Method and system for determining a measure of a physiological response due to heat

Abstract

A computer-implemented method and electronic system for determining a measure of heat-induced physiological strain in a mammal is disclosed, which comprises the steps of receiving (S13) measurements of the mammal's core body temperature (CBT) and skin temperature (ST), calculating (S14) a mean body temperature (MBT) as a function of the core body temperature (CBT) and skin temperature (ST), and calculating (S15) a heat strain index (HSI) representing the mammal's heat-induced physiological strain as a function of the mean body temperature (MBT).

Description

[Technical Field]

[0001] The present disclosure relates to methods and systems for determining measures of heat-induced physiological responses in mammals, particularly humans. [Background technology]

[0002] Aerobic performance is impaired by heat stress when studied in both laboratory and field environments. Numerous factors contribute to the heat stress experienced by the human body, including metabolic heat (which can increase during work or exercise), environmental factors (e.g., thermal radiation (e.g., from sun exposure), ambient temperature, and ambient humidity), and clothing. Mild or moderate heat stress can cause discomfort and may adversely affect physical and mental performance, but is not harmful to health. As heat stress increases and approaches human tolerance limits, the risk of heat-related illnesses, such as heat syncope, heat cramps, and heat exhaustion, increases.

[0003] While improving athletic performance in hot conditions continues to be the subject of numerous studies, measuring and quantifying the amount of heat experienced by humans also plays an important role with regard to worker health and safety, not only in industrial environments such as thermal power plants, blacksmith shops, or other industrial processes that generate large amounts of heat, but also in agricultural environments where workers may spend long periods of time in the sun.

[0004] Not only humans, but animals also suffer from heat stress. Heat stress is particularly prevalent in mammals, including pets such as dogs and cats, and livestock such as cows and horses, and the effects of heat stress are being studied. According to Godde et al. (2021), "The effects of heat stress affect all types of livestock, including reduced productivity, reduced animal welfare, reduced reproductive capacity, increased susceptibility to disease, and, in extreme cases, increased mortality."

[0005] Heat strain is an individual's overall physiological response due to heat stress and can vary from person to person. The body responds through a process called thermoregulation, which involves sweating, a specialized process that dissipates excess heat from the body. Thermoregulation uses the body's physiological resources, thus leaving fewer resources available for other tasks (e.g., mental and physical performance).

[0006] In this disclosure, the term heat strain includes the overall changes in a mammal due to heat, and particularly includes increases in body temperature (e.g., skin temperature and / or core temperature). The increase in heat can be due to internal processes associated with exercise and / or external influences.

[0007] Regular exposure to heat stress increases the body's tolerance and therefore reduces heat strain (Tyler, Reeve, Hodges, & Cheung, 2016). This process is called acclimatization. Achieving a high level of acclimatization requires physical activity under heat stress conditions similar to those expected during work (e.g., sports or physical labor). According to the prior art, for example, a person with a recent history of at least two consecutive hours of heat stress exposure (e.g., 5 days out of the last 7 days to 10 days out of the last 14 days) can be considered acclimatized. The body adapts by, for example, increasing sweating efficiency (earlier onset of sweating, more sweat production, and reduced electrolyte loss in sweat), enhancing vasodilation, lowering core and skin temperatures, and improving fluid balance and cardiovascular stability. The level of acclimatization depends on the type of heat stress (i.e., passively induced heat stress or exercise-induced heat stress, dry heat exposure or wet heat exposure, etc.), intensity, duration, frequency, and number of heat exposures (Taylor, 2014; Periard et al., 2015). If humans are not exposed to heat stress for a period of time, the degree of heat acclimatization decreases.

[0008] Human core body temperature is an important factor for determining a person's thermal state and a widely accepted proxy for estimating heat stress. However, some studies have shown that core body temperature alone is insufficient to estimate the decline in exercise performance due to heat stress. Cheuvront et al. (2003) showed that at a given core body temperature, higher skin temperatures lead to earlier exhaustion due to increased heart rate and blood flow to the skin throughout the body. A smaller temperature gradient between the skin and core forces the body to increase blood flow to the skin to achieve the same heat transport. This affects the blood available to muscles during exercise, impairing performance (Gonzalez-Alonso, et al., 1999).

[0009] International Publication No. WO2019108699 discloses a system or method for assisting an individual in acclimatizing to a hot environment before exposure to a hot environment using heart rate measurement, with or without measuring skin temperature and / or core body temperature. To provide a target for the individual's exercise level, a physiological strain index (PSI) or adaptive PSI (aPSI) is calculated for the individual, and the area under the calculated PSI / aPSI curve is used to determine the amount of heat acclimation that has occurred for that particular training session and / or previous training sessions. A drawback of the teachings of this publication is that core body temperature calculated using only heart rate is based on assumptions that do not include direct measurement of temperature and therefore are not highly reliable in all situations because they do not fully account for, for example, inter-individual differences or all exercise conditions. While this publication mentions the use of an internal temperature sensor that wirelessly transmits the individual's core body temperature, swallowing the sensor is cumbersome and not suitable for regular (especially daily) use. Furthermore, a heart rate monitor must be used to determine the physiological strain index. Additionally, the use of skin temperature by itself is prone to error as skin temperature varies depending on where on the body the temperature is located. Summary of the Invention

[0010] It is an object of the present disclosure and embodiments disclosed herein to provide methods and systems for determining measures of the physiological response of mammals, particularly humans, to heat.

[0011] In particular, it is an object of the present disclosure and embodiments disclosed herein to provide a computer-implemented method and electronic system for determining a measure of thermal physiological strain in a mammal, particularly a human, that does not suffer from at least some of the drawbacks of the prior art.

[0012] It is further an object of the present disclosure and embodiments disclosed herein to provide a computer-implemented method and electronic system for determining a measure of thermal physiological stress on a mammal, particularly a human, that does not suffer from at least some of the drawbacks of the prior art.

[0013] It is a further object of the present disclosure and embodiments disclosed herein to provide a computer-implemented method and electronic system for determining a measure of thermally induced physiological strain after acclimatization in mammals, particularly humans, that does not suffer from at least some of the drawbacks of the prior art.

[0014] It is a further object of the present disclosure and embodiments disclosed herein to provide a computer-implemented method and electronic system for determining a measure of a mammal's, and in particular a human's, readiness to perform under heat stress that does not suffer from at least some of the drawbacks of the prior art.

[0015] At least some of the steps described in relation to one of the objects of the present disclosure, particularly the first method, may be performed in relation to another of the objects of the present disclosure, particularly the second method.

[0016] The present disclosure relates to a computer-implemented method for determining a measure of thermal physiological strain in a mammal. The method includes receiving measurements of core body temperature and skin temperature of the mammal. The method includes calculating a mean body temperature as a function of the core body temperature and skin temperature. The method includes calculating a thermal strain index representing thermal physiological strain in the mammal as a function of the mean body temperature.

[0017] In one embodiment, the thermal strain index is calculated using only the average body temperature. In particular, the thermal strain index is calculated without using the heart rate.

[0018] In one embodiment, the mammal is a human.

[0019] In one embodiment, the mammal is a domesticated mammal, such as a dog, cat, cow, horse, donkey, etc.

[0020] In one embodiment, core and skin temperature measurements are taken at defined locations on the body, particularly a single defined location, and the method includes correcting the skin temperature using a predetermined correction term for the defined location. For example, the predetermined correction term includes an offset value of 0.3°C to 1°C.

[0021] In one embodiment, the defined location is the torso, particularly the chest region, more specifically the left side of the chest between the left armpit and the left hip. The predetermined correction term includes an offset value, for example, between 0.4°C and 0.8°C, preferably 0.5°C.

[0022] In one embodiment, the defined position is the arm, preferably the upper arm and / or the wrist. The predetermined correction term includes an offset value, for example, between 0.6°C and 1.0°C, preferably 0.7°C.

[0023] In one embodiment, the defined location is selected from one of the following locations: torso, upper arm, or wrist. The predetermined correction term is an individual correction term determined for a particular person. The predetermined correction term may take clothing into account.

[0024] The predetermined correction term more specifically relates to the distribution of the heat transfer coefficient between the skin and the environment over the entire skin surface.

[0025] In one embodiment, core and skin temperature measurements are taken at multiple defined locations as described herein.

[0026] In one embodiment, core body temperature measurements are taken at a defined location different from skin temperature.

[0027] In one embodiment, the skin temperature measurements include multiple skin temperature measurements taken at different defined locations.

[0028] In one embodiment, the predetermined correction term for a defined location is determined by several steps, including measuring skin temperature at a plurality of different locations on the body, preferably at least three different locations, including the defined location, calculating an average skin temperature using the plurality of measurements, and determining the predetermined correction term using the difference between the average skin temperature and the skin temperature at the defined location.

[0029] In one embodiment, the method includes receiving one or more additional physiological signals from the mammal, including heart rate, heart rate variability, galvanic skin response, sweat rate, respiration rate, oxygen saturation, heat flux, exercise data, glucose concentration, and / or lactate concentration, and further using the one or more additional physiological signals to calculate a thermal strain index.

[0030] In one embodiment, the method includes calculating a thermal strain index as a function of the mean body temperature, a lower temperature threshold, and an upper temperature threshold.

[0031] In one embodiment, the lower temperature threshold is between 36° C. and 37° C., preferably 36.7° C. The upper temperature threshold is between 38° C. and 40° C., preferably 38.9° C.

[0032] In one embodiment, the method includes calculating core body temperature as a function of heat flux (HF) at the skin and skin temperature (ST) at a defined location.

[0033] In one embodiment, the core temperature is calculated as a function of the PPG signal.

[0034] In one embodiment, core body temperature is calculated as a function of heart rate and at least one skin temperature.

[0035] In one embodiment, the core body temperature is calculated as a function of at least one skin temperature and one ambient temperature.

[0036] In one embodiment, the core body temperature is calculated as a function of at least one skin temperature, at least one ambient temperature, and heart rate.

[0037] In one embodiment, the core body temperature is calculated as a function of at least one heat flux sensor, at least one skin temperature sensor, and heart rate.

[0038] In one embodiment, the core body temperature is calculated as a function of at least one skin temperature sensor and one ambient temperature sensor.

[0039] In one embodiment, core body temperature is calculated as a function of accelerometer readings, galvanic skin response, sweat rate, and / or blood oxygen saturation.

[0040] In one embodiment, core body temperature is calculated as a function of one or more of PPG, ECG (including heart rate and / or heart rate variability), skin temperature, ambient temperature, device temperature, accelerometer, galvanic skin response, sweat rate, blood oxygen saturation, respiratory rate, and / or blood pressure.

[0041] In one embodiment, the average body temperature is calculated as a linear combination of core and skin temperatures, preferably a weighted sum including core and skin temperature coefficients.

[0042] In one embodiment, the average body temperature is calculated as a normalized weighted sum with core body temperature having a coefficient of 0.6-0.7, preferably 0.64, and skin temperature having a coefficient of 0.3-0.4, preferably 0.36.

[0043] In one embodiment, the thermal strain index is proportional to the difference between the average body temperature and the lower temperature threshold and inversely proportional to the difference between the upper and lower temperature thresholds.

[0044] The present disclosure further relates to a computer-implemented method for determining a measure of physiological stress on a mammal, particularly a human, due to heat, the method comprising calculating a heat strain index as a function of the mammal's body temperature, preferably as described herein, particularly preferably the average body temperature, as described herein, the method further comprising determining a heat strain score representing the cumulative heat stress for a period or portion thereof, the heat strain score being determined using the average and / or normalized heat strain index and the elapsed time of the period or portion thereof.

[0045] In one embodiment, the cumulative heat load is calculated as a function of the integral of the heat strain index for the hyperthermia period or portion thereof.

[0046] In one embodiment, the method includes determining an average heat strain index for the hyperthermia period or portion thereof as the arithmetic mean of a plurality of heat strain index values.

[0047] In one embodiment, the method further includes determining a normalized heat strain index for the hyperthermia period or portion thereof by calculating the i-th power of each of the plurality of heat strain index values ​​(where i is a real number between 2 and 5), calculating the average of the i-th powers, and calculating the i-th root of the average.

[0048] In one embodiment, the method further includes generating a short-term heat load for a particular time point as a weighted sum of one or more heat strain scores for one or more previous high heat periods, the weight of a given heat strain score depending on the time elapsed between the particular time point and the date of the given previous high heat period, with the weight decreasing as the time elapsed increases.

[0049] In one embodiment, the weight of a given heat strain score when calculating short-term heat load undergoes an exponential decay with a time constant of 2-10 days.

[0050] In one embodiment, the method further includes generating a long-term heat load for a particular time point as a weighted sum of one or more heat strain scores for one or more previous high heat periods, the weight of a given heat strain score depending on the time elapsed between the particular time point and the date of the given previous high heat period, the weight decreasing as the time elapsed increases, the decrease being less rapid than the decrease in weights used to generate the short-term heat load.

[0051] In one embodiment, the weight of a given heat strain score when calculating long-term heat load undergoes an exponential decay with a time constant of 5 to 30 days.

[0052] The present disclosure further relates to a method for determining a measure of a mammal's, particularly a human's, readiness to face high heat. The method includes receiving a plurality of heat strain scores of past activities, the heat strain scores preferably determined as described herein. The method further includes determining a long-term heat load, the long-term heat load preferably determined as described herein. The method further includes determining a short-term heat load, the short-term heat load preferably determined as described herein. The method includes determining a heat readiness index as a function of the long-term heat load and the short-term heat load, preferably as the difference between the long-term heat load and the short-term heat load.

[0053] In one embodiment, the method further includes generating an alarm signal (or alert signal or warning signal) when the heat strain index exceeds a heat strain index threshold and / or when the heat strain score exceeds a heat strain score threshold.

[0054] In one embodiment, the heat strain threshold is a general threshold suitable for all mammals / humans. Alternatively, or in addition, the heat strain threshold is specific to either a group of individuals or a particular individual. Groups of individuals can include occupational groups, climate zone groups, age groups, sport groups, etc.

[0055] In one embodiment, the method further includes generating a message including the heat strain index. Optionally, the method includes displaying the heat strain index on a display. Optionally, the method includes displaying the heat strain score and / or a warning message on a display.

[0056] Additionally, the present disclosure relates to a method for determining a measure of thermally induced physiological strain in a mammal, particularly a human, after adjustment for adaptation. The method includes receiving a body temperature, preferably a mean body temperature as described herein. The method includes determining a thermal strain index using the body temperature, preferably using a method as described herein. The method includes determining the thermal strain index after adjustment for adaptation. The thermal strain index after adjustment for adaptation is determined using the thermal strain index and one or more additional physiological signals of the mammal, including heart rate, heart rate variability, galvanic skin response, hydration status, body weight, sweat rate, respiration rate, oxygen saturation, heat flux, exercise data, performance data, glucose concentration, and / or lactate concentration.

[0057] In one embodiment, the method further comprises determining an acclimatization index indicative of a degree of acclimatization of the human, the acclimatization index being determined as a function of the acclimatization-adjusted thermal strain index and the thermal strain index.

[0058] In addition to the computer-implemented method for determining a measure of thermal physiological strain in a mammal, the present disclosure further relates to an electronic system for performing one or more of the methods described herein, the electronic system comprising a processor configured to perform one or more of the computer-implemented methods described herein.

[0059] In one embodiment, the electronic system further comprises a wearable device including a sensor system configured to determine core body and skin temperatures of the mammal, the sensor system being worn on the body of the mammal.

[0060] In embodiments where the mammal is a human, the sensor system is worn on the human's body in contact with the skin.

[0061] In one embodiment, the sensor system comprises a heat flux sensor configured to determine core body temperature and a temperature sensor configured to determine skin temperature. In particular, the heat flux sensor comprises a series of p-type and n-type doped semiconductors.

[0062] In one embodiment, the electronic system comprises the following sensors: a PPG sensor, an ECG sensor, a blood lactate sensor, a galvanic skin response sensor, a sweat rate sensor, one or more additional skin temperature sensors, an ambient temperature sensor, and / or an accelerometer. Preferably, the additional sensors are integrated into the wearable device.

[0063] In one embodiment, the wearable device comprises a strap for fastening the wearable device to the human body, the strap being preferably a chest strap or a wrist strap.

[0064] In one embodiment, the wearable device is worn on the torso, particularly in the chest region, more specifically on the left side of the chest between the left armpit and the left hip (apical position).

[0065] In one embodiment, the processor is integrated into a wearable device.

[0066] In one embodiment, the wearable device includes a wireless communication module configured to generate and transmit a message including the current heat strain index to one or more user devices, such as, for example, a smart watch, a mobile phone, and / or a sports computer (i.e., a running watch, a cycling computer, a rowing computer, etc.).

[0067] In one embodiment, the electronic system further comprises another electronic device, the processor being integrated into the other electronic device, the wearable device including a wireless communication module configured to transmit a message including the core body temperature and the skin temperature to the other electronic device, the other electronic device may be a user device as described herein, or the other electronic device may be a remotely located server computer.

[0068] In addition to computer-implemented methods and electronic systems for implementing the computer-implemented methods, the present disclosure further relates to a computer program product for performing one or more of the methods described herein, the computer program product including computer program code configured to control a processor to perform one or more of the methods described herein.

[0069] Furthermore, the present disclosure relates to a non-transitory computer-readable medium having stored thereon computer program code configured to control a processor to perform one or more of the methods described herein.

[0070] The above embodiments may apply to one or more of the methods herein. In particular, the features of particular embodiments are not limited to the particular method that may be construed as relating thereto; rather, one skilled in the art will recognize that the teachings of particular embodiments may be applied to other methods.

[0071] The disclosure set forth herein will be more fully understood from the detailed description and accompanying drawings given below, which should not be construed as limiting the invention as set forth in the appended claims. [Brief explanation of the drawings]

[0072] [Figure 1] 1 shows a diagram illustrating a human and showing several positions on the human body where the wearable devices described herein may be worn. [Figure 2] 1 shows a schematic front view of a wearable device with a sensor system. [Figure 3] 1 shows a schematic side view of a wearable device with a sensor system, illustrating the heat flow through the sensor system. [Figure 4] 1 shows a block diagram that schematically illustrates an electronic system comprising a wearable device and a processor. [Figure 5] FIG. 1 shows a block diagram that schematically illustrates an electronic system comprising a wearable device with an integrated processor. [Figure 6] 1 shows a diagram that schematically illustrates an electronic system that includes a wearable device connected to a user device, the user device including a processor. [Figure 7] 1 shows a diagram that schematically illustrates an electronic system including a wearable device, a server computer, and a user device. [Figure 8] FIG. 1 shows a schematic diagram of an electronic system including a wearable device with a display and a server computer. [Figure 9] FIG. 1 shows a block diagram that illustrates a schematic of a sensor system comprising a heat flux sensor and a skin temperature sensor. [Figure 10] FIG. 1 shows a block diagram that schematically illustrates a sensor system comprising a heat flux sensor, a skin temperature sensor, and optionally further sensors. [Figure 11]1 shows a flow diagram illustrating a method for determining a measure of physiological strain in a mammal due to heat, and in particular for determining a heat strain index. [Figure 12] FIG. 1 shows a flow diagram illustrating a method for determining a measure of physiological strain in a mammal due to prolonged heat exposure. [Figure 13] FIG. 1 shows a flow diagram illustrating a method for determining a measure of physiological strain regulated by adaptation as a result of heat. [Figure 14] 1 shows a flow diagram illustrating a method for determining a measure of readiness to incur high temperatures for a mammal. [Figure 15] 1 shows a flow diagram illustrating a method for determining a prescribed correction term for correcting skin temperature measurements. [Figure 16] 1 shows a time series chart of measured core body temperature, measured skin temperature, and calculated average body temperature during a low-intensity indoor cycling session. [Figure 17] 1 shows a time series chart of measured core body temperature, measured skin temperature, and calculated average body temperature during two high-intensity cycling sessions: a first outdoor session and a second indoor session. DETAILED DESCRIPTION OF THE INVENTION

[0073] Reference will now be made in detail to certain embodiments, some examples of which are illustrated in the accompanying drawings, which illustrate some, but not all, of the features. Indeed, the embodiments disclosed herein may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Wherever possible, like reference numerals will be used to refer to like components or parts.

[0074] 1 illustrates a human body 8. Several possible locations 81, 82, 83, 84, 85 on the body 8 at which wearable devices 2A, 2B, 2C, 2D, 2E can be worn are shown, although other locations are possible. Typically, a single wearable device 2A, 2B, 2C, 2D, 2E is worn at one of the locations 81, 82, 83, 84, 85. The locations 81, 82, 83, 84, 85 include the chest area 81, particularly on the left chest area (the so-called apical area), the wrist area 82 (particularly on the dorsum of the wrist), the upper arm 83, the thigh 84, and the lower leg 85. Other possible locations include the head (e.g., the forehead or temple area), the forearm, the ankle, the finger, the fingertip, the earlobe, or the inside of the ear.

[0075] For best results, wearable devices 2A, 2B, 2C, 2D, 2E are preferably worn in direct contact with the body (particularly in direct contact with the skin), however, wearable devices 2A, 2B, 2C, 2D, 2E may also be worn over one or more layers of clothing, or over hair or fur (in the case of non-human mammals).

[0076] Depending on the embodiment, wearable devices 2A, 2B, 2C, 2D, 2E may be implemented as a single device or as a distributed device having one or more connected components.

[0077] In embodiments in which wearable devices 2A, 2B, 2C, 2D, 2E are worn on a limb of body 8 or on the torso, wearable devices 2A, 2B, 2C, 2D, 2E are typically attached to body 8 by a strap or band. Wearable devices 2A, 2B, 2C, 2D, 2E may also be at least partially incorporated into clothing, particularly clothing designed to be worn against the skin.

[0078] In embodiments in which wearable devices 2A, 2B, 2C, 2D, 2E are configured to be worn on the wrist, wearable devices 2A, 2B, 2C, 2D, 2E may be implemented in the form of, for example, an electronic bracelet, an electronic cuff, or an electronic watch. In particular, wearable devices 2A, 2B, 2C, 2D, 2E may be implemented as health trackers, fitness trackers, and / or smartwatches (e.g., as shown in FIG. 8).

[0079] In embodiments in which wearable devices 2A, 2B, 2C, 2D, 2E are configured to be worn on the head, particularly in the forehead or temple region, wearable devices 2A, 2B, 2C, 2D, 2E may be implemented, for example, as electronic headbands, electronic glasses (i.e., smart glasses), electronic hats, or electronic helmets.

[0080] As described below, wearable devices 2A, 2B, 2C, 2D, and 2E include a sensor system (not shown). The sensor system may include one or more sensors directly integrated into (i.e., having a common housing with) wearable devices 2A, 2B, 2C, 2D, and 2E. However, wearable devices 2A, 2B, 2C, 2D, and 2E may also include one or more auxiliary sensors that are not directly integrated into (i.e., have a common housing with) wearable devices 2A, 2B, 2C, 2D, and 2E, but are connected to wearable devices 2A, 2B, 2C, 2D, and 2E using a wired or wireless data communication system.

[0081] 2 and 3, the wearable device 2 has a substantially rectangular housing with a front surface F and a rear surface B. The wearable device 2 is thin compared to its lateral dimensions. The wearable device 2 includes a sensor system 3 that includes, among other things, a core body temperature sensor and a skin temperature sensor.

[0082] The core body temperature sensor may be implemented using a heat flux sensor 31 and a skin temperature sensor 32, as will be described in more detail with reference to Figure 9. The sensor system 3 may include additional sensors, as will be described in more detail with reference to Figure 10.

[0083] When worn on the body 8, the rear surface B faces the body and is preferably in direct contact with the skin of the body 8. Arrows indicate the heat flux H flowing from the body 8 through the wearable device 2 from the rear surface B to the front surface F.

[0084] 4-8 show an example of an electronic system 1. The electronic system 1 comprises at least one processor 11 configured to perform one or more steps and / or functions described herein.

[0085] Depending on its configuration, the electronic system 1 further includes various components such as a memory 12, a communication interface, and / or a user interface. The components of the electronic system 1 are connected to each other via a data communication system so as to be able to transmit and / or receive data.

[0086] The term data communication system relates to a communication system that facilitates data communication between two components, devices, systems, or other entities. Depending on its configuration, the data communication system may be wired and include wired connections such as cables and / or a system bus, and / or include wireless connections such as Bluetooth (BT), Bluetooth Low Energy (BLE), ANT+, WiFi, RFID, etc. The data communication system may further include communication modules for communication over a network such as a local area network (LAN), a mobile wireless network (e.g., GSM, GPRS, CDMA2000, EDGE, and / or UTMS), and / or the Internet 5. The Internet 5 may include intermediate networks depending on the implementation.

[0087] The processor 11 may include a system on a chip (SoC), a central processing unit (CPU), and / or other more specific processing units such as a graphics processing unit (GPU), an application specific integrated circuit (ASIC), or a reprogrammable processing unit such as a field programmable gate array (FPGA).

[0088] Memory 12 comprises one or more volatile (temporary) and / or nonvolatile (non-transitory) storage components. The storage components may be removable and / or non-removable and may be fully or partially integrated with processor 11. Examples of storage components include RAM (random access memory), flash memory, a hard disk, data memory, and / or other data stores. Memory 12 includes a non-transitory computer-readable medium having stored thereon computer program code configured to control processor 11 such that electronic system 1 performs one or more steps and / or functions described herein. Depending on the embodiment, the computer program code is compiled or uncompiled program logic and / or machine code. Thus, electronic system 1 is configured to perform one or more steps and / or functions.

[0089] The computer program code defines and / or is part of a separate software application. Those skilled in the art will appreciate that the computer program code may be distributed across multiple software applications (Apps). In one embodiment, the computer program code further provides an interface, such as an API, to allow remote access to the functionality and / or data of the electronic system 1, for example, via a client application or web browser.

[0090] Although particular steps and / or functions are described herein as being performed by particular components or devices of electronic system 1, the particular steps and / or functions may be performed in whole or in part by other components or devices of electronic system 1. Furthermore, particular steps disclosed as being performed by processor 11 may be performed by wearable device 2. Additionally or alternatively, particular steps disclosed as being performed by processor 11 may be performed by multiple processors 11, particularly multiple processors 11 distributed across one or more devices of electronic system 3.

[0091] 4 to 8, the electronic system 1 includes a wearable device 2. The wearable device includes a sensor system 3.

[0092] The wearable device 2 may include a communication module configured to communicate data using a data communication system with other devices, in particular other devices of the electronic system 1. Depending on the embodiment, the communication module may be configured for wired and / or wireless communication.

[0093] Depending on the embodiment, the wearable device 2 may also include further electronic components, in particular a power source such as a battery. Other electronic components include, for example, a user interface configured to receive user input and / or provide information to the user, and comprising user input means, for example, a touchscreen, buttons, a rotating wheel, etc. The user interface may provide information to the user by means of a display (e.g., a screen, a touchscreen, and / or an AR display system), a speaker, or haptic feedback (e.g., using vibration).

[0094] In one embodiment, the wearable device 2 further comprises a module configured to determine the current time, the orientation of the wearable device 2, the position of the wearable device 2 (e.g., using a GNSS receiver, the signal strength of nearby WLAN access points, or the signal strength of nearby mobile radio transceivers), and / or ambient conditions, the ambient conditions including temperature, pressure, and / or humidity.

[0095] Furthermore, depending on the embodiment, wearable device 2 includes a processing unit separate from processor 11. The processing unit is implemented, for example, as a microprocessor that executes program code (e.g., embedded program code). Thus, the processing unit may be configured to perform one or more steps and / or functions described herein. The processing unit is connected to the sensor system and other electronic components of wearable device 2, including a battery, a user interface, a communication module, etc.

[0096] Furthermore, depending on the embodiment, the wearable device 2 comprises a memory connected to the processing unit and configured to record sensor outputs, including outputs of one or more sensors of the sensor system 3. Depending on the embodiment, the memory is integrated into the processing unit and / or the sensor system 3. In particular, the memory is configured to record core body temperature values ​​and skin temperature measurements. Furthermore, the memory may be configured to record one or more additional measurements measured by the sensor system 3, in particular one or more additional measurements from optional additional sensors of the sensor system described with reference to FIG. 10 below.

[0097] Depending on the embodiment, sensor output from one or more sensors of sensor system 3 is preprocessed (e.g., conditioned, compensated, filtered, statistically analyzed, summarized, compressed, and / or combined with the output of one or more other sensor outputs) in wearable device 2.

[0098] Depending on the embodiment, sensor output from the sensors of sensor system 3 may be used to directly determine one or more physiological signals of the mammal. For example, a skin temperature sensor may directly measure skin temperature, or an ECG sensor may be used to determine both heart rate and heart rate variability.

[0099] The sensor output from a sensor of sensor system 3 may also be combined with one or more other sensor outputs from other sensors of sensor system 3 to determine a physiological signal of the mammal. For example, the sensor outputs from multiple skin temperature sensors may be combined to determine an average skin temperature. In another example, a core body temperature measurement may be determined using the sensor outputs from one or more sensors, particularly the sensor output from heat flux sensor 31 and the sensor output from skin temperature sensor 32 (i.e., as a function of the sensor outputs from one or more sensors). The core body temperature measurement may also be adjusted using a calibration file.

[0100] The skin temperature sensor may directly provide a sensor output from the skin temperature sensor 32. However, the skin temperature measurement may be corrected based on the placement of the skin temperature sensor 32. The skin temperature measurement may also be adjusted using a calibration file.

[0101] The sensor output from the sensor system 3 may be pre-processed to remove outliers, for example using LOWESS (Locally Weighted Scatterplot Smoothing), sometimes called LOESS (Locally Weighted Smoothing).

[0102] In another example, multiple sensor output values ​​and / or physiological signal values ​​are recorded over a predetermined time interval and averaged to generate a single sensor output value and / or physiological signal value that is recorded in memory. Alternatively or additionally, representative sensor output values ​​and / or physiological signal values ​​are selected as the sensor output value and / or physiological signal value, respectively. Preferably, the sensor output values ​​and / or physiological signal values ​​are averaged over a 10-second period and recorded as a single sensor output value and / or single physiological signal value, respectively. Those skilled in the art will recognize that other periods of approximately the same length (i.e., about 1 second to about 100 seconds) are more appropriate than the exemplary value of 10 seconds, depending on the embodiment. By not recording every single sensor output value and / or physiological signal value, the amount of data stored in the memory of the wearable device 2 is reduced. Furthermore, the amount of data transmitted to the processor 11 is also reduced. Some sensor output values ​​may be averaged over different periods, for example, heart rate may be measured (or averaged) every 10 seconds, while core body temperature and skin temperature may be measured (or averaged) every 30 seconds.

[0103] In one embodiment, sensor system 3 is configured to only record the outputs of certain sensors and / or only record certain physiological signals when a particular set of conditions apply. These conditions include, for example, when the individual is active (which may be determined by the accelerometer and / or heart rate), when core body temperature and / or skin temperature are high (i.e., when core body temperature exceeds a predetermined threshold and / or when skin temperature exceeds a predetermined threshold), when the individual is experiencing heat strain and / or heat stress, and / or when a signal to start recording is received at wearable device 2. In particular, recording of core body temperature and skin temperature may depend on a high heart rate, particularly a heart rate above a predetermined heart rate threshold, indicating that the mammal is active.

[0104] Depending on the embodiment, the above-mentioned functions and / or steps relating to the conditioning, correction, pre-processing, filtering, statistical analysis and / or combination of sensor outputs are performed in the wearable device 2 itself (in particular in the processing unit of the wearable device 2) or in the processor 11 of the electronic system 1.

[0105] Depending on the embodiment, the sensor system 3 of the wearable device 2 further comprises sensors configured to measure aspects of heart rate, heart rate variability, skin perfusion, respiratory rate, respiratory rate, and / or electrodermal activity.

[0106] 4, electronic system 1 includes a wearable device 2, a processor 11, and a memory 12. Electronic system 1 may be implemented in a single device or in multiple devices communicatively coupled to each other, for example, using a data communication system described herein. For example, electronic system 1 is implemented as a wearable device 2 connected to a user device 4 (described in further detail in FIG. 6).

[0107] 5, electronic system 1 includes wearable device 2, which includes sensor system 3, processor 11, and memory 12. Wearable device 2 may be implemented in a single device or in multiple devices communicatively coupled to each other, for example, using the data communication system described herein. For example, electronic system 1 may be implemented as a wrist-worn device, such as an electronic bracelet or an electronic watch. Common examples of such wrist-worn devices include fitness trackers and smartwatches.

[0108] 6 , electronic system 1 may be distributed between wearable device 2 and user device 4. One or more of the functions and / or steps described herein may be performed, in whole or in part, on wearable device 2, and other functions and / or steps described herein may be performed, in whole or in part, within user device 4. In particular, steps and / or functions described herein as being performed on processor 11 may be performed either on wearable device 2 (processor 11 implemented in wearable device 2) or on user device 4 (processor 11 implemented in user device 4). Furthermore, when processor 11 is implemented in user device 4, wearable device 2 may have another processing unit described herein, and the functions and / or steps described herein may be performed either on the processing unit of wearable device 2 or on the processor of user device 4, or performed cooperatively between the two devices 2, 4.

[0109] Depending on the embodiment, the user device 4 is implemented as a mobile device, such as, for example, a mobile wireless telephone (e.g., a smartphone running an iOS or Android operating system), a tablet computer, a laptop computer, a smartwatch, a fitness watch, a sports computer (e.g., an electronic sports watch, a cycling computer, or a rowing computer), etc. The user device 4 may include a display 41, implemented, for example, as a touchscreen. The user device 4 is configured to be connected to the wearable device 2 using either a wired or wireless data communication system.

[0110] 6, the electronic system 1 includes a wearable device 2, a user device 4, and a server computer 6. The wearable device 2 is connected to the user device 4 using a data communication system, in particular using a short-range wireless communication protocol such as Bluetooth LE (Low Energy) or ANT+. The user device 4 is connected to the server computer 6 using a data communication system via an intermediate network 5, which may include in particular a mobile wireless network and / or the Internet. The server computer 6 may be located remotely from the wearable device 2 and the user device 4, for example in a data processing facility such as a cloud computing center.

[0111] As shown in FIG. 7 , the processor 11 and the memory 12 may be implemented in the server computer 6. However, at least some of the steps and / or functions performed by the processor 11 may be executed, in whole or in part, in the user device 4 and / or in cooperation with the user device 4. For example, the user device 4 may perform pre-processing of sensor data or physiological signals. The user device 4 may simply forward data received from the wearable device 2 to the server computer 4 (the forwarding of data may include buffering, e.g., temporary storage, of the data). For example, the server computer 6 may perform data processing and send a message to the user device 4 based on the processed data. This is illustrated by the dashed box indicating a distributed processing environment 7.

[0112] 8, electronic system 1 may include a wearable device 2 implemented as a smart device, in particular a smartwatch with a user interface 23 (e.g., including a touchscreen). Wearable device 2 is connected to server computer 1 via an intermediate network 5, which may include a mobile wireless network and / or the Internet. To that end, wearable device 2 includes, for example, a mobile wireless transceiver configured to communicate using one or more digital cellular technologies (e.g., GSM, GPRS, CDMA2000, EDGE, and / or UTMS). Steps and / or functions described herein may be performed by wearable device 2, server computer 6, and / or a combination of both.

[0113] 9 and 10 show block diagrams that schematically illustrate the sensor system 3 of the wearable device 2. As described, the sensor system 3 may be fully integrated into the wearable device 2 (e.g., integrated in the sense of structural integration by sharing a common housing with the wearable device 2), or may be partially integrated such that at least some sensors are integrated into the wearable device 2 while some other sensors are located separately and connected to the wearable device 2 using the data communication system described herein.

[0114] The sensor system 3 is configured to measure physiological signals of a mammal, particularly a human. The sensor output of a particular sensor in the sensor system 3 may directly provide a measurement of one or more physiological signals, such as a skin temperature sensor that directly provides a physiological signal of human skin temperature. In another example, the sensor output of a particular sensor may directly provide a measurement of multiple physiological signals, for example, an electrocardiogram (ECG) may provide heart rate and heart rate variability, or a photoplethysmograph (PPG) may provide heart rate, heart rate variability, skin perfusion, and / or respiratory rate.

[0115] In yet another example, the sensor output of a particular sensor can be combined with another sensor output of another sensor to determine a physiological signal, such as using the sensor output of a heat flux sensor and the sensor output of a skin temperature sensor to determine a core body temperature. For example, a combination of core body temperature and skin temperature may be used to determine an average body temperature.

[0116] As shown in FIG. 9, the sensor system 3 includes a heat flux sensor 31 and a skin temperature sensor 32 .

[0117] The heat flux sensor 31 is preferably implemented using a Seebeck element, in particular comprising a series of p-type and n-type doped semiconductors. The heat flux sensor 31 and the skin temperature sensor 32 are preferably implemented using the sensor unit disclosed in WO2018114653, the entire contents of which are incorporated herein by reference.

[0118] Alternatively, the heat flux sensor 31 may be implemented using multiple temperature sensors, for example including a first temperature sensor arranged to be in contact with the body, in particular the skin, when the wearable device 2 is worn, and a second temperature sensor arranged to provide a thermally conductive layer of predetermined thermal conductivity between the first temperature sensor and the first temperature sensor, the second temperature sensor preferably being exposed to the environment (i.e., by being arranged on a surface of the wearable device 2 opposite the first temperature sensor or in thermal contact with such a surface).

[0119] The sensor system 3 may further include a photoplethysmograph (PPG) sensor. The PPG sensor may be integrated into the same device as other sensors, such as the wearable device 2, or may be located separately from the other sensors. In particular, the PPG sensor may be implemented in a wrist-worn device such as a fitness tracker or a smartwatch. Alternatively, the PPG sensor may be located in an electronic ring worn on a finger. The PPG sensor is configured to communicate with the wearable device 2, the user device 4, and / or the processor 11, depending on the embodiment, using the data communication system described herein.

[0120] The sensor system 3 may further include an ECG sensor. The ECG sensor may be integrated in the same device as the other sensors of the sensor system 3, e.g., the wearable device 2, or may be located separately from the other sensors. In particular, the ECG sensor may be implemented in a second wearable device worn on the human torso, in particular the chest. The second wearable device may be attached to the human by a strap or belt. The (first) wearable device 2 may be attached to the human on a strap of the same belt. The ECG sensor is configured to communicate with the wearable device 2, the user device 4, and / or the processor 11, depending on the embodiment, using the data communication system described herein.

[0121] Depending on the embodiment, the sensor system 3 may be configured to determine additional physiological signals using one or more sensors, such as galvanic skin response, sweat rate, respiratory rate, oxygen saturation, blood oxygen saturation, blood pressure, glucose concentration, or lactate concentration.

[0122] The sensor system 3 may further be configured to measure other values, including environmental conditions such as ambient air temperature and ambient humidity using an ambient temperature sensor 33, or values ​​related to human position or movement. Position can be determined using a GNSS receiver (e.g., a GPS receiver). Movement can be determined by an inertial measurement unit (IMU), for example, comprising a three-axis accelerometer, a gyroscope, and / or a magnetic field sensor.

[0123] 11-15 show flow diagrams illustrating methods 110, 120, 130, 140, and 150, each of which includes several steps executed by the electronic system 1, particularly the wearable device 2 and / or the processor 11. These methods 110, 120, 130, 140, and 150 can be performed during and / or after a period of hyperthermia, i.e., a period in which a mammal experiences physiological strain due to heat. The physiological strain can be due to externally induced environmental heat and / or high physical activity. The period of hyperthermia can be a training session for an athlete or a work shift for a worker. Methods 110, 120, 130, 140, and 150 or specific steps can be performed once, intermittently, or continuously. Methods 110, 120, 130, 140, 150 or particular steps thereof may be performed on demand, i.e., when an appropriate signal to initiate the method or step is received from a user or device, or the methods or particular steps thereof may be performed automatically, for example at a predetermined time or after a predetermined period of time.

[0124] FIG. 11 shows a flow diagram illustrating a method 110 including several steps S11-S15 for calculating the thermal strain index.

[0125] In step S11, the wearable device 2, in particular the sensor system 3 of the wearable device 2, measures the core body temperature CBT and the skin temperature ST of a mammal, in particular a human being.

[0126] The core body temperature CBT may be determined using the heat flux HF and the skin temperature ST. Preferably, the heat flux HF is measured using a heat flux sensor in contact with the mammal, in particular in contact with the human skin. Preferably, the heat flux sensor is located at the same location on the body as the skin temperature sensor, for example in the same wearable device. In particular, the core body temperature CBT is determined as a function of the heat flux HF and the skin temperature ST, in particular using known algorithms or models, for example using a model of the resistance of heat flux from the core of the body to the environment through the skin. For example, the following function: CBT=ST+RB×HF is used, where RB is the thermal resistance of the body at a defined location on the body.

[0127] In another example, the core body temperature CBT is determined from the skin temperature ST and the heat flux HF using a statistical algorithm based on the skin temperature ST and the heat flux HF generated using machine learning.

[0128] The core body temperature CBT may also be determined using a temperature sensor in the form of a swallowable device equipped with a wireless transponder that transmits the core body temperature wirelessly to the wearable device 2.

[0129] In step S12, the core body temperature CBT and skin temperature ST are transmitted in transmission T1 from the wearable device 2 to the processor 11. Transmission T1 is a wired or wireless transmission, particularly using a data communication system as described herein. In embodiments where the processor 11 and the wearable device 2 are integrated, i.e., part of the same device, steps S12 and S13 may be omitted.

[0130] The wearable device 2 may be configured to perform steps S11 and S12 continuously, for example, at intervals of 1 second. As described herein, the wearable device 2 may be configured to preprocess the core body temperature CBT and the skin temperature ST.

[0131] Depending on the embodiment, the wearable device 2 may be configured to transmit other sensor outputs or other physiological signals described herein to the processor 11. In particular, transmission T1 from the wearable device 2 to the processor 11 is a transmission from a communication module of the wearable device 2 to a communication module connected to the processor 11. For example, the communication module connected to the processor 11 may be a communication module of the user device 4 or the server computer 6. Transmission T1 may be direct or indirect, for example, a direct transmission to the server computer 6 using a mobile wireless network or an indirect transmission via the user device 4.

[0132] In step S13, the processor 11 receives the core body temperature CBT and the skin temperature ST from the wearable device 2, in particular using the data communication system.

[0133] In one embodiment, processor 11 applies a correction to the skin temperature ST received from wearable device 2. Alternatively, wearable device 2 is configured to apply the correction and send the (corrected) skin temperature ST to processor 11. The skin temperature ST is corrected using a correction term, which is related to a defined location on body 8 where wearable device 2, and in particular skin temperature sensor 32, is located. Different locations may have different correction terms.

[0134] In one embodiment, the correction term is an offset value between 0.3°C and 1°C. The exact value of the offset value depends on the defined location. For example, if the defined location is the chest region of the torso, more specifically the apical position (located on the left side of the chest between the left axilla and the left hip), the offset value is between 0.4°C and 0.8°C, preferably 0.5°C.

[0135] In embodiments where the defined location is the arm, preferably the upper arm or wrist, the predetermined offset value is between 0.6°C and 1.0°C, preferably 0.7°C.

[0136] The corrected skin temperature cST is calculated using the following function, for example: cST = ST offset where ST is the skin temperature and offset is the offset value.

[0137] In one embodiment, the defined location is selected from one of the following locations: torso, upper arm, or wrist, and the predetermined correction term is a personal correction term determined for a particular person. A method for determining the personal correction term is described in further detail with reference to FIG. 15.

[0138] In step S14, processor 11 uses core body temperature CBT and skin temperature ST to calculate mean body temperature MBT, a value representing the average body temperature of a mammal that correlates well with physiological strain due to heat.

[0139] In particular, processor 11 calculates the mean body temperature MBT as a function of the core body temperature CBT and the skin temperature ST.

[0140] In particular, the processor 11 calculates the mean body temperature MBT as a linear combination of the core body temperature CBT and the skin temperature ST, preferably as expressed by the following formula: MBT = X·CBT + Y·ST where MBT is mean body temperature, CBT is core body temperature, ST is skin temperature (or may be corrected skin temperature), and X and Y are coefficients for core body temperature and skin temperature, respectively. Preferably, the coefficients are determined so that the weighted sum is a normalized weighted sum. The coefficient X has a value between 0.6 and 0.7, preferably 0.64. The coefficient Y has a value between 0.3 and 0.4, preferably 0.36.

[0141] In step S15, the processor 11 calculates the thermal strain index (HSI). The thermal strain index (HSI) is a measure of physiological strain due to heat. The thermal strain index (HSI) increases as a function of mean body temperature (MBT). For example, the HSI can be defined to be proportional to MBT. Alternatively, or in addition, the HSI can be defined as a sum of several terms, for example, as a polynomial function of mean body temperature.

[0142] In one embodiment, the heat strain index HSI is calculated such that in the absence of heat-induced physiological strain, the HSI has a value of 0. The heat strain index HSI may be calculated as a function of the difference between the current mean body temperature MBT and the resting mean body temperature rMBT.

[0143] The resting mean body temperature rMBT can also be used to calculate the lower temperature threshold LTT. For example, the lower temperature threshold LTT can be defined as 0.5°C to 1.0°C, preferably 0.7°C, higher than the resting mean body temperature rMBT. Alternatively, the lower temperature threshold LTT may be a general predetermined value, or in particular, the lower temperature threshold LTT may be a specific predetermined value that depends on the group to which the person belongs, such as occupation, climate zone, age, or sport, or may be a personal value for each person.

[0144] For mammals other than humans, a mammal-specific lower temperature threshold LTT can be used.

[0145] A personalized lower temperature threshold LTT for a human can be determined by recording the mean body temperature MBT during periods of little or no physiological strain due to heat, such as during rest and / or sleep, and using the lowest mean body temperature MBT recorded during such periods, or a low percentile of the mean body temperature MBT recorded during such periods (e.g., a low percentile between the 1st and 10th percentile of the mean body temperature MBT) to determine the lower temperature threshold LTT. As described above, the lower temperature threshold LTT can be defined as 0.5°C to 1.0°C, preferably 0.7°C, higher than the resting mean body temperature rMBT. The lower temperature threshold LTT is typically a value in the range of 36°C to 37°C. A predetermined value for the lower temperature threshold LTT that works well for humans is 36.7°C.

[0146] In one embodiment, the heat strain index (HSI) is calculated using a mean body temperature (MBT), a lower temperature threshold (LTT), and an upper temperature threshold (UTT). The upper temperature threshold (UTT) represents the mean body temperature above which mammals, particularly humans, may begin to experience serious symptoms due to heat strain, such as heat syncope, heat cramps, and heat exhaustion. The upper temperature threshold (UTT) is typically a value in the range of 38°C to 40°C. A value of the upper temperature threshold (UTT) that works well for humans is 38.9°C.

[0147] For example, the thermal strain index HSI is calculated as a function of the mean body temperature MBT, the lower temperature threshold LTT, and the upper temperature threshold UTT. In particular, the thermal strain index HSI is calculated to be proportional to the difference between the mean body temperature MBT and the lower temperature threshold LTT, and inversely proportional to the difference between the upper temperature threshold UTT and the lower temperature threshold LTT, for example, according to the following function: TIFF2026502855000002.tif9170

[0148] The proportionality factor may be selected as desired. For example, the scaling factor may be 10 or 100, such that the Heat Strain Index HSI is typically a value between 0 and 10 or 0 and 100, respectively.

[0149] In one embodiment, the heat strain index HSI further includes a heart rate component HRC that depends on the heart rate HR, in particular the individual's current heart rate HR. For example, the heart rate component HRC is calculated using the current heart rate HR, the resting heart rate rHR, and the maximum heart rate mHR, for example according to the following formula: TIFF2026502855000003.tif9170

[0150] For example, the heart rate component may be incorporated as an additive factor and / or coefficient into the calculation of the Heat Strain Index (HSI). TIFF2026502855000004.tif9170 or The file is TIFF2026502855000005.tif9170.

[0151] In one embodiment, the heat strain index HSI is calculated using additional physiological signals, in particular heart rate, heart rate variability, galvanic skin response, sweat rate, respiration rate, oxygen saturation, heat flux, exercise data, glucose concentration, or lactate concentration, which may be implemented in the form of one or more coefficients that modify the calculated heat strain index HSI and / or one or more constant values ​​that are added to or subtracted from the heat strain index.

[0152] Additionally or alternatively, these physiological signals may be used to modify the lower temperature threshold LTT and / or the upper temperature threshold UTT.

[0153] In one embodiment, the heat strain index HSI is calculated using additional personal factors such as age, sex, physiological state, and / or health, which may be implemented in the form of one or more coefficients that modify the calculated heat strain index HSI and / or one or more constant values ​​that are added to or subtracted from the heat strain index.

[0154] Additionally or alternatively, these factors may be used to modify the lower temperature threshold LTT and / or the upper temperature threshold UTT.

[0155] In optional step S16, processor 11 generates a message in response to the thermal strain index HSI. The message may be recorded in memory 12. The message may be sent back to wearable device 2, user device 2, and / or server computer 6. The message may be sent only if the thermal strain index HSI exceeds a predetermined thermal strain index threshold.

[0156] The message may include the heat strain index HSI, for example as a numerical value. Alternatively or additionally, the message may include a representation of the HSI, for example as text, a color, or an audio signal depending on the heat strain index HSI. Examples of possible representations for the heat strain index HSI with a scaling factor of 10 are shown in the table below: TIFF2026502855000006.tif22170

[0157] Depending on the exact implementation, the granularity may be finer or finer as needed.

[0158] The message may further include a warning, a warning, and / or an alert depending on the value of the Heat Strain Index HSI. The message may be configured to be displayed as an important notification on the user device 4, for example. The message may be configured to be displayed prominently and may include the use of signal colors, i.e., conspicuous colors such as orange or red that have a signal effect and are interpreted by a significant proportion of humans as a warning signal.

[0159] In one embodiment, the electronic system 1 is configured to display a message on a display, such as the display 23 of the wearable device 2 and / or the display 41 of the user device 4.

[0160] Figure 12 shows a flow diagram illustrating a method 120 that includes some steps S13 to S15 described above with reference to Figure 11, as well as further steps S17 and S18. As shown in the flow diagram, steps S13 to S15 are performed continuously, for example once per second or once per 10 seconds.

[0161] In step S17, the heat strain index HSI calculated in step S15 is used in processor 11 to determine a heat strain score HSS. The heat strain score HSS is an indicator of cumulative physiological strain due to heat and may therefore be a useful indicator for purposes of monitoring total heat exposure to ensure that physiological strain is not too great, or for monitoring acclimatization to ensure that the total amount of heat exposure is high enough to induce physiological adaptation, but not so high that it is detrimental to future performance, recovery, or acclimatization.

[0162] The Heat Strain Score HSS may be calculated "live", i.e., during a high fever period, based on the current value of the Heat Strain Index HSI during the current high fever period and past values ​​of the Heat Strain Index HSI. The Heat Strain Score HSS may also be calculated retrospectively, i.e., after a high fever period, based on multiple values ​​of the Heat Strain Index HSI calculated (and / or recorded) from the mean body temperature MBT during the high fever period.

[0163] The heat strain score HSS is calculated as a function of the average value of the heat strain index HSI during the high fever period (or part thereof) and the elapsed time P of the high fever period (or part thereof). The elapsed time P may be the entire period of the high fever, the elapsed time P up to the current moment, or a subset of the entire period. The elapsed time P may be selected, for example, for the entire day. For example, the heat strain score HSS may be calculated using the following formula: HSS=aHSI×P The average heat strain index aHSI is calculated using the following formula, where aHSI is the average of the heat strain index HSI. The average heat strain index aHSI is calculated as the average of multiple values ​​of the heat strain index HSI recorded in a certain time interval (for example, every 1 to 60 seconds, preferably every 1 to 30 seconds) using the following formula: It can be calculated according to TIFF2026502855000007.tif9170, where n is the number of values ​​of the heat distortion index HSI.

[0164] Alternatively, the heat strain score HSS can be calculated as a function of the normalized heat strain index nHSI during the high heat period (or part thereof) and the elapsed time P of the high heat period (or part thereof) using the following formula: HSS=nHSI×P is calculated according to

[0165] The normalized heat strain index nHSI is preferably calculated by taking the i-th power of multiple values ​​of the heat strain index HSI at regular time intervals (for example, every 1 to 60 seconds, preferably every 1 to 30 seconds). The index i may be a natural number or a real number, preferably between 1 and 5, most preferably 4. Then, the average of the i-th power is calculated, and then the i-th power of the average is obtained. For example, the normalized heat strain index nHSI can be calculated using the following formula: TIFF2026502855000008.tif14170, where n is the number of values ​​of the heat strain index (HSI). By normalizing the heat strain index (HSI) in this way and using the normalized heat strain index (nHSI) to calculate the heat strain score (HSS), higher values ​​of the heat strain index (HSI) contribute proportionally more to the heat strain score (HSS) than lower values. This reflects the observation that heat-induced physiological strain does not increase linearly with increasing mean body temperature (MBT), but rather increases exponentially with increasing mean body temperature (MBT).

[0166] The heat strain score HSS value obtained according to step S17 can be used as a guide to compare two hyperthermic periods with different profiles of mean body temperature MBT or different elapsed times P. However, the results depend on whether the average heat strain index aHSI or the normalized heat strain index nHSI is used. For the purposes of the following example, the heat strain score HSS is determined using the heat strain index HSI value every 10 seconds, thus resulting in 600 heat strain index HSI values ​​per hour.

[0167] A first exemplary training session has an elapsed time P of 2 hours, with both hours having a heat strain index of 6. Therefore, the average heat strain index aHSI for this training session is 6. This results in a heat strain score HSS of aHSI x 100 x P = 1200, where the factor of 100 was chosen so that an average heat strain index aHSI of 10 for one hour would result in a heat strain score HSS of 1000.

[0168] A second exemplary training session, in which the elapsed time P is 2 hours and the heat strain index HSI is 4 in the first hour and 8 in the second hour, also has an average heat strain index aHSI of 6, and therefore, a heat strain score HSS calculated using the average heat strain index aHSI logically results in the same heat strain score HSS of 1200 as the first training session. However, the total physiological strain for both sessions is not the same. In particular, the second session has greater physiological strain because the heat strain index HSI value for the second hour is greater, which results in a disproportionately greater physiological strain.

[0169] However, a more accurate measure of total physiological strain can be found using the normalized heat strain index (nHSI), which corresponds more closely to the physiological strain perceived by humans. Using the normalized heat strain index (nHSI) and a value of i=4, the first training session results in a heat strain score (HSS) of 1200, which is consistent with the results of calculations based on using the average heat strain index (aHSI). However, the second training session results in a heat strain score of 1366, reflecting a greater physiological strain for the second training session.

[0170] A scaling factor can be applied to the HSS. The scaling factor can depend on the time resolution of the HSI values ​​used, i.e., the time period between successive HSI values. For example, the scaling factor can be selected so that the HSS generated during a training session of up to 1 hour does not exceed 1,000.

[0171] In optional step S18, processor 11 generates a message including the thermal strain score HSS. The message may be recorded in memory 12. The message may be transmitted to wearable device 2, for example, for display on display 23 of wearable device 2.

[0172] A message may be sent back to the wearable device 2, the user device 4, and / or the server computer 6. The message may be sent only if the heat strain score HSS exceeds a predetermined heat strain score threshold.

[0173] The message may include the heat strain score HSS, for example, as a numeric value. Alternatively, or in addition, the message may include a representation of the heat strain score HSS, for example, text, color, or audio signal that depends on the heat strain score HSS. The following table shows examples of possible representations of the heat strain score HSS scaled so that a training session up to one hour is unlikely to exceed 1000 (i.e., a score of 1000 would require the heat strain index HSI to be 10 for the entire hour): TIFF2026502855000009.tif27170

[0174] 13 shows a flow diagram illustrating a method 130 including several steps S131-S135 for determining acclimatization-adjusted thermal strain. Method 130 may be performed by electronic system 1. Acclimatization-adjusted thermal strain considers additional physiological signals beyond just mean body temperature, and thus may more accurately and consistently account for an individual's degree of acclimatization than simply relying on core body temperature.

[0175] In step S131, a body temperature is received. Preferably, the body temperature is a mean body temperature MBT, and in particular a mean body temperature MBT calculated as described herein, for example, using core body temperature CBT and skin temperature ST.

[0176] In step S132, the body temperature is used to determine a heat strain index HSI. Preferably, the heat strain index HSI is determined according to method 110 described herein.

[0177] In step S133, one or more additional physiological signals of a mammal, particularly a human, are received. The physiological signals are preferably physiological signals that change due to the body's response to heat, particularly physiological signals that further depend on the degree of acclimatization. The physiological signals include, for example, heart rate, heart rate variability, and / or respiration rate (i.e., respiratory rate). The physiological signals may further include galvanic skin response, hydration status, body weight, sweat rate, salt and / or electrolyte concentrations in sweat, perfusion, and / or other aspects of electrodermal activity.

[0178] In step S134, an acclimatization-adjusted thermal strain index aaHSI is determined using (eg, as a function of) the thermal strain index HSI and one or more additional physiological signals.

[0179] For example, the acclimation-adjusted heat strain index (aaHSI) is determined using the heat strain index (HSI) and the heart rate (HR), for example, using the following formula: TIFF2026502855000010.tif10170, where HR is the heart rate, and HR R is the resting heart rate, and HR max is the maximum heart rate, and k HR is the heart rate index which can have values ​​between -1 and +1.

[0180] Generally, the acclimation-adjusted thermal strain index aaHSI is determined using the thermal strain index HSI and one or more coefficients and / or offset factors, which are calculated using additional physiological signals. In particular, the following equation: aaHSI=(PC A1 ×PC A2 ×…×PC An )HSI+(PC B1 +PC B2 +…+PC Bn ) where aaHSI is the acclimation-adjusted heat strain index, HSI is the heat strain index, and PC A1 is the first physiological coefficient associated with the first physiological signal, and PCA2 is the second physiological coefficient associated with the second physiological signal, and PC An is the nth physiological coefficient associated with the nth physiological signal, and PC B1 is the first offset associated with the first physiological signal, and PC B2 is a second offset associated with the second physiological signal, and PC Bn is the nth offset associated with the nth physiological signal.

[0181] An example of how the physiological coefficients can be determined for a given physiological signal is given by the following equation: where CPSV is the value of the current physiological signal, LST is the lower signal threshold, and USS is the upper signal threshold. Similarly, an example of how the physiological offset can be determined for a given physiological signal is given by the following equation: Given by TIFF2026502855000012.tif9170.

[0182] Hydration status can be determined by measuring sweat, particularly sweat rate, salt and / or electrolyte concentrations in sweat, and / or fluid loss. In particular, hydration status can be determined using sweat rate integrated over time. Furthermore, hydration status can be determined using current body weight, particularly in instances where body weight can be measured directly (e.g., an exercise bike, rowing machine, or treadmill configured to measure an individual's weight).

[0183] In optional step S135, a message is generated that includes the acclimatization adjusted thermal strain index aaHSI.

[0184] 14 shows a flow diagram illustrating a method 140 including a series of steps S141-S145 for determining a readiness index for exposure to high heat. The method 140 can be performed by the electronic system 1 described herein.

[0185] In step S141, a plurality of heat strain scores HSS are received for past high heat periods, e.g., past activities or training sessions, e.g., for past active training sessions aimed at improving heat acclimation, or activities involving exposure to heat, such as working in a sauna or hot environment.

[0186] In step S142, a long-term heat load CHL is determined. The long-term heat load CHL is a measure of the long-term physiological strain the body has experienced due to a long-term history of past heat exposures. The long-term heat load CHL can be determined for a current time t, taking into account past heat exposures. The long-term heat load CHL can also be determined for a past time t, taking into account heat exposures at even earlier heat exposures relative to the past time t.

[0187] In particular, the long-term heat load CHL is determined using the sum of the heat strain scores HSS for multiple previous high fever periods, e.g., the past 5-40 days. Preferably, the sum is weighted so that higher fever periods further in the past are weighted less than higher fever periods closer to the current time point. In particular, the weight of each heat strain score HSS depends on the time interval (i.e., number of days) between the current time point t and the time of the previous high fever period.

[0188] For example, the weight of a particular heat strain score HSS decreases linearly with time, e.g., such that the contribution of a particular heat strain score HSS becomes 0 between days 5 and 40.

[0189] For example, the weight of a particular heat strain score HSS decreases according to an exponential decay, for example with a time constant of 5 to 30 days. An example for calculating the long-term heat load CHL at a given time t is given by the following formula: where CHL(t) is the long-term heat load at a specific time point (e.g., day) t, HSS(s) is the heat strain score at a specific time point (e.g., day) s prior to t, τ is a time constant between 5 and 30 (days), and k is a scaling factor.

[0190] The time constant τ is a defined time constant that may vary between individuals, vary over time within an individual, or vary between groups of individuals. The time constant may be calibrated to a particular individual. The scaling factor k may also vary between individuals or groups of individuals.

[0191] Long-term heat load CHL is a measure of how well an individual has acclimatized to the heat and the extent to which physiological adaptations have occurred that allow the individual to perform better in high heat. A high long-term heat load CHL indicates a high degree of acclimatization to the heat. Specifically, a long-term heat load CHL, determined using an exponentially weighted sum of past heat strain scores (HSS), reflects an individual's gradual loss of physiological adaptations that allow the individual to perform in the heat.

[0192] In step S143, the short-term heat load AHL is determined. The short-term heat load AHL is a measure of the physiological strain the body has suffered due to very recent heat exposure. The short-term heat load AHL can be determined for the current time point, taking into account recent heat exposure. The short-term heat load AHL can also be determined for a past time point t, taking into account recent heat exposure relative to the past time point.

[0193] In particular, the short-term heat load AHL is determined using the sum of the heat strain scores HSS for multiple previous high fever periods, e.g., the past 2-14 days. Preferably, the sum is weighted so that more recent high fever periods are weighted less than periods closer to the current time point. In particular, the weight of each heat strain score HSS depends on the time interval (i.e., number of days) between the current time point and the time point of the previous high fever period.

[0194] For example, the weight of a particular heat strain score HSS decreases linearly with time, e.g., such that the contribution of a particular heat strain score HSS is 0 between days 2 and 14.

[0195] For example, the weight of a particular heat strain score HSS decreases according to an exponential decay, for example with a time constant of 2 to 10 days. An example for calculating the short-term heat load AHL at a given time t is given by the following formula: tif13170, where AHL(t) is the short-term heat load at a specific time point (e.g., day) t, HSS(s) is the heat strain score at a specific time point (e.g., day) s prior to t, τ is a time constant ranging from 2 to 10 (days), and k is a scaling factor.

[0196] The time constant τ is a defined time constant that may vary between individuals, vary over time within an individual, or vary between groups of individuals. The time constant may be calibrated to a particular individual. The scaling factor k may also vary between individuals or groups of individuals.

[0197] The short-term heat stress AHL is a measure of the current physiological strain an individual is under due to exposure to high heat in the very recent past. High values ​​of the short-term heat stress AHL are known to hinder performance because the body is still recovering from the heat exposure.

[0198] In step S144, the thermal readiness index HRI for a particular time point t is calculated using the long-term thermal load CHL and the short-term thermal load AHL. In particular, the thermal readiness index HRI is calculated as a linear combination of the CHL and the AHL, for example, using the following equation: HRI(t)=R0+CHL(t)-AHL(t) where HRI(t) is the thermal readiness index at a particular time t, R0 is the base readiness index which may vary for each individual, CHL(t) is the long-term heat load at time t, and AHL is the short-term heat load at time t.

[0199] The Thermal Readiness Index (HRI) is a measure of an individual's readiness to perform under heat exposure. A high HRI requires not only a high long-term heat load (CHL) (representing altitude acclimatization) but also a low short-term heat load (AHL) (representing current physiological strain resulting from recent heat exposure).

[0200] For example, to achieve a high Heat Readiness Index (HRI), an athlete may perform high Heat Strain Index (HSI) training sessions over a two or three week period, which will result in a high long-term Heat Load (CHL). However, the athlete may not perform high Heat Strain Index (HSI) training sessions for several days prior to the event, which will allow the athlete's body to recover and result in a lower short-term Heat Load (AHL) on the day of the event.

[0201] In one embodiment, a message is generated, the message including an indication of a short term heat load AHL, a long term heat load CHL, and / or a thermal readiness index HRI.

[0202] FIG. 15 shows a flow diagram illustrating a method 150 for determining a correction term for a particular individual, which is used to correct the skin temperature ST described herein. The determined correction term generally relates to the heat transfer coefficient between the skin and the environment, which varies across the skin surface and varies from person to person. The determined correction term accounts for inter-individual differences and can further take into account the presence of clothing worn on the wearable device 2, particularly the skin temperature sensor 32 of the sensor system 3. This method allows for the determination of a correction term for various defined positions. In particular, this method is used to find a correction term for the apex position, i.e., the position on the left side of the chest indicated by reference numeral 81 in FIG. 1. The method 150 includes several steps S151-S155. These steps may be performed partially or entirely in the processor 11, partially or entirely in the wearable device 2, or cooperatively between the processor 11 and the wearable device 2. Furthermore, the user device 4 and / or the server computer 6 may perform part or all of one or more of steps S151 to S155.

[0203] In a preparation step, three or more, preferably four, skin temperature sensors 32 are attached to a mammalian, particularly human, body 8. The skin temperature sensors 32 are attached to defined positions on the human body 8, for example using a selection of defined positions 81, 82, 83, 84, 85 as shown in FIG. 1 in particular. The three or more skin temperature sensors 32 preferably communicate wirelessly with the wearable device 2 and / or the user device 4. The measured skin temperatures may be transmitted to the processor 11 directly from the skin temperature sensors 32 or via an intermediate device.

[0204] In step S151, three or more skin temperature measurements are received from three or more skin temperature sensors 32. The three or more skin temperature measurements are not all identical because the sensors are placed on different parts of the body 8 having different skin temperatures.

[0205] In step S152, three or more skin temperature measurements are used to calculate an average (e.g., arithmetic mean) skin temperature value. The average skin temperature, aST, is calculated using the following formula: TIFF2026502855000015.tif9170, where the subscripts for skin temperature ST represent locations 81, 82, 83, 84 on the body 8.

[0206] In step S153, an instantaneous correction term iCT is calculated using the current value of the mean skin temperature aST. The instantaneous correction term iCT is calculated, for example, as the difference between the desired defined location (e.g., the apex location 81) and the mean skin temperature aST, using the following formula: iCT=ST 81 -aST It is calculated using

[0207] The instantaneous correction term i is recorded. As shown in Figure 15, steps S151-S154 are repeated over a particular elapsed time, such as the elapsed time of a first thermal training session. The instantaneous correction term i is recorded over this elapsed time, and the correction term is then derived therefrom.

[0208] In step S154, the correction term C is determined using the recorded values ​​of the instantaneous correction term i. The correction term C can be determined using one or more averages (e.g., arithmetic mean, median, and / or mode) of the instantaneous correction terms i.

[0209] In step S155, the correction term CT is recorded in the electronic system 1. For example, the correction term CT is recorded in the memory of the wearable device 2 and / or the memory 12 connected to the processor 11.

[0210] The above described method therefore enables the electronic system 1 to apply an individual correction term CT to skin temperature measurements taken at a defined location, such as the apex location 81, during future periods of hyperthermia.

[0211] FIG. 16 shows a time series plot of sensor output values ​​from the sensor system 3 of a wearable device 2 worn by an individual. The individual is cycling indoors at a low intensity for three hours. The core body temperature (CBT) measurement is shown to begin slightly below 37°C, rise to approximately 38°C during the second hour, and then begin to stabilize again during the third hour. Skin temperature (ST) begins below 33°C, initially rises, then falls during the second and third hours, before rising to above 34°C toward the end of the session. Mean body temperature (MBT), calculated using core body temperature (CBT) and skin temperature (ST), falls between CBT and ST, initially rises from a value of approximately 35°C toward 36°C, remains fairly stable for the first two hours, falls in response to the fall in skin temperature (ST) during the third hour, and then rises again toward 36°C toward the end of the session. The heat strain index HSI during the session remains at 0 at all times because the mean body temperature MBT remains below the lower temperature threshold LTT. Therefore, the heat strain score HSS calculated for the session is also 0.

[0212] 17 shows a time series plot of sensor output values ​​from sensor system 3 of wearable device 2 worn by an individual. The time series plot covers a four-hour period including a first outdoor high-intensity cycling session and a second indoor high-intensity cycling session.

[0213] During the outdoor session, which has an elapsed time of approximately 1 hour, the core body temperature (CBT) can be seen to increase from an initial value of approximately 37°C to above 38°C, before decreasing slightly toward the end of the session. Meanwhile, the skin temperature (ST) begins at 34°C and steadily decreases to below 30°C during the session. The mean body temperature (MBT) continues to decrease slowly and steadily during the session, decreasing from below 36°C to approximately 35°C. The decrease in mean body temperature (MBT) is a result of the sudden decrease in skin temperature (ST). The heat strain index (HSI) during the outdoor session was determined to be 0, and therefore, the outdoor session does not contribute to the heat strain score (HSS).

[0214] During the indoor sessions, which are longer than the outdoor sessions, the core body temperature CBT increases from below 38°C to above 39°C before decreasing again toward 38°C toward the end of the indoor session. The skin temperature ST increases from 34°C to above 36°C before decreasing to below 34°C at the end of the indoor session. The calculated mean body temperature MBT increases from approximately 36°C to above 38°C during the session before decreasing again to just above 36°C. The heat strain index HSI increases sharply from 0 at the beginning of the session to a value just below 9 at the point when the mean body temperature MBT (and therefore the core body temperature CBT and skin temperature ST) is at its maximum. The heat strain index HSI then decreases again to 0 before the end of the indoor session. Because the heat strain index HSI was not 0 in the indoor session, the heat strain score HSS calculated using the heat strain index HSI was 752.70 in the indoor session, and therefore also for the combined outdoor and indoor sessions.

[0215] The above-described embodiments of the present disclosure are illustrative, and those skilled in the art will recognize that at least some of the components and / or steps described in the above-described embodiments may be rearranged, omitted, or introduced into other embodiments without departing from the scope of the present disclosure.

Claims

1. 1. A computer-implemented method for determining a measure of thermal physiological strain in a mammal, comprising: receiving (S13) measurements of core body temperature (CBT) and skin temperature (ST) of the mammal; Calculating (S14) a mean body temperature (MBT) as a function of the core body temperature (CBT) and the skin temperature (ST); Calculating (S15) a heat strain index (HSI) representing physiological strain of the mammal due to heat as a function of the mean body temperature (MBT); A method comprising:

2. 2. The method of claim 1, wherein the mammal is a human being, the measurements of the skin temperature are obtained at defined locations on the human body, in particular at a single defined location, and the method comprises correcting the skin temperature (ST) using a predetermined correction term related to the defined location.

3. The predetermined correction term for the defined position is measuring skin temperature at a plurality of different locations on the body, preferably at least three different locations, including said defined location; calculating an average skin temperature using the plurality of measurements; determining the predetermined correction term using a difference between the average skin temperature and the skin temperature at the defined location; The method of claim 2 , wherein the value is determined by

4. receiving one or more additional physiological signals of the mammal, the additional physiological signals including one or more of heart rate, heart rate variability, galvanic skin response, sweat rate, respiration rate, oxygen saturation, heat flux, movement data, glucose concentration, or lactate concentration; further using the one or more additional physiological signals to calculate the heat strain index (HSI); The method according to any one of claims 1 to 3, comprising:

5. 5. The method of claim 4, comprising calculating the heat strain index (HSI) as a function of the mean body temperature (MBT), a lower temperature threshold (LTT), and an upper temperature threshold (UTT).

6. The method according to any one of claims 1 to 5, wherein the core body temperature (CBT) is calculated as a function of the heat flux at the skin (HF) and the skin temperature (ST).

7. 7. The method according to any one of claims 1 to 6, wherein the mean body temperature (MBT) is calculated as a linear combination of the core body temperature (CBT) and the skin temperature (ST), preferably a weighted sum including a core body temperature coefficient and a skin temperature coefficient.

8. 8. The method of claim 5, wherein the Heat Strain Index (HSI) is proportional to the difference between the Mean Body Temperature (MBT) and the Lower Temperature Threshold (LTT) and inversely proportional to the difference between the Upper Temperature Threshold (UTT) and the Lower Temperature Threshold (LTT).

9. 9. The method of claim 1, further comprising determining a normalized heat strain index (nHSI) for the high heat period or portion thereof by calculating the i-th power of each of a plurality of heat strain index values ​​(where i is a number between 2 and 5), calculating an average value of the i-th powers, and calculating the i-th root of the average value.

10. 10. The method of claim 1, further comprising determining (S17) a heat strain score (HSS) representative of the cumulative heat load for a high heat period or portion thereof, wherein the heat strain score (HSS) is determined using the normalized heat strain index (nHSI) and the elapsed time (P) of the high heat period or portion thereof.

11. 11. The method of claim 1, further comprising generating an Acute Heat Load (AHL) for a particular point in time as a weighted sum of one or more Heat Strain Scores (HSS) for one or more previous high heat periods, wherein the weight of a given Heat Strain Score (HSS) depends on the time interval between the particular point in time and the date of the given previous high heat period, with the weight decreasing as the elapsed time increases.

12. 12. The method of claim 11, further comprising generating a long-term heat load (CHL) for a particular point in time as a weighted sum of one or more heat strain scores (HSS) for one or more previous high heat periods, wherein the weight of a given heat strain score (HSS) depends on the time interval between the particular point in time and the date of the given previous high heat period, and the weight decreases as the elapsed time increases, the decrease being less rapid than the decrease in weights used to generate the short-term heat load (AHL).

13. 13. The method of claim 12, further comprising calculating a Thermal Readiness Index (HSB) as a function of the long term thermal load (CHL) and the short term thermal load (AHL), preferably as the difference between the long term thermal load (CHL) and the short term thermal load (AHL).

14. 14. The method of any one of claims 1 to 13, further comprising generating an alarm signal if the Heat Strain Index (HSI) exceeds a Heat Strain Index threshold and / or if the Heat Strain Score (HSS) exceeds a Heat Strain Score threshold.

15. The method of any one of claims 1 to 14, further comprising generating (S16) a message including said Heat Strain Index (HSI).

16. 16. The method of any one of claims 1 to 15, further comprising determining an acclimation-adjusted heat strain index (aaHSI) using the heat strain index (HSI) and one or more additional physiological signals of the mammal comprising one or more of heart rate, heart rate variability, galvanic skin response, hydration status, body weight, sweat rate, respiratory rate, oxygen saturation, heat flux, movement data, performance data, glucose concentration, or lactate concentration.

17. 17. The method of claim 16, further comprising determining an acclimatization index (ACI) indicative of a degree of human acclimatization, the acclimatization index being determined as a function of the acclimatization-adjusted heat strain index (aaHSI) and the heat strain index (HSI).

18. An electronic system (1) for determining a measure of thermal physiological strain in a mammal, the electronic system (1) comprising a processor (11) configured to perform the method according to any one of claims 1 to 17.

19. A wearable device (2) including a sensor system (3) configured to determine the core body temperature (CBT) and the skin temperature (ST) of the mammal, the sensor system (3) being worn on the body of the mammal.

19. The electronic system (1) of claim 18, further comprising:

20. 20. The electronic system (1) of claim 19, wherein the sensor system (3) comprises a heat flux sensor (31) configured to determine the core body temperature (CBT) and a temperature sensor (32) configured to determine the skin temperature (ST), in particular the heat flux sensor (31) comprising a series of p-type and n-type doped semiconductors.

21. 21. The electronic system (1) according to claim 19 or 20, comprising one or more of the following sensors: a PPG sensor (34), an ECG sensor (35), a blood lactate sensor, a galvanic skin response sensor, a sweat rate sensor, one or more further skin temperature sensors, an ambient temperature sensor (33), or an accelerometer, preferably said additional sensors being integrated into the wearable device (2).

22. The electronic system (1) according to any one of claims 19 to 21, wherein the wearable device (2) comprises a strap for fastening the wearable device (2) to the human body (8), in particular a chest strap or a wrist strap.

23. The electronic system (1) according to any one of claims 19 to 22, wherein the processor (11) is integrated into the wearable device (2).

24. The electronic system (1) according to any one of claims 19 to 23, wherein the wearable device (2) comprises a wireless communication module configured to generate and transmit a message comprising the current heat strain index (HSI).

25. The electronic system (1) of any one of claims 19 to 24, further comprising another electronic device (4, 6), wherein the processor (11) is integrated into the other electronic device (4, 6), and the wearable device (2) includes a wireless communication module configured to transmit a message including the core body temperature (CBT) and the skin temperature (ST) to the other electronic device (4, 6).

26. A computer program product for determining a measure of thermal physiological strain in a mammal, the computer program product comprising computer program code configured to control a processor (11) to perform the method of any one of claims 1 to 18.

Images