Description:FIELD OF THE INVENTION

[0001] The present invention relates to a system for remote health monitoring that is capable of monitoring the health by improved accuracy levels and improved segmentation process in real time.

BACKGROUND OF THE INVENTION

[0002] Remote health monitoring is a technology-driven approach that allows healthcare providers to track patients' vital signs and health parameters from a distance, often using wearable devices, sensors, and digital platforms. This system enables continuous oversight of conditions such as heart rate, blood pressure, glucose levels, and oxygen saturation, facilitating early detection of potential issues and timely interventions without the need for frequent in-person visits. By leveraging telemedicine and mobile health technologies, remote monitoring enhances patient convenience, promotes proactive management of chronic diseases, reduces healthcare costs, and improves overall health outcomes, especially for individuals in remote or underserved areas.

[0003] Traditional health monitoring typically involves in-person visits to healthcare facilities for routine check-ups, diagnostic tests, and assessments, which can be time-consuming, costly, and inconvenient for patients, especially those with chronic conditions or mobility issues. This approach often results in infrequent data collection, making it difficult to detect early signs of deterioration or manage illnesses proactively. Additionally, it can lead to delays in diagnosis and treatment, increased healthcare expenses, and limited access for individuals in remote or underserved regions, ultimately impacting the effectiveness of healthcare delivery and patient outcomes.

[0004] US20190046039A1 discloses a remote health monitoring system, method and device. The systems utilize one or more sensors, data aggregation and transmission units, mobile computing devices, processing, analytics and storage (PAS) units, and a framework based on a novel location- and power-aware communication systems and analytics to notify and manage patient health. Methods to transmit data to a PAS unit through the patients' smart phone that is connected to internet, abnormality detection in the data, advanced analytical diagnostics and communication system between the health service provider (HSP) and patient are also provided. The health monitoring systems, methods and devices allows for continuous monitoring of the patient without disrupting their normal lives, provides access even in sparsely connected and remote regions which lack good healthcare facilities, allows intervention by specialized practitioners, and sharing of resource or information in the existing healthcare facilities.

[0005] US9357921B2 discloses a system, method, devices which relate to remotely monitoring the health of an individual. The individual wears a health monitoring device, with an attached strap, capable of sensing characteristics of the individual. These characteristics may include voice level and tone, movements, blood pressure, temperature, etc. The device allows individuals to constantly monitor their health without having to physically visit a doctor or other health care professional. Wireless communication, for instance with an Internet Protocol Television (IPTV) set-top box, allows measurements to be made and evaluated by a ‘computerized’ healthcare service provider. For a more accurate evaluation, measurements are sent over the INTERNET to a service. The device communicates with services in order to diagnose the individual based upon the characteristics.

[0006] Conventionally, many system have been developed to monitor the health remotely but these systems lack monitoring the health parameters with more accurate, improved and precise levels. Additionally, the system are failed to classify maintain the patient’s privacy.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that is capable of detecting and monitoring the health parameters in more accurate, precise and improved levels helping the doctors in the diagnosis of the results. Additionally, the system is capable of filters or compresses data to save space and protect patient privacy before sending it to the computer.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of monitoring the heath parameters to give accurate precise and improved level of results in an automated manner.

[0010] Another object of the present invention is to develop a system that is capable of analyze patterns in health data for accurate diagnosis.

[0011] Another object of the present invention is to develop a system that is capable of filtering or compressing data to save space and protect patient privacy before sending it to the computer.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a system for remote health monitoring that is capable of analyzing and providing the accurate results for diagnosis.

[0014] According to an embodiment of the present invention, a system for remote health monitoring comprising a user-interface installed in a computing unit that is accessed to upload data from sensors include wearable devices or medical tools, regarding scans, and sends it to doctors for diagnosis, a gateway device that collects raw health data from sensors, where gateway device filters or compresses data to save space and protect patient privacy before sending it to the computer, a control unit that handles data from the gateway, does basic processing, the control unit checks data for errors and prepares it for secure transfer to the fog environment.

[0015] According to another embodiment of the present invention, the system further comprises of a fog computing module that processes health data close to the source, analyzes it, and sends results to the cloud or doctors, fog computing module includes a clouds controller to manage data flow between the fog layer and the cloud and a machine learning model in the fog environment that analyzes health data to predict health issues and help doctors make decisions machine learning model which uses a gradient boosting protocol to analyze patterns in health data for accurate diagnosis.

[0016] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a block diagram depicting workflow of a system for remote health monitoring.

DETAILED DESCRIPTION OF THE INVENTION

[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0019] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0020] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0021] The present invention relates to a system for remote health monitoring that is capable of monitoring the health of the patient by analyze patterns in health data for accurate diagnosis. The system ensures the improved accurate and precise results of the provided data or scans.

[0022] Referring to Figure 1, a block diagram depicting workflow of a system for remote health monitoring is illustrated. The system disclosed herein includes a user-interface installed in a computing unit that is accessed to upload data from sensors, regarding scans, and sends it to doctors for diagnosis. Where the sensor includes wearable devices or medical tools. The user interface installed in a computing unit, such as a smartphone or computer, functions as the central hub for managing data from wearable sensors or medical tools by providing an intuitive platform for users to upload, visualize, and transmit health information. When sensors collect data such as scans, vital signs, or biometric readings they transmit this information wirelessly via Bluetooth, Wi-Fi, or other communication protocols to the computing unit. The user interface processes and displays this data in real-time or upon request, allowing users or healthcare providers to review the results. Once verified, the interface facilitates secure data transmission over encrypted internet connections to cloud servers or healthcare databases, where doctors access the information for diagnosis and treatment planning.

[0023] A gateway device is installed on the computing unit that collects raw health data from sensors. It filters or compresses data to save space and protect patient privacy before sending it to the computer. It functions as an intermediary that collects raw health data from sensors, such as wearable devices or medical tools, via wireless protocols like Bluetooth, or Wi-Fi. It then performs local processing tasks such as filtering out noise, aggregating data, and compressing information to reduce data size, which helps conserve storage space and bandwidth while also enhancing privacy by removing unnecessary or sensitive details. The gateway may also implement encryption to secure data during transmission, ensuring patient confidentiality. Once processed, the gateway transmits the optimized data over secure internet connections using protocols like HTTPS or MQTT to a central server, cloud platform, for further analysis. This workflow minimizes data load on the network, protects sensitive information, and ensures that only relevant, secure, and manageable data reaches medical professionals for diagnosis and decision-making.

[0024] A control unit that handles data from the gateway, does basic processing. It checks data for errors and prepares it for secure transfer to the fog environment. The control unit that handles data from the gateway functions as an intermediary processor responsible for basic data management tasks, including error checking, validation, and formatting, to ensure data integrity before onward transmission. It receives the compressed and filtered data from the gateway via secure communication protocols such as TCP/IP, then performs error detection techniques like checksums or cyclic redundancy checks (CRC) to identify and correct data corruption or inconsistencies. The control unit may also timestamp data, organize it into structured packets, and encrypt it using standards like AES to protect patient privacy during transfer. Once verified and prepared, the control unit securely forwards the processed data to the fog environment an edge computing layer using encrypted channels for further analysis, storage, or decision-making, thereby ensuring reliable, secure, and efficient data flow from sensor collection points to higher-level processing systems.

[0025] A fog computing module that processes health data close to the source, analyzes it, and sends results to the cloud or doctors. It includes a clouds controller to manage data flow between the fog layer and the cloud. The fog computing module operates close to the health data source, such as control units, by performing real-time processing, analysis, and filtering of the collected data locally to reduce latency and bandwidth usage. It employs edge devices equipped with processing capabilities or microcontrollers to run analytics, detect anomalies, or extract relevant features from raw data. The module then summarizes or flags critical information before securely transmitting it to the cloud for further review. A cloud controller within the fog layer manages the data flow by orchestrating communication protocols, prioritizing data transmission based on urgency or importance, and ensuring synchronization between local processing and cloud storage or applications.

[0026] A machine learning model in the fog environment that analyzes health data to predict health issues and help doctors make decisions. It uses a gradient boosting protocol to analyze patterns in health data for accurate diagnosis. In a fog environment, the machine learning model, such as the gradient boosting protocol, operates locally on edge devices or fog nodes to analyze health data in real-time, enabling prompt predictions of potential health issues. The process involves training the model on historical or preprocessed health datasets like vital signs, lab results, or sensor readings to learn complex patterns and relationships indicative of specific conditions. Once integrated into the fog node, the trained model ingests live streaming data, performs feature extraction, and applies the gradient boosting technique iteratively combining weak learners (decision trees) to improve accuracy to identify anomalies or risks. This localized analysis allows for quick, accurate predictions without relying solely on cloud processing, aiding healthcare providers in early diagnosis and decision-making.

[0027] The present invention works best in the following manner, the user-interface installed in a computing unit that is accessed to upload data from sensors include wearable devices or medical tools, regarding scans, and sends it to doctors for diagnosis. The gateway device that collects raw health data from sensors filters or compresses data to save space and protect patient privacy before sending it to the computer. The control unit that handles data from the gateway, does basic processing like checks data for errors and prepares it for secure transfer to the fog environment. The fog computing module includes a clouds controller to manage data flow between the fog layer and the cloud processes health data close to the source, analyzes it, and sends results to the cloud or doctors. The machine learning model in the fog environment analyzes health data to predict health issues and help doctors make decisions using the gradient boosting protocol to analyze patterns in health data for accurate diagnosis.

[0028] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A system for remote health monitoring, comprising:

i) a user-interface installed in a computing unit that is accessed to upload data from sensors, regarding scans, and sends it to doctors for diagnosis;

ii) a gateway device that collects raw health data from sensors;

iii) a control unit that handles data from the gateway, does basic processing;

iv) a fog computing module that processes health data close to the source, analyzes it, and sends results to the cloud or doctors; and

v) a machine learning model in the fog environment that analyzes health data to predict health issues and help doctors make decisions.

2) The system as claimed in claim 1, wherein sensors include wearable devices or medical tools.

3) The system as claimed in claim 1, where gateway device filters or compresses data to save space and protect patient privacy before sending it to the computer.

4) The system as claimed in claim 1, wherein the control unit checks data for errors and prepares it for secure transfer to the fog environment.

5) The system as claimed in claim 1, the fog computing module includes a clouds controller to manage data flow between the fog layer and the cloud.

6) The system as claimed in claim 1, wherein the machine learning model which uses a gradient boosting protocol to analyze patterns in health data for accurate diagnosis.