Wearable devices for continuous monitoring of health parameters

Abstract

The present invention relates to a device for continuous monitoring of the health status of a patient or athlete without the need for blood sampling. In particular, the present invention relates to a wearable device including at least one sweat sensor for measuring sweat biomarkers, a sweat collection inlet arranged in the device for collecting sweat when the device is worn by a user, a microfluidic channel for transmitting the collected sweat from the inlet to the sweat sensor, at least one vital sign sensor arranged in the device for measuring a vital sign or physiological indication of the user, a sweat rate sensor for measuring the amount of collected sweat, and a processing unit configured to receive and process data provided by the sweat rate sensor, the vital sign biosensor, and the sweat rate measurement device. The present invention can be advantageously used in the sports medicine and / or sports health field for remote assessment of exercise and / or fatigue.

Description

[Technical field]

[0001] The present invention relates to medical devices, in particular wearable medical devices for clinical and sports use. [Background technology]

[0002] Blood testing is a standard clinical method indicating the most important physiological parameters allowing an accurate diagnosis of a patient's condition. In the health field, most analyses of metabolites of interest (various amino acids, sugars, carboxylic acids, fatty acids, ...) require blood sampling, which represents an obstacle to real-time monitoring in both clinical health and sports medicine.

[0003] Therefore, medical professionals working on clinical protocols and in the training and recovery of athletes have a need for devices that allow non-invasive, continuous and remote monitoring of biomarkers using sweat. In this respect, the use of blood is the standard, but analyzing sweat poses a challenge to obtain bioequivalence between both measurement methods.

[0004] The current clinical trend is to be minimally invasive and to combine non-invasive measurement techniques of both skin and biological fluids (urine, saliva, sweat, etc.).

[0005] There are different methods for measuring sweat volume and rate, including humidity sensors, calorimetry and volumetric measurements. Humidity and calorimetry sensors are highly sensitive but require a high degree of complexity, limiting their incorporation into portable devices. In contrast, volumetric measurements allow simple and direct monitoring by measuring the volume sampled in a controlled time. There are also visual monitoring options, which require little instrumentation and are therefore simple in a research environment. However, for continuous monitoring applications, impedance sensing (electrical resistance decreases proportional to the sample volume) is preferred, as it allows autonomous and real-time measurements.

[0006] Furthermore, there are various types of wearable devices that are worn by a user to continuously monitor daily activities such as walking, running, etc., without undue interruption or restriction. These wearable devices include electronic devices, physiological sensors configured to sense certain physiological parameters of the wearer, such as heart rate, as well as motion sensors, GPS, etc.

[0007] These known devices generally have similar configurations, are configured to monitor only one physiological parameter of the wearer, and have limited capabilities in terms of electronic processing and communication capabilities.

[0008] Thus, there is an unmet medical need for non-invasive devices and methods that measure the concentration of biomarkers in blood in an inexpensive, rapid and accurate manner. Summary of the Invention

[0009] An object of the present invention is to provide a multi-sensor device and method for remotely and continuously monitoring the health condition of patients or athletes in real time without the need for blood sampling.

[0010] The present invention can be advantageously used in the field of sports medicine and / or sports health for remote exercise and / or fatigue assessment.

[0011] The present invention, as defined in the attached independent claims, overcomes the shortcomings of the prior art by providing a wearable device that incorporates different sensors to obtain simultaneous measurements of biomarkers in sweat (metabolites, ions or amino acids, etc.) and key physiological parameters (heart and respiratory rate, blood pressure, etc.) for measuring blood lactate concentration non-invasively, continuously, in real time and remotely.

[0012] More specifically, a first aspect of the present invention provides a wearable device for continuously monitoring a health parameter of a user, the device comprising at least one sweat sensor for measuring sweat biomarkers, a sweat collection inlet disposed on the device for collecting sweat when the device is worn by a user, and a microfluidic channel for transmitting the collected sweat from the inlet to the sweat sensor.

[0013] The device further comprises at least one vital sign sensor disposed within the device for measuring a vital sign or physiological indication of the user, a sweat rate sensor for measuring the amount of sweat collected, and a processing unit configured to receive and process data provided by the sweat rate sensor, the vital sign biosensor, and the sweat rate measuring device.

[0014] Preferably, the vital sign or signature sensor is one or more of a heart rate sensor, a respiratory rate sensor, a blood pressure sensor, a temperature sensor, and an oxygen saturation sensor.

[0015] The processing unit is further configured to calculate or predict blood lactate concentration based on data provided by the sweat rate sensor, the sweat rate sensor, and the vital signs sensor, preferably in the form of electrical signals that can be processed by the processing unit to perform calculations based on the data.

[0016] In a preferred embodiment, the sweat sensor is a sweat lactate sensor and the vital signs sensor is a heart rate sensor, and the processing unit is further configured to calculate a predicted value of the blood lactate concentration based on data in the form of electrical signals provided by the sweat lactate sensor, the sweat rate sensor and the heart rate sensor, preferably by a machine learning algorithm in a manner known to those skilled in the art.

[0017] Thus, the wearable device of the present invention is a smart wearable device for lactate monitoring that incorporates a microfluidic system that ensures efficient sweat capture and continuous circulation of new sweat samples. The present invention provides real-time health status of an athlete and can inform when an athlete enters an anaerobic phase, thus generating new countermeasures for not only the athlete's health status but also the training plan of the sport.

[0018] A direct and proportional relationship between blood and sweat lactate measurements (an important biomarker in monitoring intensive physical exercise or fatigue) is unknown. Therefore, the present invention has found that by considering the addition of another parameter to sweat lactate, a more accurate bioequivalence with blood lactate can be established.

[0019] Specifically, the present invention relates to adding the subject's sweat rate, sweat volume, and heart rate parameters to the sweat lactate level for the estimation of blood lactate level, where sweat rate is also taken into account as a dilution factor for lactate and other analytes present in sweat.

[0020] Sweat rate is an indicator of the pattern of change in sweat lactate concentration and blood lactate concentration during a test. Furthermore, heart rate is suitable for estimating the intensity of a user's physical activity and is a common parameter in monitoring the condition and fatigue of athletes.

[0021] The wearable device of the present invention incorporates a microfluidic system for collecting sweat and, in contrast to prior art devices which can only provide time-separated and impractical sweat measurements, includes a sweat sample regeneration system so that new sweat measurements are constantly performed, providing an accurate real-time indication of the physical exertion performed by the user.

[0022] Furthermore, the device incorporates a communication module adapted for wireless transmission of data processed by the processing unit, preferably the estimated or calculated blood lactate concentration.

[0023] The device includes a sensing chamber and at least one of a sweat lactate sensor, a sweat conductivity sensor, a metabolite sensor, an ion sensor, and an amino acid sensor disposed within the sensing chamber. A microfluidic channel communicates the sweat inlet with the sensing chamber.

[0024] The sweat rate sensor includes a microfluidic circuit or a microfluidic reservoir in fluid communication with and disposed downstream of the sensing chamber, and a pair of electrodes disposed between the two electrodes such that a capacitance value between the two electrodes is variable depending on the amount of sweat in the reservoir. The two electrodes are a pair of opposing strips, and more preferably the electrodes are embodied as conductive flexible strips.

[0025] The microfluidic reservoirs can have any configuration, for example, they can be straight or curved conduits, or they can be formed as conduits having a serpentine configuration.

[0026] Preferably, the wearable device includes three main parts: a main housing, a means for attaching the main housing to a part of the user's body, and a replaceable consumable part configured to be manually detachable from the main housing and disposable after use.

[0027] The means for attaching the main housing to a part of the user's body preferably comprises a flexible band having two ends each connectable to the main housing.

[0028] In a preferred embodiment, the processing unit is integrally contained in the main housing, while in other preferred embodiments of the invention the processing unit or parts thereof are external to the device, for example implemented in a smartphone, smartwatch, tablet or similar device. The main housing further includes a power source, such as a battery, for powering the electronics implementing the processing unit, and electrical connectors for communicating with consumable components.

[0029] The flexible band is fitted with at least one biosensor for measuring a vital sign or physiological indication of the user, such as a heart rate sensor, so that in addition to measuring sweat, heart rate measurements are also taken with the same device and in direct contact with the skin at the same time that sweat parameters are measured.

[0030] The flexible band fits to the body as a stretchy woven tape and houses a pair of electrodes to measure heart rate. The electrodes are made of bioelectric silicone, which captures the ECG signal and extracts the heart rate. It is conductive enough to provide a high-quality heart rate signal and can withstand continuous use and washing.

[0031] The flexible band may be fitted with other sensors, such as a respiration rate sensor, to obtain additional parameters.

[0032] The consumable part has a contact surface that is provided to come into contact with the user's skin when the main housing is attached to a part of the user's body and the consumable part is coupled to the main body.

[0033] Alternatively, the means for attaching the main housing to a part of the user's body may include an adhesive material, such as an adhesive surface suitable for adhering to the user's skin, for example, the adhesive surface being provided on a surface of the consumable part that is in contact with the user's skin.

[0034] The consumable part incorporates a sweat collection inlet formed on a contact surface of the consumable part, at least one sweat sensor, a sweat rate measuring device, a microfluidic channel, and electrical connection means for electrically connecting the consumable part to the processing unit when the consumable part is mated with the main housing.

[0035] Preferably, the consumable part is embodied as a card-like part and is generally flat.

[0036] In a preferred embodiment of the wearable device, the main housing has a front surface and a back surface provided with a pair of guides opposite each other, and the consumable part has a pair of side wings, and the consumable part can be coupled to the main housing by inserting the side wings into the guides and moving the consumable part over the back surface of the main housing.

[0037] A portion of the back surface of the main housing is generally flat, and the consumable part is connectable to the main housing by moving the consumable part on a plane parallel to the generally flat portion of the back surface.

[0038] The body is provided with a pair of electrical connectors, and each end of the flexible band is fitted with a metal connector for mechanically and electrically connecting the flexible band and biosensor to the body.

[0039] The body includes a battery for powering the processing unit, and the body has a lid on an underside thereof for accessing the battery. The consumable part overlaps the lid when the consumable part is operably coupled with the main housing.

[0040] The wearable device of the present invention provides advanced, comprehensive and personalized health management capabilities through combined analysis of key metabolic and physiological health indicators such as blood glucose measurement data, in addition to measuring heart rate, oxygen saturation and body temperature, achieving accurate parity with blood glucose measurement.

[0041] In summary, the main advantages of the present invention are:

[0042] - Improved diagnostic capabilities for biological fluids such as sweat. Non-invasive, continuous and remote monitoring generates more patient data;

[0043] - some forms of wearable devices are already used by athletes (heart rate monitors) and are familiar to them in their regular training;

[0044] - If no chemical measurements from the expendable parts are required (e.g. already known training routines), the permanent part of the device (the main housing) can be used alone;

[0045] - The permanent part (main housing) is compatible with different types of wear parts depending on the parameter to be measured, making the device a useful tool not only for the end user but also for doctors and sports physicians in clinics, who can adapt the device to the requirements of their patients or studies;

[0046] - Easy to use, just insert the consumables and place the heart rate monitor. It can be attached in the same position every time, reducing the variation caused by the user's own operation;

[0047] -Can be equipped with advanced electronics with advanced capabilities compared to patch-type wearable devices, such as battery management, data processing, and flash memory when data communication is not possible;

[0048] To improve adhesion with the permanent part, the wear part can have an adhesive surface. The main application of the present invention is focused on the sports medicine or sports health field, e.g. for dehydration monitoring in athletes, however depending on the combination of sensors used the device can be adapted for different applications as described below.

[0049] Thus, in a preferred embodiment, the wearable device is configured to operate as a device for dehydration monitoring in athletes. A combination of sensors measuring sweat, conductivity, and salts such as sodium can provide real-time information about the dehydration status of an athlete during exercise. The key to improving dehydration control in testing is to create an objective scale with measurable variables and act as an alarm system for the user to allow dehydration to be dealt with in a timely manner.

[0050] In another preferred embodiment, the wearable device includes a heart rate and sweat rate measuring sensor, and the device is configured to operate as a device for monitoring the fatigue of a user.

[0051] In another preferred embodiment, the wearable device is configured to operate as a nocturnal hypoglycemia monitoring device. In this embodiment, the device is a non-invasive device with sensors for measuring heart rate, sweat, conductivity and blood glucose levels compatible with hypoglycemia. The user wears the device at night and, if an episode of this type is detected, the device can generate an alarm. Adding a predictive model allows to predict the onset of an episode in advance and to prevent and mitigate its consequences.

[0052] In another preferred embodiment, the wearable device is configured to operate as a device for remote monitoring of peritoneal dialysis, in this embodiment, the device is composed of a heart rate sensor, an accelerometer, a sweat rate sensor, and sensors for measuring conductivity, ions such as sodium, and lactate.

[0053] The present invention also relates to a wearable device for continuously monitoring health parameters of a user, the device comprising at least one sweat sensor for measuring sweat biomarkers, a sweat collection inlet arranged in the device for collecting sweat when the device is worn by a user, a microfluidic channel for transmitting the collected sweat from the inlet to the sweat sensor, at least one vital sign sensor arranged in the device for measuring a vital sign or physiological indication of the user, a sweat rate sensor for measuring the amount of collected sweat, and a processing unit configured to receive and process data provided by the sweat sensor, the vital sign biosensor, and the sweat rate sensor. The vital sign sensor includes at least a heart rate sensor, and the processing unit is further configured to calculate the concentration of the biomarkers in blood based on the data provided by the sweat biomarker sensor, the sweat rate sensor, and the heart rate sensor, preferably by a machine learning algorithm.

[0054] In a preferred embodiment, the sweat biomarker sensor is selected from the list consisting of a sweat lactate sensor and a sweat glucose sensor, hi another preferred embodiment, the sweat biomarker sensor is a sweat lactate sensor.

[0055] More preferably, the sweat biomarker sensor is a sweat lactate sensor and the processing unit is configured to calculate the blood lactate concentration based on data provided by the sweat lactate sensor, the sweat rate sensor and the heart rate sensor, preferably by a machine learning algorithm.

[0056] In another preferred embodiment, the wearable device further comprises a sweat rate sensor, and the processing unit is further configured for monitoring fatigue of the user.

[0057] In another preferred embodiment, the wearable device further comprises a conductivity sensor and an ion sensor, and the processing unit is further configured for monitoring the athlete's dehydration based on data provided by the sweat rate sensor, the conductivity sensor and the ion sensor.

[0058] In another preferred embodiment, the sweat biomarker sensor is a sweat glucose sensor and further includes a conductivity sensor, and the processing unit is configured to calculate a blood glucose concentration based on the data provided by the heart rate sensor, the sweat rate sensor, the conductivity sensor and the sweat glucose sensor, preferably by a machine learning algorithm. More preferably, the processing unit is further configured to monitor nocturnal hypoglycemia based on the data provided by the heart rate sensor, the sweat rate sensor, the conductivity sensor and the sweat glucose sensor. Even more preferably, the sweat glucose sensor is adapted to detect a sweat glucose concentration of less than 55 mg / L.

[0059] In another preferred embodiment, the sweat biomarker sensor is a sweat lactate sensor and / or a sweat glucose sensor, the device further comprises an accelerometer, a conductivity sensor, and an ion sensor, preferably a sodium sensor, and the processing unit is further configured to monitor peritoneal dialysis based on data provided by the heart rate sensor, the accelerometer, the sweat rate sensor, the conductivity sensor, the ion sensor, and the sweat lactate sensor and / or the sweat glucose sensor.

[0060] A second aspect of the invention is a method for non-invasively measuring the concentration of a biomarker in blood, comprising the steps of: a) receiving the subject's sweat biomarker concentration, sweat volume collected, and heart rate; b) calculating a value representing the concentration of the biomarker in blood using a multiparametric regression model; and and c) determining that the calculated value is the concentration of the biomarker in the blood.

[0061] In a preferred embodiment, the biomarker is selected from the list consisting of lactate and glucose, hi another preferred embodiment, the biomarker is lactate.

[0062] In another preferred embodiment, the biomarker is lactate and the method comprises measuring blood lactate concentration based on sweat lactate levels, sweat rate and heart rate.

[0063] In another preferred embodiment, the subject's biomarker concentration in sweat, the amount of sweat collected, and / or the heart rate are received from a wearable device according to any embodiment of the first aspect of the invention.

[0064] A third aspect of the present invention relates to a method for non-invasively or minimally invasively assessing a condition of a subject, the method comprising non-invasively or minimally invasively measuring a concentration of a biomarker in blood based on the concentration of a biomarker in sweat.

[0065] In a preferred embodiment, the method includes determining the concentration of the biomarker in the subject's blood based on the concentration of the biomarker in the subject's sweat, sweat rate, and heart rate.

[0066] In another preferred embodiment, the biomarker is selected from the list consisting of lactate and glucose. In another preferred embodiment, the biomarker is lactate.

[0067] In another preferred embodiment, the subject's biomarker concentration in sweat, the amount of sweat collected, and / or the heart rate are received from a wearable device according to any embodiment of the first aspect of the invention. [Brief description of the drawings]

[0068] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings, in which: [Figure 1] FIG. 1 is a perspective view of a preferred embodiment of the present invention including a mounting band or strap. [Diagram 2] FIG. 13 is a perspective view showing a state in which the main housing and the consumable parts are partially connected. [Diagram 3] FIG. 11 is another perspective view showing a portion of the end of the band and the main housing as viewed from above. [Figure 4] FIG. [Diagram 5] FIG. 4 is a perspective view of the main housing as viewed from below. [Figure 6] FIG. 2 is an exploded perspective view of components of the main housing. [Figure 7A] FIG. 4 is a perspective view of the main housing as viewed from below. [Figure 7B] FIG. 13 is a bottom perspective view of the main housing with some of the consumable parts attached. [Figure 7C] FIG. 13 is a bottom perspective view of the main housing with the consumable parts fully joined. [Figure 8] Schematic diagram of a sweat rate sensor, showing four stages (A-D) of filling of the microfluidic channel. [Figure 9] FIG. 2 is a perspective view of the consumable device from the side opposite the surface that contacts the skin. [Figure 10] FIG. 2 is a schematic plan view of a consumable device. [Figure 11] FIG. 11 is a cross-sectional view of the consumable device taken along section AA in FIG. [Figure 12] 11 is another cross-sectional view of the consumable device taken along section line BB of FIG. 10. [Figure 13] 11 is a graph showing characteristics of a sweat rate sensor. [Figure 14] 1 is a graph showing the effect of sweat conductivity on capacitance measured by a sweat rate sensor. [Figure 15A] 1 shows a scheme of the exercise test carried out in the examples. [Figure 15B] FIG. 1 illustrates a data distribution diagram for machine learning for prediction, in accordance with one or more embodiments of the present invention. [Figure 16A] 4 is a characteristic graph of a lactate concentration sensor. [Figure 16B]1 is a graph comparing current measurements of a standard potentiostat and a dedicated (proposed) potentiostat in accordance with one or more embodiments of the present invention. [Figure 17A] FIG. 13 is a diagram showing an example of the operating mechanism of a sweat rate sensor using a living body. [Figure 17B] 1 is a graph illustrating in vitro characterization of a sweat rate sensor in accordance with one or more embodiments of the present invention. [Figure 18] (A) is a graph showing a correlation plot between the two blood lactate measurement systems, and (B) is a graph showing a Bland-Altman plot showing the agreement between the two systems. [Figure 19] 1 is a graph showing the relationship between blood lactate level and sweat lactate level. [Figure 20] 1 is a table showing the root mean square error (RMSE) of the different multi-parametric approaches tested. [Figure 21] 1 is a graph showing a correlation plot of a multi-layer perceptron (MLP) model. [Figure 22A] 22 shows a plot showing the correlation plot of the Multilayer Perceptron (MLP) model of FIG. 21 when the lactate concentration is truncated at 10.5 Mm. [Figure 22B] 1 shows two examples of continuous sweat-based blood lactate monitoring according to one or more embodiments of the present invention. [Figure 23] For the MLP model, a plot showing the importance of each input parameter within the model is shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0069] FIG. 1 shows an exemplary embodiment of a wearable device 1 according to the present invention, comprising a main housing 2, a flexible band or strap 3 for attaching the main housing to a part of a user's body, and a pair of electrical and mechanical connectors or snap buttons 4 on the main housing 2 for mechanically and electrically connecting the ends of the flexible band with the main housing 2.

[0070] The flexible band 3 is implemented as a stretchable woven tape with a female connector at the end for coupling with a snap button 4. The flexible band 3 is conventionally fitted with at least one biosensor in a known manner for measuring a vital sign or physiological indication of the user, for example a pair of electrodes for measuring heart rate. The electrodes are made of bioelectrical silicone to capture an electrocardiogram signal and extract the heart rate, are sufficiently conductive to obtain a high quality heart rate signal, and can withstand continuous use and washing. The vital sign or indication sensor is at least one of a heart rate sensor, a respiration rate sensor, a blood pressure sensor, a body temperature sensor, and an oxygen saturation sensor.

[0071] As shown more clearly in FIG. 2, the device 1 includes a consumable part 5 configured to be manually coupled and uncoupled from the main housing 2, the consumable part 5 having a contact surface 6 arranged to contact the user's skin when the consumable part 5 is coupled to the main housing 2 and the main housing 2 is attached to the user's body, for example by a flexible band 3.

[0072] As shown in Figure 6, the main housing 22 is formed by two matable parts, a base 2a and a lid or cover 2b, which define a space in which an electronic circuit 7 is enclosed. This electronic circuit 7 implements a processing unit, sensor instrumentation, battery management, a data processing module for wirelessly transmitting the data processed by the processing unit, and a communication module.

[0073] The main housing 2, and particularly the base 2a, has a receptacle 8 for receiving a battery 9 for powering the electronic circuitry 7, and a lid 10 for opening and closing the receptacle 8.

[0074] The consumable part 5 is generally flat, and the main housing 2 and the consumable part 5 are configured such that the consumable part 5 can be coupled to the main housing 2 by moving the consumable part 5 flush with or in a plane parallel to the generally flat underside 19 of the main housing 2, as shown in Figures 2, 7B and 7C, thereby allowing the lid 10 to be accessed only when the consumable part 5 is removed from the main housing 2.

[0075] In particular, as can be seen in FIGS. 7A-7C, when the consumable part 5 is operatively coupled to the main housing 2, the consumable part 5 overlaps the lid 10. As shown in FIG.

[0076] The consumable part 5 incorporates a sweat collection inlet 11 formed on a contact surface 6 of the consumable part for collecting sweat when the device is worn by a user, at least one sweat sensor, a sweat rate measuring device, and a microfluidic channel for transmitting collected sweat from the inlet 11 to the sweat sensor.

[0077] In particular, the consumable part 5 includes a sensing chamber and at least one of a sweat lactate sensor, a sweat conductivity sensor, a metabolite sensor, an ion sensor, and an amino acid sensor disposed within the sensing chamber. A microfluidic channel communicates the sweat inlet 11 with the sensing chamber.

[0078] The consumable part 5 is fabricated as a stack of layers of plastic material, with the microfluidic channels formed by laser or die cutting. The integrated sensor is electrochemical in nature and therefore requires electrodes which are fabricated by screen printing.

[0079] A pair of electrical connectors 12 are provided on the underside 13 of the base 2a of the main housing 2 for providing electrical communication between the sensors located on the consumable parts 5 and the processing unit 7. These connectors 12 are well known spring biased connectors which establish electrical contact with corresponding electrical pads 17 provided on the consumable parts 5 when the consumable parts 5 are coupled to the main housing 2, in a well known manner.

[0080] The consumable parts 5 can be mechanically coupled and uncoupled from the main housing 2 in a manner that is quick and intuitive for the user, while ensuring functionality and a good electrical connection with the main housing 2.

[0081] The main housing 2 has a back surface 13 with a pair of opposing guides 15 between which a space or pocket is formed for receiving the consumable part 5 therein. The consumable part 5 has a pair of side wings 16 that are sized and configured to fit into the space formed between the guides 15, and the consumable part 5 can be coupled to the main housing 2 by inserting the side wings 16 into the guides 15, respectively, and moving the consumable part on the back surface 13 of the main housing 2 as shown in the sequence of Figures 7A, 7B, and 7C.

[0082] The sweat rate sensor 18 is represented diagrammatically in Figure 8 and includes a microfluidic circuit or microfluidic reservoir 19 in fluid communication with and located downstream of the sensing chamber 21. As shown more clearly in Figure 10, sweat enters the inlet 11 and flows along the microfluidic channel 22 and the sensing chamber 21, gradually filling the reservoir 19. Furthermore, the sweat rate sensor 18 comprises a pair of electrodes (20, 20') and a microfluidic reservoir 19 located between the two electrodes such that a capacitance value between the two electrodes changes depending on the amount of sweat in the reservoir.

[0083] When sweat enters the microfluidic reservoir 19, the capacitance between the two electrodes changes (due to the difference in dielectric constant between air and water, the main component of sweat). This method not only provides a very stable response with changes in the amount of sweat, but also has the advantage of being integrated since the device layers only require two layers of copper tape, a cheap and readily available material. Furthermore, there is no need to fabricate electrodes inside the channel, and the sensitivity is across the entire measurement area of ​​the copper layer.

[0084] The electrodes (20, 20') are able to measure the progressive sweat flow filling the reservoir 19 with little effect from the ionic strength of the sweat sample. Since the local sweat flow at each measurement interval can be estimated (because the time and amount of sweat progression are known), the sweat lactate measurements can be corrected as a dilution factor.

[0085] Experimental validation of this novel sweat rate sensor was performed by injecting a controlled amount with a syringe pump into a microfluidic reservoir while monitoring the capacitance between the two electrodes. The microfluidic reservoir consisted of a long serpentine microfluidic channel fabricated using adhesive and laser cutting, sandwiched between electrodes made of copper conductive tape. A solution of 100 mM NaCl, which mimics the ionic strength of typical sweat, was injected at a constant flow rate of 5 μL / min. In Figure 13, we can see that when the injection begins, the capacitance starts to increase linearly until the solution reaches the edge of the detection area. The liquid is then continued to be injected, while the capacitance remains stable. By converting the time to the injected volume, a calibration curve of the capacitance and volume of the sweat rate sensor can be constructed.

[0086] Most impedance-based sweat volume or sweat rate sensors respond highly dependent on the conductivity or ionic strength of the sweat sample. This is due to the resistive nature of these sensors, where the free ions in sweat have a larger effect than capacitive measurements (like the proposed sensor). This can be observed in Figure 14, where the capacitance increase between an empty and a filled sweat volume sensor is measured for solutions of different ionic strength (0-100 mM NaCl solutions). It turns out that, although there is a small increase in the capacitance increase when the solution contains high concentrations of ions, in the range of interest (low concentration of 30 mM to high concentration of 100 mM), the changes caused by the conductivity of the sample are within the limits of the sensor variation.

[0087] Preferably, the two electrodes are a pair of conductive flexible strips facing each other.

[0088] The consumable part 5 can be fabricated as a stack of layers of plastic material in which the microfluidic channels and sensing chambers are laser-cut or die-cut. The electrochemical nature of the integrated sensor requires electrodes which are fabricated by screen printing.

[0089] As shown in FIGS. 11 and 12, in this particular embodiment, the sweat inlet 11, the microfluidic channel 22 and the sensing chamber 21 are positioned above the microfluidic reservoir 19.

[0090] The present invention is also a wearable device 1 for continuous monitoring of health parameters of a user, comprising at least one sweat sensor for measuring sweat biomarkers, a sweat collection inlet 11 arranged in the device for collecting sweat when the device is worn by a user, a microfluidic channel 22 for transmitting collected sweat from the inlet to the sweat sensor, at least one vital sign sensor arranged in the device for measuring a vital sign or physiological indication of the user, a sweat rate sensor 18 for measuring the amount of collected sweat, and a processing unit 7 adapted to receive and process data provided by the sweat sensor, the vital sign biosensor and the sweat rate sensor 18.

[0091] According to this embodiment, the vital signs sensor includes at least a heart rate sensor, and the processing unit 7 is further configured to calculate the concentration of biomarkers in the blood based on the data provided by the sweat biomarker sensor, the sweat rate sensor 18 and the heart rate sensor, preferably by a machine learning algorithm.

[0092] More preferably, the sweat biomarker sensor is selected from the list consisting of a sweat lactate sensor and a sweat glucose sensor. Thus, the device 1 is configured to monitor the concentration of lactate and / or glucose in blood based on the data provided by the respective sweat biomarker sensor, the sweat rate sensor and the heart rate sensor.

[0093] More preferably, the sweat biomarker sensor is a sweat lactate sensor and the processing unit is adapted to calculate the blood lactate concentration based on data provided by the sweat lactate sensor, the sweat rate sensor and the heart rate sensor, preferably by means of a machine learning algorithm.

[0094] A second aspect of the present invention is a method for non-invasively measuring a biomarker concentration in blood, comprising the steps of: a) receiving the subject's sweat biomarker concentrations, collected sweat volume and heart rate; b) calculating a numerical value representing the concentration of the biomarker in the blood by a multiparametric regression model; and c) determining that the calculated value is the concentration of the biomarker in the blood.

[0095] In some embodiments where the calculated values ​​of step b) are not on a standard scale, the method may include the intermediate step of calculating a standardized value representing the concentration of the biomarker in the blood from the calculated values ​​of step b) prior to measuring the concentration of the biomarker in the blood.

[0096] In a preferred embodiment of the second aspect of the invention, the biomarkers are selected from the list comprising or consisting of lactate and glucose.

[0097] Thus, the method is for non-invasively measuring the concentration of lactate or glucose in the blood, and upon receiving the concentration of lactate or glucose in the subject's sweat, the amount of sweat collected and the heart rate, values ​​representative of the concentration of lactate or glucose in the blood, respectively, can be calculated, and the calculated values ​​can be ultimately determined to be the lactate concentration in the blood or the glucose concentration in the blood, respectively.

[0098] In a preferred embodiment of the second aspect of the present invention, the biomarker is lactate. Advantageously, this allows non-invasive monitoring of blood-related conditions associated with lactate. As shown in Example 1, the blood lactate concentration determined from non-invasive measurement of the subject's sweat lactate, sweat rate and heart rate not only has a high correlation with the determined blood lactate concentration when measured directly, but the provided value is a standard value of blood lactate concentration. Thus, the method is for non-invasively measuring blood lactate concentration, and upon receiving the subject's sweat lactate concentration, sweat rate and heart rate, a value representing the blood lactate concentration can be calculated, and the calculated value can be finally determined to be the blood lactate concentration.

[0099] In another preferred embodiment of the second aspect of the invention, the concentration of such biomarkers in the subject's sweat, the amount of sweat collected and / or the heart rate are obtained from a device 1 according to any embodiment of the first aspect of the invention.

[0100] A third aspect of the present invention relates to a method for assessing a condition of a subject, the method comprising non-invasively or minimally invasively measuring a concentration of a biomarker in the blood based on the concentration of the subject's biomarker in sweat.

[0101] More preferably, the method comprises non-invasively or minimally invasively measuring the concentration of the biomarker in the subject's blood based on the concentration of the biomarker in the subject's blood, sweat rate, and heart rate. More preferably, the biomarker is lactate or glucose.

[0102] In a preferred embodiment of the third aspect of the invention, the biomarkers are selected from the list consisting of lactate and glucose.

[0103] In another preferred embodiment of the third aspect of the invention, the concentration of such biomarkers in the subject's sweat, the amount of sweat collected and / or the heart rate are obtained from a device 1 according to any embodiment of the first aspect of the invention.

[0104] The present invention will now be described by the following examples, which are merely illustrative and should not be considered as limiting the scope of the present invention in any way.

[0105] Example: Non-invasive measurement of blood lactate levels To test the feasibility of blood lactate prediction using only non-invasive measurements, a bioequivalence study was performed. Sweat lactate was measured using a commercially available lactate sensor adapted to the concentration range found in sweat, a dedicated wireless device, and a microfluidic sampling patch. Sweat rate was measured using a sweat rate sensor already validated in other studies, which relies on visual inspection of the sweat front in a microfluidic channel. Heart rate was measured using a heart rate monitor (chest strap). Blood lactate was measured using a portable meter that uses disposable test strips together with a laboratory colorimetric meter. All these measurements were performed simultaneously during the volunteers' exercise test (Figure 15A).

[0106] These measurements, along with the subjects’ metadata (age, height, weight) and derived parameters such as ELER (Exhaustion and Lactate Excretion Rate), were used to create a dataset for predicting blood lactate using a machine learning algorithm (Figure 15B). The ELER parameters consist of a combination of sweat lactate (SL), sweat rate (SR), and heart rate (HR), providing the model with the relationship between the variables, i.e., the physiological relationship. For example, SR is used to correct for the dilution factor of sweat lactate in irregularly generated samples. HR helps provide the user’s physiological situation in terms of exercise intensity and fatigue.

[0107] Therefore, the aim of this study was to validate the ability of a multi-parametric model to predict blood lactate levels with sufficient accuracy, overcoming challenges specifically associated with sweat analysis and lactate monitoring. Indeed, recent wearable technology was used to replicate a tool applicable in real-life scenarios combined with the rigor of physiological studies.

[0108] In vitro characterization The sweat sensor used in this study was first characterized in the laboratory. The sweat lactate sensor was characterized over the concentration range of interest using artificial sweat samples to reproduce the sample matrix and interferences in the sensor response. It was confirmed that the sweat sensor used could measure up to 40 mM with a good linear response (Figure 16A). Furthermore, our dedicated instrument showed comparable values ​​to a commercial potentiostat (Palmsens) (Figure 16B).

[0109] The sweat rate sensor consists of a microfluidic patch that collects sweat in a microfluidic channel. Dye paper placed in the inlet gives the sweat color, facilitating visual determination of the fluid front. Knowing the geometric dimensions of the microfluidic channel and taking successive photographs, the volume of sweat collected over a period of time can be calculated. The instantaneous sweat rate, a variable used in the model, was calculated from the increment in volume over a period of time. Figure 17A shows an in vivo demonstration of the sensor's working mechanism. The sensor was calibrated in vitro by injecting water using a syringe pump and comparing the flow rate to a set value (Figure 17B). It also verified that the laser cutting method of fabrication is sufficiently reproducible, since there was no significant difference in the flow rate calculations using the average dimensions of the microfluidic channel or the dimensions of the individual devices.

[0110] In vivo testing The in vivo test method consisted of the following steps: a) Before applying the sampling patch, the skin is cleaned to remove contaminants and ensure good adhesion. First, the skin is washed with ethanol using sterile gauze, followed by a pure water wash to completely remove any ethanol that may interfere with sweating, and finally dried using sterile gauze. After that, the sampling patch is applied to the subject's chest and a heart rate strap monitor is attached. b) Begin the test on the cycloergometer or running track, details of which are given below. c) Once the subject began to sweat, usually 10-15 minutes after the start of the test, blood lactate, sweat lactate, sweat rate, and heart rate were simultaneously measured. d) Blood lactate levels were measured by earlobe puncture using Lactate Pro 2 and test strips (Arklay, Japan) and LactatePLUS and test strips (Nova Biomedical, USA). For some subjects, blood was collected via capillary and later tested in a laboratory using a reference meter (colorimeter, Diaglobal, Germany). e) For sweat lactate measurement, the Lactate Pro 2 test strip was placed in the dedicated reader and the mobile app started receiving the measurement signal. When the test strip was brought close to the collection area of ​​the sample collection patch and sweat entered the sensing chamber, a peak appeared on the measurement screen due to the onset of lactate oxidation. The chronoamperometric data can be stored time-traceable for subsequent analysis. Excess sweat on the collection area of ​​the sample collection patch was wiped off with a cotton swab. The test strip was discarded and a new one was used for the next measurement. f) For sweat rate measurements, photographs were taken with a smartphone so that the fluid front could be distinguished (it was noted that using a flash improves contrast), and the images were stored in a time-trackable form for later analysis. g) Heart rate measurements were performed using a heart rate strap with time-tracked access and transfer of heart rate data to a personal mobile app for post-test analysis. h) This process was repeated for each measurement on a given subject, with varying frequency depending on the test, until the test was completed. Typically, 4-8 measurements were made on a subject.

[0111] Field Testing Different exercise tests were performed by subjects for a full bioequivalence study with the aim of obtaining a wide range of lactate concentrations. The exercise tests were carried out between February 2021 and April 2021 on volunteers, athletes from the Centre d'Alt Rendiment (CAR) and students from the Institut Nacional d'Educacio Fisica (INEFC). The dataset included 152 measurements of 32 subjects. The protocols implemented were designed to mimic real tests and trainings performed by athletes and volunteers. In addition to the equipment, the cycloergometer and the running track, we were able to distinguish between incremental and constant loads when setting the intensity of the exercise. It made sense to combine both activity types, since both give different responses to lactate levels. This increased the variability of the data, making the post-hoc analysis more robust.

[0112] subject : Age: 21±4 years ·Gender: 21 men, 11 women Height: 1.74±0.09m Weight: 66±9kg Training level: 20 amateurs, 12 athletes Cycloergometer test: Progressive strength - 6 subjects, N = 38 measurements, average number of measurements = 6.33 measurements / subject Warm up for 10 minutes with a load of 50~100W power. After warming up, the power was increased by 30W for 3 minutes. Measurements were taken every 3 minutes. The load was increased to the subject's maximum or to the maximum load on the cycloergometer. After reaching maximum load, there will be 3 minutes of active rest followed by 3 minutes of complete rest. Measurements will also be taken after each of these periods. ·Running track test constant intensity 22 subjects, N=92 measurements, average measurements=4.18 measurements / subject Subject was already warmed up. -Four series of 1,200-2,000m were run. Measurements were taken after a three-minute rest period at the end of each series. The time between measurements varied depending on the subject's pace, but was close to six minutes. Triathlon (Cycloergometer-Running Track) Test - 4 subjects, N = 21 measurements, average measurements = 5.25 measurements / subject Subject was already warmed up. After cycling for 10 minutes, participants ran a 400m track. After each transition, they rested for 3 minutes and then took three consecutive measurements. The time between measurements depended on the participant's pace, but was approximately 10 minutes.

[0113] Data analysis The analysis process began with extracting data from the sensors. For sweat lactate measurements, the mean current in the stable region of chronoamperometry was used directly. For sweat rate measurements, the procedure described in the in vitro characterization was applied. Heart rate was extracted from the heart rate monitoring source for the same test time as the other measurements. Lactate excretion rate (LER) is the product of sweat lactate concentration and sweat rate, taking into account volume dilution. LER was divided by heart rate (HR) to create the Exertion and Lactate Excretion Rate (ELER) parameter, which introduces the expected relationship between the independent variables. The purpose of the ELER parameter is to provide the model with an initial logical relationship between the independent variables. Conversely, if this relationship does not exist in the data, the model's performance will be compromised.

[0114] Preprocessing of all models used starts with centering and scaling all continuous variables to have a mean of 0 and a standard deviation of 1. This improves numerical stability and allows continuous variables with different values ​​to be used together. The initial set of models used to predict blood lactate levels are multiparametric linear models including LM (linear model), PLS (partial least squares), or PCR (principal component regression). Due to the complexity of the data, it is expected that nonlinearity will need to be incorporated using the neural network algorithm MultiLayer Perceptron (MLP).

[0115] The structure of an MLP consists of an input layer (independent variables), a hidden layer and an output layer (dependent variables). The MLP has a single hidden layer with neurons (hidden units), which are mathematical expressions consisting of weighted inputs (obtained from supervised backpropagation learning) and which only produce outputs above a certain threshold. This threshold is controlled by an activation function (which can be of a different nature), a nonlinear term added to the model. The MLP used was from the CARET package in R, the number of neurons in the hidden layer was adjusted (from 1 to 10, the final optimized number was 5 neurons) and the activation function used was a rectified linear function.

[0116] The statistical metric used to test the model's prediction accuracy is RMSE (Root Mean Square Error), which is the square root of the square of the difference between the measured values ​​(blood lactate levels) and the predicted values ​​(according to the trained model), and provides information about the inaccuracy of the predictions in the same units as the variables, making them easier to understand.

[0117] Variability in blood lactate measurements The same blood samples were measured using two measurement systems currently available for lactate measurement (Diaglobal colorimetric meter in the laboratory and Lactate Plus electrochemical portable meter) to investigate the variability associated with the field measurement methods. Sufficient measurements were obtained to perform a cross-correlation, as shown in Figure 18. Figure 18A is a correlation plot of Lactate Plus (portable meter and test strips) and Diaglobal (optical meter) for blood lactate measurement. Figure 18B is a Bland-Altman plot showing the agreement between the methodologies.

[0118] A significant deviation of 1.3 mM (RMSE) was observed between the two methods, which is an accepted standard error in the field of sports and is the standard accuracy required for lactate measurement tools.

[0119] Bioequivalence Once the dataset was built, the first step was to find the relationship between blood lactate and sweat lactate. It became apparent that it was not feasible to obtain a direct correlation using a simple correlation plot (see Figure 19) and that a multi-parametric approach needed to be used.

[0120] The first models applied were multiparametric regression models PCR, PLS and LM, whose RMSE values ​​are shown in Figure 20. All these algorithms are linear, which allows for simpler and less computationally intensive predictions. However, the performance initially obtained was not satisfactory enough to be used as a reliable tool for predicting blood lactate levels. Not only the accuracy (reflected by the RMSE value) but also the stability of the predictions was highly dependent on the training data used and was not reproducible at all. On the other hand, MLP showed less variability with respect to the training data and showed stable predictions along with improved prediction accuracy.

[0121] A correlation plot combining multiple predictions using the MLP algorithm is shown in Figure 21. It is clear that high blood lactate values ​​are underrepresented in the dataset and have the lowest prediction accuracy. Moreover, the validity of predicting lactate values ​​above 10 mM is limited since the athletes are well above the anaerobic threshold. Thus, by filtering out high blood lactate values, the prediction performance improved significantly, as shown in Figure 22A. The improvement was not only in absolute terms, as seen in the RMSE value (which decreased to 1.56 mM, quite close to the aforementioned standard value of 1.3 mM), but also in the ability to track the evolution of blood lactate levels over time for a given user (Figure 22B).

[0122] An important feature of the MLP model used is the relative importance of each independent variable in the prediction (Figure 23). The most important parameter was found to be ELER, a parameter derived from the remaining measurements, confirming the correctness of the assumed relationship. The next most important variables were the individual measurements of heart rate, sweat lactate and sweat rate. These results support the idea that the non-invasive measurements performed are the basis for the lactate bioequivalence results presented in this study and that there are no confounding factors such as subject characteristics or test method.

[0123] Thus, the feasibility of predicting blood lactate levels from non-invasive measurements was verified. The same strategy can be applied to other blood biomarkers. This kind of device could provide the next leap in sweat monitoring, providing useful and meaningful data for both users and expert clinicians.

Claims

1. A wearable device for continuously monitoring a user's health parameters, comprising: at least one sweat sensor for measuring sweat biomarkers; a sweat collection inlet disposed on the wearable device for collecting sweat when the wearable device is worn by a user; a microfluidic channel for delivering collected sweat from the sweat collection inlet to a sweat sensor; at least one vital sign sensor disposed within the wearable device for measuring a vital sign or physiological indication of the user; a sweat rate sensor for measuring the volume of collected sweat; a processing unit configured to receive and process data provided by the sweat biomarker sensor, the vital signs biosensor, and the sweat rate sensor; the vital sign sensor includes at least a heart rate sensor; The wearable device, wherein the sweat biomarker sensor is a sweat lactate sensor, and the processing unit is further configured to calculate a blood lactate concentration based on data provided by the sweat lactate sensor, the sweat rate sensor, and the heart rate sensor.

2. The wearable device described in claim 1, wherein the processing unit calculates the blood lactate concentration using a machine learning algorithm.

3. The wearable device of claim 1 , further comprising a communication module adapted for wireless transmission of data processed by the processing unit.

4. The wearable device of claim 1 , further comprising a sensing chamber, wherein the sweat lactate sensor is disposed within the sensing chamber, and the microfluidic channel communicates the sweat collection inlet with the sensing chamber.

5. The wearable device of claim 4 , further comprising at least one sensor of a sweat conductivity sensor, a metabolite sensor, an ion sensor, or an amino acid sensor disposed within the sensing chamber.

6. The wearable device of claim 1 , further comprising at least one vital sign or symptom sensor selected from the group consisting of a respiration rate sensor, a blood pressure sensor, a body temperature sensor, and an oxygen saturation sensor.

7. The wearable device of claim 1 , wherein the sweat rate sensor includes a microfluidic circuit or a microfluidic reservoir in fluid communication with and disposed downstream of the sensing chamber.

8. The wearable device of claim 7, wherein the sweat rate sensor includes a pair of electrodes arranged opposite each other and a microfluidic reservoir in fluid communication with the sensing chamber, and the capacitance value between the two electrodes is variable depending on the amount of sweat in the reservoir.

9. The wearable device of claim 8 , wherein the two electrodes are a pair of strips facing each other, and the microfluidic reservoir is disposed between the two electrodes.

10. A wearable device as described in claim 9, wherein the electrodes are embodied as conductive strips.

11. A wearable device as described in claim 9, wherein the electrodes are embodied as conductive flexible strips.

12. Wearable devices: a main housing enclosing at least a portion of the processing unit therein; means for attaching the main housing to a part of a user's body; a consumable part configured to be manually detachably attached to the main housing, the consumable part having a contact surface configured to contact the user's skin when the main housing is attached to a part of the user's body and the consumable part is coupled to the main housing; The consumable part includes a sweat collection inlet formed on the contact surface of the consumable part, at least one sweat sensor, a sweat rate measurement device, and a microfluidic channel. The wearable device of claim 1 , further comprising an electrical connection means for electrically connecting the consumable part to the processing unit when the consumable part is coupled to the main housing.

13. 13. The wearable device of claim 12, wherein the means for attaching the main housing to a portion of the user's body includes a flexible band having opposite ends each connectable to the main housing, the flexible band comprising at least one biosensor for measuring a vital sign or physiological indication of the user.

14. The wearable device of claim 12 , wherein the means for attaching the main housing to a portion of a user's body comprises an adhesive surface suitable for adhering to a user's skin.

15. A wearable device as described in claim 14, wherein the adhesive surface is provided on the contact surface of the consumable part.

16. The wearable device of claim 1 , further comprising a sweat rate sensor, and wherein the processing unit is further configured for monitoring fatigue of a user.

17. 10. The wearable device of claim 1, further comprising a conductivity sensor and an ion sensor, wherein the processing unit is further configured to monitor dehydration of the athlete based on data provided by the sweat rate sensor, the conductivity sensor, and the ion sensor.

18. 10. The wearable device of claim 1, wherein the sweat biomarker sensor is a sweat glucose sensor and further includes a conductivity sensor, and the processing unit is configured to calculate a blood glucose concentration based on data provided by the heart rate sensor, the sweat rate sensor, the conductivity sensor, and the sweat glucose sensor.

19. The wearable device described in claim 18, wherein the processing unit calculates the blood glucose concentration using a machine learning algorithm.

20. 20. The wearable device of claim 18, wherein the processing unit is further configured to monitor for nocturnal hypoglycemia based on data provided by a heart rate sensor, a sweat rate sensor, a conductivity sensor, and a sweat glucose sensor.

21. The wearable device of claim 20, wherein the sweat glucose sensor is configured to detect glucose sweat concentrations of less than 55 mg / L.

22. 2. The wearable device of claim 1, wherein the sweat biomarker sensor is a sweat lactate sensor and / or a sweat glucose sensor, the wearable device further includes an accelerometer, a conductivity sensor, and an ion sensor, and the processing unit is further configured to monitor peritoneal dialysis based on data provided by the heart rate sensor, the accelerometer, the sweat rate sensor, the conductivity sensor, the ion sensor, and the sweat lactate sensor and / or the sweat glucose sensor.

23. The wearable device described in claim 22, wherein the ion sensor is a sodium sensor.

24. 24. A computer-implemented method for non-invasively measuring a biomarker concentration in blood in a wearable device according to any one of claims 1 to 23, comprising: a) receiving a subject's sweat biomarker concentration, sweat volume collected, and heart rate; b) calculating a value representative of the concentration of the biomarker in the blood by a multiparametric regression model; c) determining that the calculated value is the concentration of the biomarker in the blood; The method comprises measuring blood lactate concentration based on sweat lactate level, sweat rate, and heart rate, wherein the biomarker is lactate.

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