Provided is an apparatus (100) for transporting sweat droplets (112) to a sensor. The apparatus comprises a chamber (102) for filling with sweat. The chamber has an inlet (104) lying adjacent the surface of the skin (106), which inlet permits sweat to enter and fill the chamber. The chamber has an outlet (114) from which a sweat droplet protrudes once the chamber has been filled. The apparatus further comprises a fluid transport assembly which is designed to enable the sweat droplet protruding from the outlet to become detached from the outlet of the chamber. The sweat droplet is subsequently transported by the fluid transport assembly to the sensor. Once the protruding droplet has been released from the outlet, the outlet is made available for a subsequent sweat droplet to protrude therefrom upon further filling of the chamber. The released sweat droplet is transported via the fluid transport assembly at least as fast as the subsequent sweat droplet protrudes from the outlet such that the respective sweat droplets do not contact each other before reaching the sensor. Thus, the apparatus supplies sweat to the sensor in a dropwise manner. Further provided is a system comprising the apparatus and a sensor, and a method for transporting sweat droplets to a sensor.

Provided is a system (300) for determining a sweat rate per gland and measuring biomarker concentration. The system comprises an apparatus and a sensor (166). The apparatus receives sweat from the skin and transports the sweat as discrete sweat droplets to the sensor. The sensor senses each of the counted sweat droplets. The system further comprises a processor which counts the number of sensed sweat droplets during a time period. The processor also determines time intervals between consecutive sensed sweat droplets, and receives a measure of the volume of each of the counted sweat droplets. The time intervals and the measure of the volume are then used by the processor to identify sweat burst and rest periods of the sweat gland or glands producing the sweat. This identification process necessarily involves assigning the sweat burst and rest periods to the sweat gland or glands, such that the processor is permitted to determine the number of sweat glands involved in producing the sweat. The sweat rate per gland may then be determined from the number of sweat droplets, the measure of the volume of each of the counted sweat droplets, and the determined number of sweat glands. Further provided is a method for determining a sweat rate per gland.

Disclosed is a hormone electrochemical biosensor, e.g. an amperometric biosensor, for the detection of a hormone and measurement of the concentration of a hormone. The disclosed hormone biosensor comprises a hormone-catalyzing enzyme, such as KSDH1. Also described herein are systems comprising an amperometric biosensor, e.g., chronoamperometric biosensor and methods of using the chronoamperometric biosensor.

A wearable patch system comprises a sensor (14) for measuring at least one sweat parameter, and a controller (16) for indicating the need to replace the patch. The controller is configured to receive the at least one sweat parameter and compare the at least one sweat parameter to a first threshold. The controller is also configured to determine the replacement information based on the total amount of time during which the at least one sweat parameter exceeds the first threshold; and/or a value indicative of the at least one sweat parameter in respect of the total amount of time during which the at least one sweat parameter exceeds the first threshold. The controller is further configured to output an indication that the wearable patch needs (12) replacing based on the replacement information.

A device (1) for determining sweat parameters of a user is provided, which device comprises a microfluidic structure (10) having a collection chamber (16) configured to collect sweat from a first skin area (i), and a sensor (12) configured to determine a sweat parameter from sweat from the first skin area (i). The device also comprises an evaporation control chamber (14), which is connected to the microfluidic structure (10), configured to utilize fluid collected at a second area (ii) to moisten the microfluidic structure (10). The moistening of the microfluidic structure (10) aims to increase the available sweat for the sensor to determine a sweat parameter, by increasing the humidity inside the microfluidic structure (10) and thus, decreasing the evaporation of sweat. A method for determining sweat parameters of a user is also provided.

Devices and methods are described herein for directly and accurately measuring sweat flow rates using miniaturized thermal flow rate sensors. The devices (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500) include the flow rate sensors (220, 320, 420, 520, 620, 720, 820, 920, 1020, 1120, 1220, 1320, 1420) in or adjacent to a microfluidic component (230, 330, 430, 530, 630, 730, 830, 930, 1030, 1130, 1230, 1330, 1430, 1530) of a wearable sweat sensing device. The devices and methods optimize the sensitivity of the flow rate sensors, while minimizing the presence of noise, in order to accurately and directly measure sweat flow rates.

Systems, devices, methods, and kits for monitoring one or more physiologic and/or physical signals from a subject are disclosed. A system including patches and corresponding modules for wirelessly monitoring physiologic and/or physical signals is disclosed. A service system for managing the collection of physiologic data from a customer is disclosed. An isolating patch for providing a barrier between a handheld monitoring device with a plurality of contact pads and a subject is disclosed.

Novel tools and techniques are provided for physiological monitoring. A method includes receiving, with a computing system, physiological data of a user, analyzing, with the computing system, the received physiological data of the user to identify at least one of one or more body states or one or more transitions between body states of the user, and determining, with the computing system, at least one physiological state of the user, based at least in part on an analysis of the identified at least one of the one or more body states or the one or more transitions between body states of the user. The method further includes sending, with the computing system and to a user device, the determined at least one physiological state of the user, and displaying, on a display screen of the user device, the determined at least one physiological state of the user.

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

The present disclosure provides a method for sweat volume detection based on sweat print imaging, comprising: obtaining a raw data set, the raw data set including at least one sweat print image formed by dropping different volumes of sweat on a test paper under different environmental parameters; obtaining a color value extreme of the sweat print image and dividing the sweat print image into at least one region; calculating a sweat error based on a total sweat collection volume and a real sweat volume; obtaining a trained error prediction model; obtaining a to-be-tested sweat print image and current environmental parameters; inputting the current environmental parameters and a count of regions of the to-be tested sweat print image into the trained error prediction model to determine a sweat error of the to-be-tested sweat print image; calculating a sweat volume and a sweat rate based on the sweat error.