IL305118B2

IL305118B2 - Perspiration sensor data analysis

Info

Publication number: IL305118B2
Application number: IL305118A
Authority: IL
Inventors: Re’em Hemi; Genut Menachem
Original assignee: Yopi Tech R& D Ltd; Re’em Hemi; Genut Menachem
Priority date: 2023-08-10
Filing date: 2023-08-10
Publication date: 2024-11-01
Also published as: IL305118B1; WO2025032589A1; IL305118A

Description

PERSPIRATION SENSOR DATA ANALYSIS FIELD OF THE INVENTION id="p-1" id="p-1" id="p-1"

[0001] The present invention relates generally to the field of external measurement of body fluids or tissues and associated medical conditions.

BACKGROUND id="p-2" id="p-2" id="p-2"

[0002] The popularity of wearable biosensor devices continues to expand, as these devices have come to play a major role in the medical and sports monitoring industries. These fitness tracking devices come in many shapes and forms. They can measure heartbeat, steps climbed and sleep parameters. They can also connect data to applications for goal setting, diet tracking, and other health issues. id="p-3" id="p-3" id="p-3"

[0003] Many athletes use wearable technologies to optimize their performance and to collect data about their training. Additionally, people on a diet use such devices to track their energy consumption (e.g. calories burned) as well as the caloric intake. However, while wearable technology has been around almost a decade, and wearable technology has rudimentary biometric data. id="p-4" id="p-4" id="p-4"

[0004] Often such devices are linked to smartphones or are designed to incorporate internal apps, such as smartwatch applications, that provide health and fitness-related feedback to users. Health and fitness tracking devices may monitor basic physiological parameters such as heartbeat, steps, sleep phases, etc. id="p-5" id="p-5" id="p-5"

[0005] US Patent Publication 2018/0263539, to Javey, et al., describes that sodium in sweat [Na+] can potentially serve as an important biomarker for dehydration monitoring.

According to Javey, a wearable device may be configured to determine, from sensor measurements, the likelihood or presence of wearer dehydration, hyponatremia, hypokalemia, muscle cramps, ischemia, and/or pressure ulcers, and may provide corresponding alerts and reports. id="p-6" id="p-6" id="p-6"

[0006] contagious id="p-7" id="p-7" id="p-7"

[0007] A high blood serum level of potassium has been implicated as the cause of many disease risks and physiological disorders, and the potassium is known to change during unstable cardiovascular conditions. Another physiological parameter with important health ramifications is oxygen consumption (VO2), also referred to as oxygen uptake, and particularly peak VO2 during physical activity, such as training exercises. As described in the article, (JACC: Heart Failure, Vol. 4, no. 8, 2016), by Malhotra, et al., VO2, as measured during cardiopulmonary exercise testing (CPET), indicates functional capacity. Parameters of carbon dioxide output (VCO2) and ventilation (VE), together with VO2, can also indicate maladaptive responses during exercise.

SUMMARY id="p-8" id="p-8" id="p-8"

[0008] Embodiments of the present invention provide systems and methods for generating measures of user health. Providing a user with real-time feedback of physiological parameters during exercise can improve user wellness and reduce health risks.

BRIEF DESCRIPTION OF DRAWINGS id="p-9" id="p-9" id="p-9"

[0009] For a better understanding of various embodiments of the invention and to show how the same may be carried into effect, reference is made, by way of example, to the accompanying drawings. Structural details of the invention are shown to provide a fundamental understanding of the invention, the description, taken with the drawings, making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. In the figures: id="p-10" id="p-10" id="p-10"

[0010] Fig. 1is a schematic diagram of a system for generating and reporting measures of user health, according to embodiments of the present invention; id="p-11" id="p-11" id="p-11"

[0011] Fig. 2is a block diagram of components of a system, implementing a process for generating and reporting measures of user health, according to embodiments of the present invention; id="p-12" id="p-12" id="p-12"

[0012] Fig. 3is a flow chart of a process for generating and reporting measures of user health, according to embodiments of the present invention; id="p-13" id="p-13" id="p-13"

[0013] Fig. 4 is a schematic diagram of a neural network model correlating sensor data with physiological parameters, according to embodiments of the present invention; id="p-14" id="p-14" id="p-14"

[0014] Fig. 5 is a graph showing a correlation between sensor data of potassium electrolyte in sweat with oxygen consumption (VO 2), over time, according to embodiments of the present invention; and, id="p-15" id="p-15" id="p-15"

[0015] Figs. 6A and 6Bare graphs showing oxygen consumption (VO2) during exercise, over a period of multiple breaths by a user, showing, respectively, VO2 measured and VO2 predicted by a biomarker correlation model.

DETAILED DESCRIPTION id="p-16" id="p-16" id="p-16"

[0016] Embodiments of the present invention provide systems and methods for generating and reporting measures of user health. The measures of user health may also be applied to generate wellness recommendations. id="p-17" id="p-17" id="p-17"

[0017] Fig. 1 is a schematic diagram of a system 100 for generating and reporting measures of user health, according to embodiments of the present invention. System 100 includes a wearable, sensor device 102 , which may include skin sensors for measuring components of perspiration, as well as additional sensors that may be both perspiration and non-perspiration sensors. Sensor device 102 may be configured as multiple wearable devices, which may be wearable as an at that are exposed to skin perspiration. id="p-18" id="p-18" id="p-18"

[0018] Sensors of sensor device 102 include at least one sensor for measuring an electrolyte component of perspiration that is either potassium (i.e., K+) or sodium (i.e., Na+), or both. That is, the sensors of device 102 may include electrolyte sensors for both potassium and sodium, and may also include one or more additional electrolyte sensors, such as sensors for id="p-19" id="p-19" id="p-19"

[0019] sensors of device 102 may also include sensors for detecting levels of metabolites such as glucose, lactate, ethanol, and uric acid. Additionally, or alternatively, device 102 may include sensors for detecting additional perspiration components, such as zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, a cytokine, a hormone or hormone precursor. Device 102 may also include at least one non- perspiration sensor, such as a heart rate sensor, a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor. id="p-20" id="p-20" id="p-20"

[0020] As described further hereinbelow, a processor, either incorporated with sensor device 102 or incorporated with a mobile computing device 104 (which may be, for example, a smartphone or smartwatch), receives the signals generated by the sensors of device 102 and processes the signals to determine levels of the perspiration components indicated by the signals, as well as physiological parameters correlated with the signals. In some embodiments the processor is also configured to present indicators of the physiological parameters to the user, typically on a screen of mobile computing device 104 . Alternatively, or additionally, the processor may send the physiological parameters to a remote server 106 for further processing, for example, to record a history of a information that can be correlated with further health parameters, including wellness parameters. In further embodiments, a biomarker correlation model and a wellness correlation model, described further hereinbelow, are implemented by the server, where the physiological parameters, and the wellness correlation model is trained to correlate the physiological parameters with wellness guidelines. id="p-21" id="p-21" id="p-21"

[0021] Fig. 2is a block diagram of the system 100 , showing components of the sensor device 102 and of the mobile computing device 104 . The sensor device 102 includes perspiration sensors 202 potassium and sodium. Additional perspiration sensors 202 may include electrolyte sensors, such as sensors for id="p-22" id="p-22" id="p-22"

[0022] sensors of device 102 may also include sensors for detecting levels of metabolites such as glucose, lactate, ethanol, and uric acid. Additionally, or alternatively, device 102 may include sensors for detecting additional perspiration components, such as zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, or a cytokine. id="p-23" id="p-23" id="p-23"

[0023] Device 102 may also include at least one non-perspiration sensor 204 , such as a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor. id="p-24" id="p-24" id="p-24"

[0024] Signals from the sensors, typically generated as analog voltages, may be acquired at a sensor processor 206 , which may use transmission protocols known in the art, such as Bluetooth or radio-frequency identification (RFID) transmission protocols, to transmit the signals to the mobile computing device 104 . id="p-25" id="p-25" id="p-25"

[0025] In some embodiments, sensor processor 206may also execute applications stored in the processor memory, such as a biomarker correlation model 220a and a wellness correlation model 230a . Alternatively, these applications may be executed by a mobile device processor 210 , these applications in the alternative implementation being indicated as biomarker correlation model 220b and a wellness correlation model 230b . (Hereinbelow, the biomarker correlation model of the alternative implementations is referred to as biomarker correlation model 220a/b , and the alternatives of the wellness correlation model are referred to as the wellness correlation model 230a/b .) id="p-26" id="p-26" id="p-26"

[0026] The biomarker correlation model 220a/b may be trained, using multiple data instances, that typically includes multiple physiological parameters. Input data includes the signals from the perspiration sensors, the signals being indicative of levels of the respective perspiration components. Input data may also include the signals from the non-perspiration sensors, indicative of their respective biomarker levels.

Input data may also include user specific data 222a/b , such as user age, weight, gender, and height. The system may also be configured to continuously update the biomarker correlation model as new user-specific data is obtained. Training of the biomarker correlation model may be based on known machine learning (i.e., artificial intelligence) methods. The model may also be trained to provide real-time anomaly detection, to identify and notify a user of abnormal physiological parameters. id="p-27" id="p-27" id="p-27"

[0027] The input data are typically correlated by the biomarker correlation model with at least two physiological parameters that are particularly relevant during athletic training, these two physiological parameters being measures of oxygen consumption (VO2) and carbon dioxide elimination (VCO2). Additional physiological parameters that may be correlated by the biomarker correlation model may include: calories burned, carbohydrates, fats and proteins burned, potassium blood concentration, body loss of sodium, lactic acid accumulation, glucose blood concentration, insulin blood concentration, bone health, stress level, infection likelihood (e.g., COVID-19), likelihood of kidney stones, likelihood of type 2 diabetes, likelihood of congestive heart failure, heart attack risk, hypertension risk, stroke risk, likelihood of adrenal insufficiency, heart rate variability (HRV), and risk of liver disease. id="p-28" id="p-28" id="p-28"

[0028] The sensor processor may also be configured to calculate a level of respiratory exchange ratio (RER) from the ratio VCO2/VO2. In further embodiments, a subset of the biomarker correlation model is a heart risk correlation model trained to correlate RER and VO2 levels with levels of risk of coronary artery blockage. The processor may apply the values of VCO2 and VO2 values to calculate RER, which together with the VO2 value (i.e., level) to the heart risk correlation model to estimate a risk of coronary artery blockage. id="p-29" id="p-29" id="p-29"

[0029] The wellness correlation model 230a/b , also referred to herein as a wellness model, is typically trained to correlate the physiological parameters with health and wellness guidelines. The correlated health and wellness guidelines may include, for example, warning alerts. Alerts may indicate that certain physiological parameters predicted by the biomarker correlation model indicate that the user is at risk. The alerts may also make recommendations regarding actions that a user should take immediately, which may include measures such as: reducing an activity level, resting, ingesting carbohydrates, and/or drinking water. The health and wellness guidelines may also include exercise recommendations for the user to change a workout type and/or an exercise form. The health and wellness guidelines may also include nutrition recommendations that include, for example: food to consume during a current training exercise; food to consume prior to and/or following specific exercises; and/or a recommended caloric intake. id="p-30" id="p-30" id="p-30"

[0030] After a correlated health and wellness guideline is determined, it may be transmitted to the user interface as a further indicator of a user physiological parameters. The input to the wellness model, like the biomarker model, may also include accumulated data, such as trend data, which may be stored on the mobile computing device or at the server (i.e., a remote computing device, such as a cloud computing device). In some embodiments, the wellness model is implemented on the server, which receives the input of estimated physiological parameters from the mobile computing device. id="p-31" id="p-31" id="p-31"

[0031] As described above, a user may use the sensor device while exercising, with sensor measurements being made in real time, so that the user can receive real-time feedback.

At the mobile computing device 104 , a transmission from the sensor device, typically wireless, may be received by the mobile device processor 210 . When the sensor processor 206is not configured to execute the applications described above, the sensor processor may simply transmit sensor data to the mobile device processor and the applications (i.e., the models) may be executed at the mobile device processor. id="p-32" id="p-32" id="p-32"

[0032] Typically, the mobile device processor transmits one or more indicators of the values of the multiple physiological parameters to a user interface, such as the computing device screen 224 . The presentation of the physiological parameters on the screen, also ensor data is being received. - at intervals of up to 15 minutes. id="p-33" id="p-33" id="p-33"

[0033] The physiological parameters presented as user feedback may also include the calculated level of RER. The physiological parameters may also be stored, for example in memory storage of the mobile computing device or of the remote server, for subsequent analysis and reporting to the user. The user specific data 222a/b are also typically stored in such memory storage, and may be entered by the user from the computing device screen 224 , or may be accessed by the mobile computing device from remote sources, such as the remote server 106 . The mobile computing device 104 also typically includes a data storage module for securely storing and managing user-specific data and physiological measurements. In addition, data encryption and secure communication protocols are typically employed to protect user privacy and data integrity. id="p-34" id="p-34" id="p-34"

[0034] Fig. 3is a flow chart of key steps of a process 300 for generating and reporting measures of user health, according to embodiments of the present invention. At a first step 312 physiological parameters. The input data includes perspiration sensor data, user specific data, and optionally non-perspiration sensor data. id="p-35" id="p-35" id="p-35"

[0035] In operation, typically while a user is exercising, the mobile computing device acquires data from the perspiration sensors, and optionally from non-perspiration sensors, at a step 314 . Next, at a step 316 , the acquired data, together with stored, user specific data, is applied to the biomarker correlation model to determine (i.e., predict/estimate) values of physiological parameters. As described above, the physiological parameters may include many biomarkers, but typically include at least levels of VCO and VO. id="p-36" id="p-36" id="p-36"

[0036] At a step 318 , the processor may optionally correlate the physiological parameters with wellness guidelines, as described above. id="p-37" id="p-37" id="p-37"

[0037] At a step 320 , The processor then transmits the indicators of physiological parameters, optionally including the predicted wellness guidelines, to a user interface to provide real-time user feedback. Fig. 4 is a schematic diagram of a biomarker correlation model 400 , configured as a neural network. The model correlates sensor data with physiological parameters, according to embodiments of the present invention. Embodiments of the present invention include multiple correlation models, as described above, which may be configured by methods known in the art, such as by neural networks or random forest algorithms. The biomarker correlation model is typically trained using a large dataset encomp dataset acquired from at least 10, and more commonly several hundred users, or more. A d at least two different classifications of age and/or race. id="p-38" id="p-38" id="p-38"

[0038] In the exemplary biomarker correlation model 400 , two incremental stages of a neural network, indicated as Tn and Tn+1 are shown. Both stages have identical layers, as follows. id="p-39" id="p-39" id="p-39"

[0039] The model accepts input 402 , typically including: input from one or more sodium potassium channels (similarly, there are typically two); additionally, in some embodiments, additional sensor input, as described above; and user specific input, such as age, weight, and gender. Additional user specific input may include factors such as race, height, and other personal data known to correlate with the target physiological parameters. Output, generated from the output layer 404 , includes the physiological parameters that the biomarker correlation model is trained to associate with the input, such as VO and VCO. Additional output may include additional physiological parameters, as described above. (Health and wellness guidelines are typically output by a separately trained model.) id="p-40" id="p-40" id="p-40"

[0040] As indicated by model 400 short- 406 input being derived at each incremental time stage by the LSTM layer of the previous time stage. id="p-41" id="p-41" id="p-41"

[0041] Output of the LSTM layers is fed to a feed forward layer 408 , which generates the output. Between the LSTM and the feed forward layer, there may also be additional n 410 . The softplus layer applies the activation function Y = log(1 + ex), ensuring that the output is always positive. (Note that the function Y = log(1 + ex) is a smooth continuous version of the neural network reluLayer.) Testing of the exemplary model on real time sensor data generated values for predicted VO2 closely aligned with values measured by breathing equipment, indicating the utility of the exemplary model. id="p-42" id="p-42" id="p-42"

[0042] Fig. 5 is a graph showing a correlation between sensor data of potassium electrolyte (referred to herein as potassium) in sweat with oxygen consumption (VO2), over time, according to embodiments of the present invention. As indicated, VO increases as measured potassium decreases, indicating an inverse correlation that is incorporated into the correlation model. id="p-43" id="p-43" id="p-43"

[0043] Figs. 6A and 6Bare graphs showing oxygen consumption (VO2) during exercise, over a period of multiple breaths by a user, showing, respectively, VO2 measured and VO2 predicted by the exemplary biomarker correlation model shown in Fig. 4 , as described above.

As shown, the predicted VO2 is closely aligned with the actual value, indicating the utility of the exemplary model. id="p-44" id="p-44" id="p-44"

[0044] It is to be understood that processing elements shown or described herein are preferably implemented by one or more computers in computer hardware and/or in computer software embodied in a non-transitory, computer-readable medium in accordance with conventional techniques, such as employing a computer processor, a memory, I/O devices, and a network interface, coupled via a computer bus or alternate connection arrangement. id="p-45" id="p-45" id="p-45"

[0045] include any processing device, such as, for example, one that includes a CPU (central processing unit) and/or other processing circuitry (e.g., GPUs), and may refer to more than one processing device. Various elements associated with a processing device may be shared by other processing devices. id="p-46" id="p-46" id="p-46"

[0046] with a processor or CPU, such as, for example, RAM, ROM, a fixed memory device (e.g., hard drive), a removable memory device (e.g., diskette, tapes), flash memory, etc. Such memory may be considered a computer readable storage medium. id="p-47" id="p-47" id="p-47"

[0047] more input devices (e.g., keyboard, mouse, scanner, HUD, etc.) for entering data to the processing unit, and/or one or more output devices (e.g., speaker, display, printer, HUD, AR, VR, etc.) for presenting results associated with the processing unit. id="p-48" id="p-48" id="p-48"

[0048] Embodiments of the invention may include a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the invention. id="p-49" id="p-49" id="p-49"

[0049] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), Blue-Ray, magnetic tape, Holographic Memory, a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. id="p-50" id="p-50" id="p-50"

[0050] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. A network adapter card or network interface in each computing/processing device may receive computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. id="p-51" id="p-51" id="p-51"

[0051] Computer readable program instructions for carrying out operations of the invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the invention. id="p-52" id="p-52" id="p-52"

[0052] Where aspects of the invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention, it will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. id="p-53" id="p-53" id="p-53"

[0053] These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the function/steps specified in the flowchart and/or block diagram block.

These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/step specified in the flowchart and/or block diagram block or blocks. id="p-54" id="p-54" id="p-54"

[0054] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/steps specified in the flowchart and/or block diagram block or blocks. id="p-55" id="p-55" id="p-55"

[0055] Any flowchart and block diagrams included herein illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which may include one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order shown herein. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. id="p-56" id="p-56" id="p-56"

[0056] The descriptions of the various embodiments of the invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. id="p-57" id="p-57" id="p-57"

[0057] EXAMPLES id="p-58" id="p-58" id="p-58"

[0058] Examples of the present invention may include the following configurations. id="p-59" id="p-59" id="p-59"

[0059] Example 1 is a system for physiological analysis and feedback having multiple of levels of multiple respective perspiration components. One of the perspiration components may be potassium. In addition to the multiple sensors, the system includes one or more processors having memory including instructions that when executed implement processing steps including: a) receiving the perspiration data; b) applying to the perspiration data a biomarker correlation model, wherein the biomarker correlation model is trained to correlate input including the multiple perspiration components with output including multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO2) and carbon dioxide elimination (VCO2); c) determining, by the biomarker correlation model, values of the multiple physiological parameters including the values of VO2 and VCO2; d) calculating a level of respiratory exchange ratio (RER) from the ratio VCO2/VO2; and e) transmitting, in real time, one or more indicators of the values of the multiple physiological parameters to a user interface, wherein the indicators include the calculated level of RER. id="p-60" id="p-60" id="p-60"

[0060] Example 2 is a system including the features of example 1, and the one or more processors are further configured to execute steps of calculating a risk of coronary artery blockage by applying the calculated VO2 levels to a heart risk correlation model trained to correlate VO2 levels with levels of risk of coronary artery blockage. id="p-61" id="p-61" id="p-61"

[0061] Example 3 is a system including the features of example 1, with or without any of the features of the additional examples listed above, further including determining that the risk is above a preset threshold and transmitting an alert to the user to consult a medical care professional. id="p-62" id="p-62" id="p-62"

[0062] Example 4 is a system including the features of example 1, with or without any of the features of the additional examples listed above, and the perspiration data includes sodium, in addition to potassium. id="p-63" id="p-63" id="p-63"

[0063] Example 5 is a system including the features of example 1, with or without any of the features of the additional examples listed above, and configured to transmit the indicators to the user interface in real time . id="p-64" id="p-64" id="p-64"

[0064] Example 6 is a system including the features of example 1, with or without any of the features of the additional examples listed above, and in which the biomarker correlation model is trained to correlate the multiple perspiration components, together with user specific data, with the output including multiple physiological parameters. id="p-65" id="p-65" id="p-65"

[0065] Example 7 is a system including the features of example 6, with or without any of the features of the additional examples listed above, and the user specific data includes a id="p-66" id="p-66" id="p-66"

[0066] Example 8 is a system including the features of example 1, with or without any of the features of example 2, and the multiple sensors include two sodium sensor and two potassium sensors, and wherein the biomarker correlation model is trained on input including data from the sodium sensors and from the potassium sensors. id="p-67" id="p-67" id="p-67"

[0067] Example 9 is a system including the features of example 1, with or without any of the features of the additional examples listed above, and the multiple physiological parameters include a level of the u id="p-68" id="p-68" id="p-68"

[0068] Example 10 is a system including the features of any of the above examples, and the multiple perspiration components, in addition to potassium and sodium, include at least one electrolyte from among the electrolytes magnesium, chloride, ammonia, and calcium. id="p-69" id="p-69" id="p-69"

[0069] Example 11 is a system including the features of any of the above examples, and the multiple perspiration components include at least one metabolite from among the metabolites glucose, lactate, ethanol, and uric acid. id="p-70" id="p-70" id="p-70"

[0070] Example 12 is a system including the features of any of the above examples, and the multiple perspiration components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, or a cytokine. id="p-71" id="p-71" id="p-71"

[0071] Example 13 is a system including the features of any of the above examples, and further includes at least one non-perspiration sensor generating non-perspiration data, wherein the one or more processors are further configured to apply the perspiration and non- perspiration data, together with the user specific data, to the biomarker correlation model to determine the multiple physiological parameters, wherein the biomarker correlation model is trained to correlate the perspiration and the user specific data, together with the non- perspiration data, with the multiple physiological parameters. id="p-72" id="p-72" id="p-72"

[0072] Example 14 is a system including the features of example 13, and the at least one non-perspiration sensor is at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor. id="p-73" id="p-73" id="p-73"

[0073] Example 15 is a system including the features of any of the above examples, and the biomarker correlation model is an artificial intelligence model, such as a neural network or a random forest classifier. Example 16 is a system in which the biomarker correlation model is trained with a large dataset encompassing a diverse range of population demographics. Example 17 includes any of the above features and is configured to update the biomarker correlation model continuously as new user-specific input data is received. id="p-74" id="p-74" id="p-74"

[0074] Example 18 is a system including the features of any of the above examples, and the perspiration data is received at intervals ranging up to once per 15 minutes. id="p-75" id="p-75" id="p-75"

[0075] Example 19 is a system including the features of any of the above examples, and the user interface is a user interface of either a smartwatch or a smartphone. id="p-76" id="p-76" id="p-76"

[0076] Example 20 is a system including the features of any of the above examples, and further including a wearable device comprising at least one of the one or more processors and the multiple sensors. id="p-77" id="p-77" id="p-77"

[0077] Example 21 is a system including the features of any of the above examples, and the multiple sensors are integrated into a wearable device configured as an armband, headband, chest strap, ring, headphone, or tattoo sticker. id="p-78" id="p-78" id="p-78"

[0078] Example 22 is a system including the features of any of the above examples, and further configured to apply the values of the physiological parameters to a wellness correlation model, correlating the physiological parameters with a set of health and wellness guidelines, to determine a correlated health and wellness guideline, and according to the correlated health and wellness guideline, to provide a personalized recommendation to the user for optimizing user performance and well-being (wherein the correlated health and wellness guideline is one of the multiple indicators of the values of the multiple physiological parameters transmitted to the user interface). id="p-79" id="p-79" id="p-79"

[0079] Example 23 is a system including the features of example 22, and the correlated health and wellness guideline includes a warning alert for the user to perform at least one of: reducing an activity level, resting, ingesting carbohydrates, and drinking water. id="p-80" id="p-80" id="p-80"

[0080] Example 24 is a system including the features of example 22, and the correlated health and wellness guideline includes an exercise instruction for the user to change a workout type and/or an exercise form. id="p-81" id="p-81" id="p-81"

[0081] Example 25 is a system including the features of example 22, and the correlated health and wellness guideline includes at least one of a nutrition indicator that includes one or more of: 1) food to consume during a current training exercise, 2) food to consume prior to and/or following specific exercises, and 3) a recommended caloric intake. id="p-82" id="p-82" id="p-82"

[0082] Example 26 is a system including the features of example 1, and the one or more processors includes a distributed processing system including a local processor and a remote processor, wherein the local processor receives the perspiration data and determines the one or more physiological parameters and wherein the remote processor is configured to receive the determined values of the physiological parameters from the local processor and to apply the wellness correlation model to determine the correlated health and wellness guideline. id="p-83" id="p-83" id="p-83"

[0083] Example 27 is a method for physiological analysis and feedback implemented by one or more processors having associated memory with instructions that when executed implement steps comprising: receiving the perspiration data; applying to the perspiration data a biomarker correlation model, wherein the biomarker correlation model is trained to correlate the multiple perspiration components with multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO 2) and carbon dioxide elimination (VCO2); determining, by the correlation model, values of the multiple physiological parameters including the values of VO2 and VCO2; calculating a level of respiratory exchange ratio (RER) from the ratio VCO 2/VO2; and transmitting one or more indicators of the values of the determined physiological parameters to a user interface, wherein the indicators include the calculated level of RER. id="p-84" id="p-84" id="p-84"

[0084] Further examples of the present invention include the features of the method of example 28 and any additional features of examples 2-27.

Claims

1. A system for physiological analysis and feedback comprising: multiple sensors configured to be worn by a user on the in real time, perspiration data indicative of levels of multiple respective perspiration components, wherein at least one of the perspiration components is potassium; and one or more processors having memory including instructions that when executed implement steps of: a) receiving the perspiration data; b) applying to the perspiration data a biomarker correlation model, wherein the biomarker correlation model is trained to correlate input data including the multiple perspiration components with output including multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO 2) and carbon dioxide elimination (VCO2); c) determining, by the biomarker correlation model, values of the multiple physiological parameters including the values of VO2 and VCO2; d) calculating a level of respiratory exchange ratio (RER) from the ratio VCO2/VO2; and e) transmitting one or more indicators of the values of the multiple physiological parameters to a user interface, wherein the indicators include the calculated level of RER.

2. The system of claim 1, wherein the one or more processors are further configured to execute steps of calculating a risk of coronary artery blockage by applying the calculated VO levels to a heart risk correlation model trained to correlate VO levels with levels of risk of coronary artery blockage.

3. The system of claim 2, further comprising determining that the risk is above a preset threshold and transmitting an alert to the user to consult a medical care professional.

4. The system of claim 1, wherein the perspiration data includes sodium.

5. The system of claim 1, wherein the indicators are transmitted to the user interface in real time.

6. The system of claim 1, wherein the biomarker correlation model is trained to correlate the multiple perspiration components, together with user specific data, with the output including multiple physiological parameters.

7. The system of claim 6 , weight, and gender.

8. The system of claim 1, wherein the multiple sensors include two sodium sensor and two potassium sensors, and wherein the biomarker correlation model is trained on input including data from the two sodium sensors and from the two potassium sensors.

9. The system of claim 1, wherein the multiple physiological parameters include a level of .

10. The system of claim 1, wherein the multiple perspiration components include at least one electrolyte from among the electrolytes

11. The system of claim 1, wherein the multiple perspiration components include at least one metabolite from among the metabolites glucose, lactate, ethanol, and uric acid.

12. The system of claim 1, wherein the multiple perspiration components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, a hormone, a hormone precursor, or a cytokine.

13. The system of claim 1, further comprising at least one non-perspiration sensor generating non-perspiration data, wherein the one or more processors are further configured to apply the perspiration and non-perspiration data, together with the user specific data, to the biomarker correlation model to determine the multiple physiological parameters, wherein the biomarker correlation model is trained to correlate the perspiration and the user specific data, together with the non-perspiration data, with the multiple physiological parameters.

14. The system of claim 13 wherein the at least one non-perspiration sensor is at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, a heart rate monitor measuring heart rate variability (HRV), and a blood glucose monitor.

15. The system of claim 1, wherein the biomarker correlation model is an artificial intelligence model.

16. The system of claim 1, wherein the biomarker correlation model is trained with a large dataset encompassing a diverse range of population demographics.

17. The system of claim 1, wherein the one or more processors are further configured to update the biomarker correlation model continuously as new user-specific input data is received.

18. The system of claim 1, wherein the perspiration data is received at intervals of up to once per 15 minutes.

19. The system of claim 1, wherein the user interface is a user interface of either a smartwatch or a smartphone.

20. The system of claim 1, further comprising a wearable device comprising at least one of the one or more processors and the multiple sensors.

21. The system of claim 1, wherein the multiple sensors are integrated into a wearable device configured as an

22. The system of claim 1, wherein the one or more processors further configured to apply the values of the physiological parameters to a wellness correlation model, correlating the physiological parameters with a set of health and wellness guidelines, to determine a correlated health and wellness guideline, and, according to the correlated health and wellness guideline, to provide a personalized recommendation to the user for optimizing user performance and well-being.

23. The system of claim 22, wherein the correlated health and wellness guideline includes a warning alert for the user to perform at least one of: reducing an activity level, resting, ingesting carbohydrates, and drinking water.

24. The system of claim 22, wherein the set of health and wellness guidelines includes an exercise instruction for the user to change a workout type and/or an exercise form.

25. The system of claim 22, wherein the set of health and wellness guidelines includes at least one of a nutrition indicator that includes one or more of: 1) food to consume during a current training exercise, 2) food to consume prior to and/or following specific exercises, and 3) a recommended caloric intake.

26. The system of claim 1, wherein the one or more processors are further configured to provide at the user interface a historical analysis of a user's physiological parameters over time.

27. The system of claim 1, wherein the user interface integrates with a social network or online platform to enable data sharing and engagement with other users.

28. The system of claim 1, wherein the user interface includes audio or visual feedback to guide a user in achieving optimal physiological parameters.

29. The system of claim 1, wherein the multiple sensors are detachable and can be repositioned on different areas of the user's body.

30. The system of claim 1, further comprising a data storage module for securely storing and managing user-specific data and physiological measurements.

31. The system of claim 1, wherein the one or more processors employ data encryption and secure communication protocols to protect user privacy and data integrity.

32. The system of claim 1, wherein the one or more processors perform real-time anomaly detection to identify and notify the user of abnormal physiological patterns.

33. The system of claim 1, wherein the one or more processors comprise a distributed processor including a local processor and a remote processor, wherein the local processor receives the perspiration data and determines the one or more physiological parameters and wherein the remote processor is configured to receive the determined values of the physiological parameters from the local processor and to apply the wellness correlation model to determine the correlated health and wellness guideline.

34. A method for physiological analysis and feedback implemented by one or more processors having associated memory with instructions that when executed implement steps comprising: a) receiving perspiration data; b) applying to the perspiration data a biomarker correlation model, wherein the biomarker correlation model is trained to correlate multiple perspiration components acquired with the perspiration data, including at least a measure of potassium with multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO2) and carbon dioxide elimination (VCO2); c) determining, by the correlation model, values of the multiple physiological parameters including the values of VO2 and VCO2; d) calculating a level of respiratory exchange ratio (RER) from the ratio VCO2/VO2; and e) transmitting, in real time, one or more indicators of the values of the determined physiological parameters to a user interface, wherein the indicators include the calculated level of RER.

35. The method of claim 34, further comprising receiving non-perspiration data from at least one non-perspiration sensor, wherein the correlation model correlates the perspiration and the non-perspiration data to the multiple physiological parameters.

36. The method of claim 35, wherein receiving the non-perspiration data from at least one non-perspiration sensor comprises receiving the non-perspiration data from at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor.

37. The method of claim 34, wherein the multiple physiological parameters include a level .

38. The method of claim 34, wherein the multiple physiological parameters include a risk of coronary artery blockage.

39. The method of claim 34, further comprising determining that the risk is above a preset threshold and transmitting an alert to the user to consult a medical care professional.

40. The method of claim 34, wherein the indicators are transmitted to the user interface in real time.

41. The method of claim 34, wherein the biomarker correlation model is trained to correlate the multiple perspiration components, together with user specific data, with the output including multiple physiological parameters.

42. The method of claim 34, wherein gender.

43. The method of claim 34, wherein the multiple perspiration components include at least one electrolyte from a list of electrolytes, and wherein the list of electrolytes includes at least sodium,

44. The method of either claim 34, wherein the multiple perspiration components include at least one metabolite from among the metabolites glucose, lactate, ethanol, and uric acid.

45. The method of claim 34, wherein the multiple perspiration components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, or a cytokine.

46. The method of claim 34, wherein the steps further comprise applying to the determined values of the physiological parameters a wellness correlation model, correlating the physiological parameters with a set of health and wellness guidelines, to determine a correlated health and wellness guideline, and wherein the correlated health and wellness guideline is one of the indicators of the values of the one or more determined physiological parameters transmitted to the user interface.

47. The method of claim 46, wherein the correlated health and wellness guideline includes a warning alert for the user to perform at least one of: reducing an activity level, resting, ingesting carbohydrates, and drinking water.

48. The method of claim 46, wherein the set of health and wellness guidelines includes an exercise instruction for the user to change a workout type and/or an exercise form.

49. The method of claim 46, wherein the set of health and wellness guidelines includes at least one of a nutrition indicator that includes one or more of: 1) food to consume during a current exercise, 2) food to consume prior to and/or following specific exercises, and 3) a recommended caloric intake.

50. The method of claim 34, wherein the processor is a distributed processor including a local processor and a remote processor, wherein the local processor receives the perspiration data and determines the one or more physiological parameters and wherein the remote processor receives the determined values of the physiological parameters from the local processor and applies the wellness correlation model to determine the correlated health and wellness guideline.