A system and a method for health diagnosis using breath of user and lifestyle assessment

Abstract

A system (100) for health diagnosis using breath of user and lifestyle assessment is disclosed The system includes a breath analyzer device (110) that includes a breath chamber (120) to receive exhaled breath and detect concentration level of acetone, hydrogen, ethanol, hydrogen, ammonia, carbon monoxide, hydrogen sulphide, 5 lung capacity, hydrogen, methane, ammonia and hydrogen sulphide. The plurality of sensors (130) captures a photoplethysmography signal. A receiving module (160) receives data and the photoplethysmography signal. A disorder score generation module (170) analyses the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive. A nutrient score module (180) calculates a 10 nutrient score. A lifestyle score module (190) calculates a lifestyle score. A display module (200) displays diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface.

Description

A SYSTEM AND A METHOD FOR HEALTH DIAGNOSIS USING BREATH OF USER AND LIFESTYLE ASSESSMENTEARLIEST PRIORITY DATE:This Application claims priority from a complete patent application filed in India having Patent Application No. 202441080750, filed on 23rd day of October 2024, and titled “A SYSTEM AND A METHOD FOR HEALTH DIAGNOSIS USING BREATH OF USER AND LIFESTYLE ASSESSMENT”.FIELD OF INVENTIONEmbodiments of the present disclosure relate to the field of health diagnosis, and more particularly, a system and a method for health diagnosis using breath of user and lifestyle assessment.BACKGROUNDIn the field of health diagnostics and screening, existing methods are based on invasive procedures, often require laboratory tests to obtain blood samples from a user. This procedure imposes a significant logistical burden, and causes inconvenience to the user, as the user needs to schedule appointments, visit a laboratory, and have their blood drawn, which is time-consuming. Moreover, these tests are performed periodically, limiting the ability to perform continuous or real-time condition monitoring. As a result, the user does not receive immediate feedback on a health status, which can delay detection of potential health disorders and hinder proactive health management.Hence, there is a need for an improved system for health diagnosis which addresses the aforementioned issue(s).OBJECTIVE OF THE INVENTIONAn objective of the invention is to analyze exhaled breath from a user for diagnosing various health conditions, including diabetes, liver disorder, respiratory disorder, digestive disorder, and kidney disorder, heart rate, heart rate variability, peripheral capillary oxygen saturation, and blood pressure.Another objective of the invention is to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters on an user interface.Yet, another objective of the invention is to enable the user to view diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface, facilitating real-time monitoring and management of the user health.BRIEF DESCRIPTIONIn accordance with an embodiment of the present disclosure, a system for health diagnosis using breath of user and lifestyle assessment, is provided. The system includes a breath analyzer device that includes a breadth chamber adapted to receive an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period. The breath analyzer device also includes a plurality of sensors positioned inside the breath chamber and configured to detect if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic. The plurality of sensors is also configured to detect if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user. Further, the plurality of sensors is configured to detect if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user. Furthermore, the plurality of sensors is configured to detect if aconcentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user. Furthermore, the plurality of sensors is configured to detect if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user. Moreover, the plurality of sensors is configured to capture a photoplethysmography signal from the plurality of sensors wherein the photoplethysmography signal is used to extract a plurality of health parameters. The system includes a processing subsystem hosted on a server configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a receiving module configured to receive data and the photoplethysmography signal from the plurality of sensors. Further, the processing subsystem includes a disorder score generation module operatively coupled to the receiving module wherein the disorder score generation module is configured to analyze the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module utilizes a machine learning model. Furthermore, the processing subsystem includes a nutrient score module operatively coupled to the disorder score generation module wherein the nutrient score module is configured to calculate a nutrient score based on an input received from the user wherein the input includes at least one of the calories, carbohydrates, protein, fat, and fiber. Moreover, the processing subsystem includes a lifestyle score module operatively coupled to the nutrient score module wherein the lifestyle score module is configured to calculate a lifestyle score based on the input received from the user wherein the input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score. Additionally, the processing subsystem includes a display module operatively coupled to the lifestyle score module wherein the display module is configured to allow the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestylescore, and the plurality of health parameters via a user interface configured in the user device.In accordance with another embodiment of the present disclosure, a method for health diagnosis using breath of user and lifestyle assessment, is provided. The method includes receiving, by a breath chamber of a breath analyzer device, an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period. The method also includes detecting, by a plurality of sensors of the breath analyzer device, if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic. Further, the method includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user. Further, the method also includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user. Furthermore, the method includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user. Additionally, the method includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user. Further, the method includes capturing, by the plurality of sensors of the breath analyzer device, a photoplethysmography signal from the plurality of sensors wherein the photoplethysmography signal is used to extract a plurality of health parameters. Furthermore, the method includes receiving, by a receiving module, data and the photoplethysmography signal from the plurality of sensors. Furthermore, the method also includes analyzing, by a disorder score generation module, the data to generate adiabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module utilizes a machine learning model. Moreover, the method includes calculating, by a nutrient score module, a nutrient score based on an input received from the user wherein the input includes at least one of the calories, carbohydrates, protein, fat, and fiber. Additionally, the method includes calculating, by a lifestyle score module, a lifestyle score based on the input received from the user wherein the input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score. Further, the method includes allowing, by a display module, the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device.BRIEF DESCRIPTION OF THE DRAWINGSThe disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:FIG. 1 is a block diagram representation of a system for health diagnosis using breath of user and lifestyle assessment in accordance with an embodiment of the present disclosure;FIG. 2 is a block diagram of an exemplary embodiment of a system for health diagnosis using breath of user and lifestyle assessment of FIG. 1 in accordance with an embodiment of the present disclosure;FIG. 3 is a perspective view of a breath analyzer device of FIG.1 in accordance with an embodiment of the present disclosure;FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;FIG. 5 (a) illustrates a flow chart representing the steps involved in a method for health diagnosis using breath of user and lifestyle assessment, in accordance with an embodiment of the present disclosure; andFIG. 5(b) illustrates continued steps of the method of FIG. 5(a) in accordance with an embodiment of the present disclosure.Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.DETAILED DESCRIPTIONFor the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listedor inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.Embodiments of the present disclosure relates to a system for health diagnosis using breath of user and lifestyle assessment, is provided. The system includes a breath analyzer device adapted to receive an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period. The breath analyzer device also includes a plurality of sensors positioned inside the breath chamber and configured to detect if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic. The plurality of sensors is also configured to detect if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user. Further, the plurality of sensors is configured to detect if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user. Furthermore, the plurality ofsensors is configured to detect if a concentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user. Furthermore, the plurality of sensors is configured to detect if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user. Moreover, the plurality of sensors is configured to capture a photoplethysmography signal from the plurality of sensors wherein the photoplethysmography signal is used to extract a plurality of health parameters. The system includes a processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a receiving module configured to receive data and the photoplethysmography signal from the plurality of sensors. Further, the processing subsystem includes a disorder score generation module operatively coupled to the receiving module wherein the disorder score generation module is configured to analyze the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module utilizes a machine learning model. Furthermore, the processing subsystem includes a nutrient score module operatively coupled to the disorder score generation module wherein the nutrient score module is configured to calculate a nutrient score based on an input received from the user wherein the input includes at least one of the calories, carbohydrates, protein, fat, and fiber. Moreover, the processing subsystem includes a lifestyle score module operatively coupled to the nutrient score module wherein the lifestyle score module is configured to calculate a lifestyle score based on the input received from the user wherein the input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score. Additionally, the processing subsystem includes a display module operatively coupled to the lifestyle score module wherein the display module is configured to allow the user to view the diabetic score, liver disorder score, respiratory disorder score, digestivedisorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device.FIG. 1 is a block diagram for health diagnosis using breath of user and lifestyle assessment, is provided in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (140) hosted on a server (145). In one embodiment, the server (145) may include a cloud-based server. In another embodiment, parts of the server (145) may be a local server coupled to a user device (not shown in FIG.l). The processing subsystem (140) is configured to execute on a network (150) to control bidirectional communications among a plurality of modules. In one example, the network (150) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (150) may include both wired and wireless communications according to one or more standards and / or via one or more transport mediums. In one example, the network (150) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (150) may also include communications over a terrestrial cellular network, including, a global system (100) for mobile communications (GSM), code division multiple access (CDMA), and / or enhanced data for global evolution (EDGE) network.The system (100) includes a breath analyzer device (110). The breath analyzer device (110) includes a breath chamber (120) and a plurality of sensors (130).The breath chamber (120) is adapted to receive an exhaled breath from an oral cavity (mouth) of a user through a breath flow tube for a predetermined period. Typically, the breath flow tube channels the exhaled breath from the user directly into the breath chamber (120), ensuring that a breath sample is captured efficiently and with a minimal loss. In other words, the breath chamber (120) is where the exhaled breath is collected and analyzed. The breath flow tube channels ensures that the breath goes directly intothe breath chamber (120) without being lost or dispersed. Further, the breath is collected in the breath chamber (120) for a specific amount of time, which is predetermined. This ensures that the sample is adequate for analysis.The design of the breath flow tube channels and the breath chamber (120) ensures that the exhaled breath is captured efficiently, meaning that the system (100) collects as much of the breath as possible with minimal loss. This is important for obtaining an accurate sample for analysis. The plurality of sensors (130) are positioned inside the breath chamber (120) and are responsible for detecting specific chemical compounds in the breath. Specifically, the plurality of sensors (130) are configured.to detect if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic. This is because certain levels of these substances in breath can be associated with diabetes.Typically, the plurality of sensors (130) is embedded on a printed circuit board of the breath chamber (120). The printed circuit board also includes an ultraviolet light that eliminates bacteria in the breath exhaled from the user.Further, the plurality of sensors (130) is configured to detect if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user.Further, the plurality of sensors (130) is also configured to detect if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user.Furthermore, the plurality of sensors (130) is configured to detect if a concentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user.Moreover, the plurality of sensors (130) is configured to detect if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user.Typically, the plurality of sensors (130) captures the concentration level in parts per million and parts per billion levels.Typically, the breath exhaled includes various volatile organic compounds (VOC’s). By analyzing the specific VOCs (acetone, hydrogen, ethanol, ethanol, hydrogen, ammonia, carbon monoxide, hydrogen sulfide, lung capacity, hydrogen, methane, ammonia, hydrogen sulfide, acetone), the score generation module generates the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, and kidney disorder score.Additionally, the plurality of sensors (130) are configured to capture a photoplethysmography (PPG) signal from the plurality of sensors (130). The photoplethysmography signal is used to extract a plurality of health parameters. Typically, the photoplethysmography signal is analyzed by the machine learning model to provide the plurality of health parameters. Typically, the machine learning model is trained on a large dataset of many examples of the photoplethysmography signal, along with corresponding plurality of health parameters. The training process allows the machine learning model to learn how to recognize patterns and anomalies in the PPG signal that correlate with specific health parameters. The plurality of health parameters includes, but is not limited to heart rate, heart rate variability, peripheral capillary oxygen saturation, and blood pressure. Through a training process, the machine learning model learns to recognize patterns and anomalies in the PPG signal that correlate with the plurality of parameters.Referring back to paragraph

[0022] , the processing subsystem (140) includes a receiving module (160), a disorder score generation module (170), a nutrient score module (180), a lifestyle score module (190), and a display module (200).The receiving module (160) is configured to receive data and the photoplethysmography signal from the plurality of sensors (130).The disorder score generation module (170) is operatively coupled to the receiving module (160). The disorder score generation module (170) is configured to analyze the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score. The disorder score generation module (170) utilizes the machine learning model. For example, when analysing the diabetic score, the disorder score generation module (170) first collects the data from the exhaled breath, such as the concentration level of a biomarkers related to glucose metabolism. The biomarkers includes acetone, hydrogen, and ethanol. This data is then fed into the machine learning model, which has been trained on a large dataset of known diabetic and non-diabetic cases. The machine learning model analyses patterns in the user data, comparing the patterns to the patterns found in the training data. Based on this comparison, the machine learning model predicts likelihood of the diabetes and assigns a diabetic score that reflects the user risk level. For instance, a higher concentration of certain biomarkers might correlate with a higher diabetic score, indicating a greater risk. Similar processes are used for generating scores for liver disorders, respiratory disorders, digestive disorders, and kidney disorders, with each score reflecting the model's assessment of the user's health based on the data analysed.The nutrient score module (180) is operatively coupled to the disorder score generation module (170). The nutrient score module (180) is configured to calculate a nutrient score based on an input received from the user. The input includes at least one of the calories, carbohydrates, protein, fat, and fiber. For example, if the user inputs 600 grams of carbohydrates, 20 grams of protein, 15 grams of fat, and 10 grams of fiber,the nutrient score module (180) calculates the nutrient score based on the inputs. The nutrient score module (180) compares the input to assess the user meal’s overall quality. If the meal aligns well with the recommended daily intake for nutrients, the nutrient score module (180) generates a high nutrient score, indicating a balanced meal. If the user intake is unbalanced — such as being high in fat but low in fiber — the nutrient score might be lower, suggesting that improvements could be made to your diet.Typically, the nutrient score is calculated as follows:Nutrient Score:if bmi > 25:if gender == 'female' and (bmr - 500) < 1200:bmr threshold = max(bmr - 500, 1200)elif gender == 'male' and (bmr - 500) < 1500:bmr threshold = max(bmr - 500, 1500)else:bmr threshold = bmr - 500elif bmi < 18.5:bmr threshold = bmr + 500else:bmr threshold = bmrnutrient_values = {'Calories': bmr threshold,'Carbohydrates': bmr_threshold * 0.5 / 4,'Protein': bmr_threshold * 0.2 / 4,'Fat': bmr_threshold * 0.3 / 9,'Fiber': bmr threshold * 0.01Nutrient score = 0.39999804*Calories (kcal)+ 0.2000078*Carbohydrates (g)+ 0.20000783*Protein (g)+ 0.15001755*Fat (g) +0.0499989 l*Fiber (g)BMR refers to basal metabolic rate and BMI refers to body mass index.The lifestyle score module (190) is operatively coupled to the nutrient score module (180). The lifestyle score module (190) is configured to calculate a lifestyle score based on the input received from the user. The input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score.Typically, the lifestyle score is calculated as follows:Lifestyle Score:water_weight = 0.1alcohol weight = 0.1sleep_weight = 0.2exercise_weight = 0.15smoking_weight = 0.1nutrition_weight = 0.35recommended nutrition = 100base sleep requirement = 7recommended_water = ((weight / 0.453592) *0.5)* 29.57 recommended_water=recommended_water+((exercise_hours / 30)* 12*29 •57)recommended alcohol = 0 # No alcohol consumption is recommended recommended_exercise=calculate_recommended_exercise_duration(age , gender, weight)recommended smoking = 0age modifier = calculate required sleep(age)recommended sleep = base sleep requirement + age modifier# Calculate the deviations from recommended values nutrition deviationl = (nutrition scorel - recommended nutrition) nutrition deviation = (nutrition scorel - recommended nutrition) / recommended nutrition * 100 water deviationl = (water consumption -recommended water) water deviation = (water consumption recommended water) / recommended water * 100 alcohol deviation = (alcohol consumption - recommended alcohol)# Calculate the sleep deviation and provide reduction or increase suggestionsif sleep hours >= recommended sleep:sleep deviationl = (sleep hours - recommended sleep)sleep deviation = (sleep hours - recommended sleep) / sleep hours * 100sleep_score = 100 * (1 - abs(sleep_deviation / 100))sleep suggestion = f'Reduce sleep hours by {abs(sleep_deviationl):.2f} Hr"else:sleep deviationl = (recommended sleep - sleep hours) sleep deviation = (recommended sleep - sleep hours) / sleep hours * 100sleep_score = 100 * (1 - abs(sleep_deviation / 100))sleep suggestion = f" Increase sleep hours by {abs(sleep_deviationl):.2f}Hr"# Check if exercise duration exceeds recommended durationif exercise hours > recommended exercise:exercise_deviation = 0 # Set deviation to 0exercise_score = 100 # Set score to 100else:exercise deviationl = (exercise hours - recommended exercise) exercise_deviation = (exercise_hours - recommended_exercise) / recommended exercise * 100 exercise score = 100 * (1 - abs(exercise_deviation / 100))# Calculate the individual scores for each parameternutrition scorel = 100 * (1 - abs(nutrition_deviation / 100))exercise_score = 100 * (1 - abs(exercise_deviation / 100))water_score= 100 * (1 - abs(water_deviation / 100))alcohol score = 100 if alcohol deviation == 0 else 0 alcohol deviation = alcohol score smoking score = 100 if smoking deviation == 0 else 0 smoking deviation = smoking scoresleep_score = 100 * (1 - abs(sleep_deviation / 100))Lifestyle score formula =(nutrition_weight * nutrition_scorel) + (exercise_weight * exercise_score) +(water_weight * water_score) + (alcohol_weight * alcohol_score) + (smoking_weight * smoking_score) + (sleep_weight * sleep_score))FIG. 2 is a block diagram of an exemplary embodiment of a system for health diagnosis using breath of user and lifestyle assessment of FIG. 1 in accordance with an embodiment of the present disclosure. Further, the processing subsystem (140) includes a recommendation module (210) operatively coupled to the lifestyle score module (190). The recommendation module (210) provides suggestions to the user to improve the lifestyle score and the nutrient score via the machine learning model.In a non-limiting example, consider a scenario where the user ‘X’ utilizes the breath analyzer device (110). As the user ‘X’ exhales breath into the breath chamber (120), the plurality of sensors (130) positioned inside the breath chamber (120) analyzes the exhaled breath for various biomarkers. Elevated levels of acetone, hydrogen, and ethanol suggest potential diabetes, while elevation levels of ethanol, hydrogen, and ammonia levels indicate possible liver issues. Elevated levels of carbon monoxide and hydrogen sulfide point to respiratory disorder, and the elevated levels of hydrogen and methane indicate digestive disorder. The elevated levels of ammonia, hydrogen sulfide, and acetone, which could signal kidney disorder. Further, the plurality of sensors (130) captures PPG signals which detects the plurality of health parameters. Subsequently, the user ‘X’ provides input indicating that he / she has consumed 2,000 calories, 150 grams of carbohydrates, 80 grams of protein, 70 grams of fat, and 25 grams of fiber in a particular day. The nutrient score module (180) calculates the nutrient score. Further, the user ‘X’ provides information about their lifestyle habits: a nutrition weight of 0.4, an exercise score of 70, a water intake score of 80, an alcohol consumption score of 50, and a sleep score of 90. The lifestyle score module (190) then calculates their lifestyle score by integrating these inputs, reflecting their overall health and habits. Moreover, the display module (200) displays all diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters simultaneously, the recommendation module (210) offers personalized advice to help the user X improve their health based on their scores and analyzed data. Further, the system (100) utilizes the machine learning model to predict potential future health issues. Moreover, the exhaled breath is expelled from the breath chamber (120) to a breath outlet using a vacuum pump (125) to undergo dynamic cleaning processes of the breath chamber (120) and to maintain a consistent clean and reliable testing environment for the user following each breath analysis.FIG. 3 is a perspective view of a breath analyzer device of FIG. 1 in accordance with an embodiment of the present disclosure. Typically, the breath analyzer device (110) is a key component of the system (100) for lifestyle and health diagnosis, designed to analyze the exhaled breath of a user to detect various health conditions.FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (145) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem of FIGI. The processing subsystem (140) further has following modules: a receiving module (160), a disorder score generation module (170), a nutrient score module (180), a lifestyle score module (190), and a display module (200).A receiving module (160) configured to receive data and the photoplethysmography signal from the plurality of sensors (130). Further, the processing subsystem (140) includes a disorder score generation module (170) operatively coupled to the receiving module (160) wherein the disorder score generation module (170) is configured to analyse the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module (170) utilizes a machine learning model. Furthermore, the processing subsystem (140) includes a nutrient score module (180) operatively coupled to the disorder score generation module (170) wherein the nutrient score module (180)is configured to calculate a nutrient score based on an input received from the user wherein the input includes at least one of the calories, carbohydrates, protein, fat, and fiber. Moreover, the processing subsystem (140) includes a lifestyle score module (190) operatively coupled to the nutrient score module (180) wherein the lifestyle score module (190) is configured to calculate a lifestyle score based on the input received from the user wherein the input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score. Additionally, the processing subsystem (140) includes a display module (200) operatively coupled to the lifestyle score module (190) wherein the display module (200) is configured to allow the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device.The bus (220) as used herein refers to internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bitserial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.FIG. 5 illustrates a flow chart representing the steps involved in a method for health diagnosis using breath of user and lifestyle assessment, in accordance with an embodiment of the present disclosure. The method (400) includes receiving, by a breath chamber of a breath analyzer device, an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period in step 410.In one embodiment, the breath chamber (120) includes a pressure sensor configured to detect pressure of the exhaled breath.In another embodiment, the plurality of sensors (130) captures the concentration level in parts per million and parts per billion levels.Yet, in another embodiment, the exhaled breath is expelled from the breath chamber (120) to a breath outlet using a vacuum pump to undergo dynamic cleaning processes of the breath chamber (120) and to maintain a consistent clean and reliable testing environment for the user following each breath analysis.The method (400) also includes detecting, by a plurality of sensors of the breath analyzer device, if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic in step 420.Further, the method (400) includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user in step 430.Furthermore, the method (400) includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user in step 440.In one embodiment, the exhaled breath is expelled from the breath chamber (120) to a breath outlet using a vacuum pump to undergo dynamic cleaning processes of the breath chamber (120) and to maintain a consistent clean and reliable testing environment for the user following each breath analysis.Furthermore, the method (400) also includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the hydrogen and methane in theexhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user in step 450.Moreover, the method (400) includes detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user in step 460.Additionally, the method (400) includes capturing, by the plurality of sensors of the breath analyzer device, a photoplethysmography signal from the plurality of sensors wherein the photoplethysmography signal is used to extract a plurality of health parameters in step 470.In one embodiment, the photoplethysmography signal is analyzed by the machine learning model to provide the plurality of health parameters wherein the plurality of health parameters includes at least one of the heart rate, heart rate variability, peripheral capillary oxygen saturation, and blood pressure.Further, the method (400) includes receiving, by a receiving module, data and the photoplethysmography signal from the plurality of sensors in step 480.Further, the method (400) also includes analyzing, by a score generation module, the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module utilizes a machine learning model in step 490.In one embodiment, the machine learning module is configured to analyze a respiratory pattern of the user and provide a respiratory score which corresponds to a respiratory health of the user.In another embodiment, the machine learning model is trained with a data set comprising a plurality of lifestyle scores, nutrient scores and respiratory scores obtained from past analysis.Furthermore, the method (400) includes calculating, by a nutrient score module, a nutrient score based on an input received from the user wherein the input includes at least one of the calories, carbohydrates, protein, fat, and fiber in step 500.Moreover, the method (400) includes calculating, by a lifestyle score module, a lifestyle score based on the input received from the user wherein the input includes at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score in step 510.Additionally, the method (400) includes allowing, by a display module, the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device in step 520.Various embodiments of the system (100) for health diagnosis using breath of user and lifestyle assessment as described above various advantages for detecting and monitoring various health conditions such as diabetes, liver disorders, respiratory disorders, digestive disorders, and kidney disorders. By utilizing the plurality of sensors (130) to detect specific biomarkers in the exhaled breath, the system (100) provides real-time and precise analysis, allowing for early diagnosis and personalized health management. Further, integration of the machine learning model enhances accuracy of health scores and predictions, enabling tailored recommendations to improve the user's lifestyle and nutritional habits. Further, by using machine learning technology, the system (100) not only assesses current health but also predicts thelikelihood of future diseases. It provides the user with valuable insights into potential health risks, allowing him / her to take proactive measures.The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing subsystem ” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure.Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

26WE CLAIM:

1. A system (100) for health diagnosis using breath of user and lifestyle assessment comprising:characterized in that,a breath analyzer device (110) comprising:a breath chamber (120) adapted to receive an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period; a plurality of sensors (130) positioned inside the breath chamber (120) and configured to:detect if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic;detect if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user;detect if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user;detect if a concentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user;detect if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user; andcapture a photoplethysmography signal from the plurality of sensors (130) wherein the photoplethysmography signal is used to extract a plurality of health parameters; anda processing subsystem (140) hosted on a server (145) wherein the processing subsystem (140) is configured to execute on a network (150) to control bidirectional communications among a plurality of modules comprising:a receiving module (160) configured to receive data and the photoplethysmography signal from the plurality of sensors (130);a disorder score generation module (170) operatively coupled to the receiving module (160) wherein the disorder score generation module(170) is configured to analyse the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module(170) utilizes a machine learning model;a nutrient score module (180) operatively coupled to the disorder score generation module (170) wherein the nutrient score module (180) is configured to calculate a nutrient score based on an input received from the user wherein the input comprises at least one of the calories, carbohydrates, protein, fat, and fiber;a lifestyle score module (190) operatively coupled to the nutrient score module (180) wherein the lifestyle score module (190) is configured to calculate a lifestyle score based on the input received from the user wherein the input comprises at least one of the nutrition weight, nutrition score, exercise weight,exercise score, water weight, water score, alcohol weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score; anda display module (200) operatively coupled to the lifestyle score module (190) wherein the display module (200) is configured to allow the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device.

2. The system (100) as claimed in claim 1, comprising a recommendation module (210) operatively coupled to the lifestyle score module (190) wherein the recommendation module (210) provides suggestions to the user to improve the lifestyle score and the nutrient score via the machine learning model.

3. The system (100) as claimed in claim 1, wherein the photoplethysmography signal is analyzed by the machine learning model to provide the plurality of health parameters wherein the plurality of health parameters comprises at least one of the heart rate, heart rate variability, peripheral capillary oxygen saturation, and blood pressure.

4. The system (100) as claimed in claim 1, wherein the breath chamber (120) comprises a pressure sensor configured to detect pressure of the exhaled breath.

5. The system (100) as claimed in claim 1, wherein the plurality of sensors (130) captures the concentration level in parts per million and parts per billion levels.

6. The system (100) as claimed in claim 1, wherein the user device utilizes an artificial intelligence technique configured to predict probability of future one or more diseases based on the data collected from the plurality of sensors (130).

297. The system (100) as claimed in claim 1, wherein the exhaled breath is expelled from the breath chamber (120) to a breath outlet using a vacuum pump to undergo dynamic cleaning processes of the breath chamber (120) and to maintain a consistent clean and reliable testing environment for the user following each breath analysis.

8. The system (100) as claimed in claim 1, wherein the machine learning module is configured to analyse a respiratory pattern of the user and provide a respiratory score which corresponds to a respiratory health of the user.

9. The system (100) as claimed in claim 1, wherein the machine learning model is trained with a data set comprising a plurality of lifestyle scores, nutrient scores and respiratory scores obtained from past analysis.

10. A method (400) for health diagnosis using breath of user and lifestyle assessment comprising:characterized in that,receiving, by a breath chamber of a breath analyzer device, an exhaled breath from an oral cavity of a user through a breath flow tube for a predetermined period; (410)detecting, by a plurality of sensors of the breath analyzer device, if a concentration level of acetone, hydrogen, and ethanol in the exhaled breath deviates from a predetermined threshold thereby indicating that the user is diabetic; (420) detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ethanol, hydrogen and ammonia in the exhaled breath deviates from a predetermined threshold thereby indicating a liver disorder in the user;30detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of carbon monoxide, hydrogen sulphide and lung capacity in the exhaled breath deviates from a predetermined threshold thereby indicating a respiratory disorder in the user; (440)detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the hydrogen and methane in the exhaled breath deviates from a predetermined threshold thereby indicating a digestive disorder in the user; (450) detecting, by the plurality of sensors of the breath analyzer device, if a concentration level of the ammonia, hydrogen sulphide and acetone deviates from a predetermined threshold thereby indicating a kidney disorder in the user; (460) capturing, by the plurality of sensors of the breath analyzer device, a photoplethysmography signal from the plurality of sensors wherein the photoplethysmography signal is used to extract a plurality of health parameters; (470) receiving, by a receiving module, data and the photoplethysmography signal from the plurality of sensors; (480)analyzing, by a disorder score generation module, the data to generate a diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score wherein the disorder score generation module utilizes a machine learning model; (490)calculating, by a nutrient score module, a nutrient score based on an input received from the user wherein the input comprises at least one of the calories, carbohydrates, protein, fat, and fiber; (500)calculating, by a lifestyle score module, a lifestyle score based on the input received from the user wherein the input comprises at least one of the nutrition weight, nutrition score, exercise weight, exercise score, water weight, water score, alcohol31weight, alcohol score, smoking weight, smoking score, sleep weight and sleep score; and (510)allowing, by a display module, the user to view the diabetic score, liver disorder score, respiratory disorder score, digestive disorder score, kidney disorder score, nutrition score, lifestyle score, and the plurality of health parameters via a user interface configured in the user device. (520)

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