Smart Adaptive Personalized Wearable Thermal Management System

Abstract

The invention relates to a personalized adaptive thermal management system which includes a heat exchanger, a thermoelectric module, and a wearable interface. The wearable interface is in communication with a contact plate on one side of the thermoelectric module and the heat exchanger is in communication with a contact plate on another side of the thermoelectric module. The thermoelectric module is connected to a user interface which allows for efficient control of the thermoelectric module to enhance efficiency. The wearable interface is an article of clothing such as a vest, hat, helmet, collar, shirt, backpack, or other item and the system leverages data regarding the user and the environment to provide enhanced performance.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63 / 641,478, filed May 2, 2024, which is herein incorporated by reference in its entirety.BACKGROUND

[0002] The human body is a heat generator and a thermally sensitive system. A large fraction of the energy input to human body as food (chemical energy) is converted to thermal energy (heat) and must be dissipated to the environment to maintain a healthy core temperature. In normal conditions, a human body's thermoregulatory system enables the heat dissipation to the environment and regulation of the core body temperature.

[0003] The thermoregulatory system's capacity to dissipate heat to the environment is dependent on ambient temperature, humidity, sunlight intensity, elevation, cardiovascular system's health, age, and several other parameters. In hot and hot-humid conditions the thermoregulatory system's capacity to dissipate heat to the environment quickly deteriorates. Over time, reduced heat dissipation to the environment (or reverse heat flow in extreme heat wave cases) may lead to thermal energy (heat) accumulation in human body and increase in core temperature above the healthy level. Elevated core temperatures lasting over an extended period (heat stress) may lead to heat stroke, permanent damage to vital organs (kidney, brain, heart, and liver), and eventually death.SUMMARY

[0004] The present disclosure is directed to an adaptive personalized wearable thermal management system with both cooling and heating functionality. In certain disclosed embodiments, the adaptive personalized thermal management system monitors several parameters including ambient conditions, user's biomarkers, metabolic activity level, and motion in order provide the required heat exchange capacity (needed cooling / heating power) and to maintain a comfortable and healthy thermal condition. Unlike existing technologies, the disclosed adaptive personalized thermal management system adjusts its thermal response (heating or cooling) constantly based on the data provided by its sensors, available external data, and user's own thermal profile. In certain embodiments, the accuracy of the disclosed adaptive personalized thermal management system improves over time as the system builds up a more accurate personalized profile for the wearer over a wide range of climate conditions, metabolic activity levels, and other parameters it utilizes.

[0005] To avoid heat stress and heat related illnesses, external cooling capacity can be provided to help the human body to stay in a healthy and comfortable temperature level. The current disclosure describes a wearable thermal management system, which enables a human body to maintain a healthy thermal equilibrium with the environment and keep human body temperature at a healthy and comfortable level.

[0006] Disclosed herein is a personalized adaptive thermal management system which includes a heat exchanger, a thermoelectric module in communication with the heat exchanger, a wearable interface in communication with the thermoelectric module and a user interface in communication with the thermoelectric module, wherein the user interface enables control of at least one temperature effect provided by the wearable interface. In certain embodiments, the personalized adaptive thermal management system further includes a first contact plate disposed between the thermoelectric module and the heat exchanger. The first contact plate and thermoelectric module permit heat transfer between the heat exchanger and the thermoelectric module. The system may also include a second contact plate disposed between the wearable interface and the thermoelectric module. The second contact plate and the thermoelectric module permit heat transfer between the wearable interface and the thermoelectric module.

[0007] In some embodiments, the heat exchanger is an air-cooled heat-exchanger. In some embodiments the heat exchanger is a multiphase heat exchanger. In some embodiments, the heat exchanger is a single-phase heat exchanger. In some embodiments, the heat exchanger is a phase change heat exchanger. In some embodiments, the phase change heat exchanger is a phase change material (PCM). IN some embodiments, the PCM comprises ice.

[0008] In certain embodiments, the wearable interface further comprises at least one sensor. The sensor or sensors may be configured to measure at least one of motion, temperature, acceleration, humidity, and light intensity.

[0009] The system may also further include a processor, where the processor is connected to the at least one sensor and the thermoelectric module, and where processor is configured to utilize information from the at least one sensor to control the thermoelectric module. The processor may be configured to use the information from the at least one sensor to calculate a desired voltage to be applied to the thermoelectric module.

[0010] In certain embodiments the wearable interface comprises a vest or a backpack. In some embodiments, the vest may include an integrated water dispenser, air circulation system, sensors, and customizable cooling channels for dynamic temperature regulation. In some embodiments the water dispenser system includes a reservoir or bladder for holding water, and a network of tubes or channels for distributing the water evenly throughout the vest. In some embodiments, the air circulation system includes tubes integrated into the vest and connected to an air pump, and further comprising a plurality of openings disposed throughout the vest and configured to allow air flow to evaporate water from a wet vest.

[0011] In certain embodiments the processor is configured to utilize sensor data to monitor a user's physiological parameters and at least one environmental data measurement to dynamically adjust a system cooling mode. In some embodiments the sensors monitor a user's body temperature, ambient temperature and humidity, and adjust at least one of a water flow and air flow based on the monitoring. In some embodiments, the system contains an alarm system to alert a user or a caregiver for the user of the system.BRIEF DESCRIPTION OF THE FIGURES

[0012] A full and enabling disclosure of the present subject matter, including the best mode thereof to one of ordinary skill in the art, is set forth more particularly in the remainder of the specification, including reference to the accompanying figures in which:

[0013] FIG. 1A is a block diagram of an exemplary control system for managing a user's thermal state.

[0014] FIG. 1B is a block diagram showing exemplary inputs to machine-learned models implemented on a computing device.

[0015] FIG. 2 is a block diagram showing the flow of heat from the wearable thermal interface unit and the environment.

[0016] FIG. 3 is a simplified diagram of an embodiment consistent with this disclosure.

[0017] FIG. 4 is a simplified diagram of an embodiment consistent with this disclosure.

[0018] FIG. 5 is a simplified diagram of an embodiment consistent with this disclosure.DETAILED DESCRIPTION

[0019] Reference will now be made in detail to various embodiments of the disclosed subject matter, one or more examples of which are set forth below. Each embodiment is provided by way of explanation of the subject matter, not limitation thereof. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made in the present disclosure without departing from the scope or spirit of the subject matter. For instance, features illustrated or described as part of one embodiment, may be used in another embodiment to yield a still further embodiment.

[0020] In general, the present disclosure is directed to a cooling vest designed to offer superior temperature regulation in diverse environments and activities. Certain embodiments are provided below which describe various functional pieces of the adaptive personal thermal management system. Control of the various components of the system may be informed by computing systems such as those contained in FIG. 1.

[0021] This patent application discloses a personalized adaptive thermal management system. The disclosed system is smart, lightweight, quiet, compact, and easy to use. In certain embodiments, the system is enhanced with AI operation features which control or inform the control mechanisms of the system. The system determines the required / needed cooling / heating power based on an array of inputs from the user and the environment and delivers the optimum cooling / heating power. These inputs may include but are not limited to the user's metabolic activity level, heartrate, weight, height, body temperature, breathing rate, and other biomarkers in addition to ambient temperature, humidity, sunlight intensity and other parameters. In certain embodiments, the system generates a personalized and dynamic response to each user based on the user's thermal profile. The minuscule thermal mass, rapid response, and compact and efficient design of the system enables it to follow the input array closely and generate the personalized thermal response tuned to maintain the wearer in a thermally comfortable zone and prevent heat stress, heat stroke, and other heat / cold related illnesses.

[0022] Turning now to a detailed discussion of the figures, FIGS. 1A and 1B present block diagrams of an exemplary control system for managing a user's thermal state. These control systems may utilize advanced modeling techniques featuring AI-enhanced control, or may utilize conventional means of modeling or control.

[0023] FIG. 1A depicts a block diagram of an example computing system 100 that performs training of a machine learning model 120 according to example embodiments of the present disclosure. The system 100 includes a user computing device 102, a server computing system 130, and a training computing system 150 that are communicatively coupled over a network 180.

[0024] The user computing device 102 includes one or more processors 112 and a memory 114. The one or more processors 112 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 114 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing device 102 to perform operations.

[0025] In some implementations, the user computing device 102 can store or include one or more machine learning models 120. For example, the machine learned models 120 can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other types of machine-learned models, including non-linear models and / or linear models. Neural networks can include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks or other forms of neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models).

[0026] In some implementations, the one or more machine learning models 120 can be received from the server computing system 130 over network 180, stored in the user computing device memory 114, and then used or otherwise implemented by the one or more processors 112.

[0027] More particularly, the machine learned model 120 may be used to aggregate and categorize data relating to temperature, humidity, heart rate, blood oxygen level, humidity, or any following described parameters.

[0028] Additionally or alternatively, one or more machine learning models 140 can be included in or otherwise stored and implemented by the server computing system 130 that communicates with the user computing device 102 according to a client-server relationship. For example, the machine learning models 140 can be implemented by the server computing system 130 as a portion of a web service Thus, one or more models 120 can be stored and implemented at the user computing device 102 and / or one or more models 140 can be stored and implemented at the server computing system 130.

[0029] The user computing device 102 can also include one or more user input components 122 that receives user input. For example, the user input component 122 can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other means by which a user can provide user input.

[0030] The server computing system 130 includes one or more processors 132 and a memory 134. The one or more processors 132 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 134 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 134 can store data 136 and instructions 138 which are executed by the processor 132 to cause the server computing system 130 to perform operations.

[0031] In some implementations, the server computing system 130 includes or is otherwise implemented by one or more server computing devices. In instances in which the server computing system 130 includes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof.

[0032] As described above, the server computing system 130 can store or otherwise include one or more machine learning models 140. For example, the models 140 can be or can otherwise include various machine-learned models. Example machine-learned models include neural networks or other multi-layer non-linear models. Example neural networks include feed forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models).

[0033] The user computing device 102 and / or the server computing system 130 can train the models 120 and / or 140 via interaction with the training computing system 150 that is communicatively coupled over the network 180. The training computing system 150 can be separate from the server computing system 130 or can be a portion of the server computing system 130.

[0034] The training computing system 150 includes one or more processors 152 and a memory 154. The one or more processors 152 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 154 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 154 can store data 156 and instructions 158 which are executed by the processor 152 to cause the training computing system 150 to perform operations. In some implementations, the training computing system 150 includes or is otherwise implemented by one or more server computing devices.

[0035] The training computing system 150 can include a model trainer 160 that trains the machine-learned models 120 and / or 140 stored at the user computing device 102 and / or the server computing system 130 using various training or learning techniques, such as, for example, backwards propagation of errors. For example, a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function). Various loss functions can be used such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and / or various other loss functions. Gradient descent techniques can be used to iteratively update the parameters over a number of training iterations.

[0036] In some implementations, performing backwards propagation of errors can include performing truncated backpropagation through time. The model trainer 160 can perform a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained.

[0037] In particular, the model trainer 160 can train the machine learning models 120 and / or 140 based on a set of training data 162. The training data 162 can include, for example, past physiological information, past ambient information, or any of the following described parameters.

[0038] In some implementations, if the user has provided consent, the training examples can be provided by the user computing device 102. Thus, in such implementations, the model 120 provided to the user computing device 102 can be trained by the training computing system 150 on user-specific data received from the user computing device 102. In some instances, this process can be referred to as personalizing the model.

[0039] The model trainer 160 includes computer logic utilized to provide desired functionality. The model trainer 160 can be implemented in hardware, firmware, and / or software controlling a general purpose processor. For example, in some implementations, the model trainer 160 includes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, the model trainer 160 includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium such as RAM, hard disk, or optical or magnetic media.

[0040] The network 180 can be any type of communications network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof and can include any number of wired or wireless links. In general, communication over the network 180 can be carried via any type of wired and / or wireless connection, using a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0041] FIG. 1B depicts a set of exemplary modes operating on a computing device 190. As depicted, cooling mode 191, heating mode 192, or other user customized modes 193 may receive various inputs in the operation of these modes. For example, sensors 194 may provide sensor data, context manager 195 may provide context data, device state variables 196 may provide device state data, and other additional components 197 may provide additional data (e.g., statistical data).

[0042] In some implementations, the input to the machine-learned model(s) of the present disclosure can be statistical data. Statistical data can be, represent, or otherwise include data computed and / or calculated from some other data source. The machine-learned model(s) can process the statistical data to generate an output. As an example, the machine-learned model(s) can process the statistical data to generate a recognition output. As another example, the machine-learned model(s) can process the statistical data to generate a prediction output. As another example, the machine-learned model(s) can process the statistical data to generate a classification output. As another example, the machine-learned model(s) can process the statistical data to generate a segmentation output. As another example, the machine-learned model(s) can process the statistical data to generate a visualization output. As another example, the machine-learned model(s) can process the statistical data to generate a diagnostic output.

[0043] In some implementations, the input to the machine-learned model(s) of the present disclosure can be sensor data. The machine-learned model(s) can process the sensor data to generate an output. As an example, the machine-learned model(s) can process the sensor data to generate a recognition output. As another example, the machine-learned model(s) can process the sensor data to generate a prediction output. As another example, the machine-learned model(s) can process the sensor data to generate a classification output. As another example, the machine-learned model(s) can process the sensor data to generate a segmentation output. As another example, the machine-learned model(s) can process the sensor data to generate a visualization output. As another example, the machine-learned model(s) can process the sensor data to generate a diagnostic output. As another example, the machine-learned model(s) can process the sensor data to generate a detection output.

[0044] FIG. 2 illustrates a high-level block diagram of one embodiment adaptive personalized thermal management system 200. User 210 is equipped with wearable thermal interface unit 220 which provides heating and cooling to user 210. In the disclosed embodiment, User 210 is also able to interact with user interface unit 230. However, in certain embodiments user interface unit 230 may not be present, or may be an extremely simplified interface. Throughout FIG. 2, thermal exchange occurs at exchanges 205.

[0045] User interface unit 230 is configured to interact with and inform power and control unit 240. Power and control unit 240 is operatively connected to heat exchange components: wearable thermal interface unit (WITU) 220, active thermal unit (ATU) 250, and heat exchange unit (HEU) 260. Power and control unit provides and adjusts power to each of these units. In certain embodiments, sensors and electronics are housed in a water-resistant, durable casing. The power and control unit and associated sensors and electronics may be of a compact, lightweight design for seamless integration into the wearable thermal interface unit.

[0046] Wearable thermal interface unit (WITU) 220 interfaces with user 210 and may be in the form of a vest, hat, helmet, neck collar, shirt, pants, shorts, or other wearable item, such as a backpack or a portion of a backpack. Further, wearable thermal interface unit 220 may be a combination of these wearable items, e.g., a vest and a hat, vest hat and collar, etc. The wearable item(s) of wearable thermal interface unit 220 includes a fluid circulation system with tubing, contact surfaces, and materials that facilitate heat transfer from the skin surface to the circulating fluid, or vice-versa.

[0047] Active thermal unit (ATU) 250 is itself operatively connected to wearable thermal interface unit 220 and may exchange heating and cooling with wearable thermal interface 220. Active thermal unit 250 adjusts the temperature of the working fluid which flows into the wearable thermal interface unit 220. Active thermal unit 250 may employ a single or a combination of cooling-heating mechanisms to change the temperature of the working fluid circulating through the wearable thermal interface unit and active-thermal unit. For example, in certain embodiments, ATU 250 may utilize thermoelectric modules (TECs) coupled with an air heat exchanger. In another embodiment, ATU 250 may utilizes TECs coupled with a liquid heat-exchanger. In another embodiment, ATU 250 may utilize TECs coupled with a phase-change-heat-exchanger, such as solid-liquid, liquid-vapor, solid-vapor, or a combination of all three. In other embodiments, a combination of TECs coupled with different heat-exchangers may be utilized, e.g., both an air and liquid heat-exchanger, an air and phase-change heat-exchanger, a liquid and phase-change heat exchanger, or a combination of all three. In each instance, TEC operation may be optimized based on the teachings contained in this disclosure.

[0048] Furthermore, active thermal unit 250 may exchange heating and cooling with heat exchange unit 260. Heat exchange unit 260 is configure to interface and provide exchange of hot / cold with the outside environment (or relevant ambient environment). When adaptive personalized thermal management system 200 is operating in a cooling mode, heat exchange unit 260 operates as a heat dissipation mechanism, absorbing the total energy from user 210 and waste heat generated by ATU 250.

[0049] User interface unit 230 allows control of adaptive personalized thermal management system. In certain embodiments, the user interface associated with the user interface unit is provided using an app on a user's phone, smart watch, or other similar device. In other embodiments, the user interface may be provided by attachment to the wearable systems, with either a wired or wireless connection to power and control unit. In other embodiments, a combination of an app-based interface and a directly connected interface may be used. User interface unit 230 allows the user to control various settings and functions of adaptive personalized thermal management system, including, for example, setting the desired temperature, choosing from existing present programs, or activating a personalized adaptive mode, which may incorporated various AI-based methods or may otherwise use adaptive logic based on the input of various items of the input array.

[0050] In operation, the adaptive personalized thermal management system is configured to operate utilizing an array of input data which informs power and control unit 240. Input data may include both user-specific data and environment specific data. For example, user-specific data may include, but is not limited to, a user's temperature, motion, heart rate, bloody oxygen level, breathing rate, and metabolic activity level. Environment-specific data may include, but is not limited to temperature, humidity, sunlight intensity, elevation, and windspeed. These data may be considered as point-in-time measurements, historical data, or use predictive information, for example.

[0051] Adaptive personalized thermal management system 200 via power and control unit 240 may be operated in a manual mode through user input via the user interface. System 200 may also be operated using adaptive operation which leverages an integrated AI system such as the one disclosed in connection with FIGS. 1A and 1B. In other modes of operation, adaptive personalized thermal management system may utilize previously programmed operations. For example, a user undergoing medical treatments where cooling or heating may be valuable for the health or comfort of a patient, a programmed response may be utilized. Similarly, if certain emergency response situations (e.g., heat stroke, overheating, or known intense thermal environments) are encountered, a programmed treatment may be utilized. Additionally, a combination of any of the above modes may be utilized.

[0052] An operating mode for adaptive personalized thermal management system may be considered a Thermal Operating Mode comprising a cooling mode and a heating mode. In a cooling mode, circulating fluid is cooled to the desired temperature by Active Thermal Unit 250. The cooled fluid will then enter a fluid distribution system embedded in the structure of the wearable item (e.g., vest, hat, neck collar, etc.) and is brought into thermal contact with the user's skin (either directly or indirectly across clothing) where it provides the required cooling power to maintain user's temperature at a healthy level. As a result of heat exchange with the user's body the fluid temperature increases. The warm fluid then flows back to the wearable system heat-exchange unit to be cooled again and repeat the cycle again.

[0053] In a heating mode, an operation similar to the cooling mode occurs, but essentially in reverse. Circulating fluid is warmed up in Active Thermal Unit 250 to a temperature higher than the user's body temperature. The circulating fluid then enters the wearable fluid distributing system and warms the skin. The cooled fluid then flows back to Active Thermal Unit 250 to repeat the cycle.

[0054] Certain non-limiting examples of possible use cases for the wearable the following examples are given in more detail below.Workplace Applications

[0055] In one example, the personalized adaptive thermal management system may be deployed in workplace environments. For example, the personalized adaptive thermal management system may protect workers exposed to heat from heat stress and prevents decline in workplace productivity. Through its use, the system helps workers to stay thermally comfortable and prevents dehydration. Due to its continuous monitoring of user vital data, the personalized adaptive thermal management system can alert the user to stop working when heat stress / stroke is imminent.Medical Applications

[0056] Multiple different medical applications may employ the personalized adaptive thermal management system, including in diagnostic, safety and prevention, and emergency response settings. Furthermore, the personalized adaptive thermal management system may be used in connection with other medical treatments which may cause heating or cooling sensation to the user, e.g., chemotherapy treatments. Some exemplary aspects of the diagnostic, safety and prevention, and emergency response uses are detailed below:Diagnosis

[0057] The ability of the human body to regulate its temperature and withstand extreme heat or cold is often closely linked to the health of its cardiovascular and other essential systems. By utilizing the array of inputs available to the personalized adaptive thermal management system the system may build a dynamic personal thermal profile for each user over time. An artificial intelligence system may utilize the thermal profile in addition to monitored parameters to provide the thermal response. The thermal profile for each user and his / her health data monitored by multiple sensors can be utilized for a wide range of medical diagnosis purposes.Safety and Prevention

[0058] The personalized adaptive thermal management system safeguards against dehydration, thermal stress, heat stroke, and heat-related illnesses by ensuring the human body remains within a healthy thermal state. The system may alleviate strain on the thermoregulatory system and provide particular benefits to the cardiovascular system and heart through maintaining this desired thermal state.Response to Heat-Related Emergencies

[0059] The personalized adaptive thermal management system provides a safer and more efficient alternative to existing treatments. In contrast to existing procedures and treatments the system provides a controlled and safe treatment by precise control of the cooling power and intensity, thus preventing the human body from exposure to extremely cold temperatures. The personalized adaptive thermal management system may be used in situations of both extreme heat and extreme cold, useful in preventing both heat stroke and hypothermia.

[0060] FIG. 3 depicts an embodiment of the personalized adaptive thermal management system utilizing an air-cooled heat exchanger. Turning first to heating portions of the system, thermoelectric module (TE) warm side contact plate 310 is associated with TE warm side 330. The thermoelectric module relies on the Peltier effect, where an electric current causes a temperature difference, allowing for heat flow in the desired direction and for precise temperature control. TE warm side contact plate 310 is in communication with warm side heat exchanger 350. Outgoing heat fluid 360 is expelled from warm side heat exchanger 350 and directed to air-cooled heat exchanger 370. Fan 382 is an exemplary air-flow inducing device that is used to induce air-cooling to heat exchanger 370. Fluid 380 is expelled from air-cooled heat exchanger 370 back to warm side heat exchanger 350. In some embodiments, fan 382 may be omitted. In some embodiments, heat from fluid is dissipated using a heat exchanger where the environmental heat exchange occurs through other means such as liquid or phase-change heat exchange / transfer.

[0061] Turning now to cool side portions of the system, TE cold side contact plate 320 is associated with TE cold side 340. Cold side heat exchanger 390 cools and expels outgoing cooled fluid 317. Outgoing cooled fluid 317 is supplied to wearable system 311 (e.g., wearable thermal interface unit) which further comprises sensing elements 313. As described above with personalized adaptive thermal management system 200, sensing elements may include user-specific sensing elements and environment-specific sensing elements, including but not limited to motion, temperature, acceleration, humidity, light intensity, etc. After circulation through wearable system 311, heated fluid 312 is provided back to cold side heat exchanger 390.

[0062] Fluids 360, 380, 312, and 317 may be directed using fluid pumps, including micropump technology.

[0063] As depicted in FIG. 3, power, sensing, and control system (PSCS) 314 interfaces with TE warm side 330 and TE cold side 340, via connections 315, and is also connected to user interface unit 316 and wearable system 311 including sensing elements 313. Connections 315 between power, sensing, and control system 314 and TE warm side 330 and cold side 340 provide a capability for optimized control and operation of thermoelectric modules. PSCS 314 may utilize the computing system disclosed in FIGS. 1A and 1B to optimize the applicable cooling / heating mode.

[0064] Although only one thermoelectric module is shown, in certain embodiments, a plurality of thermoelectric modules may be deployed. The personalized adaptive thermal management system is configured to effectively and efficiently determine ideal operating parameters for the thermoelectric modules. In certain embodiments, contact plate 310 and / or contact plate 320 may be omitted.

[0065] The personalized adaptive thermal management system may utilize its components, alone or in combination, to enhance (maximize) machine learning efficiency, prolong battery operating life, enhance response time, and increase power to mass ratio.Dynamic Power Optimization

[0066] Thermoelectric modules, when used as cooling systems, have a very narrow window of high efficiency. Their performance declines quickly as temperature difference between their cold side and hot side exceed a few degrees Celsius and as their power intake exceeds above or drops below a certain power window.

[0067] The personalized adaptive thermal management system PSCS monitors the temperature on the hot and cold side of each TE module and adjusts the electric power (voltage) applied to each TE module based on the real-time temperature readings and the total cooling power requirement which is determined by another section of the PSCS. This ensures the delivery of the desired cooling power at any given time while maximizing the efficiency of the system. This feature enables the personalized adaptive thermal management system to prolong battery life per charge cycle and maximize total cooling power per unit mass of the system.

[0068] In one embodiment, the temperature of the hot sides and cold sides of an array of thermoelectric modules are monitored continuously. The array may include all thermoelectric modules in a given system or a subset of the thermoelectric modules for a given system. Average temperatures of the hot sides and cold sides are calculated, and an temperature difference between the average hot side and average cold side are calculated.

[0069] Because thermoelectric modules can be inefficient if not operating within an optimized ranged, the personalized adaptive thermal management system calculates an optimized voltage to be applied to the array of TE modules. The optimized voltage is the voltage which results in a maximum coefficient of performance (COP) based on the temperature difference between the average hot and average cold sides.

[0070] The array of TE modules may be in a series configuration or a parallel configuration. In parallel configuration, the optimized voltage is applied evenly across the array of TE modules. In series configured, the optimized voltage multiplied by the number of TE modules in the array is applied to the two ends of the TEC array.

[0071] COP is calculated as a function of voltage and temperature difference for each TEC model. In other words, COP=f(V, ΔT), Vopt=g(COPmax, ΔT, NTEC), and thus the optimal V may be calculated for any given ΔT to maximize COP.

[0072] In addition to controlling the overall heating and cooling of the system by applying a voltage to a given TE module or TE module array, personalized adaptive thermal management system may control overall heating and cooling by adjusting the pumping rate of micropumps and / or the speed of air-flow inducing fan (or its equivalent when the environmental heat exchange occurs through other means such as liquid or phase-shift).Hybrid Heat Dissipation for Ultra-Low Thermal Resistance

[0073] The personalized adaptive thermal management system may utilize a number of different of heat transfer mechanisms and systems to lower the temperature on the hot (warm) side of the TE modules and increase the efficiency and performance of the ATU and HEU systems. This may include optimized geometry to boost heat transfer and reduced thermal resistance, optimized fluid flow by adjusting the power applied to micropumps, adjustment of power applied to the fans, reducing resistance to fluid flow using non-wetting surfaces, and employing phase-change heat exchangers to reduce thermal resistance. Any one or combination of the aforementioned enhancements may be utilized.

[0074] Furthermore, at least the following heat transfer mechanisms may used to dissipate heat to the environment: Liquid microchannel cooling couple with air-cooled heat exchange to air, closed two-phase liquid-vapor system, closed two-phase system solid-liquid, open two-phase system liquid-vapor, open multiphase system, direct heat exchange with ambient (air, liquid, etc.), liquid cooling with heat exchange to surrounding water (for diving applications).

[0075] In addition to employing optimized power settings other the remaining components of the system are optimized for weight and power efficiency. For example, battery power provided to the unit may be provided by lithium-ion batteries which may optionally be charged by lightweight solar panels. Lightweight versions of sensors and materials may be employed to reduce the overall impact on the user and thus avoid a counter-productive increase in body temperature caused by excess exertion.

[0076] FIG. 4 depicts an embodiment of the personalized adaptive thermal management system utilizing a multi-phase heat exchanger. Turning first to heating portions of the system, thermoelectric module (TE) warm side contact plate 410 is associated with TE warm side 430. TE warm side contact plate 410 is in communication with multiphase heat exchanger 450. The opposing end of multiphase heat exchanger 460 is in communication with fins 470 which provide extended surface area for optimized heat exchange to the environment. Fan 480 is an exemplary air-flow inducing device that is used to induce air-cooling to fins 470 and heat exchanger 460. In some embodiments, fan 480 may be omitted.

[0077] Turning now to cool side portions of the system, TE cold side contact plate 420 is associated with TE cold side 440. Cold side heat exchanger 490 cools and expels outgoing cooled fluid 417. Outgoing cooled fluid 417 is supplied to wearable system 411 (e.g., wearable thermal interface unit) which further comprises sensing elements 413. As described above with personalized adaptive thermal management system 200, sensing elements may include user-specific sensing elements and environment-specific sensing elements, including but not limited to motion, temperature, acceleration, humidity, light intensity, etc. After circulation through wearable system 411, heated fluid 412 is provided back to cold side heat exchanger 490.

[0078] Fluids 412, and 417 may be directed using fluid pumps, including micropump technology.

[0079] As depicted in FIG. 4, power, sensing, and control system (PSCS) 414 interfaces with TE warm side 430 and TE cold side 440, via connections 415, and is also connected to user interface unit 416 and wearable system 411 including sensing elements 413. Connections 415 between power, sensing, and control system 414 and TE warm side 430 and cold side 440 provide a capability for optimized control and operation of thermoelectric modules. PSCS 414 may utilize the computing system disclosed in FIGS. 1A and 1B to optimize the applicable cooling / heating mode.

[0080] Although only one thermoelectric module is shown, in certain embodiments, a plurality of thermoelectric modules may be deployed. The personalized adaptive thermal management system is configured to effectively and efficiently determine ideal operating parameters for the thermoelectric modules, as discussed above with respect to FIG. 3. In certain embodiments, contact plate 410 and / or contact plate 420 may be omitted.

[0081] FIG. 5 depicts an embodiment of the personalized adaptive thermal management system utilizing a phase change heat exchanger. Turning first to heating portions of the system, thermoelectric module (TE) warm side contact plate 510 is associated with TE warm side 530. TE warm side contact plate 510 is in communication with phase change heat exchanger 550. In some embodiments, the heat changer is a single-phase heat exchanger. In some embodiments, the phase change heat exchanger is comprised of ice or other phase-change materials (PCMs).

[0082] Turning now to cool side portions of the system, TE cold side contact plate 520 is associated with TE cold side 540. Cold side heat exchanger 590 cools and expels outgoing cooled fluid 517. Outgoing cooled fluid 517 is supplied to wearable system 511 (e.g., wearable thermal interface unit) which further comprises sensing elements 513. As described above with personalized adaptive thermal management system 200, sensing elements may include user-specific sensing elements and environment-specific sensing elements, including but not limited to motion, temperature, acceleration, humidity, light intensity, etc. After circulation through wearable system 511, heated fluid 512 is provided back to cold side heat exchanger 590.

[0083] Fluids 512, and 517 may be directed using fluid pumps, including micropump technology.

[0084] As depicted in FIG. 5, power, sensing, and control system (PSCS) 514 interfaces with TE warm side 530 and TE cold side 540, via connections 515, and is also connected to user interface unit516 and wearable system 511 including sensing elements 413. Connections 515 between power, sensing, and control system 514 and TE warm side 530 and cold side 540 provide a capability for optimized control and operation of thermoelectric modules. PSCS 514 may utilize the computing systems disclosed in FIGS. 1A and 1B to optimize the applicable cooling / heating mode.Although only one thermoelectric module is shown, in certain embodiments, a plurality of thermoelectric modules may be deployed. The personalized adaptive thermal management system is configured to effectively and efficiently determine ideal operating parameters for the thermoelectric modules, as discussed above with respect to FIG. 3. In certain embodiments, contact plate 510 and / or contact plate 520 may be omitted.

[0085] In conjunction with the above adaptive personalized thermal management system, a specialized cooling vest may be utilized as the wearable thermal interface unit. The cooling vest may be comprised of various components which enhance its functionality. Although all of the listed components may be utilized in the water vest, any combination of the below components may be employed. Furthermore, any combination of the below components may be utilized in the personalized adaptive thermal management system, regardless of the form of the particular WTIU (e.g., vest, hat, neck collar).Component 1: Cooling Fabric

[0086] In certain embodiments, the improved cooling vest is made from lightweight, breathable, and moisture-wicking materials, designed to conform to the body's shape for efficient cooling and comfort. The vest may be comprised of a blend of phase change materials (PCMs) and high-conductivity fibers.

[0087] The vest is engineered for maximum thermal conductivity while maintaining breathability and comfort. In operation, the vest absorbs and dissipates body heat, providing a cooling effect directly to the skin. The thermal control elements are seamlessly integrated into the vest's inner layer, enhancing the machine learning cooling efficiency by working in tandem with the water distribution and air circulation systems.Component 2: Water Distribution Network

[0088] In certain embodiments, the improved cooling vest features a reservoir or a bladder which holds water, and a network of customizable tubes for even water distribution across the vest, enabling effective cooling. The tubing network may be comprised of flexible, lightweight, and durable medical-grade silicone tubing. The tubing network may be set out in a customizable network layout to evenly distribute cooling water across the vest. According to one mode of operation, the cooling vest circulates water from the reservoir, through the vest, to absorb body heat before recirculation or replacement.

[0089] The water distribution network connects with the hydration reservoir, pump system, and modular connectors for easy maintenance and configuration adjustments.

[0090] The water distribution network may incorporate smart sensors into the water dispenser system that can monitor the wearer's body temperature and adjust the water flow accordingly. This ensures optimal cooling and prevents overcooling.

[0091] In some embodiments, the water distribution network may employ cooling microfluidics, integrating microfluidic channels within the vest fabric that circulate cooling fluids or gels.

[0092] In some embodiments, the water distribution network may utilize multi-flavored cooling and may supply potable water to the user via an ingestion tube. Flavor cartridges or pods may be added to the water dispenser system, allowing users to enjoy a refreshing burst of different flavors while staying cool. This can enhance the machine learning cooling experience and make it more enjoyable.

[0093] The water distribution system may employ a modular design. The modular design may comprise a modular water dispenser system that allows users to customize the placement and number of tubes or channels in the vest. Users can tailor the cooling distribution to their specific needs and preferences and select specific cooling zones.Component 3: Air Circulation System

[0094] In certain embodiments, the improved cooling vest features an air circulation system which integrates tubes with small openings in several places to allow air flow, powered by an eco-friendly air pump, to enhance evaporate the water from the wet vest, enhancing the cooling effect. The air circulation system may incorporate miniaturized, energy-efficient fans and breathable mesh panels. Components of the air circulation system may be strategically placed to optimize airflow within the vest, enhancing evaporative cooling.

[0095] In operation, the air circulation system may actively pull in cooler external air and expel warmer air, significantly boosting the vest's cooling capacity. In certain embodiments, the air circulation system may work in conjunction with the cooling fabric and water distribution network to maximize cooling effects.Component 4: Smart Control Unit

[0096] In certain embodiments, the improved cooling vest features Smart Sensors and AI Integration which monitors physiological and environmental parameters to dynamically adjust cooling intensity and modes for optimal performance. The cooling vest may offer remote adjustments via a smartphone app and provide haptic feedback for intuitive control without visual aid. An employed algorithm may learn from user feedback and environmental interactions to optimize cooling efficiency and comfort over time.

[0097] In certain embodiments, sensors and electronics are housed in a water-resistant, durable casing. The smart control unit and associated sensors and electronics are compact, lightweight design for seamless integration into the vest.

[0098] The smart control unit, with associated sensors and electronics may be configured to monitor temperature, humidity, and user activity to dynamically adjust cooling intensity. Further, the subsystem may include a user interface for manual control and feedback. This offers wireless and haptic feedback control for user-friendly adjustment of settings. It allows users to adjust the water flow and cooling intensity of the vest remotely. This can be done through a smartphone app or a wearable device, providing convenience and customization options.

[0099] In certain embodiments, the smart control unit is integrated centrally to the vest's operation, coordinating the function of all systems, ensuring optimal performance based on environmental conditions and user preferences.Component 5: Energy Harvesting and Storage

[0100] In certain embodiments, the improved cooling vest employs solar panels, kinetic energy from movement, and thermoelectric elements to power the cooling systems sustainably. This allows the vest to generate its own energy and ensures continuous cooling even in remote or outdoor environments.

[0101] The energy harvesting and storage subsystem may use lightweight solar panels, kinetic energy harvesters, and high-capacity, low-profile batteries. The energy harvesting and storage subsystem is designed for efficiency, durability, and minimal impact on vest weight and flexibility.

[0102] The energy harvesting and storage subsystem is designed to collect and store energy from renewable sources, powering the vest's cooling systems. It may, for example, directly power the pump, fans, and control unit, with smart management to prioritize energy use and ensure sustained operation. For example, in situations where certain systems are not needed (e.g., a fan) the system may shut down operation of the fan to conserve power resources.Component 6: Hydration and Nutrient Dispenser

[0103] In certain embodiments, the improved cooling vest features a hydration and nutrient dispenser. The hydration and nutrient dispense may be constructed from food-grade materials, ensuring safety and durability. In certain embodiments, the hydration and nutrient dispenser integrates with the water reservoir, featuring controllable valves for precise delivery.

[0104] The hydration and nutrient dispenser may provide hydration and nutrient solutions directly to the wearer, customizable based on individual needs. In some embodiments the improved cooling vest's water dispenser system may be combined with a hydration bladder, allowing users to drink water directly from the vest. This eliminates the need for separate water bottles or hydration packs, providing convenience and efficiency. Beyond water, it can store and dispense electrolyte solutions or cooling fluids with nutritional benefits. The hydration and nutrient dispenser may link to the smart control unit for automated or manual dispensation based on sensor feedback and user input.Component 7: Self-Cleaning Mechanism

[0105] In certain embodiments, the improved water vest may also include a self-cleaning mechanism within the water dispenser system to prevent the growth of bacteria or mold, ensuring the vest remains hygienic and safe to use for extended periods. It uses nanotechnology and UV light for self-cleaning, ensuring the system remains hygienic.Component 8: Algorithms for Learning Thermal Patterns and Heat Stroke / Heat Exhaustion / Heat Related Illnesses Diagnosis, Prediction and Prevention

[0106] In certain embodiments, the improved cooling vest (or other WITU / wearable system) features a suite of sensors deployed in the WITU / wearable system and an associated detection algorithm, implemented in the power and control unit 240 or in the PSCS. To effectively prevent heat stroke and monitor the wearer's vital signs, the improved cooling vest may be equipped with an array of advanced sensors, including:

[0107] Skin Temperature Sensors: To monitor the surface temperature of the skin, providing immediate data on the body's heat exchange with the environment.

[0108] Heart Rate Monitors: To track the wearer's heart rate, a critical indicator of stress and overheating.

[0109] Hydration Sensors: To measure skin hydration levels, offering insights into potential dehydration, a key factor in heat stroke risk.

[0110] Galvanic Skin Response Sensors: To assess sweat levels, another vital parameter in understanding the body's effort to cool itself down.

[0111] Respiration Sensors: These sensors measure breathing rate and depth, providing critical data on the wearer's metabolic heat production and stress levels. Rapid, shallow breathing may indicate heat stress or the onset of heat-related illness.

[0112] Accelerometers: By monitoring gait stability and movement patterns, accelerometers can detect early signs of physical fatigue, disorientation, or muscle weakness, which are potential precursors to heat stroke.

[0113] These sensors may, alone or in combination, feed data into an AI-driven analytics system that can predict the onset of heat stress or stroke by analyzing trends and deviations in these vital signs. Upon detecting a potential risk, the system can automatically adjust the cooling intensity and mode of the vest, prioritizing rapid reduction in body temperature, initiate an emergency protocol that includes increasing fluid circulation (either through the active cooling or PCM systems) and / or send alerts.Component 9: Machine Learning Predictive Models

[0114] In certain embodiments, the improved cooling vest utilizes machine learning (ML) techniques. Accordingly the vest may predict the risk of heat stroke more accurately by analyzing data from skin temperature, heart rate, hydration levels, galvanic skin response, respiration rates, and gait stability. Innovative ML approaches may include:

[0115] Time-Series Analysis: To track changes in vital signs over time, identifying patterns that precede heat stress conditions.

[0116] Feature Engineering: Creating composite indicators from multiple sensor readings to better capture the physiological state leading to heat stress.

[0117] Anomaly Detection: Identifying deviations from the wearer's normal physiological patterns, which could indicate the onset of heat-related illnesses.

[0118] Predictive Modeling: Using algorithms like Random Forests or Gradient Boosting Machines to integrate real-time data and historical trends, offering predictions on heat stroke risk.

[0119] Deep Learning: Applying neural networks, especially recurrent neural networks (RNNs) or long short-term memory (LSTM) models, to capture complex temporal dependencies among the physiological signals.

[0120] By feeding the ML model with continuous data streams from the improved cooling vest's sensors, it can learn to recognize the onset of dangerous conditions early on. This proactive approach allows for immediate adjustments to cooling strategies and the initiation of alerts and emergency protocols, potentially saving lives by preventing heat stroke before it occurs.

[0121] Furthermore, the system's ability to communicate with external devices and services ensures that in case of a critical alert, detailed information about the wearer's condition and location can be instantly shared with emergency responders or caregivers, enhancing the efficiency of the response and the likelihood of a positive outcome.Component 10: Health Monitoring and Emergency Response

[0122] In certain embodiments, the improved cooling vest features the ability to integrate with health monitoring sensors deployed in the WITU or wearable system that can detect signs of heat exhaustion or dehydration and initiate emergency cooling protocols or alerts.

[0123] In some embodiments, the improved cooling vest may integrate with other smart devices including through enhanced connectivity features for seamless integration with smartphones, smartwatches, and other IoT devices for health and fitness tracking.

[0124] In some embodiments the improved cooling vest may employ Social and Safety Networking, a feature allowing users in close proximity to form a network, sharing environmental data and safety alerts, enhancing group safety in high-risk conditions.

[0125] The vest can be configured to send real-time alerts and health data to multiple sites, such as smartphones, local monitoring stations, or directly to emergency services like ambulances or other caregivers. This feature ensures that in the event of a critical health alert, the user can receive immediate assistance. The integration of GPS and communication technologies enables precise location tracking, ensuring quick response times for emergency personnel.

[0126] This comprehensive approach combines proactive monitoring with automated cooling adjustments and emergency notification capabilities, offering a robust system for preventing heat-related illnesses in high-risk environments.

[0127] To further advance the improved cooling vest, one or more of the below integrations and enhancements may be utilized:

[0128] Phase Change Material Integration: In some embodiments, the improved cooling vest may incorporate microencapsulated phase change materials (PCMs) into the vest's fabric and cooling channels. PCMs are substances that absorb and release thermal energy during the process of melting and freezing, providing substantial temperature regulation without continuous energy input. This feature is especially beneficial in environments where active cooling is not feasible or when energy conservation is a priority.

[0129] Evaporative Cooling Enhancement: In certain embodiments, the improved cooling vest may utilize a highly breathable and moisture-permeable layer that allows for effective evaporative cooling in dry conditions. This system uses the body's natural heat to evaporate water from the reservoir, drawing heat away from the wearer and reducing the skin temperature.

[0130] Active Cooling with Thermoelectrics: In certain embodiments, the improved cooling vest may implement a network of thermoelectric coolers that directly cool the skin or circulate chilled water through the vest's channels. Thermoelectric coolers operate on the Peltier effect, where an electric current causes a temperature difference, allowing for precise temperature control. This mode is particularly effective in humid conditions where evaporative cooling is less efficient.

[0131] Virtual Reality Cooling: Incorporating VR or AR to manipulate the wearer's perception of temperature, using sensory inputs that can make the user feel cooler through visual and auditory cues.Adaptive Mode Switching Based on Environmental Conditions

[0132] In certain embodiments, the improved cooling vest may be equipped with smart sensors and an AI-driven control unit that continuously monitors the ambient humidity, temperature, and the wearer's physiological parameters.

[0133] In dry, hot conditions, the system prioritizes evaporative cooling for energy efficiency and comfort. In humid environments, where evaporative cooling is less effective, the system automatically switches to active cooling using TE modules or activates the PCM to maintain optimal temperature without relying on evaporation. In certain embodiments, the AI controller may attempt to predict environmental changes and adjust the cooling strategy preemptively, ensuring continuous comfort and preventing thermal stress.

[0134] In some embodiments, to enhance the improved cooling vest, integrating a hybrid cooling system that combines phase change material (PCM), evaporative cooling, and active cooling technologies will significantly improve its versatility and efficiency in various environmental conditions.

[0135] Energy Harvesting: In certain embodiments, the improved cooling vest may be enhanced with energy-harvesting technologies such as piezoelectric fibers that generate power from the wearer's movements, supplementing the energy supply for the active cooling system.

[0136] Some of the benefits of the personalized adaptive thermal management system and improved cooling vest, whether used alone or in combination are described below. However, such a list is certainly not a limitation of the benefits and is merely a short description of some exemplary benefits.

[0137] 1. Temperature Regulation: Cooling vests help regulate body temperature by providing a cooling effect. They can help prevent overheating and heat-related illnesses, such as heat exhaustion or heat stroke, in hot environments or during intense physical activities.

[0138] 2. Enhanced Comfort: Wearing a cooling vest can provide immediate relief and comfort in hot conditions. The cooling sensation can help reduce discomfort, fatigue, and sweating, allowing individuals to stay focused and perform better.

[0139] 3. Increased Endurance: By keeping the body cool, cooling vests can help improve endurance and prolong physical performance. They can reduce the strain on the cardiovascular system and prevent early fatigue, enabling individuals to engage in activities for longer durations.

[0140] 4. Improved Recovery: Cooling vests can aid in post-workout or post-activity

[0141] recovery. They help lower the body's core temperature, reduce inflammation, and promote faster recovery of muscles and tissues.

[0142] 5. Versatility: Cooling vests can be used in a wide range of environments and activities. They are beneficial for outdoor workers, athletes, firefighters, military personnel, and individuals participating in sports, exercise, or recreational activities in hot climates.

[0143] 6. Portability: Most cooling vests are lightweight and portable, allowing for easy wearability and mobility. They can be worn under clothing or as standalone garments, making them convenient for use in various settings.

[0144] 7. Customizable Cooling: Some cooling vests offer adjustable cooling settings, allowing users to control the level of cooling based on their preferences and needs. This customization ensures optimal comfort and effectiveness.

[0145] The foregoing detailed description has been given for clearness of understanding only, and no unnecessary limitation should be understood therefrom. While the present invention has been described with reference to preferred embodiments and several alternative embodiments, which embodiments have been set forth in considerable detail for the purposes of making a complete disclosure of the invention, such embodiments are merely exemplary and are not intended to be limiting or represent an exhaustive enumeration of all aspects of the invention. The scope of the invention therefore shall be defined solely by the claims. Further, it will be apparent to those of skill in the art that numerous changes may be made in such details without departing from the spirit and the principles of the invention. It should be appreciated that the present invention is capable of being embodied in other forms without departing from its essential characteristics.

Claims

1. A personalized adaptive thermal management system comprising:a heat exchanger;a thermoelectric module in communication with the heat exchanger;a wearable interface in communication with the thermoelectric module; anda user interface in communication with the thermoelectric module, wherein the user interface enables control of at least one temperature effect provided by the wearable interface.

2. The personalized adaptive thermal management system of claim 1, further comprising a first contact plate disposed between the thermoelectric module and the heat exchanger;wherein the first contact plate and thermoelectric module permit heat transfer between the heat exchanger and the thermoelectric module; anda second contact plate disposed between the wearable interface and the thermoelectric module;wherein the second contact plate and the thermoelectric module permit heat transfer between the wearable interface and the thermoelectric module.

3. The personalized adaptive thermal management system of claim 1, wherein the heat exchanger is an air-cooled heat-exchanger.

4. The personalized adaptive thermal management system of claim 1, wherein the heat exchanger is a multiphase heat exchanger.

5. The personalized adaptive thermal management system of claim 1, wherein the heat exchanger is a single-phase heat exchanger.

6. The personalized adaptive thermal management system of claim 1, wherein the heat exchanger is a phase change heat exchanger.

7. The personalized adaptive thermal management system of claim 6, wherein the phase change heat exchanger comprises a phase change material (PCM).

8. The personalized adaptive thermal management system of claim 7, wherein the PCM comprises ice.

9. The personalized adaptive thermal management system of claim 2, wherein the wearable interface further comprises at least one sensor.

10. The personalized adaptive thermal management system of claim 9, wherein the at least one sensor measures at least one of motion, temperature, acceleration, humidity, and light intensity.

11. The personalized adaptive thermal management system of claim 10, further comprising a processor, wherein the processor is connected to the at least one sensor and the thermoelectric module, and wherein processor is configured to utilize information from the at least one sensor to control the thermoelectric module.

12. The personalized adaptive thermal management system of claim 11, wherein processor is configured to use the information from the at least one sensor to calculate a desired voltage to be applied to the thermoelectric module.

13. The personalized adaptive thermal management system of claim 12, wherein the wearable interface comprises a vest.

14. The personalized adaptive thermal management system of claim 12, wherein the wearable interface comprises a backpack.

15. The personalized adaptive thermal management system of claim 13, the vest further comprising an integrated water dispenser system, air circulation system, sensors, and customizable cooling channels for dynamic temperature regulation.

16. The personalized adaptive thermal management system of claim 15, wherein the water dispenser system includes a reservoir or bladder for holding water, and a network of tubes or channels for distributing the water evenly throughout the vest.

17. The personalized adaptive thermal management system of claim 15, wherein the air circulation system includes tubes integrated into the vest and connected to an air pump, and further comprising a plurality of openings disposed throughout the vest and configured to allow air flow to evaporate water from a wet vest.

18. The personalized adaptive thermal management system of claim 12, wherein the processor is configured to utilize sensor data to monitor a user's physiological parameters and at least one environmental data to dynamically adjust a system cooling mode.

19. The personalized adaptive thermal management system of claim 17, wherein the sensors monitor a user's body temperature, ambient temperature and humidity, and adjust at least one of a water flow and air flow based on the monitoring.

20. The personalized adaptive thermal management system of claim 12, the system further comprising an alarm system to alert a user or a caregiver for the user.

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