[0001] The present disclosure relates generally to field of health monitoring. More particularly, the present disclosure provides a health monitoring system and device to monitor health and transmission of contagious diseases caused due to bacteria, virus, fungi, and other microorganisms of entities.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Transmission of infection through microbes like virus, bacteria and fungi in entities are common and sometimes causes serious diseases like pandemic COVID- 19 and other similar contagious diseases. Though, certain measures can be taken to avoid transmission of infection like social distancing, sanitization, boosting immunity, and the likes. But, following these measures and keeping them in mind every time can be bit difficult for entities. The occurrence of contagious diseases is associated with symptoms like cold, fever, cough, breathing problem, and the likes. therefore, health monitoring of entities is essential to identify symptoms and accordingly take precautions. Heath of the entities can be monitored with help of various means. There are devices and systems existing in market that aids in monitoring health of the entities.
[0004] Some of the devices available for health monitoring and configuration of similar devices is not suitable for entities. The entities can be school students, kids, but not limited to the likes. therefore, comfort of the entities is must while designing of health monitoring devices and systems. Also continuous monitoring and automatic updating of symptoms associated with the entities can help in prevention from contagious diseases. Therefore, devices and systems with similar features is required which not only monitors health of the entities continuously and updates itself but also alerts the entities to take necessary precautions timely.
[0005] There is a need to overcome above mentioned prior art by bringing solution which alerts entities to take necessary precaution and measures to avoid transmission of contagious diseases and continuously monitors symptoms and updates itself automatically based on past occurrence of the symptoms. Also, the solution is affordable, cost effective, and benefits society.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] It is an object of the present disclosure to provide a system and a device for monitoring health and spreading of contagious diseases, which is easy to use and is quite comfortable for the school going kids.
[0008] It is an object of the present disclosure to provide a system and a device that facilitates prediction of severity of disease with help of the machine learning module.
[0009] It is an object of the present disclosure to provide a system and a device for monitoring health and spreading of contagious diseases, which is highly scalable, affordable, and cost effective.
[0010] It is an object of the present disclosure to provide a system and a device that aids in monitoring and catching early signs and symptoms of contagious diseases like COVID-19 and other diseases.
[0011] It is an object of the present disclosure to provide a system and a device where the device is capable of being worn 24/7, comfortably and can be a good replacement of following social distancing for entities like school going kids and other similar entities.
[0012] It is an object of the present disclosure to provide a system and a device that facilitates in alerting the entities for symptoms like fever, cold, cough and the likes and helps in preventing spread of contagious diseases and other similar diseases.

SUMMARY
[0013] The present disclosure relates generally to field of health monitoring. More particularly, the present disclosure provides a health monitoring system and device to monitor health and transmission of contagious diseases caused due to bacteria, virus, fungi, and other microorganisms of entities.
[0014] An aspect of the present disclosure pertains to a to a health monitoring device including a first set of sensors configured to detect temperature and pulse of one or more entities and correspondingly generate a first set of signals, a second set of sensors configured to detect cough, cold associated with the one or more entities , and correspondingly generate a second set of signals, a first processing unit operatively coupled with the first set of sensors and the second set of sensors, where the first processing unit may include one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors and configured to extract a third set of signals and a fourth set of signals from the first set of signals and the second set of signals respectively, and where the third set of signals may pertain to temperature and pulse parameters and the fourth set of signals may pertain to cold, cough parameters, compare the temperature and pulse parameters, and the cough and cold parameters with a dataset, where the dataset comprises predefined limit ranges, generate a set of warning signals, when at least one of the temperature and pulse parameter, and cold cough parameters are beyond the predetermined limit ranges, and where the device may be configured to transmit the temperature and pulse parameters, and the cough and cold parameters, and the set of warning signals to one or more mobile computing devices.
[0015] In an aspect, the first set of sensors may include any or a combination of thermocouple, temperature sensor, and pulse sensor, and where the second set of sensors may include any or a combination of cough detector, cold detector, and respiration sensor.
[0016] In an aspect, the device may include a communicating unit operatively coupled with the processing unit, and configured to communicatively couple the one or more mobile computing devices with the processing unit, and where the communicating unit may include any or a combination of Wireless Fidelity (Wi-Fi) module, Bluetooth module, Li-Fi module, optical fiber, Wireless Local Area Network (WLAN), and ZigBee.
[0017] In an aspect, the device may be adapted in form of wrist band, watch, anklet, and may be configured to be clipped with clothes wrists and waist.
[0018] In an aspect, the processing unit may be configured to update the first dataset based on the extracted third set of signals and the fourth set of signals.
[0019] In an aspect, the updating of the first dataset facilitates predetermining health of the one or more entities.
[0020] In an aspect, the device may include a power source configured to supply electric power to the device.
[0021] In an aspect, the power source may include any or a combination of rechargeable battery, rechargeable cells, solar cell, solar battery, electrochemical cells, storage battery, and secondary cell.
[0022] Another aspect of the present disclosure pertains to a health monitoring system including one or more devices associated with the one or more entities, where each of the one or more devices may include a first set of sensors configured to detect temperature and pulse of the one or more entities and correspondingly generate a first set of signals, a second set of sensors configured to detect cough, cold associated with the one or more entities , and correspondingly generate a second set of signals, a first processing unit configured to extract a third set of signals and a fourth set of signals from the first set of signals and the second set of signals respectively, and where the third set of signals may pertain to temperature and pulse parameters and the fourth set of signals may pertain to cold, cough parameters, compare the temperature and pulse parameters, and the cough and cold parameters with a first dataset, where the first dataset may include predefined limit ranges. The system may include a server including one or more processors coupled with a memory, and configured to communicatively couple the server with the one or more devices, and where the server may be configured to create a training and testing dataset based on received first dataset, where the training and testing dataset may facilitate the system to predetermine the health of the one or more entities associated with the one or more devices.
[0023] In an aspect, the system may be configured to communicatively couple with one or more mobile computing devices, and transmit a set of warning signals based on the compared temperature and pulse parameters, and the cough and cold parameters to the one or more mobile computing devices, and where the one or more mobile computing devices may include any or a combination of cell phone, laptop, palmtop, I pad, and tablet.

BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0025] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0026] FIG. 1 illustrates a network implementation of proposed health monitoring system to , in accordance with an embodiment of the present disclosure.
[0027] FIG. 2 illustrates a block diagram of the proposed health monitoring device, in accordance with an embodiment of the present disclosure.
[0028] FIG. 3 illustrates exemplary functional components of first processing unit of the proposed health monitoring system and device, in accordance with an embodiment of the present disclosure.
[0029] FIG. 4 illustrates an exemplary view of components of the proposed health monitoring system and device, in accordance with an embodiment of the present disclosure.
[0030] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAIL DESCRIPTION
[0031] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.
[0032] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0033] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0034] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0035] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this invention will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0036] While embodiments of the present invention have been illustrated and described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claim.
[0037] The present disclosure relates generally to field of health monitoring. More particularly, the present disclosure provides a health monitoring system and device to monitor health and transmission of contagious diseases caused due to bacteria, virus, fungi, and other microorganisms of entities.
[0038] FIG. 1 illustrates a network implementation of proposed health monitoring system to, in accordance with an embodiment of the present disclosure.
[0039] As illustrated in FIG. 1, the proposed system 100 (also referred to as system 100, herein) can include a communication unit 102, one or more entities 108 ( 108-1, 108-2, 108-3…..108-N, collectively referred as entities 108, and individually referred to as entity 108), one or more devices 106 (106-1, 106-2, 106-3….106-N, collectively referred to as devices 106, and individually referred to as device 106, herein) associated with the entities 108, server 114, and one or more mobile computing devices 112 (112-1, 112-2, 112-3…112-N, also referred collectively as mobile computing devices 112, and individually referred to as mobile computing device 112, herein).
[0040] In an embodiment, the system 100 can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing system, a computing device, a network device and the like. Further, the device 106 is communicatively coupled with the mobile computing device 112 through a communication unit 102, such as Wi-Fi, Bluetooth, Li-Fi, or an application, that can reside in the mobile computing device 112. In an implementation, the system 100 can be accessed by the communication unit 102 or a server 114 that can be configured with any operating system, including but not limited to, AndroidTM, iOSTM, and the like.
[0041] In an embodiment, the devices 106 can communicate with the system 100 through the communication unit 102. The mobile computing device 112 can include any or a combination of cell phones, mobiles, laptops, computers, a smart camera, a smart phone, a portable computer, a personal digital assistant, a handheld device, computer, and the likes.
[0042] Further, the communication unit 102 can be a networking module like wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the networking module can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0043] In an embodiment, the devices 106 can be communicatively coupled with help of a communication module, where the communication module can include any or a combination of Bluetooth low energy (BLE), ZigBee, and the likes.
[0044] In an illustrative embodiment, the device 106 can include a first set of sensors 202, a second set of sensors 204, a first processing unit 206. The first processing unit 206 can be operatively coupled with the first set of sensors 202 and the second set of sensors 204. The device 106 can be configured to detect symptoms of fever, cold, cough, and the likes with help of the first set of sensors 202, and the second set of sensors 204, and can captures pattern of the symptoms at early stages. The device 106 can be configured to identify computational intelligence approach to predict the symptoms before intense occurrence in the entities 108 with help of the first processing unit 206, where the first processing unit 206 can be configured with machine learning modules and machine learning techniques.
[0045] In an illustrative embodiment, computational intelligence can be applied to the first processing unit 206 to automate process of prediction in health monitoring of the entities 208. The prediction can include early detection of symptoms like cold, fever, and likes with help of the first set of sensors 202 and the second set of sensors 204. In yet another illustrative embodiment, the machine learning techniques can include any or a combination of Artificial Neural Network (ANN), Evolutionary Computation, Swarm Intelligence, Artificial Immune System, Fuzzy System. the machine learning modules and the techniques can be configured to create a training and testing dataset based on the first set of signals and the second set of signals received from the first set of sensors 202, and the second set of sensors 204.
[0046] In an illustrative embodiment, the server 114 can include one or more processors coupled with a memory, and configured to communicatively couple with the devices 106. In another illustrative embodiment, the server 114 can be configured to receive pattern of the symptoms from the one or more processors including the first processing unit 206 where the more or more processors can be configured to receive the pattern of the symptoms from the first set of sensors 202, and the second set of sensors 204 in form of the first set of signals and the second set of signals respectively. The server 114 can be configured to create a training and testing dataset based on the received temperature and pulse parameters, and the cough and cold parameters, and where the training and testing dataset facilitates the system 100 to predetermine the health of the entities 108 associated with the devices 106.
[0047] In an illustrative embodiment, the server 114 or a cloud server 114 can be communicatively coupled with the mobile computing device 112 and the devices 106, where the mobile computing device 112 can include any or a combination of mobile phones, laptop, palmtop, tablet, and the likes. in an illustrative embodiment, the server 114 can be configured to create a training and testing dataset based on the received patterns of the symptoms from the first set of sensors 202 and the second set of sensors 204, where the server 114 can include one or more processors along with the first processing unit 206. The training and testing dataset received at the server 114 or the cloud server 114 can be communicated to the mobile computing device 208 associated with the entities 108 with help of the communication unit 102, where the communication unit 102 can include any or a combination of Wireless Fidelity (Wi-Fi), Bluetooth, and Li-Fi, optical fiber, Wireless Local Area Network (WLAN), ZigBee, and the likes.
[0048] FIG. 2 illustrates a block diagram of the proposed health monitoring device, in accordance with an embodiment of the present disclosure.
[0049] As illustrated in FIG. 2, the proposed device 106 (also referred to as device 106) can include a first set of sensors 202, a second set of sensors 104, a first processing unit 206. In an illustrative embodiment, the first processing unit 206 can be operatively coupled to the first set of sensors 202, the second set of sensors 204. The device 106 can be communicatively coupled with mobile computing device 112. The device 100 can facilitate in predetermining health of entities 108 with help of training and testing dataset created by a server 114.
[0050] In an embodiment, the first set of sensors 202 can be configured to detect temperature and pulse of entities 108 and correspondingly generate a first set of signals. In an illustrative embodiment, the first set of sensors can include any or a combination of thermocouple, temperature sensor, pulse sensor, and the likes. In another illustrative embodiment, the first set of sensors 202 can be configured to detect the temperature and pulse attributes of the entities 108 and convert the temperature and the pulse attributes in electrical form as first set of electrical signals. The first set of electrical signals can be transmitted to the first processing unit 206.
[0051] In an embodiment, the second set of sensors 204 can be configured to detect cough, cold associated with the entities 108, and correspondingly generate a second set of signals. In an illustrative embodiment, the second set of sensors 204 can include any or a combination of cough detector, cold detector, respiration sensor, and the likes. In another illustrative embodiment, the, the second set of sensors 202 can be configured to detect the cough and cold attributes associated with the entities 108 and convert the cough and cold attributes in electrical form as second set of electrical signals. The second set of electrical signals can be transmitted to the first processing unit 206.
[0052] In an embodiment, the first processing unit 206 can be configured to receive the first set of electrical signals, and the second set of electrical signals from the first set of sensors 202 and the second set of sensors 204 respectively. In an illustrative embodiment, the first processing unit 206 can be micro processor, microcontroller, Arduino Uno, At mega 328, and other similar processing unit 206. In yet another illustrative embodiment, the first processing unit 206 can be configured to convert the received first set of signals and the second set of signals from electrical form to machine readable or binary form with help of sub processing units like extraction unit, comparison unit, signal generation unit, and other unit respectively.
[0053] In an embodiment, the first processing unit 206 can be configured to extract a third set of signals and a fourth set of signals from the first set of signals and the second set of signals respectively. The third set of signals can pertain to temperature and pulse parameters and the fourth set of signals can pertain to cold, cough parameters. The first processing unit 206 can be configured to compare the temperature and pulse parameters, and the cough and cold parameters with a dataset, where the dataset can include predefined limit ranges. The first processing unit 206 can be configured to generate a set of warning signals, when at least one of the temperature and pulse parameter, and cold cough parameters are beyond the predetermined limit ranges.
[0054] In an embodiment, the computing device 208 can be configured to receive the set of alert signals from the first processing unit 206. The set of alert signals can be transmitted to the computing device 208 as SOS alert, emergency alerts, and the likes. in an illustrative embodiment, the computing device 208 can be any or a combination of cell phone, laptop, palmtop, I pad, tablet, and the likes. in another illustrative embodiment,
[0055] FIG. 3 illustrates exemplary functional components of first processing unit of the proposed health monitoring system and device, in accordance with an embodiment of the present disclosure.
[0056] As illustrated in an embodiment, the first processing unit 206 can include one or more processor(s) 302. The one or more processor(s) 302 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 302 are configured to fetch and execute computer-readable instructions stored in a memory 304 of the first processing unit 206. The memory 304 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 304 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0057] In an embodiment, the first processing unit 206 can also include an interface(s) 306. The interface(s) 306 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 306 may facilitate communication of the first processing unit 206 with various devices coupled to the first processing unit 106. The interface(s) 306 may also provide a communication pathway for one or more components of first processing unit 206. Examples of such components include, but are not limited to, processing engine(s) 308 and data 310.
[0058] In an embodiment, the processing engine(s) 308 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 308. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 308 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 308 may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 308. In such examples, the first processing unit 206 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to first processing unit 206 and the processing resource. In other examples, the processing engine(s) 308 may be implemented by electronic circuitry. A database 310 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 308.
[0059] In an embodiment, the processing engine(s) 308 can include an extraction unit 312, a comparison unit 314, a signal generation unit 316, and other unit (s) 318. The other unit(s) 318 can implement functionalities that supplement applications or functions performed by the device 106 or the processing engine(s) 308.
[0060] The database 310 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 308.
[0061] It would be appreciated that units being described are only exemplary units and any other unit or sub-unit may be included as part of the device 106. These units too may be merged or divided into super- units or sub-units as may be configured.
[0062] In an embodiment, the first processing unit 206 can be configured to extract a third set of signals and from the first set of signals with help of the extraction unit 312, where the first set of signals can be received from the set of first set of sensors 202. The first processing unit 206 can be configured to extract a fourth set of signals from the second set of signals, where the second set of signals can be received from the second set of sensors 204 with help of the extraction unit 312. The third set of signals can pertain to temperature parameters, and the pulse parameters associated with entities 108 and the fourth set of signals can pertain to cough and cold parameters associated with the entities 108. In another embodiment, the first processing unit 206 can be configured to compare the temperature parameters, pulse parameters, and cough and cold parameters with first dataset and a second dataset respectively, where the dataset can include predetermined limit ranges with help of the comparison unit 314 and generate a set of alert signals, when at least one of the temperature parameters and pulse parameters, and the cough and cold parameters are beyond the predetermined limit ranges with help of the signal generation unit 316.
[0063] In an illustrative embodiment, the first processing unit 206 can be configured with machine learning modules and machine learning techniques. In another illustrative embodiment, computational intelligence can be applied to the first processing unit 206 to automate process of prediction in health monitoring of the entities 208.The prediction can include early detection of symptoms like cold, fever, and likes with help of the first set of sensors 202 and the second set of sensors 204. In yet another illustrative embodiment, the machine learning techniques can include any or a combination of Artificial Neural Network (ANN), Evolutionary Computation, Swarm Intelligence, Artificial Immune System, Fuzzy System. the machine learning modules and the techniques can be configured to create a training and testing dataset based on the first set of signals and the second set of signals received from the first set of sensors 202, and the second set of sensors 204.
[0064] In an illustrative embodiment, the extraction unit 312 can be configured to receive the first set of signals and the second set of signals from the first set of sensors 202 and the second set of sensors 204 respectively in form of electrical signals. The extraction unit 312 can be configured to extract the third set of signals from the first set of signals and the fourth set of signals from the second set of signals respectively in machine readable form or binary form. In another illustrative embodiment, the extraction unit 312 can transmit the extracted third set of signals and the extracted fourth set of signals in machine readable form to the comparison unit 314.
[0065] In an illustrative embodiment, the extraction unit 312 can be configured to extract the temperature parameters and pulse parameters associated with the entities 108 from the first set of signals, where the first set of signals can be generated by the first set of sensors 202. The extraction unit 312 can be configured to extract the cough and cold parameters associated with the entities 108 from the second set of signals, where the second set of signals can be generated by the second set of sensors 204. The extracted temperature and pulse parameters, and the cough and cold parameters after comparison with the first dataset and the second dataset respectively can facilitate in predetermining health of the entities 108.
[0066] In an illustrative embodiment, the comparison unit 314 can be configured to receive the extracted temperature parameters, pulse parameters and the cough and cold parameters from the extraction unit 312 in machine readable form. The comparison unit 314 can facilitate in comparing the extracted temperature parameters and the pulse parameters with a first dataset, where the first dataset can pertain to predefined limit ranges. The comparison unit 314 can receive the extracted temperature parameters and the pulse parameters from the extraction unit 312, and can compare with the first dataset stored in database 310. The predefined limit ranges can include threshold values pertaining to the temperature parameters and the pulse parameters associated with the entities 108. The comparison unit 314 can compare the extracted temperature parameters and the pulse parameters, and can facilitate in finding whether the extracted temperature parameters and the pulse parameters has reached the predefined limit ranges. In another illustrative embodiment, the threshold value can include limit rang of thirty-seven degrees centigrade but not limited to the likes for temperature parameters and limit range of sixty to hundred beats per minute for adults, but not limited to the likes for pulse parameters.
[0067] In an illustrative embodiment, the comparison unit 314 can facilitate in comparing the extracted cough and cold parameters with a second dataset, where the second dataset can pertain to predefined limit ranges. The comparison unit 314 can receive the extracted cough and cold parameters from the extraction unit 312, and can compare with the second dataset stored in database 310. The predefined limit ranges can include threshold values pertaining to the cough and cold parameters associated with the entities 108. The comparison unit 314 can compare the extracted cough and cold parameters, and can facilitate in finding whether the extracted cough and cold parameters has reached the predefined limit ranges.
[0068] In an illustrative embodiment, the comparison unit 314 can receive the extracted temperature parameters, pulse parameters, and the cough and cold parameters in machine readable form. The comparison unit 314 can facilitate in comparing the received extracted temperature parameters, pulse parameters, and the cold and cough parameters in machine readable form with help of a comparator. The comparator can enable comparing the extracted temperature parameters, pulse parameters, and the cough and cold parameters with the predefined limit ranges. The comparator can include an analogue comparator or a digital comparator. The digital comparators can compare the extracted temperature parameters, pulse, and cough and cold parameters with the predefined limit ranges. The digital comparators can facilitate comparison with help of logic gates such as AND, NOT or NOR gates. The digital comparator can be configured to accept the extracted temperature parameters, pulse parameters, and the cough and cold parameters in the machine readable form. Further three conditions can be applicable for the comparison of the extracted temperature parameters, pulse parameters, and the cough and cold parameters with the predefined limit ranges.
[0069] In an illustrative embodiment, the three conditions associated with the digital comparator can include a first condition, which can prevail when the extracted temperature parameters, pulse parameters, and the cough and cold parameters are found equal to the predefined limit ranges, a second condition can prevail when the extracted temperature parameters, pulse parameters, and the cough and cold parameters are found beyond the predefined limit ranges, and the third condition can prevail when the extracted temperature parameters, pulse parameters, and the cough and cold parameters are found less than the predefined limit ranges. The digital comparator can compare and transmit the compared temperature parameters, pulse parameters, and the cough and cold parameters to the signal generation unit 316.
[0070] In an embodiment, the signal generation unit 316 can be configured to receive the compared temperature parameters, pulse parameters, and the cough and cold parameters in machine readable form. The signal generation unit 316 can be configured to generate a set of alert signals when at least one of the compared temperature parameters, pulse parameters, and the cough and cold parameters are found beyond the predefined limit ranges. In an illustrative embodiment, the signal generation unit 316 can be configured to generate the set of alert signals, when the compared temperature parameters are found beyond the threshold value, where the threshold value can include the limit range of thirty-seven degrees centigrade but not limited to the likes. When the temperature parameters associated with the entities 108 are found beyond thirty-seven degrees centigrade by the comparison unit 314, the signal generation unit 316 can be configured to generate the set of alert signals and transmit the set of alert signals to mobile computing devices 208.
[0071] In an illustrative embodiment, the signal generation unit 316 can be configured to generate the set of alert signals, when the compared pulse parameters are found beyond the predefined limit ranges. In another illustrative embodiment, the signal generation unit 316 can be configured to generate the set of alert signals, when the compared pulse parameters are found beyond the threshold value, where the threshold value can include the limit range of sixty to hundred beats per minute for adults, but not limited to the likes. When the pulse parameters associated with the entities 108 are found beyond hundred beats per minute, but not limited to the likes, by the comparison unit 314, the signal generation unit 316 can be configured to generate the set of alert signals and transmit the set of alert signals to the mobile computing devices 208.
[0072] In an illustrative embodiment, the server 114 of the system 100 can include one or more processing unit, along with first processing unit 206. The one or more processing unit can include one or more processors, where the one or more processors can be configured to receive the first set of signals, and the second set of signals from the first set of sensors 202 and the second set of sensors 204 respectively, with help of the sub units like the extraction unit 312, the comparison unit 314, the signal generation unit 316, and the other unit(s) 318, and process the first set of signals and the second set of signals. The processing of the first set of signals and the second set of signals can include extraction, comparison, and signal generation. The server 114 or the one or more processors can be configured to create a training and testing dataset based on the received first set of signals and the second set of signals, and where the training and testing dataset created by the server 114 or the one or more processors can facilitate in predetermining health of the entities.
[0073] FIG. 4 illustrates an exemplary view of components of the proposed health monitoring system and device, in accordance with an embodiment of the present disclosure.
[0074] As illustrated in FIG. 3, the system 100 can include a device 106, mobile computing device 208, and server 114. The device 106 can include a first set of sensors 202 such as a thermocouple, and a pulse sensor but not limited to the likes. the device 106 can include a second set of sensors 204 like cough detector, respiration sensor, but not limited to the likes. the server 112 can be a cloud server communicatively coupled with the mobile computing device 208 with help of the communication unit 102. In an illustrative embodiment, the first set of sensors 202 can be configured to detect temperature attributes and pulse attributes associated with the entities 108, and correspondingly generate a first set of signals. The second set of sensors 204 can be configured to detect cold and cough attributes and correspondingly generate a second set of signals.
[0075] In an illustrative embodiment, the system 100 can be divided into two sub-systems namely, a detection unit and a communication sub-system. The detection unit 106 can be the device 100, where the device 106 can be adapted in form of wrist band, watch, anklet, and configured to be clipped with clothes wrists and waist of the entities 108. The detection unit can include the first set of sensors 202, and the second set of sensors 204 configured to be trained for patterns of symptoms relating to fever, cough, cold, and the likes associated with the entities 108. The training for patterns of the symptoms can be done with help of a first processing unit 206, where the first processing unit 206 can be configured with machine learning modules and techniques. The first set of sensors can include thermocouple sensor, resistance thermo detector, and pulse sensor but not limited to the likes, and the second set of sensors can include quvium’s cough monitor, cough detector but not limited to the likes.
[0076] In an illustrative embodiment, after assembling and training the patterns of the symptoms based on the received first set of signals and the second set of signals from the first set of sensors 202 and the second set of sensors 204 respectively, the device 106 can be configured to algorithmically detect possibilities of ill health before occurrence with help of the first processing unit 206, where the first processing unit 206 can be configured with machine learning techniques and modules. The machine learning techniques can include any or a combination of Artificial Neural Network (ANN), evolutionary computation, swarm intelligence, artificial immune system, fuzzy systems, and the likes.
[0077] In an illustrative embodiment, the communication sub system can include a server 114 or a cloud server 114 communicatively coupled with the mobile computing device 208, where the mobile computing device 208 can include any or a combination of mobile phones, laptop, palmtop, tablet, and the likes. in an illustrative embodiment, the server 114 can be configured to create a training and testing dataset based on the received patterns of the symptoms from the first set of sensors 202 and the second set of sensors 204, where the server 114 can include one or more processors along with the first processing unit 206. The training and testing dataset received at the server 114 or the cloud server 114 can be communicated to the mobile computing device 208 associated with the entities 108 with help of the communication unit 102, where the communication unit 102 can include any or a combination of Wireless Fidelity (Wi-Fi), Bluetooth, and Li-Fi, optical fiber, Wireless Local Area Network (WLAN), ZigBee, and the likes.
[0078] In an illustrative embodiment, the training and testing dataset can be received by the mobile computing device 208 associated with the entities. The mobile computing device 208 can be configured to receive asset of alert signals from the first processing unit 206, where the alert signals can be configured to alert the entities 108 and take precautions accordingly. In another illustrative embodiment, the training and testing dataset can facilitate in predicting and analyzing severity of the symptoms associated with the entities 108, and in predetermining health of the entities 108.
[0079] In an illustrative embodiment, the device 106 can communicatively couple with services like hospital with help of their server and can facilitate in predetermining health of the entities 108. In another illustrative embodiment, the device 106 can be cost effective, and aids in benefitting society.
[0080] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0081] As shown in FIG. 5, computer system 500 includes an external storage device 520, a bus 530, a main memory 540, a read only memory 550, a mass storage device 560, communication port 570, and a processor 580. A person skilled in the art will appreciate that computer system may include more than one processor and communication ports. Examples of processor 580 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor 580 may include various modules associated with embodiments of the present invention. Communication port 570 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port 570 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0082] In an embodiment, the memory 540 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory 550 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 580. Mass storage 560 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0083] In an embodiment, the bus 530 communicatively couples’ processor(s) 580 with the other memory, storage and communication blocks. Bus 5can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 580 to software system.
[0084] In another embodiment, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 530 to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 570. External storage device 520 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0085] Thus, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular name.
[0086] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0087] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, ` components, or steps that are not expressly referenced.
[0088] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0089] The present disclosure provides a system and a device for monitoring health and spreading of contagious diseases, which is easy to use and is quite comfortable for the school going kids.
[0090] The present disclosure provides a system and a device that facilitates prediction of severity of disease with help of the machine learning module.
[0091] The present disclosure provides a system and a device for monitoring health and spreading of contagious diseases, which is highly scalable, affordable, and cost effective.
[0092] The present disclosure provides a system and a device that aids in monitoring and catching early signs and symptoms of contagious diseases like COVID-19 and other diseases.
[0093] The present disclosure provides a system and a device where the device is capable of being worn 24/7, comfortably and can be a good replacement of following social distancing for entities like school going kids and other similar entities.
[0094] The present disclosure provides a system and a device that facilitates in alerting the entities for symptoms like fever, cold, cough and the likes and helps in preventing spread of contagious diseases and other similar diseases.

Claims:1. A health monitoring device comprising:
a first set of sensors configured to detect temperature and pulse of one or more entities and correspondingly generate a first set of signals;
a second set of sensors configured to detect cough, cold associated with the one or more entities, and correspondingly generate a second set of signals;
a first processing unit operatively coupled with the first set of sensors and the second set of sensors, wherein the firs processing unit comprises of one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors and configured to:
extract a third set of signals and a fourth set of signals from the first set of signals and the second set of signals respectively, and wherein the third set of signals pertain to temperature and pulse parameters and the fourth set of signals pertain to cold, cough parameters;
compare the temperature and pulse parameters, and the cough and cold parameters with a dataset, wherein the dataset comprises predefined limit ranges;
generate a set of warning signals, when at least one of the temperature and pulse parameter, and cold cough parameters are beyond the predetermined limit ranges,
and wherein the device is configured to transmit the temperature and pulse parameters, and the cough and cold parameters, and the set of warning signals to one or more mobile computing devices.
2. The device as claimed in claim 1, wherein the first set of sensors comprises any or a combination of thermocouple, temperature sensor, and pulse sensor, and wherein the second set of sensor comprises any or a combination of cough detector, cold detector, and respiration sensor.
3. The device as claimed in claim 1, wherein the device comprises of a communicating unit operatively coupled with the processing unit, and configured to communicatively couple the one or more mobile computing devices with the processing unit, and wherein the communicating unit comprises any or a combination of Wireless Fidelity (Wi-Fi) Module, Bluetooth Module, Li-Fi Module, optical fiber, Wireless Local Area Network (WLAN), and ZigBee.
4. The device as claimed in claim 1, wherein the device is adapted in form of wrist band, watch, anklet, and is configured to be clipped with clothes wrists and waist.
5. The device as claimed in claim 1, wherein the processing unit is configured to update the first dataset based on the extracted third set of signals and the fourth set of signals.
6. The device as claimed in claim 5, wherein the updating of the first dataset facilitates predetermining health of the one or more entities.
7. The device as claimed in claim 1, wherein the device comprises a power source configured to supply electric power to the device.
8. The device as claimed in claim 7, wherein the power source comprises any or a combination of rechargeable battery, rechargeable cells, solar cell, solar battery, electrochemical cells, storage battery, and secondary cell.
9. A health monitoring system comprising:
one or more devices associated with the one or more entities, wherein each of the one or more devices comprises:
a first set of sensors configured to detect temperature and pulse of the one or more entities and correspondingly generate a first set of signals;
a second set of sensors configured to detect cough, cold associated with the one or more entities, and correspondingly generate a second set of signals;
a first processing unit configured to:
extract a third set of signals and a fourth set of signals from the first set of signals and the second set of signals respectively, and wherein the third set of signals pertain to temperature and pulse parameters and the fourth set of signals pertain to cold, cough parameters;
compare the temperature and pulse parameters, and the cough and cold parameters with a dataset, wherein the dataset comprises predefined limit ranges;
a server comprising one or more processors coupled with a memory, and configured to communicatively couple the server with the one or more devices.
and wherein the server is configured to create a training and testing dataset based on received dataset, wherein the training and testing dataset facilitates the system to predetermine the health of the one or more entities associated with the one or more devices.
10. The system as claimed in claim 9, wherein the system is configured to communicatively couple with one or more mobile computing devices, and transmit a set of warning signals based on the compared temperature and pulse parameters, and the cough and cold parameters to the one or more mobile computing devices, and wherein the one or more mobile computing devices comprise any or a combination of cell phone, laptop, palmtop, I pad, and tablet.