4 shows the impact of exercise on fluid movement across the capillary membrane. Exercise leads to significant increases in systolic pressure and is typically associated with vasodilation. The combination of both influences will result in elevated CHP (41 ). The difference relative to normal conditions (42) becomes significant. The impact of the increase in CHP is reflected in the net increase in fluid movement into the interstitial space, as shown in the second graph (43).

[0128] Notably, the reabsorption oncotic pressure (OPr) in FIG. 3, and FIG. 4 has been maintained as constant for illustration purposes. This assumption is valid at the initiation of exposure to heat or exercise. As the individual becomes dehydrated, the oncotic pressure will increase due to volume loss in the plasma space.

[0129] In a state of dehydration, where an individual has lost 2% or more of their total body water and has ceased exercising, the body undergoes critical changes in fluid dynamics, particularly affecting capillary hydrostatic pressure (CHP), oncotic pressure (OPr), and the net filtration rate.

[0130] Dehydration leads to a decrease in plasma volume as fluid is lost from the vascular compartment. This reduction in plasma volume impacts capillary hydrostatic pressure (CHP) differently along the capillary bed. CHP may decrease slightly on the arterial side due to a reduction in arterial pressure, but the more significant change occurs on the venous side. In the venous capillaries, there is little to no smooth muscle to maintain pressure, and CHP drops more substantially as the plasma volume decreases. This marked reduction in venous CHP diminishes the filtration force moving fluid out of the capillaries into the interstitial space.

[0131] Simultaneously, reabsorption oncotic pressure (OPr) increases as the plasma volume decreases, leading to a higher concentration of plasma proteins like albumin. This elevated OPr enhances the osmotic pull of fluid from the interstitial space back into the capillaries.

[0132] The combination of decreased CHP, particularly on the venous side, and increased OPr shifts the balance of forces in favor of reabsorption rather than filtration. The net filtration rate,which is the balance between the outward pushing force of CHP and the inward pulling force of OPr, decreases. This results in a net fluid movement from the interstitial space back into the plasma compartment.

[0133] This physiological adjustment is essential for maintaining circulatory stability during dehydration. The body attempts to restore plasma volume and sustain blood pressure despite the overall fluid deficit by promoting fluid reabsorption into the plasma space. This compensatory mechanism is critical in preventing further reductions in effective circulating volume (ECV) and maintaining blood flow to vital organs.

[0134] FIG. 5 illustrates the above condition in graphical form. First, the state of dehydration leads to an increase in OPr (54). The state of dehydration also impacts the slope and magnitude of CHP (51 ). As shown, the CHP is below the normal condition (52). These alterations in CHP and OPr create a net fluid movement into plasma space (53). This type of fluid redistribution has been observed in many studies and is an important compensatory mechanism.

[0135] Fluid movement across cellular membranes

[0136] Cellular membranes are selectively permeable, allowing water to move freely while restricting the passage of larger solutes like sodium. This selective permeability makes cellular membranes subject to osmotic forces, where water moves rapidly in response to changes in osmolality, striving to equalize solute concentrations on both sides of the membrane. This immediate response is vital for maintaining cellular integrity and function, especially during activities that cause rapid shifts in fluid and solute levels, such as intense exercise.

[0137] On the other hand, capillary membranes operate under the influence of Starling forces, a balance between hydrostatic pressure, which pushes fluid out of capillaries, and oncotic pressure, which pulls fluid back into the capillaries. Unlike the rapid osmotic adjustments across cellular membranes, the movement of fluids across capillary membranes occurs more gradually, often stabilizing over several minutes to hours. The distinct differences in how these membranes function and respond to physiological changes define a complex redistribution of fluids during dehydration, deprivation, and rehydration.

[0138] The Effective Circulating Volume Paradigm: A Revolutionary Departure from Traditional Methods

[0139] All existing hydration assessment methods share a fundamental conceptual limitation: they attempt to measure structural volumes rather than functional circulatory adequacy.Whether quantifying plasma volume through indicator dilution, estimating total body water through bioimpedance, or tracking mass changes, these approaches treat the body as a collection of measurable compartments rather than addressing the physiological parameter that the cardiovascular system actually defends — effective circulating volume (ECV). ECV represents a new measurement paradigm that captures the functional adequacy of venousreturn and cardiac filling pressure, providing direct insight into circulatory effectiveness rather than static volume quantification.

[0140] This distinction is not merely academic but represents a fundamental breakthrough in understanding what matters physiologically. Traditional methods may indicate normal structural volumes while missing critical functional deficits, or conversely, may suggest dehydration when compensatory mechanisms have successfully maintained circulatory adequacy. The ability to compute an ECV observable proxy through its direct influence on cardiac performance via systolic time intervals provides unprecedented access to the functional parameter that governs circulatory effectiveness and drives the body's fluid regulatory responses. This functional specificity ensures that hydration assessment reflects actual physiological capacity rather than structural measurements, offering uniquely valuable information that directly correlates with the body's circulatory performance and regulatory responses — information that no existing method can provide.

[0141] Realities of Hydration Assessment

[0142] Effective Circulating Volume is the Critical Regulatory Setpoint

[0143] Effective circulating volume functions as the body's primary hemodynamic node — the central control point around which all cardiovascular regulation is organized. Unlike other physiological parameters that can fluctuate within wide ranges, ECV must be maintained within remarkably tight limits because it directly determines cardiac filling pressure and, consequently, the heart's ability to generate adequate stroke volume and cardiac output. This critical relationship means that even small deviations in ECV immediately threaten organ perfusion and oxygen delivery, making its defense the body's highest physiological priority.

[0144] The body's regulatory architecture reflects this priority through sophisticated feedback mechanisms that coordinate multiple organ systems to defend ECV. When ECV decreases, pressure-sensitive baroreceptors, volume-sensitive receptors, and osmoreceptors detect the deviation and generate error signals that trigger immediate compensatory responses. These responses are not isolated reactions but coordinated system-wide adjustments: the cardiovascular system increases heart rate and peripheral resistance, the kidneys activate renin-angiotensin-aldosterone pathways to retain sodium and water, the hypothalamus releases antidiuretic hormone to reduce urine output, and fluid redistribution mechanisms draw water from interstitial and intracellular compartments to restore plasma volume.

[0145] The body continuously faces two distinct types of fluid challenges that threaten ECV stability. Exogenous perturbations — sweating, urinary losses, fluid intake, and respiratory water loss — represent external forces that directly add or remove fluid from the system. However, the body's most powerful compensatory mechanism involves endogenous perturbations through transcapillary refill, where fluid redistributes from interstitial spaces back into the plasma compartment via altered Starling forces. This process demonstrates remarkable efficiency: research by Nose et al. showed that subjects experiencing a 9.4% plasma volume decreaseimmediately post-exercise recovered to only 2.3% below baseline within 30 minutes through transcapillary refill alone, representing a 75% restoration without any fluid intake. (Nose, Hiroshi, et al. "Shift in body fluid compartments after dehydration in humans." Journal of Applied Physiology 65.1 (1988): 318-324.) Similar findings by Fortney et al. and Convertino et al. have documented plasma volume recoveries of 50-80% during post-exercise periods, demonstrating that transcapillary refill can mobilize substantial interstitial fluid reserves to defend plasma volume, (Fortney, S. M., et al. "Effect of blood volume on sweating rate and body fluids in exercising humans." Journal of Applied Physiology 51.6 (1981 ): 1594-1600., Convertino, Victor A. "Blood volume response to physical activity and inactivity." The American journal of the medical sciences 334.1 (2007): 72-79.)

[0146] This adaptive response is beautifully demonstrated during exercise-induced dehydration. As sweating creates a constant exogenous fluid loss, the body's internal compensatory mechanisms intensify progressively. FIG. 6 illustrates this dynamic relationship during a 120- minute exercise session (61) where external dehydrating forces are met with increasingly powerful internal restoration mechanisms. The body mobilizes fluid from interstitial spaces through altered Starling forces, reduces urine production, and triggers thirst responses — all coordinated to minimize ECV reduction despite ongoing fluid losses.

[0147] Perhaps most revealing is the deprivation period (62) shown in FIG. 6, where subjects remain dehydrated but consume no fluid. During this phase, body weight remains constant while significant internal fluid redistribution occurs through transcapillary refill, demonstrating that the body prioritizes ECV maintenance over total body water preservation (63). The cardiovascular system can effectively mobilize substantial fluid from other compartments to defend the critical ECV setpoint, even when total body water continues to decline. During rehydration, the rate of recovery can exceed fluid intake (64). If full fluid replacement with electrolytes is provided, the subject may exhibit a degree of fluid overload (65).

[0148] When ECV falls below its defended range, the resulting cascade of physiological consequences demonstrates why this parameter demands such tight regulation. Inadequate ECV immediately reduces venous return and cardiac preload, forcing the heart to operate on a less favorable portion of the Frank-Starling curve. This compromises stroke volume and cardiac output, threatening perfusion pressure to vital organs. The cardiovascular system responds by increasing heart rate and peripheral vasoconstriction, but these compensatory mechanisms have limits and metabolic costs that ultimately drive the body's coordinated efforts to restore ECV through the mechanisms described above.

[0149] The ability to compute an ECV observable proxy in real-time represents a fundamental paradigm shift that enables an entirely novel approach to fluid kinetics modeling. Historically, physiologists have understood ECV's central regulatory role but have been forced to build fluid models around measurable surrogates — plasma volume, total body water, or bioimpedance estimates — because ECV itself remained inaccessible to direct measurement. This limitationhas constrained fluid kinetics research to structural compartment models that describe what can be measured rather than what the body actually regulates. For the first time, real-time ECV observable proxy through plethysmographic monitoring makes it possible to construct kinetic models centered on the body's true regulatory setpoint rather than its measurable consequences. This breakthrough enables the development of ECV-informed fluid models that can predict and quantify the body's compensatory responses — transcapillary refill rates, hormonal adjustments, and inter-compartmental redistributions — based on deviations from the defended ECV setpoint. Such models represent a fundamental advance from descriptive fluid accounting to predictive physiological modeling, offering unprecedented insight into how the body's sophisticated regulatory mechanisms coordinate to maintain circulatory adequacy.

[0150] Feedback Control Based on Use of ECV

[0151] FIG. 7 illustrates an ECV-centered feedback control system for fluid balance assessment. Unlike traditional approaches that attempt to measure individual fluid compartments directly, this system uses a real-time ECV observable proxy as the primary sensor to detect deviations from the body's defended physiological setpoint.

[0152] The control loop monitors ECV proxy alteration and compares it to baseline values to generate an error signal indicating filling pressure and cardiac function hydration status. When disturbances occur (sweating, fluid intake, etc.), the system detects the resulting ECV proxy changes through cardiovascular monitoring rather than trying to measure hard-to-measure, or unmeasurable, compartments like interstitial fluid or plasma volume. The physiological response mechanisms — transcapillary refill between plasma and interstitial spaces — are then modeled and quantified based on the ECV proxy deviation magnitude.

[0153] This represents a fundamental advance because it centers the control system on the parameter the body regulates (ECV) rather than structural volumes that are difficult or impossible to measure accurately. Using ECV as both the sensor and setpoint, the system can predict and track the body's compensatory fluid redistributions, enabling real-time assessment of total body water changes through a measurable, functionally relevant parameter. This approach transforms fluid balance monitoring from static compartment measurement to dynamic regulatory assessment, providing unprecedented insight into the body's sophisticated fluid management mechanisms.

[0154] Limitations of Existing Physiological Fluid Models

[0155] The quest to understand and predict human fluid dynamics has produced several sophisticated physiological models, each representing significant advances in our comprehension of complex fluid exchange mechanisms. These models have provided invaluable insights into the fundamental principles governing fluid movement between body compartments. However, despite decades of development and refinement, a critical gap remains: none of these models can translate real-time measurements of effective circulating volume (ECV) into actionable estimates of total body water changes.

[0156] The original Starling principle, formulated over a century ago, established the foundational concept that fluid movement across capillary membranes depends on the balance between hydrostatic and oncotic pressures. This pioneering model assumed rapid fluid equilibration between compartments, predicting that transcapillary refill would occur within minutes following plasma volume depletion. While revolutionary in its time, subsequent research has revealed fundamental timing inaccuracies in these assumptions. Studies consistently demonstrate that plasma volume restoration following hemorrhage or dehydration occurs over hours to days, not minutes. For example, after moderate blood loss, initial refill rates may reach 90-120 mL / hour in the first hours but slow dramatically, requiring 30-40 hours for complete restoration. The original Starling model's prediction of rapid equilibration simply does not match physiological reality.

[0157] The revised Starling principle and glycocalyx model represents a sophisticated evolution in our understanding of capillary fluid dynamics. This modern framework correctly identifies the endothelial glycocalyx as a critical barrier that fundamentally alters fluid exchange kinetics, explaining why transcapillary refill is much slower than originally predicted. The model elegantly describes the steady-state condition where capillaries predominantly filter fluid along their length, with minimal direct reabsorption occurring at the venous end. Research has confirmed the glycocalyx's role in creating an exclusion zone that opposes fluid reabsorption and necessitates lymphatic return for volume regulation. This framework has proven particularly valuable for understanding intravenous fluid therapy, explaining why crystalloid resuscitation from hypovolemia is more effective than the pre-revision Starling principle predicted. While this model provides excellent physiological insight and accurately describes the mechanisms underlying delayed transcapillary refill, it remains primarily descriptive rather than predictive. The revised Starling principle explains what happens but offers limited capability for quantitative modeling of dynamic fluid redistributions in response to physiological perturbations.

[0158] The Guyton circulatory model stands as both a masterpiece of integrative physiology and a testament to the complexity of whole-system fluid regulation. This comprehensive model incorporates multiple feedback loops, hormonal responses, renal function, cardiovascular adjustments, and inter-compartmental fluid movements to create a complete picture of circulatory homeostasis. The model's strength lies in demonstrating how various organ systems coordinate to maintain fluid balance, providing unparalleled insight into the integrated nature of physiological regulation. However, the Guyton model's very comprehensiveness becomes its operational limitation, see FIG. 8. With thousands of parameters and dense interconnected feedback loops, the model requires extensive computational resources and specialized expertise to operate. More critically for practical applications, the model's complexity makes it unsuitable for real-time assessment or clinical decision-making where rapid, actionable information is essential.

[0159] The Hahn model represents a significant advance in practical fluid kinetics, specifically developed to address the clinical realities of intravenous fluid therapy, (Hahn, Robert G. "Sequential recruitment of body fluid spaces for increasing volumes of crystalloid fluid." Frontiers in Physiology 15 (2024): 1439035.). Drawing from extensive human studies involving 326 experiments across multiple clinical settings, this model demonstrates the sequential recruitment of body fluid spaces as crystalloid volumes increase. The model shows that small volumes (250-500 mL) expand only the central plasma compartment, moderate volumes (500-1000 mL) extend into a rapidly exchanging interstitial space (Vt1 ), and larger volumes (>1 L) recruit a slow-exchange "third space" (Vt2) when Vt1 expansion reaches 700- 800 mL. This threshold-based recruitment dramatically prolongs fluid retention, with plasma half-life extending from 30 minutes to 14 hours when all three compartments are filled. The model has proven invaluable for understanding IV crystalloid distribution kinetics and optimizing perioperative fluid management. However, the Hahn model's scope reflects its specific development purpose: it was designed for controlled IV infusion scenarios and cannot model oral fluid intake, complex patterns of insensible losses, or the variable urine output responses that occur during natural dehydration and rehydration cycles.

[0160] Despite their individual strengths, none of these sophisticated models can perform the essential task of translating real-time ECV observable proxies into actionable estimates of total body water changes. This fundamental limitation stems from their focus on specific clinical applications rather than the broader challenge of hydration assessment. Existing models have been developed primarily for drug delivery optimization, ICU fluid management, and resuscitation from hemodynamically unstable shock states — scenarios involving acute volume changes and invasive monitoring in controlled clinical environments. Current fluid management protocols focus on shock resuscitation where patients receive liters of intravenous fluids and require vasopressor support, fundamentally different from detecting dehydration at levels that do not result in hemodynamic instability.

[0161] More critically, none of these models are designed to use the time history of ECV observable proxies to estimate exogenous fluid movements for the determination of changes in total body water. The existing frameworks treat fluid assessment as snapshot measurements or short-term therapeutic interventions, rather than continuous monitoring of physiological status over extended periods. This represents a fundamental gap in translational capability — the inability to convert functional circulatory adequacy measurements into structural fluid balance assessments across the full spectrum of hydration states, from optimal hydration through mild dehydration to the early stages of volume depletion before hemodynamic compromise occurs.

[0162] Translating effective circulating volume (ECV) measurements into total body water (TBW) estimates is inherently complex because it requires the real-time integration of multiple dynamic variables operating on different time scales and with shifting interdependencies. ECV must be interpreted against a backdrop of continuous fluid redistribution — influenced by thecurrent degree of dehydration, the full history of preceding ECV values, variable measurement intervals, and the simultaneous, often independent changes in fluid intake, sweat rate, urine output, and insensible losses.

[0163] The mathematical challenge goes far beyond static fluid accounting. Accurate inference demands time-series modeling in which each ECV value is conditioned not only on the present physiological state but on the entire temporal trajectory that preceded it. The same ECV reading can represent vastly different TBW states depending on whether it occurs during the onset of dehydration, ongoing loss, compensatory redistribution, or active rehydration. Compounding this, the ECV-TBW relationship is not fixed — it evolves as compensatory mechanisms such as transcapillary refill, hormonal modulation, and inter-compartmental fluid shifts continuously adjust to preserve circulatory adequacy.

[0164] Despite decades of research, these realities explain why no model has yet bridged the gap between a functional measurement of ECV and a structural quantification of TBW. The solution demands deep physiological insight and mathematical frameworks capable of handling time-dependent, multi-variable systems embedded with feedback loops, regulatory delays, and shifting boundary conditions.

[0165] Core System Elements

[0166] ECV Observable Proxy Demonstration Using Systolic Time Intervals with Positional Change

[0167] FIG. 9 presents standing data obtained via finger photoplethysmography during a progressive dehydration test. As dehydration progresses, heart rate (HR) increases and left-ventricular ejection time (LVET) shortens, reflecting reduced preload and thus decreased effective circulating volume (ECV). The device computes a subject-normalized index from the relative changes in HR and LVET with respect to an initial condition; treating these paired changes as a Euclidean distance (radial displacement) in the HR-LVET plane yields a metric that increases monotonically as ECV falls. Because the index relies on relative changes in two coordinated cardiac-function variables, it is robust to inter-subject differences in baseline physiology — for example, highly conditioned athletes may exhibit larger LVET excursions and lower baseline HR than deconditioned subjects, yet the normalized distance consistently tracks the same trajectory.

[0168] The progressive alteration of a measured metric during a dehydration protocol by itself cannot distinguish whether the declining index reflects ECV specifically, plasma volume, or some other spurious metric. However, the ECV proxy represents the functionally available blood volume for cardiac filling, while plasma volume is a static anatomical measurement of total blood fluid content. To demonstrate ECV specificity, photoplethysmography data were collected while the subject performed a supine-to-standing postural maneuver (supine — > stand — > supine — > stand). Upon standing, gravitational pooling redistributes approximately 500-800 mL of blood to the lower extremities within 30 seconds, acutely reducing venous return and effectivecirculating volume without altering total plasma content. This physiological separation enables definitive discrimination between ECV (functional) and plasma volume (anatomical) measurements. ECV falls within seconds of postural transition, whereas plasma volume remains essentially unchanged over this short interval and would require hours to reflect any meaningful change through traditional measurement methods such as Evans Blue dye or radiodilution techniques.

[0169] Consistent with this physiology, HR rises and LVET shortens promptly upon standing within 30 seconds, producing a dramatic decrease in LVET and a corresponding increase in heart rate, see FIG. 10. FIG. 11 is a violin plot of HR and LVET as the stand and supine positions. FIG. 12 shows the change in the EVC proxy as calculated by a Euclidean distance metric as the subject moves through positional changes. The top plot shows the individual measurements by pulse. The lower plot shows average values for a 30-second measurement. FIG. 12 illustrates the reversible and repeatable nature of positional changes. The postural challenge demonstrated immediate ECV proxy sensitivity with measurable changes in the HR- LVET indexes occurring within 30 seconds of postural transition, demonstrating temporal resolution that plasma volume measurements cannot achieve.

[0170] In certain embodiments, an effective circulating volume (ECV) proxy is computed from heart rate (HR) and left-ventricular ejection time (LVET) using complementary feature combinations. The proxy may be derived from either signal independently (e.g., monotonic mappings of HR or LVET), from joint ratios (e.g., HR / LVET or LVET / HR), from baseline- normalized changes (e.g., percent or z-score deltas), from a geometric aggregation such as the Euclidean distance in a feature space spanned by AHR and ALVET, or from hybrid constructs that couple a ratio of changes with a Euclidean-distance term to encode both magnitude and direction of joint deviation. The mapping from these features to an ECV score may be linear or non-linear (e.g., weighted sums, logistic transforms, kernel or learned functions), fixed- parameter or adaptive, and may apply posture- or context-specific weighting; any monotonic transformation that preserves the ordering of circulatory adequacy is within scope.

[0171] Accordingly, FIG. 10, FIG. 11 , and FIG. 12 together establish that the disclosed HR- LVET method both quantifies the magnitude of ECV proxy reduction during dehydration and proves specificity that the ECV proxy is sensitive to change in filling pressure through its immediate, posture-dependent response — behavior that a plasma-volume measurement, as a structural compartment quantity, would not exhibit on the same time scale. This temporal discrimination definitively separates functional circulatory volume (ECV) from anatomical blood volume (plasma volume), providing a specific measurement tool for clinical applications where this distinction is critical, such as heart failure management, dehydration assessment, and cardiovascular monitoring.

[0172] The above discussion and figures define and demonstrate that effective circulating volume (ECV) measurement proxy with posture sensitivity is a definable measurementtechnique and a quantitative determination of circulatory adequacy that incorporates one or more such systolic time intervals to detect a reproducible, directionally consistent change in cardiac preload in response to a gravitational fluid shift caused by a change in posture — such as supine-to-stand, stand-to-supine, or passive leg raise — occurring within a defined time window and exceeding a defined magnitude threshold, and exhibiting reversibility upon return to the baseline posture. In a cohort of 116 observations, heart rate (HR) and left-ventricular ejection time (LVET) were measured in supine and standing postures. Defining differences as AHR = HRsupine- HRstand and AET = ETsupine- ETstand, the aggregated results were: AHR = -10.0 bpm (SD 8.8, N=116) and AET = +61.3 ET_d2 units (SD 27.8, N=116). Expressed as ratios of change relative to supine, (supine-stand)Zsupine, the mean percent differences were -17.4% for HR (SD 16.0%) and +18.9% for LVET (SD 8.1%) across the same 116 observations. These measurements show a directionally consistent orthostatic response — HR increases and LVET shortens with standing — and establish that both absolute differences and normalized ratios provide a robust posture-sensitive response. Because these posture-induced variations in systolic time intervals manifest within seconds and without net change in total body water, their detection constitutes a specific functional signature of ECV proxy measurements and enables discrimination from static compartmental volume measurements such as plasma volume. It is important to note that the use of effective circulating volume (ECV) measurements that have posture sensitivity does not require a positional change. ECV proxy can be determined in a standing position only.

[0173] The ECV-Centric Hydration Model

[0174] The ECV-Centric Hydration Model (ECHM) represents a fundamental departure from traditional fluid assessment approaches by positioning effective circulating volume as the central organizing principle for hydration monitoring. Unlike previous models that attempt to measure or estimate individual fluid compartments, the ECHM recognizes that ECV serves as the body's primary fluid regulation target — the functional parameter that cardiovascular regulatory systems actively defend through coordinated physiological responses.

[0175] When disturbances occur — whether from fluid intake, sweating, blood loss, or other perturbations — the body's multiple physiological systems work synergistically to restore circulatory adequacy through complex responses including transcapillary fluid shifts, hormonal modulation, and renal function adjustments. The ECHM captures this regulatory reality by using ECV observable proxies as the foundation for a physiologically-based model that draws on control theory principles to estimate fluid balance dynamics in the presence of known and unknown disturbances.

[0176] The ECV-Centric Hydration Model (ECHM) is an elegant and effective model defined by functional elements that impact and govern hydration in the human. The model architecture is defined by a three-component system, see FIG. 13. The ECHM operates through a connectedthree-component architecture that transforms the traditionally intractable problem of ECV-to- TBW translation into a solvable kinetic framework:

[0177] Component 1 : Measured Effective Circulating Volume

[0178] Real-time ECV observable proxies through plethysmographic monitoring of systolic time intervals provide the central input that drives model calculations. This measured parameter serves as both the regulatory setpoint the body defends and the primary variable from which all other fluid movements can be inferred. The ECV observable proxy forms the centroid of the model, directly connected to both internal (endogenous) fluid compartments and external (exogenous) fluid sources, enabling continuous assessment of circulatory adequacy.

[0179] Component 2: Modeled Endogenous Fluxes

[0180] Endogenous fluxes encompass all fluid movements within the body's circulatory boundaries, including fluid redistribution between plasma, interstitial, and intracellular compartments. These internal fluid shifts represent the body's compensatory mechanisms for maintaining ECV. The model accounts for the kinetic realities of fluid movement by incorporating multiple compartments, and can include fast-exchanging interstitial space, slow-exchanging interstitial space, and intracellular compartments. The model also accounts for the user's posture, which exerts a gravitational influence.

[0181] Crucially, endogenous fluxes can be effectively modeled based on prior ECV observable proxies and the historical trajectory of ECV changes. These prior measurements, which can often include an euvolemic baseline when available, provide the reference context necessary for interpreting current ECV proxy measurements. The model leverages kinetic coefficients that define fluid movement rates between compartments, enabling prediction of transcapillary refill rates, inter-compartmental redistributions, and compensatory volume shifts. These endogenous transformations follow predictable patterns that can be characterized through the temporal history of ECV changes and established physiological principles governing fluid movement between body compartments.

[0182] Component 3: Estimated Exogenous Fluxes

[0183] Exogenous fluxes represent all fluid movements that cross the boundaries between the body's internal circulation and the external environment. These include fluid ingestion (stomach to circulation), fluid losses through sweat and respiration (circulation to external environment), and sensible losses, including urine production and fecal water content. Importantly, fluid that resides in the stomach or bladder is considered outside the circulatory boundaries — these represent staging areas that do not actively participate in homeostatic regulation until absorption occurs (stomach) and have no impact after excretion into the bladder.

[0184] The model treats exogenous fluxes as unknown disturbances or perturbations that influence total body water but can be estimated through the difference between measured ECV changes and predicted endogenous responses. For development purposes, the model can incorporate a gastric absorption coefficient that governs the rate at which ingested fluidtransitions from the external staging area (stomach) into the circulating volume. For urine production estimation, the model can include a urine production estimation tied to ECV.

[0185] Kinetic Framework and Mathematical Implementation of the ECHM employs an integrated kinetic framework that transforms discrete ECV observable proxies into continuous estimates of fluid balance changes. One embodiment of the model incorporates four primary compartments arranged in a physiologically-consistent linked configuration:

[0186] Effective Circulating Volume Compartment: the volume available to the heart.

[0187] Fast Interstitial Space: Rapidly equilibrating with plasma through conventional Starling forces

[0188] Slow Interstitial Space: The "third space" represents the slower interstitial fluid movement as observed during capillary refill.

[0189] Intracellular Compartment: Connected to the interstitial spaces through osmotic gradients. The contribution of fluid from the intracellular space can often be included in the slow interstitial, which results in a mode simplification.

[0190] With a three-compartment system, the mathematical framework utilizes four intercompartmental coefficients governing fluid movement between plasma, fast interstitial, slow interstitial, and intracellular spaces. If the model is used to predict ECV from measured weight used to estimate changes in TBW, then the model can include a gastric absorption coefficient and a urine production coefficient.

[0191] This coefficient-based approach enables the model to account for individual transfer rates to and from compartments while maintaining computational tractability. The linked compartment configuration ensures that predicted fluid movements respect established physiological principles, including the sequential recruitment of fluid spaces and the timedependent nature of compensatory responses.

[0192] Illustrative Simulation of Fluid Movement during Dehydration, Deprivation and Rehydration

[0193] To illustrate the fluid shifts and dynamics that occur within ECV, fast interstitial, and slow interstitial spaces, a simulated dehydration study was modeled using the ECV-Centric Hydration Model. The modeled study represents a controlled dehydration protocol involving a linear decrease of 1 .5 liters in total body water over 120 minutes through simulated sweat loss, followed by a deprivation period of 120 minutes (120-240 minutes), and concluding with rapid replenishment of total fluid lost through single fluid consumption beginning at 240 minutes, see FIG. 14. The simulation models a 100 kg individual with 50% body water content, resulting in a baseline total body water of 50 liters. Therefore, a 1 .5 liter fluid loss represents a 3% change in total body water. This idealized simulation illustrates the organized, predictable changes in fluid compartments that occur in response to exogenous influences — changes that remain hidden when measuring only total body water but are revealed through compartmental analysis.

[0194] The simulation models three distinct phases of external fluid perturbation, marked by the vertical lines in the graphs. During the initial 120 minutes, the subject experiences progressive dehydration through controlled sweat loss, creating a continuous exogenous outflow from the system that removes exactly one liter of fluid. The deprivation period from 120-240 minutes (120 minutes total) maintains the dehydrated state without any fluid intake, eliminating exogenous fluid inputs while the body's internal compensatory mechanisms continue to operate. Finally, rapid rehydration begins at 240 minutes with immediate fluid consumption, representing a sudden exogenous fluid addition back to the system.

[0195] Total body water is shown in the left upper plot, demonstrates the expected total body water changes that would be measured in such a study. TBW declines linearly during active sweat loss over the first 120 minutes, reaching exactly 3% loss (1 .5 liters from the baseline) at the end of the sweating phase. During the deprivation period from 120-240 minutes, TBW remains constant at -3.0%, reflecting the absence of both fluid intake and additional losses. Rehydration after 240 minutes shows the characteristic rapid return toward baseline as the consumed fluid is absorbed and distributed throughout the body.

[0196] The right graph reveals an organized response within the body's fluid compartments — responses that demonstrate the sophisticated nature of physiological fluid management and cannot be detected by measuring total body water alone. Each compartment exhibits distinct, predictable patterns that reflect the body's hierarchical priorities for maintaining circulatory function.

[0197] During the dehydration phase (0-120 minutes), the ECV Proxy (solid line) shows an initial decline but shows a decreasing slope, demonstrating the cardiovascular system's regulatory priority of defending circulatory adequacy. The fast interstitial compartment (dashed line) shows progressive decline reaching approximately -15%, serving as an available fluid reservoir. The slow interstitial compartment (dash-dot line) exhibits delayed but ultimately significant depletion approaching -5%, representing the body's large fluid reserves that become mobilized during sustained dehydration. As used herein, the “slow interstitial” compartment is a lumped, functional reservoir within the extravascular interstitial space that exchanges with the plasma compartment on a longer time scale than the remainder of the interstitium. It is not an anatomically bounded space; rather, it represents tissue regions and microvascular beds whose transcapillary and lymphatic conductances are relatively low, so that perturbations in plasma volume equilibrate with a delayed response. This reservoir captures the portion of interstitial fluid that refills plasma slowly after a loss (or accepts fluid slowly after a load). The delay reflects features such as lower capillary surface-area / permeability, higher tissue compliance, diffusion barriers, and limited lymphatic return. This lumped functional reservoir can be used as a mechanism to include intracellular contribution as well.

[0198] The deprivation period (120-240 minutes) provides the most compelling demonstration of the body's organized internal fluid management. While total body water remains perfectlyconstant, no external fluid exchange occurs; substantial internal redistribution occurs through endogenous fluid movements. The ECV improves, rising from -2.5% back toward baseline. Thus, the degree of transcapillary refill is consistent with the physiological studies of Nose et al. and others. Concurrently, both interstitial compartments lose volume, reaching maximum depletion of approximately -5%. This represents active transcapillary refill in action, where the cardiovascular regulatory system systematically draws fluid from interstitial reserves to defend the critical ECV parameter that determines cardiac filling and circulatory adequacy.

[0199] During rehydration (240+ minutes), the fluid compartments return to a euvolemic state. The ECV recovers most rapidly, reflecting the body's regulatory priority of restoring circulatory adequacy as the first response to fluid availability. The interstitial compartments recover more gradually, with distinct kinetic patterns — the fast interstitial space begins refilling promptly while the slow interstitial compartment follows a more prolonged recovery trajectory back to baseline.

[0200] This simulation demonstrates that the ECV-Centric Hydration Model successfully captures the organized, predictable patterns of fluid compartment responses to exogenous influences — patterns that remain completely invisible when measuring only total body water changes. The model reveals how the body systematically prioritizes ECV defense through coordinated internal fluid redistributions, maintaining circulatory function even during periods when total body water appears stable. This capability to visualize and quantify the hidden compensatory mechanisms provides insight into the body's sophisticated fluid management strategies.

[0201] An ECHM Embodiment for Translating Functional Measurements to Total Body Water Changes

[0202] The ECV-Centric Hydration Model (ECHM) represents one embodiment of an innovative approach to translating real-time effective circulating volume measurements into quantitative estimates of total body water changes. This educational overview examines how this particular embodiment integrates functional cardiovascular assessment with deterministic fluid kinetics to provide insight into the body's dynamic fluid status. Various other embodiments and implementations of ECV-centric hydration modeling are possible and contemplated within the scope of this invention.

[0203] Core Model Architecture

[0204] This embodiment of the ECHM operates on the principle that ECV changes, measured through plethysmographic monitoring of systolic time intervals, can be used to reverse-engineer the underlying fluid perturbations affecting total body water. The model accomplishes this through a three-compartment kinetic framework that mirrors the body's actual fluid distribution:

[0205] VcO (Circulating Volume): The ECV proxy measure representing the defended hemodynamic node where ECV proxy changes are directly related to cardiac performance metrics. This compartment serves as the primary sensor for the body's regulatory responses.

[0206] Vt1 (Fast-Exchange Interstitial Volume): The rapidly equilibrating interstitial fluid space that communicates directly with plasma compartment through transcapillary exchange governed by Starling forces. This compartment captures the immediate buffering capacity that defends plasma volume during fluid perturbations.

[0207] Vt2 (Slow-Exchange Interstitial Volume): The slowly equilibrating interstitial fluid space that provides longer-term fluid storage and represents the body's strategic reserve for maintaining circulatory adequacy. This compartment fills sequentially when fast-exchange capacity is exceeded.

[0208] The ECV-Centric State Observer: Information-Driven Forcing Function Recovery

[0209] The ability to create an estimation of total body water from cardiovascular measurements necessitated the integration of sophisticated physiological modeling with advanced controls engineering concepts. Traditional physiological approaches focus on measuring structural compartments, while controls engineering provides mathematical frameworks for estimating unmeasurable system states from observable outputs. This embodiment leverages controls theory principles — including state observers, observable proxies, and inverse problem solving — to bridge the gap between what can be measured (cardiovascular function) and what needs to be known (fluid balance dynamics).

[0210] This embodiment of the ECHM operates as a sophisticated state observer that treats effective circulating volume as the defended hemodynamic quantity determining venous return and cardiac filling. Rather than attempting to measure ECV directly, the system uses systolic time intervals as an observable proxy for ECV and employs a compact multi-compartment fluid model to solve the inverse problem of recovering the information-driven forcing function — the unknown exogenous flux that best explains observed cardiovascular changes.

[0211] State Observer: In control theory, a state observer is a mathematical construct that estimates the internal states of a dynamic system based on measurable outputs. The observer uses a model of the system dynamics combined with real-time measurements to infer unmeasurable variables. In this embodiment, the state observer estimates fluid compartment volumes and flows based on cardiovascular performance indicators.

[0212] Observable Proxy: An observable proxy is a measurable parameter that correlates with and provides information about an unmeasurable parameter of interest. Systolic time intervals serve as the observable proxy for ECV because changes in effective circulating volume directly influence cardiac preload, which manifests as measurable changes in ventricular ejection timing and heart rate patterns.

[0213] Inverse Problem: An inverse problem involves determining unknown inputs or parameters that produced observed outputs, working backward from effects to causes. This embodiment solves the inverse problem of determining what exogenous fluid flux pattern could have produced the observed cardiovascular changes, rather than predicting cardiovascular changes from known fluid inputs.

[0214] Information-Driven Forcing Function: The forcing function represents external inputs to the system — in this case, net fluid gains or losses. It is "information-driven" because it is derived from the cardiovascular measurement information rather than direct measurement of fluid inputs / outputs. The algorithm recovers this forcing function by optimizing it to match observed cardiovascular responses. The use of an information-driven forcing function is the basis upon which unknown exogenous fluxes are determined.

[0215] Per-lnterval Inversion Process: This embodiment employs a per-interval inversion methodology, which assumes a constant exogenous flux during each measurement interval and optimizes this parameter to minimize end-of-interval mismatch between predicted and observed ECV changes. This approach converts central-volume information into a time-stamped profile of external fluid flux with physiologic attribution. The infrequency of measurement data in the processed dehydration data necessitated this type of inversion methodology. In practice, it would be desirable to have measurements about every 10 minutes.

[0216] Time-series framing. The foregoing constitutes a time-series analysis of the ECV observable proxy: the model processes a sequence of irregularly spaced measurements, propagates states across each measurement interval, and, when needed, subdivides long gaps to a nominal cadence so that interval-wise exogenous rates and cumulative TBW change remain stable despite variable sampling.

[0217] Managing variable sampling frequency: Plethysmographic signals are motion-sensitive, so usable (quality-accepted) samples may be irregularly spaced in time due to many issues, include athletic participation. The ECHM handles this by operating per measurement interval, where each interval is the actual elapsed time between two adjacent accepted samples. For each interval, the model assumes a constant exogenous fluid rate, propagates the compartment model over that interval’s actual duration, and chooses the rate that best aligns the model- predicted ECV observable proxy at the end of the interval with the observed value. Repeating this step across all intervals yields a time-resolved exogenous flux and cumulative TBW change that are robust to variable sampling; denser sampling simply improves timing resolution. Optionally, the accepted ECV-proxy series may be resampled to a nominal cadence for reporting without changing the per-interval fit.

[0218] Nominal-cadence subdivision for widely spaced samples: When adjacent accepted plethysmographic samples are widely separated in time, the ECHM mitigates instability by subdividing the elapsed time into nominal-duration sub-intervals (e.g., about ten minutes). For each sub-interval the model assumes a constant exogenous fluid rate, propagates the compartment model across the sub-interval, and selects the rate so that the model-predicted ECV observable proxy at the next sub-interval boundary minimizes the difference to the target trajectory, with the final boundary aligned to the next accepted sample. This gap subdivision yields a time-resolved exogenous flux and TBW change that are robust to variance in sampling frequency, while preserving the original anchor measurements.

[0219] Inter-Compartment Conductances: The system employs physiologically-based rate constants that govern fluid exchange between compartments. Specifically:

[0220] k12: Transfer coefficient from circulating volume (ECV) to fast interstitial compartment

[0221] k21 : Transfer coefficient from fast interstitial to circulating volume (ECV)

[0222] k23: Transfer coefficient from fast interstitial to slow interstitial compartment

[0223] k32: Transfer coefficient from slow interstitial to fast interstitial compartment

[0224] These conductances enable the model to predict how fluid redistributions while maintaining computational efficiency suitable for real-time implementation.

[0225] Real-Time Implementation Capability: The interval-wise inversion solutions can be computed on-device as new samples arrive, enabling continuous monitoring and therapy guidance. This real-time capability transforms hydration assessment from retrospective analysis to proactive physiological management, supporting diagnostic insight and closed-loop fluidtherapy guidance.

[0226] Uniform Timeline Processing: Observations are interpolated and resampled to a uniform cadence (typically ten-minute steps) so each adjacent pair defines a closed interval for inversion. This standardization enables consistent optimization across varying measurement frequencies while maintaining temporal resolution sufficient for detecting rapid physiological changes.

[0227] Information-Driven Forcing Recovery: Each optimization interval solves for the exogenous flux that, when applied to the forward-propagated compartment model, minimizes the mismatch between predicted and observed central volume percent at the interval endpoint. This process effectively recovers a time-resolved forcing function that timestamps external events (fluid intake, sweat losses, urine output) without requiring manual diaries or direct measurement of these processes.

[0228] Time Series Analysis Framework: The ECV-Centric Hydration Model operates as a sophisticated time series system where each measurement builds upon the complete history of prior observations to create a continuous physiological narrative, see FIG. 15. The baseline euvolemic state establishes the temporal reference point, while each subsequent ECV observable proxy measurement updates the model's understanding of fluid compartment dynamics through sequential estimation. This feed-forward architecture propagates the solved state from each interval as the initial condition for the next measurement period, enabling the system to maintain a current status of fluid movement.. The temporal dependency is critical because identical ECV measurements can represent vastly different physiological states depending on the trajectory that preceded them — whether reflecting dehydration onset, ongoing compensation, or recovery phases. By maintaining awareness of the complete measurement sequence and accumulated fluid balance changes, the time series framework transforms discrete cardiovascular measurements into actionable insights about dynamic hydration status over extended monitoring periods.

[0229] Subject-Specific Baseline Initialization: Each individual's baseline total body water can be computed anthropometrically (Watson formula) and used to initialize the three compartments (Vc, Vt1 , Vt2) as subject-specific fractions rather than population averages. This personalization ensures that the recovered forcing function accurately reflects individual physiological responses rather than statistical approximations.

[0230] Example Implementation: Single Subject Exercise Study

[0231] The capabilities of this ECHM embodiment are illustrated in FIG. 16, which shows realtime fluid tracking for a single subject during a demanding exercise protocol. The study involved a 6km run over approximately 60 minutes followed by consumption of 315mL of electrolytecontaining fluid, followed by an additional 6km run, then incremental rehydration with specific fluid boluses: 200mL at -180 minutes, 150mL at -225 minutes, and 200mL at -270 minutes.This example demonstrates one application where functional ECV monitoring provides valuable insight, though other exercise protocols and monitoring scenarios are equally applicable to this modeling approach.

[0232] Expected vs. Modeled Total Body Water: The upper left panel compares two different approaches to TBW assessment. The "Expected TBW" (solid line) represents a reference methodology based on weight measurements with a Watson estimated body water percentage — treating the body as having fixed water content ratio. The "Modeled TBW" (thick dashed line) shows the ECHM estimates based solely on the time course of ECV observable proxies derived from cardiovascular information. The close agreement between these independent methods validates the model's accuracy across the entire exercise portion, including the initial 6km dehydration phase, the mid-phase consumption of fluid, the second 6km run, and subsequent stepped rehydration interventions.

[0233] Fiducial Measurement Points: The vertical gray lines mark critical measurement periods where both weight-based assessments and ECV observable proxies were obtained. These fiducial marks represent the temporal anchor points that enable comparing the reference method (expected) and the predicted (modeled) process.

[0234] Compartmental Fluid Dynamics: The upper right panel reveals the sophisticated internal fluid redistributions that weight-based methods cannot detect. While total body water (small- dashed line) shows the overall fluid balance during the total dehydration study, the individual compartments tell a more nuanced story. The ECV experiences fluctuations reflecting alterations in filling pressure and overall cardiovascular performance. The fast-exchange interstitial space (Vt1) shows the intermediate buffering response, while the slow-exchange space (Vt2) demonstrates longer-term adaptive changes and the delayed effects of fluid interventions.

[0235] External Fluid Integration Process: The bottom right panel shows the "Estimated fluid in / out per minute," which captures both the ongoing fluid losses during exercise (negative values during the first 90 minutes) and the discrete positive spikes corresponding to themeasured fluid intakes: the 315mL during exercise, followed by the 200mL, 150mL, and 200mL rehydration boluses at 180, 225, and 270 minutes respectively. The estimated fluid in / out per minute also accounts for residual sweats that occurs after the run and urine output.

[0236] Inverse Problem Solution: Each step in the fluid rate estimation represents a solved inverse problem: given the measured ECV change between two fiducial points, what external fluid flux pattern could have produced this cardiovascular response? The method integrates the kinetic equations forward in time while iteratively adjusting the external fluid rate until the predicted ECV change matches the observed measurement. This process effectively reconstructs the complete fluid balance story from functional cardiovascular data alone, correctly identifying not only the timing and magnitude of fluid interventions but also the ongoing exercise-induced losses.

[0237] Scatter Plot Evaluation: the bottom left panel shows the relationship between the weight referenced TBW and the estimated or predicted TBW by the ECHM process. Examination of the plot shows an R-squared of 0.984. Overall perform is very good across a range of dehydration values.

[0238] Cross-Population Validation Example

[0239] The correlation analysis shown in FIG. 17 shows total body prediction results versus weight derived TBW references across approximately 25 subjects and a wide range of hydration states. The correlation demonstrates consistent model performance across diverse physiological conditions with data points spanning from +1 % hyperhydration to -10% dehydration. This example validation represents one approach to testing model accuracy, though other validation methodologies and subject populations could be employed to assess different aspects of model performance.

[0240] Incorporating Observable Parameters for Improved Performance

[0241] The overall prediction performance of the hydration system can be augmented by incorporating additional physiological measurements that influence exogenous or endogenous fluxes. The enclosed information is illustrative, and additional parameters may add value.

[0242] Parameters affecting capillary hemodynamics include, but are not limited to, continuous blood pressure, pulse pressure, pulse-transit time, perfusion index, and PPG waveform morphology. These parameters provide measurable indicators that influence capillary hydrostatic pressure. Brief orthostatic challenges (supine-to-stand or passive leg raise) are particularly valuable as they reveal preload sensitivity and functional volume status, offering high-yield discrimination between true volume depletion and simple vasodilation.

[0243] Autonomic and respiratory coupling signals are critical contextual filters for interpreting cardiovascular responses. Heart rate variability, electrodermal activity, respiratory sinus arrhythmia, and cardiorespiratory synchronization help distinguish volume-mediated changes from other physiological drivers. This distinction is essential because an elevated heart rate following exercise may present identically to dehydration-induced tachycardia. For mostcardiovascular measurements, establishing a true baseline requires the subject to return to physiological equilibrium. Using normalized respiratory rate to indicate that the subject has returned to baseline provides a reliable mechanism for distinguishing between exercise-induced and volume-related cardiovascular responses.

[0244] Environmental and metabolic information provides physiologically-grounded constraints on expected fluid redistribution. Skin temperature gradients, activity intensity, ambient conditions (temperature, humidity), and circadian timing establish priors for sweat losses, vascular tone regulation, and normal physiological variation that must be accounted for in the Starling force calculations.

[0245] The composition of fluid replacement significantly impacts model performance, as drinking plain water versus water with replacement electrolytes produces fundamentally different effects on plasma osmolality and oncotic pressure. Plain water dilutes plasma sodium and reduces oncotic gradients, while electrolyte solutions maintain or restore normal osmotic relationships — differences that directly influence the Starling forces governing capillary fluid exchange and must be explicitly modeled for accurate hydration prediction.

[0246] Example Embodiments

[0247] Dual-Parameter Hydration and Cardiovascular Capability Monitoring Using a MultiSensor Ring

[0248] In one embodiment, the hydration monitoring system is implemented as a wearable ring configured to acquire and process multiple physiological signals relevant to both hydration status and cardiovascular performance. The ring as shown in FIG. 19 comprises an optical system for measurement of a photoplethysmogram (PPG) signal from digital vascular bed using detector (1901 ), and emitter (1902); a temperature sensor (1903) configured to measure skin surface temperature; one or more motion sensors configured to determine activity level (1905) and posture; and an electrodermal activity (EDA) sensor (1904) configured to measure changes in sympathetic nervous system activity. Respiratory rate is derived from cyclical variations in the PPG waveform and / or other suitable noninvasive measurement methods.

[0249] At the initiation of daily use, the subject performs a baseline assessment in a standing position. The processing unit compares the measured respiratory rate to a stored euvolemic standing baseline value obtained under normovolemic conditions. When the respiratory rate is at or near the baseline value, indicating the subject is at physiological rest and free from transient exertional influences, the system records (i) a baseline effective circulating volume (ECV) measurement, and (ii) a baseline total body water (TBW) value, calculated from anthropometric data and stored reference conditions.

[0250] During the course of the day, the system continuously updates two complementary measurements: (i) point-in-time ECV, representing the instantaneous cardiovascular capability of the subject and reflecting the adequacy of cardiac preload and venous return at the time of measurement; and (ii) integrated TBW change, representing the cumulative change in totalbody water from the established baseline, calculated using the ECV-Centric Hydration Model (ECHM) and incorporating measured physiological parameters, environmental conditions, and model-estimated endogenous and exogenous fluid fluxes.

[0251] During periods of physical exertion, such as occupational activity in elevated ambient temperatures, the ECV observable proxy provides an indication of whether cardiovascular performance is being sustained or reduced. The TBW change measurement simultaneously tracks the cumulative hydration status over time, independent of short-term fluctuations in ECV caused by activity or posture.

[0252] In a post-activity recovery phase, the subject may initiate a recovery assessment. The system measures current ECV and TBW change relative to the morning baseline and determines the extent to which cardiovascular capability and total fluid balance have been restored. The system further prompts the user to input details of any planned or current fluid intake, including the type of solution (e.g., plain water, electrolyte-containing beverage, sports drink) and, if available, the approximate sodium or electrolyte content. The processing unit adjusts its physiological model to account for the osmotic and oncotic effects of the fluid composition on inter-compartmental fluid shifts.

[0253] Based on these inputs and the current measurements, the system calculates a recommended fluid volume and composition required to restore both ECV and TBW to baseline values. The recommendation may include staged rehydration strategies, timing intervals, and specific electrolyte concentrations to optimize both integrated hydration status and point-in-time cardiovascular capability, while avoiding overhydration.

[0254] This embodiment demonstrates the synergistic value of simultaneously providing an instantaneous measurement of cardiovascular capability (ECV) and an integrated measure of total body water change from baseline (TBW change). The combination of these two distinct yet complementary parameters enables the system to provide physiologically relevant, individualized guidance for hydration management, recovery optimization, and sustained cardiovascular performance.

[0255] Hydration and Cardiovascular Capability Monitoring via Sensor-Integrated Sports Eyewear for Intermittent-Burst Athletic Events

[0256] In another embodiment, the hydration and cardiovascular capability monitoring system is implemented in sports eyewear, such as glasses designed for athletic use, see FIG. 20. The eyewear incorporates a noninvasive sensor system positioned along the interior temple region, configured to acquire photoplethysmography (PPG) signals from the temporal artery and / or adjacent vasculature. The sensor system includes one or more light emitters (4201) and a detectors (4203 and 4202) configured to measure pulse waveforms with sufficient resolution to derive left ventricular ejection time (LVET) and heart rate (HR). The eyewear may also include additional sensors such as motion detectors (4204) to identify periods of relative inactivity, skin temperature sensors for contextual physiological assessment, and ambient light sensors toadapt signal acquisition parameters for varying environmental conditions. The device also contains a small speaker to prove information to the user (4205)

[0257] This embodiment is specifically designed for sports and activities characterized by repeated bursts of high-intensity exertion followed by short rest or pause intervals. Representative examples include soccer, ultimate frisbee, lacrosse, pickleball, tennis, basketball, hockey, and similar competitive sports.

[0258] Baseline Measurement

[0259] Prior to the start of the event, the athlete puts on the sports eyewear and performs a baseline measurement under normovolemic, resting conditions. The system can compare this baseline measurement with prior baselines to determine the validity of the current baseline. Athletes can be very excited or nervous before the start of an event, so an elevated heart rate or other physiological variances may be present. In such a scenario, the system can default to a stored baseline or combine prior baseline measurements. The system measures and records a baseline effective circulating volume (ECV) and a baseline total body water (TBW) value, the latter determined through the ECV-Centric Hydration Model (ECHM) using anthropometric data and stored reference parameters. This baseline serves as the reference for integrated hydration status tracking and instantaneous cardiovascular capability assessment throughout the event.

[0260] Opportunistic Data Acquisition During Activity

[0261] During gameplay, the eyewear opportunistically acquires HR and LVET data during naturally occurring periods of reduced head movement and physical activity, such as during timeouts, referee stoppages, substitutions, changeovers, between plays, or when the athlete is awaiting engagement. The motion detection system identifies these periods of relative inactivity, triggering the PPG system to collect high-quality waveform data without requiring the athlete to stop play or alter posture specifically for measurement.

[0262] Per-lnterval Inversion Process for Variable Sampling Cadence

[0263] Because the timing and duration of low-activity intervals vary in competitive sports, this embodiment utilizes the Per-lnterval Inversion Process to manage irregular measurement frequency. Each available measurement interval is treated as a closed segment, and the system estimates the exogenous fluid flux for that interval by comparing the observed ECV change to the model-predicted endogenous fluid redistribution. The inversion process compensates for variable data cadence by dynamically adjusting model parameters and interpolating fluid status between measurement points, enabling accurate estimation of both point-in-time ECV and integrated TBW change despite uneven sampling.

[0264] Real-Time In-Event Optimization

[0265] Any athlete participating in these intermittent-burst sports benefits from maintaining normal cardiac function and optimal hydration status throughout competition. During short pauses — timeouts, halftime, changeovers, or sideline rests — the eyewear can communicate the current ECV state to the athlete in real time via an integrated display, bone-conduction audiooutput, or wireless communication to a mobile device or sideline tablet. This information allows athletes and coaches to make immediate adjustments — such as targeted fluid intake, electrolyte supplementation, or pacing strategy — based on the actual functional circulatory capacity at that moment, rather than on generalized hydration guidelines.

[0266] Between-Game and Between-Day Recovery Guidance

[0267] Many of the above sports are played in multi-game or multi-day tournament formats. In such cases, the eyewear-based system also provides recovery optimization between matches. By continuing to compute an ECV observable proxy during post-game rest intervals and integrating these values with model-estimated TBW change, the system determines whether the athlete’s cardiovascular capability and hydration status have returned to baseline. The system further prompts the athlete to log post-game fluid intake, including the type of beverage and electrolyte content, allowing the model to account for the osmotic and oncotic impact of the chosen fluid on compartmental fluid redistribution.

[0268] Based on these measurements and inputs, the system generates a personalized recovery plan for the interval between games. This plan may specify staged rehydration volumes, electrolyte ratios, and timing to ensure that the athlete starts subsequent matches with both optimal integrated hydration status and maximal point-in-time cardiovascular capability.

[0269] Sideline Point-of-Care Scanner for Team Sports (Direct ECV-Proxy, No Prior Baseline)

[0270] In heat-exposed, high-intensity team practices (e.g., American football), coaching and medical staff desire a rapid, stand-alone assessment to identify players trending toward functional hypovolemia and heat risk. In this embodiment, hydration status is assessed directly from the ECV observable proxy without estimating total body water or solving an inverse problem. Because teams often manage pre-practice hydration routines, it is reasonable (but not required) to assume the squad begins practice near euvolemia; the system's objective is to detect players who become at-risk during practice and to prompt timely intervention.

[0271] The device as shown in FIG. 21 , is a portable, battery-powered point-of-care scanner includes an optical sensor system (not shown) configured to receive a finger (2101) in the optical sample area with ambient light protection. The system has a display (2102) for presenting measurement results and risk classification. The device incorporates the necessary signal processing, motion detection, and data storage components (2103) to perform real-time ECV proxy calculations and baseline comparisons.

[0272] The unit can operate fully offline and may optionally synchronize summary data to a sideline tablet or secure cloud service for team management purposes.

[0273] The athlete places a finger into the measurement area (2101 ) while standing in a relaxed position. The device acquires a short time window (approximately 10-20 seconds) of plethysmographic data. The plethysmographic data is reviewed for quality prior to providing a measurement result.

[0274] Accepted measurement segments undergo real-time processing: the signal is detrended and filtered, individual heartbeats are detected to extract interbeat intervals (heart rate), and aortic-valve opening and closure events are identified from the plethysmographic waveform to compute left ventricular ejection time (LVET). From the processed LVET and heart rate measurements, the device computes the ECV observable proxy using established normalization algorithms.

[0275] Because no prior per-subject baseline may be available in team sports scenarios, the device automatically generates a baseline from one or more of the following approaches:

[0276] A matched-cohort baseline is selected by nearest-neighbor or k-nearest-neighbor search in a feature space containing demographic and contextual parameters including age, sex, height / weight or BMI, position group / workload classification, heat acclimatization status, measurement device site, and body posture during measurement.

[0277] A physiology-matched Baseline is selected based on the similarity of systolic time intervals and pulse morphology characteristics measured during the current capture session.

[0278] A simulated baseline generation is a process that creates an artificial or simulated baseline by using multiple physiological characteristics measured during the current capture session, including systolic time intervals, pulse morphology features, and other readily observable parameters, to generate a personalized simulated baseline specific to that subject's current physiological state.

[0279] The baseline-generation engine can blend multiple approaches using weighted combinations and implement fallback protocols to conservative cohort baselines when confidence metrics are insufficient. If conditions permit during subsequent measurement sessions, the system may store subject-specific euvolemic baselines for enhanced accuracy in future assessments.

[0280] The unit can operate fully offline and may optionally synchronize summary data to a sideline tablet or secure cloud service for team management.

[0281] Alternative Modeling Approaches

[0282] In certain embodiments, the determination of total body water (TBW) changes, hydration status, and related physiological parameters is performed using predictive models that may operate through direct mapping approaches or intermediate physiological modeling approaches. While one class of embodiments employs physiologically-based models that calculate intermediate parameters such as effective circulating volume (ECV) before determining TBW, alternative embodiments employ machine learning, artificial intelligence, or statistical models that directly predict TBW changes and hydration status from sensor measurements without explicit calculation of intermediate physiological parameters.

[0283] In some embodiments, the predictive model is configured to bypass intermediate physiological calculations and directly map sensor inputs to target outputs. These sensor inputs include but are not limited to left ventricular ejection time (LVET), heart rate (HR), respiratoryrate, skin temperature, motion data, electrodermal activity, and environmental measurements. The target outputs may comprise total body water absolute values and changes, hydration status classifications such as euhydrated, mildly dehydrated, or severely dehydrated states, fluid loss rates and cumulative fluid deficits, personalized hydration recommendations and timing, performance impact predictions related to hydration status, and recovery time estimates for rehydration.

[0284] In some example embodiments, the model is configured for temporal sequence processing, wherein predictions depend on current measurement inputs from multiple sensor modalities, historical measurement trajectories over configurable time windows ranging from minutes to hours to days, contextual environmental data including ambient temperature, humidity, and altitude, individual baseline patterns established through continuous monitoring, and activity-specific physiological responses learned from user behavior patterns. This temporal dependency enables the system to capture dynamic physiological processes and adaptation responses, distinguish between acute and chronic hydration changes, predict future hydration needs based on activity patterns and environmental forecasts, and adapt predictions to individual physiological variability and training status.

[0285] The predictive models may comprise various neural network architectures optimized for physiological signal processing. Sequential neural networks include recurrent neural networks (RNNs) with specialized architectures for physiological signal processing, long short-term memory (LSTM) networks optimized for long-term physiological dependency modeling with multi-layer configurations incorporating attention mechanisms for sensor fusion, bidirectional processing for incorporating future context windows, and stacked architectures for hierarchical feature learning. Gated recurrent units (GRU) may be employed with computational efficiency optimizations for real-time deployment, and attention-based RNNs may automatically weight the importance of different time steps and sensor inputs.

[0286] Transformer-based architectures represent another class of suitable models, including time series transformers with physiological signal-specific attention mechanisms, multi-head attention systems for capturing relationships between different sensor modalities, positional encoding adapted for irregular sampling rates common in wearable devices, encoder-decoder transformers for sequence-to-sequence hydration prediction, and vision transformers adapted for waveform pattern recognition in continuous physiological signals.

[0287] Convolutional neural network approaches may include temporal convolutional networks (TCNs) with dilated convolutions for capturing multi-scale temporal patterns, one-dimensional CNN architectures for feature extraction from raw physiological waveforms, ConvLSTM hybrid models combining spatial pattern recognition with temporal sequence modeling, and ResNet- inspired architectures with skip connections for deep physiological signal processing.

[0288] Advanced statistical and probabilistic models suitable for implementation include state space and sequential models such as Kalman filters and extended Kalman filters for noise-robust state estimation with uncertainty quantification, particle filters for non-linear, nonGaussian physiological state tracking, hidden Markov models with physiological state definitions for discrete hydration status modeling, Gaussian process regression for uncertainty-aware predictions with limited training data, and Bayesian neural networks providing prediction uncertainty estimates for clinical decision support.

[0289] Autoregressive and time series models may comprise vector autoregression (VAR) for jointly modeling multiple correlated physiological signals, ARIMA and SARIMA models with seasonal components for circadian and activity-based patterns, structural time series models with explicit trend, seasonal, and irregular components, Prophet-style decomposition models adapted for physiological forecasting, and neural ordinary differential equations (NODEs) for continuous-time physiological modeling.

[0290] Ensemble and hybrid approaches may incorporate multi-model architectures including ensemble methods combining multiple neural network architectures with uncertainty quantification, boosting and bagging approaches for improving prediction robustness across diverse physiological conditions, meta-learning frameworks for rapid adaptation to new users with limited calibration data, and multi-task learning systems simultaneously predicting TBW, hydration status, and performance metrics.

[0291] Physics-informed neural networks represent a specialized class of hybrid models incorporating physiological constraints as regularization terms, neural ODEs with physiological differential equation constraints, graph neural networks modeling physiological system interactions, and attention mechanisms that can optionally focus on physiologically-relevant intermediate calculations when available.

[0292] The Al-based predictive models may be configured for real-time processing through on- device processing using edge-optimized neural network architectures with quantization and pruning for low-power operation, streaming inference with sliding window processing for continuous prediction updates, and adaptive sampling adjusting sensor collection frequency based on prediction confidence and physiological state. Cloud-hybrid processing configurations may include federated learning for model improvement while preserving user privacy, cloudbased ensemble inference combining multiple model predictions, and distributed processing for computationally intensive transformer or large ensemble models.

[0293] Personalization and adaptation capabilities include individual calibration through transfer learning from population models to individual-specific models, few-shot learning for rapid adaptation to new users, continual learning with catastrophic forgetting mitigation for long-term user adaptation, and domain adaptation for different activity types, environmental conditions, or measurement devices. Contextual adaptation may incorporate multi-domain models adapting predictions based on activity type, environmental conditions, and user demographics, seasonal adaptation accounting for long-term physiological adaptation and training status changes, andreal-time model selection choosing optimal prediction models based on current context and data quality.

[0294] Uncertainty quantification and clinical integration features may include prediction reliability through Bayesian uncertainty estimation providing confidence intervals for all predictions, out-of-distribution detection identifying when current conditions exceed training data coverage, model ensemble disagreement as a proxy for prediction uncertainty, and calibrated probability outputs for clinical decision support integration. Clinical decision support capabilities may comprise risk stratification identifying users at high risk for dehydration-related performance or health impacts, personalized intervention timing optimizing hydration reminder frequency and urgency, integration with electronic health records and clinical monitoring systems, and regulatory compliance with medical device standards for Al-based diagnostic tools.

[0295] The Al models may be trained and validated using multi-modal datasets including laboratory-controlled hydration studies with gold-standard TBW measurements, field studies with real-world activity and environmental conditions, synthetic data generation using physiological simulation models, and cross-population validation ensuring model generalization across demographic groups. Validation methodologies may include time-series cross-validation respecting temporal dependencies in physiological data, leave-one-subject-out validation for assessing individual generalization, external validation on independent datasets and different sensor hardware, and clinical validation against established hydration assessment methods.

[0296] This comprehensive modeling approach ensures coverage of the full spectrum of Al- driven predictive modeling strategies, from direct sensor-to-TBW mapping to sophisticated ensemble methods, enabling robust implementation flexibility across diverse technological platforms while maintaining adaptability for future developments in artificial intelligence and machine learning methodologies.AI-Enhanced Predictive Modeling via Continuous LVET and HR

[0297] The continuous monitoring of left ventricular ejection time (LVET) and heart rate (HR) provides a dynamic and integrated approach to hydration assessment, offering real-time insights into both effective circulating volume (ECV) and total body water (TBW) without requiring independent corrections. By continuously tracking LVET and HR, the system captures not only the current state of the ECV but also reflects real-time physiological changes and external perturbations, such as exercise intensity, heat stress, and other conditions that can lead to dehydration. The HR serves as an effective proxy for these external stressors, making it possible to observe and quantify their impact on hydration status. When combined with advanced Al learning models and physiological models, this continuous data stream allows for sophisticated predictive analytics. Al models can leverage historical and real-time data to enhance predictions about fluid balance and TBW shifts, adapting to individual physiological responses and environmental factors. This integration of continuous LVET and HRmeasurements with Al-driven predictive models ensures a comprehensive and adaptive hydration monitoring system, capable of providing precise, real-time hydration assessments and forecasts that are responsive to both internal physiological changes and external influences.

[0298] Off-Device and Hybrid Processing Embodiments.

[0299] In some embodiments, the estimator, the ECV-centric Hydration Model (ECHM), and the inverse solver execute on a remote computing service. A wearable or client device performs signal acquisition and local quality gating to produce an ECV observable proxy (and optionally summary features), transmits these data over a network (e.g., Bluetooth— >phone->cellular / Wi- Fi) to the remote computing service, and receives results including time-resolved exogenous fluid flux and cumulative TBW change. In hybrid embodiments, the client computes the ECV proxy and runs ECHM opportunistically on-device, but falls back to the remote service when local resources or data density are insufficient. To reduce bandwidth and protect privacy, the system may transmit derived features or the ECV proxy rather than raw waveforms. Communications may be encrypted and results cached for offline display.

[0300] Those skilled in the art will recognize that the present invention can be manifested in a variety of forms other than the specific embodiments described and contemplated herein. Accordingly, departures in form and detail can be made without departing from the scope and spirit of the present invention as described in the appended claims. While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims. The disclosed embodiments can be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments can also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which can be read and executed by one or more processors. A machine-readable storage medium can be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).

Claims

ClaimsWhat is claimed is:

1. A hydration monitoring system, comprising:(a) an optical sensor system configured to acquire plethysmographic data from a subject;(b) an analysis module configured to determine from the plethysmographic data (i) left ventricular ejection time (LVET) as a time interval between aortic-valve opening and aortic-valve closure and (ii) an interbeat interval;(c) an estimator configured to compute an effective circulating volume (ECV) observable proxy from the LVET and interbeat interval;(d) an ECV-centric Hydration Model (ECHM) executable by a processor and configured to:(i) model endogenous fluid fluxes between intravascular and extravascular compartments based at least in part on time-varying ECV proxy values; and(ii) estimate exogenous fluid fluxes crossing a body-environment boundary as an information-driven forcing function that, when applied to a compartment model, minimizes a mismatch between predicted and observed ECV proxy trajectories;(e) a calculator configured to integrate the estimated exogenous fluid fluxes over time to determine changes in total body water (TBW) relative to a baseline; and(f) an output interface configured to present hydration status information comprising at least one of: the ECV proxy, modeled endogenous fluxes, estimated exogenous fluxes, TBW change, or guidance for fluid replacement.

2. The system of claim 1 , wherein the ECV observable proxy is posture-sensitive, exhibiting a reproducible, directionally consistent change within 10-60 seconds of a supine-to- stand transition.

3. A method for assessing hydration status, comprising:(a) acquiring plethysmographic data;(b) determining LVET and an interbeat interval from the data;(c) computing an ECV observable proxy from LVET and interbeat interval;(d) applying an ECV-Centric hydration model (ECHM) that (i) models endogenous fluid fluxes and (ii) estimates exogenous fluid fluxes as an information-driven forcing function consistent with the observed ECV proxy;(e) integrating the estimated exogenous fluxes to determine TBW change relative to a baseline; and(f) outputting at least one of ECV proxy, TBW change, or fluid-intake guidance.

4. A method of determining changes in total body water (TBW), comprising:(a) acquiring plethysmographic data from a subject using an optical sensor system and deriving one or more systolic time intervals representing durations within and between predetermined cardiac cycle events;(b) computing, from the systolic time intervals, an ECV observable proxy that constitutes a functional assessment of circulatory adequacy and is correlated with but distinct from a static physical compartment volume;(c) executing an ECV-centric Hydration Model (ECHM) that models endogenous fluid fluxes based at least in part on the ECV proxy;(d) determining an information-driven forcing function — representing exogenous fluid flux — on a per-measurement-interval basis by solving an inverse problem such that application of the forcing function to the ECV-centric Hydration Model (ECHM) minimizes a mismatch between model-predicted and observed ECV proxy values;(e) computing TBW change at a current time point as the time integral of the estimated exogenous fluxes from a baseline time point; and(f) outputting to a user interface the TBW change and guidance for hydration status and cardiac-efficiency optimization.

5. The method of claim 3 or 4, wherein estimating the information-driven forcing function comprises assuming a constant exogenous fluid rate within each measurement interval and selecting that rate by minimizing a difference between a model-predicted ECV proxy at the end of the interval and the observed ECV proxy.

6. The method of claim 3 or 4, wherein when the elapsed time between adjacent plethysmographic samples that satisfy a signal-quality threshold exceeds a threshold, the system subdivides the elapsed time into sub-intervals of about ten minutes, assumes a constant exogenous fluid rate within each sub-interval, and selects the rates by minimizing a difference between model-predicted and observed ECV observable proxy values at the next accepted sample, thereby reducing sensitivity to irregular sampling.

7. A system for recovering a time-resolved exogenous fluid flux from cardiovascular measurements, comprising:(a) an input configured to receive a time series of effective circulating volume (ECV) observable proxy values;(b) a compartment model comprising at least a circulating volume and an interstitial compartment with inter-compartment conductances;(c) an inverse solver configured, for each measurement interval, to assume a constant exogenous fluid rate over the interval and to select the rate by minimizing a discrepancy between a model-predicted ECV observable proxy at an interval endpoint and an observed ECV observable proxy; and(d) an output configured to provide the time-resolved exogenous fluid flux and a cumulative change in total body water (TBW).

8. A method for time-series assessment of hydration status, comprising:(a) receiving a time-ordered stream of measurements indicative of an effective circulating volume (ECV) observable proxy;(b) initializing a state estimate representing at least a circulating-volume compartment and one or more extra-vascular compartments;(c) for each measurement interval having an elapsed time, executing a sequential update that:(i) propagates an immediately prior state estimate through a physiological compartment model to obtain a predicted state at an interval endpoint;(ii) determines an exogenous fluid flux by reducing a discrepancy between a model-predicted ECV proxy and the measured ECV proxy at the interval endpoint, subject to physiological constraints; and(iii) conditions the predicted state on the measured ECV proxy to produce a posterior state estimate; thereby generating a sequence of estimates in which each estimate depends on at least the immediately prior estimate and the current accepted measurement;(d) integrating the determined exogenous fluid flux over time to compute a change in total body water (TBW) relative to a baseline; and(e) outputting hydration status information, including at least one of: the ECV proxy, modeled endogenous fluxes, the determined exogenous flux, the change in TBW, or fluid-intake guidance.

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