Field of invention:

The present invention relates to cellular telecommunication-based e-health and wellness systems and more particularly to an advanced health monitoring and educating system that efficiently and precisely guides the individuals for their health and diet.

Background of the invention:

Nowadays life is very hectic and people are very busy. Although health is main aspect of the life, people don’t get time to visit the doctor for the health checkup due to their busy schedules. On the other hand, availability of the doctor as per individual’s requirement is difficult to match. Moreover, visiting to a doctor or a health specialist is tiresome, lengthy and not preferable in situations like pandemic. It is observed in general survey that people are not getting enough time for cooking hence they prefer to consume junk food. Generally people order food from outside which is unhealthy and causes bad impact on health like obesity and other diseases like blood pressure, diabetes, hormonal imbalance and likewise.

However, the development of health related device or technology has grown beyond imagination. Currently, many technologies, device and mobile applications are used to provide the diet plan and the workout regime, calories counter based on general user parameters. In the prior art various health related mobile application and system, devices are available. Each type has its own disadvantages and limitations.

At present, there are many health related technologies and system are available in the market. Currently known health systems can enable user to provide diet plan and workout only with specific contents. There are few efforts seen in the art to provide best healthcare system, to give combined plan on workout and balanced diet. However, these systems do not provide suggestions or recommendations based on past medical history, allergies, and genetic information of the user. The technologies known in the art fail to provide organ related findings, probability of risk factors and to educate the people about their own health based on said finding. The conventional systems have limitations to provide knowledge to the user about alarming condition of health, or alarms for visiting to doctor or suggesting diet plan based on individual users’ lifestyle.

Accordingly, there exists a need for an advanced health monitoring and educating system that overcomes all the aforementioned drawbacks and limitations of the prior art.

Summary of the invention:

In an embodiment, the present invention provides an advanced health monitoring and education system that is accessible by a plurality of electronic devices and a plurality of biometric devices for monitoring health of pluralities of users. The said system includes a cloud computing platform that is configured to process data received from the users. The said system includes a cloud web application module and a mobile web application module that are configured to facilitate access to the users through the electronic devices. The said system includes an administrator application module that is configured to facilitate access to at least one administrator through the electronic devices. The said system includes a processing unit that processes the data received from the users. The said system includes a user interface unit that is configured to communicate with the processing unit. The said system includes a processing unit that processes the data received from the administrators. The said system includes a user interface unit that is configured to communicate with the processing unit. The said system includes an authentication and identification module that is configured to authenticate the data entered by users. The said system includes a server load balancer module that is configured to prioritize data requests of the users. The said system includes an auto scaling module that is configured to create an elastic container service (ECS) instance to scale received service data requests. The said system includes a web server cluster module that is configured to flexibly cluster a plurality of servers as per service data requests. The said system includes a scanner interface handling module that receives data from the biometric devices. The said system includes a data operation and visualization engine that processes data received from the scanner interface handling module. The said system includes an Artificial Intelligence (Al) and machine learning (ML) module that bidirectionally communicates and processes the data received from the data operation and visualization engine. The said system includes a monitoring service module that is configured to maintain and log details of error, exception and flaws. The said system includes a message queue module that stores cache data in at least one cache server. The said system includes a database that stores the data. The said system includes at least one reporting server that is configured to fetch data from the database and communicate to the users. The said system includes a cloud file storage that stores data reports.

The user interface unit includes a reports module to receive data report of the users from the reporting server module. The processing unit includes a user data processing module, a diet processing module and a workout processing module. The user data processing module facilitates the users to send the data by filling forms. The diet processing module is configured to receive diet plan report as per the condition and model trained data set from Al and ML module. The diet processing module is configured to provide data and set the baseline for diet- related training data set. The workout processing module is configured to provide data and set the baseline for workout-related training data. The processing unit includes a report processing module, a diet processing module and a workout processing module. The report processing module is configured to provide data and set the baseline for health-related training data set.

The authentication module is configured to authenticate the users by comparing user data with the Active Directory or Light-weight Directory Access Protocol (LDAP) server. The load balancer module is configured to distribute user data requests to optimize processing of user data. The autoscaling module is configured to add or remove ECS instances as per the user data service requests. The web server cluster module is configured to add and remove the number of servers in the web server cluster module based on the number of requests received. The data operation and visualization engine is a core processing component that is configured to process data. The Al and ML module is configured to provide the data to the classifier data set as baseline for the training data set in Al & ML algorithms. The monitoring processing module is configured to log the process status, failure and other information.

In another embodiment, the present invention provides a method for creating health report of users by utilizing the advanced health monitoring and education system, wherein said method comprising an initial step of creating and registering a new user with the system. In next step, the said system is accessed through the mobile web application module and cloud web application module. In further step, the registered user is authenticated through the authentication module. In next step, the biometric data from the users is scanned through the scanner interface handling. In further step, the scanned biometric data is processed through the data operation and data visualization engine and the Al and ML module. In next step, the processed report is stored inside the database and reported to the users.

In one more embodiment, the present invention provides a method for preparing data by the administrators thereby utilizing the advanced health monitoring and education system, wherein said method comprising an initial step of creating and registering administrators with said system. In next step, the system is accessed through the administrator application module followed by authenticating the registered administrators through the authentication module. In further step, the data of the users is processed through the Al and ML module. In next step, the users are restricted from completely altering the workout report and diet report through the report server module followed by generating diet and workout report by combining inputs received from diet processing module and workout processing module.

In yet another embodiment, the present invention provides a method for creating diet plan and workout plan for the users thereby utilizing the advanced health monitoring and education system of the present invention, wherein said method comprising and initial step of receiving diet plan and workout plan from the data operation and visualization engine thereby communicating through the diet processing module and workout processing module respectively. In next step, the diet plan and workout plan are processed as per condition and model trained data set in Al and ML module. In further step, the unplanned diet plan and workout plan is modified and verified through the report processing module. In final step, the planned diet is stored in the data base and cloud file storage and made available for accessing by the users.

Brief description of the drawings:

FIG. 1 shows an environmental representation diagram of an advance health monitoring and education system in accordance with the present invention;

FIG. 2 is a system architecture diagram of the advance health monitoring and education application system of FIG. 1;

FIG. 3 is an architectural representation of a mobile application module and a cloud application module of the advance health monitoring and education system of FIG. 1;

FIG. 4 is an architectural representation of an administrator application module of the advance health monitoring and education system of FIG. 1;

FIG. 5 is a flowchart showing creation of a final report for user of the advance health monitoring and education system of FIG. 1 ;

FIG. 6 is a flowchart showing creation of a diet plan for user of the advance health monitoring and education system of FIG. 1; FIG. 7 is a flowchart showing creation of a workout plan for user of advance health monitoring and education system FIG. 1; and

FIG. 8 is a flowchart showing a process for data preparation for the advance health monitoring and education system FIG. 1.

Detailed Description of the invention

Before the present invention is disclosed and described, it is to be understood that the embodiments are not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. Other embodiments can be practiced or carried out in various ways. Embodiments may not achieve any or all of the listed advantages; equally, this is not an exhaustive list of all possible advantages of the disclosure

Embodiments are herein described, by way of example only, with reference to the accompanying drawings. It is stressed that the particulars shown are intended for the purpose of illustrative discussion of the preferred embodiments, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the embodiments.

In the description and in the claim the term “ML” is defined broadly as Machine learning which learn and adapt by using various algorithm and statistical models of advance health monitoring and education system.

In the description and in the claim the term “Al” is defined broadly as Artificial Intelligence which simulate of human intelligence by machines of advance health monitoring and education system.

Accordingly, the present invention provides an advanced health monitoring and educating system designed to monitor and educate the user about the health by providing the suggestion and customized diet plan based on the patient’s health history and the life style in particular. The information and results generated by the advanced health monitoring and educating system is very informative and unique as it consider the factors like, genetic, allergies to person, history of the particular body system or the organ of the user, sleeping patterns. The said system may be used as to keep track on the health checkup of the user on regular basis. This prediction can instigate the user to approach any doctor for the enveloping issues

The present invention is further illustrated and described by an exemplary embodiment as an advanced health monitoring and educating system which should not be construed as limiting the scope of the invention because various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims.

Referring to FIG.l, an advanced health monitoring and education system 100 is described hereinafter. The advance health monitoring and education system 100 (hereinafter referred to as “the system 100”) allows a plurality of users 105 (hereinafter referred to as “user 105/ users 105”) to feed and store their health related data into the system 100 and process said stored data using ML algorithms and Al technologies to monitor their health. In the context of the present invention, the users 105 are allowed to access the system 100 using electronic devices 110 such as Laptops, Desktops, Handheld devices, PDA’s, Smart Devices and the like. The system 100 has a plurality of administrators 105a (hereinafter referred to as “administrators 105a”) that are provided with highest level of access rights. The administrators 105a preferably handle and maintain data related to the users 105 to provide health monitoring and educating inputs to the users 105.

The electronic devices 110 are configured with the secure authentication channels such as biometric devices 115. The biometric devices 115 include but are not limited to retina scanners, fingerprint scanners, face recognition scanner, and the like. The verified user data is processed preferably on a cloud computing platform 120 which includes a cloud computer that uses ML algorithms and AL technique to process the received data. The system 100 includes a database 125 and at least one cache server 127. The database 125 stores a processed data therein. The reporting servers 130 communicate with the users 105 to display stored data upon request.

Referring to FIGS. 1 and 2, a system architecture 200 adapted for the advanced health monitoring and education system 100 is described hereinafter. The users 105 use the electronic devices 110 to access the system 100 through either a cloud web application module 205 or a mobile application module 215. The administrators 105a access the system 100 through an administrator application module 210. The system 100 includes an authentication and identification module 220, a server load balancer module 225, an auto scaling module 230, a web server cluster module 235, a scanner interface handling module 240, a data operation and visualization engine 245, an Artificial Intelligence and machine learning module 250 (hereinafter referred to as “Al and ML module 250”), a monitoring service module 255, a message queue module 260, a cloud file storage 265, the a reporting server 130 and the cache server 127.

The users 105 are allowed to access the system 100 and communicate with the authentication and identification service module 220 as per the predetermined access specifiers configured therein. The authentication and identification service module 220 (hereinafter referred to as “authentication module 220”) authenticates the user request by validating the username and password entered by the users 105. The authentication module 220 validates the username and password by communicating with the Active Directory or Light-weight Directory Access Protocol (LDAP, hereinafter) server. The authentication module 220 is installed as a virtual appliance and communicates with a local directory using LDAP over Secure Sockets Layer (SSL, hereinafter). The authentication module 220 allows the system 100 to operate in Demilitarized Zone (DMZ, hereinafter), Local Area Network (LAN, hereinafter) or both based on the mode of operation.

The server load balancer module 225 (hereinafter referred to as “load balancer module 225”) receives the request from authentication module 220. The load balancer module 225 is configured to prioritize the response to specific requests from the users 105. The load balancer module 225 distributes the requests received from the users 105 to optimize the processing of the system 100. The auto scaling module 230 receives requests from the load balancer module 225. The auto scaling module 230 utilizes Elastic Container Service (ECS, hereinafter) to automatically adjust the number of elastic computing resources based on the service requests received from the users 105. The auto scaling module 230 is configured to add and remove ECS instance depending upon the quantity of service request. The instance of ECS ensures sufficient computing capabilities for the received users 105 requests.

The web server cluster module 235 is a group of two or more independent servers operating as a single system for optimized performance of the system 100. The system 100 decides the ratio of number of servers in the web server cluster module 235 based on the number of requests received from the load balancer module 225 and the auto scaling module 230. The load balance module 225 utilizes capacity and algorithm which is also considered by the web server cluster module 235 in adding the number of servers. The scanner and interface handling module 240 receives data from the biometric devices 115.

The data operation and visualization engine 245 receives data from the scanner interface handling module 240. The data operation and visualization engine 245 is a core processing component that processes data received from Scanner Interface handling module 240 and communicates bidirectionally with Al and ML module 250. The data operation and visualization engine 245 is configured to store results received from the Al and ML module 250 in the cache server 125 through the message queue module 260 that reduces the response time of data operations. The processed data is stored in the database 127.

The reporting server 130 accesses the report stored in the database 127 and displays the reports with the users 105. Further, the reporting server 130 also stores a copy of generated report inside the cloud file storage 265. The monitor service module 255 is configured to catch exceptions, errors and flaws generated from the load balancing module 225, the auto scaling module 230, the web server cluster module 235, the data operation and visualization engine 245 and the Al and ML module 250.

Referring to FIGS. 2 and 3, the system architecture 300 adapted for the users 105 to access the system 100 through cloud web application 205 and the mobile application 215 includes a User Interface unit 310, a Processing Unit 320 and a Database Unit 330. The User interface unit 310 includes the cloud web application module 205, the mobile application module 215, a reports module 335 and the biometric devices 115. The Processing unit 320 includes the authentication module 220, the scanner interface handling module 240, a user data processing module 340, the data operation and data visualization engine 245, the reporting server module 130, a diet processing module 345, a workout processing module 350 and the monitoring service module 255. The database unit 330 includes the database 125, the cloud file storage 265 and the cache server 127. The user interface unit 310, the processing unit 320 and the database unit 330 bidirectionally communicate with each other.

In context of the present invention, the users 105 access the system 100 through the either mobile application or through computers, laptops, desktops. The mobile users access the system 100 using mobile application module 215 whereas the laptop or desktop users access the system 100 using the cloud web application module 205. The authentication module 220 validates the users 105 into the system 100 by verifying the details like username and password entered by the users 105. The scanner interface handling module 240 is configured to communicate with biometric devices 115 for receiving data from the users 105.

The user data processing module 340 module receives data from the scanner interface handling module 240. The user data processing module 340 is configured to fill in the input forms of advance health monitor and education system 100. The data operation and data visualization engine 245 processes data received from the user data processing module 340, the scanner interface handling module 240 and Al and ML module 250 (refer FIG. 2). The Al and ML module 250 stores the data into the cache storage 127 and database 125. The reporting server module 130 requests the data from the database 125 and converts the received data into “.pdf’ file format. The “.pdf’ file is sent to the report module 335 so that the users 105 read the report. The reporting server module 130 also stores the report generated inside the cloud file storage 265.

The system 100 is configured to advice the user 105 regarding medical conditions of that user. In an exemplary situation, the user 105 receives alert from the system 100 regarding high body fats that might affect the users 105 bone. In another exemplary situation, the user gets an alert regarding excess weight by calculating Body Mass Index (BMI). In yet another exemplary situation, the users 105 receive an alert regarding any eye related disease, chickenpox, melasma or the like.

The diet processing module 345, receives the Diet plan according to the condition and model trained data set from Al and ML module 250. In context of the present invention, the diet plan is a report generated by the data operation and visualization engine 245 as per the data received from the users 105. The diet processing module 345 receives extra information about the diet plan by calling conditional and additional info service to the data operation and visualization engine 245. The system 100 allows the users 105 to export the diet plan in “.pdf’ file format. The diet plan has a hyper link that redirects the user to the predefined website to explore additional details regarding the health conditions. The cloud file storage 265 stores the diet plan stored thereby allowing the user to edit the intake or update the details regarding a specific diet. In an exemplary situation, the diet plan shows eggs has certain calories then if user 105 select that item then the amount of calories to be consumed on that day are automatically reduced from the total calories intake. Further, the users 105 select from the list of food items of their own choice in different sections like breakfast, lunch, snacks and dinner. The report processing module 440 restricts the users 105 to choose combination of any such food items in any particular section that effect the users 105 health or plan as diagnosed by Al and ML module 250 during biometric scanning or past user history or allergies. Further, the system 100 is configured to send a reasoned notification to the users 105 as to why the particular combination is not allowed.

The workout processing module 350 receives the workout plan according to the condition and model trained data set from Al and ML module 250. In context of the present invention the workout plan is a report generated by the data operation and visualization engine 245 as per the data received from the users 105. The workout processing module 350 receives extra information about the workout plan by calling conditional and additional info service to the data operation and visualization engine 245. The system 100 allows the users 105 to export the workout plan in “.pdf’ file format. The workout plan has a hyper link that redirects the user to the predefined website to explore additional details regarding the users 105 health. The workout plan stored in cloud file storage 265 allows the user 105 to access the data remotely and to maintain and track the history of the workout.

The Monitoring Processing module 255 is configured to log the process status, failure and other data that help system 100 to monitor. The Monitoring Processing module 255 module stores the data to cloud file storage to track the history.

Referring to FIG. 4, the system architecture 400 adapted for the administrators 105a for accessing the system 100 through administrator web application module 210 includes a User Interface unit 410, a Processing Unit 420 and a Database Unit 430. The User interface unit 410 includes the administrator web application module 210 and a reports module 435. The Processing unit 420 includes the authentication module 220, the Al and ML module 250, the reporting server 130, the data operation and data visualization engine 245, a report processing module 440, a diet processing module 445, a workout processing module 450 and the monitoring service module 255. The database unit 430 includes the database 125, the cloud file storage 265 and the cache server 127. The user interface unit 410, the processing unit 420 and the database unit 430 bidirectionally communicate with each other. In context of the present invention, the administrators 105a access the system 100 through the administrator web application module 210 preferably through electronic devices 110. The authentication module 220 validates the administrators 105a into the system 100 by verifying the details like username and password entered by the users 105a. The Al and ML module 250 is configured to provide the data to the classifier data set which works as baseline for the training data set in Al & ML algorithms. The reporting server 130 includes a restriction handling platform that is configured to restrict users 105 while allowing to manually modify the Diet or Work out report. The reporting server module 130 notifies the users 105 upon entering incorrect data. The data operation and data visualization engine 245 module processes data received from the user data processing module 340, the scanner interface handling module 240 and Al and ML module 250 (refer FIG. 2). The Al and ML module 250 stores the data into the cache storage 127 and the database 125.

The report processing module 440 is configured to provide data and set the baseline for health-related training data set which communicates with data operation and data visualization module 245. The administrator 105a will prepare the data either through inputs received from a health specialist or based on the input forms and data stored in the database 125. The Diet processing module 445 is configured to provide data and set the baseline for diet-related training data set which communicates with the data operation and data visualization module 245. The workout processing module 450 is configured to provide data and set the baseline for workout-related training data set which communicates with the data operation and data visualization module 245. The administrators 105a either prepare the data through health specialist or on input forms in the system 100 and stores the same in the database 125. The Monitoring Processing module 225 is configured to log the process status, failure and other information that help system to monitor. The reports module 435 receives the report from report processing module 440. Referring to FIG. 5, an operational flow for creating report of users 105 through cloud web application 205 and mobile web application 215 is described hereinafter. In an initial step 505, the new users 105 must register themselves with the system 100 by entering requested login details. In the next steps 510 and 515, the registered users 105 log into the system 100 by entering the appropriate details. In step 520, the authentication module 220 validates the registered user by comparing the details with the Active Directory or LDAP server. In next step 525, the users 105 view the dashboard of the system displaying information regarding the respective users 105. In further step 530, the system 100 checks for available biometric devices 110 with the user 105.

In steps 535, 540 and 545, the system 100 is configured to scan biometric credentials of the users 105 through the scanner interface handling module 240. In the step 535, the scanner interface handling module 240 scans face of the user 105 for facial parameters like color, face, skin texture or the like. In the step 540, the scanner interface handling module 240 scans the retina of the user 105 for parameters like eye Cornea thickness, lens thickness and the like. In the step 545, the scanner interface handling module 240 scans the fingers of the user 105 for parameters like finger pulse, blood pressure, calibration, or the like. In next steps 550 and 570, the data operation and data visualization engine 245 processes the scan data using Al and ML module 250 and stores the data in the data base 125. Contrarily, in step 555, the user 105 enters the data manually in case if the user 105 does not have biometric devices. In step 555, the users 105 enter the data like height, weight, blood group, gender and the like. In further step 560, the user 105 enters his health history along with diseases if any, like low blood pressure, heart disease, diabetes and the like.

In next steps 565 and 570, the data operation and data visualization engine 245 processes the user data using Al and ML module 250 and stores the data in the data base 125. In next step 575 and 580, the final report generated is stored inside the cloud file storage 265 through the reporting server module 130. In the final step 585, a help link regarding generated report is created that redirects the user 105 to a website displaying detailed information regarding his/her health.

Referring to FIG. 6, the process flow of diet plan creation for the users 105 through the diet processing module 345 is described hereinafter. In an initial step 605, the users 105 receive the diet plan report generated by the data operation and visualization engine 245 as per the data received from the users 105. The users 105 receive diet plan through the diet processing module 345. The diet plan is processed as per the condition and model trained data set in Al and ML module 250. In next step 610, if the diet is planned then the system 600 forwards the planned diet towards step 635. In step 635, the planned diet is received by the users 105 through the report module 335. In next step 640, the planned diet is stored in the cloud file storage 265 so the users 105 can remotely access the diet report.

In context of the present invention, the system 600 forwards the unplanned diet plan towards step 615 wherein the users 105 can adjust/ modify the diet plan 615 as per his/her requirements and forwards to a next step 620. In step 620, the diet processing module 345 verifies the diet plan and sends the diet plan either to step 625 if the predefined conditions in diet plan are declined or sends the diet plan towards step 630 if the predefined conditions in diet plan made by users 105 are accepted. In this step 620, the predefined conditions are set by the administrators 105a through the report processing module 440. In next steps 630 and 635, the diet plan is validated and saved in database 125 and cloud file storage 265.

Referring to FIG. 7, the process flow of creating the workout plan through the workout processing module 350 for the users 105 is described hereinafter. In an initial step 705, the users 105 receive the workout plan report generated by the data operation and visualization engine 245 as per the data received from the users 105. The users 105 receive workout plan through the workout processing module 350. The workout plan is processed as per the condition and model trained data set in Al and ML module 250. In next step 710, if the workout report is planned then the system 700 forwards the planned workout report towards step 735. In step 735, the planned workout is received by the users 105 through the report module 335. In next step 740, the planned workout report is stored in the cloud file storage 265 so the users 105 can remotely access the workout plan report.

In context of the present invention, the system 700 forwards the unplanned workout report plan towards step 715 wherein the users 105 adjust/ modify the workout plan 715 as per his/her requirements and forwards the same towards step 720. In step 720, the workout processing module 350 verifies the workout plan and sends the workout plan either to step 725 if the predefined conditions in workout plan are declined or sends the workout plan towards next step 730 if the predefined conditions in workout plan made by users 105 are accepted. In this step 720, the predefined conditions are set by the administrators 105a through the report processing module 130. In next steps 730 and 735, the workout plan is validated and saved in database 125 and cloud file storage 265.

Now referring to FIG.8, the process flow for data preparation adapted for administrators 105a is described hereinafter. In an initial step 805, the new administrators 105a register themselves with the system 100 by entering predefined details. In the next steps 810 and 815, the administrators 105a log into the system 100 by entering the appropriate details. In step 820, the authentication module 220 validates the registered administrator 105a by comparing the details with the Active Directory or LDAP server. In next step 825, the administrator 105a views the dashboard of the system 100 for displaying information regarding the respective users 105.

In next step 830, the administrators 105a process/ prepare the user data through the Al and ML module 250. In step 835, the administrators 105a configure the reporting server module 130 with predefined restrictions that prevent the users 105 from completely altering the generated diet report or workout report. In next step 840, the administrators also generate results/ report by combining inputs received from diet processing module 445 and workout processing module 450. In the final step 845, the data is prepared, processed and classified in data operation and visualization module 245 and Al and ML module 250.

Example:

In context of the present invention an exemplary data report of the user 105 entered in the system 100 is shown in Table 1. Similarly, the diet and workout plan report based on the data entered by the user 105 is shown in Table 2.

Table 1- User Data Health Report

Table 2- Diet Plan and Workout Plan Report

The foregoing description of specific members of the present invention has been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching.

The embodiments were chosen and described in order to best explain the principles of the present invention and its practical application, to thereby enable others, skilled in the art to best utilize the present invention and various members with various modifications as are suited to the particular use contemplated.

It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the scope of the present invention.