A SYSTEM AND METHOD FOR HEALTH PARAMETER MONITORING USING IOMT DEVICES AND ADAPTIVE MACHINE LEARNING MODELS

Abstract

The present invention relates to a system and method for health parameter monitoring using IoMT devices integrated with adaptive machine learning models. The system includes a network of interconnected IoMT devices that collect various health parameters from patients in real time, such as heart rate, blood pressure, glucose levels, and physical activity. The collected data is pre-processed to remove noise and detect anomalies before being analysed by an adaptive machine-learning model. The adaptive model continuously updates based on real-time data, providing personalized health recommendations and generating alerts for healthcare providers. The system also ensures interoperability between multiple IoMT devices, consolidating data for holistic analysis. Privacy measures are incorporated to protect patient information during data collection, transmission, and analysis. This invention provides proactive health management, real-time monitoring, and personalized care, facilitating early detection of potential health issues and timely medical intervention, thereby improving patient outcomes and healthcare delivery.

Specification

Description:[001] The present invention relates to the field of healthcare and medical technology, particularly to a system and method for monitoring health parameters using the Internet of Medical Things (IoMT) devices integrated with adaptive machine learning models.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] The need for accurate and timely health monitoring has increased significantly, driven by an aging population and the rise in chronic diseases. Current health monitoring systems rely heavily on traditional visits to healthcare providers or static wearable devices that provide only basic health information.
[004] Conventional monitoring solutions often lack real-time analytics and fail to integrate data from various sources. This limitation makes it challenging for healthcare providers to obtain a comprehensive understanding of a patient's health condition, thereby compromising timely and effective treatment.
[005] Most current wearable health devices are designed to collect a limited number of health parameters, such as heart rate and physical activity. However, they often fail to interpret these parameters meaningfully or adapt to changing health conditions, thus providing limited insight to both patients and healthcare professionals.
[006] The use of machine learning models in healthcare is also constrained by the need for static training data, which quickly becomes outdated as patient conditions change. These models require frequent retraining to remain effective, which is time-consuming and computationally intensive.
[007] Additionally, the Internet of Medical Things (IoMT) provides a platform for integrating multiple health monitoring devices, but there is a lack of effective systems that can manage the large volume of data generated by these devices in a reliable and efficient manner. The existing solutions often do not address issues related to data privacy, standardization, and real-time data fusion.
[008] Another significant drawback of prior art is the absence of a personalized adaptive mechanism that can adjust health monitoring based on the specific needs of the individual. Current systems often provide generic health recommendations that do not account for individual differences in physiology, lifestyle, and medical history.
[009] The lack of interoperability among different IoMT devices also limits their utility. Most devices operate in silos, unable to communicate or share data effectively. This lack of coordination results in fragmented health information that is difficult for healthcare providers to interpret holistically.
[010] Therefore, there is a pressing need for an improved system and method that can integrate multiple IoMT devices, adapt machine learning models in real-time to evolving patient data, and provide actionable insights to enhance health monitoring, disease prevention, and overall patient care.
SUMMARY OF THE PRESENT INVENTION
[011] According to an embodiment, the present invention discloses a system for health parameter monitoring using IoMT devices, comprising a network of interconnected IoMT devices that collect health parameters from a patient in real-time, an adaptive machine learning model that analyzes the collected health parameters, and a secure wireless communication network for transmitting the data. The adaptive machine learning model is continuously updated to provide personalized health recommendations.
[012] In another embodiment, the present invention discloses that the health parameters collected by the system include heart rate, blood pressure, glucose levels, physical activity, and oxygen saturation levels. The system is configured to detect health anomalies and generate real-time alerts for healthcare providers.
[013] In a further embodiment, the present invention discloses a central processing unit that preprocesses the collected health parameters by filtering noise and detecting anomalies before the data is input into the adaptive machine learning model. This preprocessing step ensures the quality and reliability of the health data.
[014] In yet another embodiment, the present invention discloses that the system is capable of integrating multiple IoMT devices, ensuring interoperability and enabling the consolidation of data from wearable sensors, smart home devices, and other connected medical instruments.
[015] In one embodiment, the present invention discloses a method for monitoring health parameters using IoMT devices and adaptive machine learning models, wherein health parameters are collected in real-time from a plurality of IoMT devices, preprocessed to remove noise, and then analyzed using an adaptive machine learning model. The adaptive model continuously updates based on real-time data to provide personalized health recommendations.
[016] In another embodiment, the present invention discloses that the method includes collecting health parameters such as heart rate, blood pressure, glucose levels, physical activity, and oxygen saturation levels. The method further involves generating real-time alerts for healthcare providers upon detecting anomalies in the patient's health data.
[017] In a further embodiment, the present invention discloses that the preprocessing step involves filtering out noise and detecting anomalies to ensure the quality and reliability of the collected health parameters before analysis by the adaptive machine learning model.
[018] In yet another embodiment, the present invention discloses that the adaptive machine learning model dynamically adjusts its parameters to reflect changes in the patient's health condition, thereby improving the accuracy of health monitoring and the effectiveness of personalized recommendations.
[019] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[020] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[021] FIG. 1 illustrates a block diagram of the health monitoring system comprising IoMT devices, adaptive machine learning models, and cloud infrastructure, in accordance with an embodiment of the present invention.
[022] FIG. 2 illustrates a flowchart depicting the data collection, processing, and analysis workflow of the system, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[023] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[024] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[025] According to an embodiment, the present invention discloses a system and method for health parameter monitoring using IoMT devices integrated with adaptive machine learning models. The system is designed to continuously collect health parameters from a patient in real-time, analyze the data using adaptive algorithms, and provide personalized health recommendations to improve patient outcomes. The system addresses the need for a more comprehensive, adaptable, and responsive health monitoring solution that can proactively identify health issues and support timely medical intervention.
[026] The system comprises a network of IoMT devices that can be integrated into the patient's environment to gather real-time data. These IoMT devices include wearable sensors, smart home devices, and connected medical instruments that can monitor various health parameters such as heart rate, blood pressure, glucose levels, physical activity, and oxygen saturation. The IoMT devices communicate with a central processing unit using a secure wireless communication network to transmit health data in real-time.
[027] The central processing unit is responsible for preprocessing the health data before analysis by the adaptive machine learning model. The preprocessing step involves filtering out noise, detecting anomalies, and standardizing the data format to ensure the quality and reliability of the health parameters collected. Preprocessing is essential for eliminating potential inaccuracies and providing a high-quality data set to the machine learning model.
[028] The core of the system is the adaptive machine learning model, which is designed to analyze the preprocessed health data to generate meaningful insights regarding the patient's health status. Unlike traditional machine learning models that rely on static training data, the adaptive model in the present invention is continuously updated based on real-time data. This allows the model to dynamically adjust its parameters to reflect changes in the patient's health condition. The model can detect health anomalies, predict potential health issues, and generate alerts for healthcare providers when immediate intervention is required.
[029] The adaptive machine learning model is capable of learning from the patient's historical health data and adjusting its behavior over time. It uses real-time inputs to refine its predictions and recommendations, ensuring that the patient receives personalized health management. This adaptive nature of the machine learning model makes it particularly effective in monitoring chronic health conditions where the patient's health status may vary significantly over time.
[030] The system also ensures interoperability between different IoMT devices, allowing data from multiple devices to be consolidated and analyzed holistically. This is achieved through standardized communication protocols that facilitate the seamless exchange of information among wearable sensors, smart home devices, and other connected medical instruments. The interoperability feature enables a comprehensive view of the patient's health, providing healthcare providers with a more complete understanding of the patient's condition.
[031] In one embodiment, the system is configured to generate real-time alerts for healthcare providers upon detecting anomalies in the patient's health data. These alerts are designed to notify healthcare providers of potential health risks, allowing for timely intervention and reducing the risk of serious health events. The alerts can be communicated through various channels, including mobile devices, computer systems, and cloud-based healthcare platforms.
[032] The method for monitoring health parameters using IoMT devices and adaptive machine learning models involves several steps. First, health parameters are collected in real-time from a plurality of IoMT devices. These health parameters include vital signs and activity levels that are essential for monitoring the patient's overall health. The collected data is then preprocessed to remove noise and detect anomalies, ensuring that only reliable and high-quality data is used for analysis.
[033] The preprocessed data is transmitted to the adaptive machine learning model, which analyzes the data to provide insights into the patient's health condition. The model dynamically adjusts its parameters based on the incoming data to provide personalized health recommendations. These recommendations may include lifestyle adjustments, alerts for healthcare providers, or other forms of intervention aimed at improving the patient's health outcomes.
[034] The adaptive nature of the machine learning model allows it to respond effectively to changes in the patient's health condition, providing a level of personalization that is not possible with traditional health monitoring systems. By continuously updating the model with real-time data, the system ensures that the health monitoring process remains accurate, reliable, and responsive to the patient's evolving needs.
[035] Referring now to FIG. 1 illustrates a block diagram of the health monitoring system comprising IoMT devices, adaptive machine learning models, and cloud infrastructure, in accordance with an embodiment of the present invention. The figure illustrates the architecture and workflow of a health monitoring system that leverages a network of interconnected IoMT (Internet of Medical Things) devices integrated with an adaptive machine learning model to continuously monitor patient health in real-time and provide actionable insights to healthcare providers.
[036] The system begins with a network of IoMT devices, which include wearable sensors, smart home devices, and other connected medical instruments. These devices are responsible for continuously collecting various health parameters from the patient, such as heart rate, blood pressure, glucose levels, physical activity, and more. The collected health data is then transmitted through a secure wireless communication network, ensuring the data is transmitted reliably and securely to the next component while maintaining the privacy and security of sensitive patient information.
[037] Upon transmission, the data is received by the central processing unit (CPU), which is responsible for preprocessing the data to ensure its quality and reliability. Preprocessing is a crucial step that involves filtering out noise, standardizing the data, and detecting anomalies, all of which help ensure that only high-quality data is used for further analysis. This step reduces inaccuracies and prepares the data for effective interpretation by the adaptive machine learning model.
[038] The preprocessed data is then analyzed by an adaptive machine learning model, which is designed to provide meaningful insights regarding the patient's health status. Unlike traditional models, the adaptive machine learning model continuously learns from real-time inputs and adjusts its parameters dynamically, reflecting changes in the patient's health condition. This adaptability is particularly important for chronic disease management, where the patient's condition can change significantly over time. The model's analysis helps to identify patterns, detect health anomalies, and predict potential health issues, offering a comprehensive understanding of the patient's health.
[039] The adaptive machine learning model is also continuously updated with real-time data. This continuous model update mechanism allows the system to refine its predictions and improve the accuracy of its analysis, ensuring that the patient receives timely and personalized health insights. If any health anomalies are detected during the analysis, the system generates real-time alerts for healthcare providers. These alerts are sent to notify healthcare professionals of any potential health risks, enabling them to intervene in a timely manner and prevent the patient's condition from worsening.
[040] Based on the analysis, the system also generates personalized health recommendations for the patient. These recommendations are tailored to the patient's unique health condition, medical history, and lifestyle, providing targeted guidance aimed at improving overall health and managing specific conditions. By offering personalized recommendations, the system ensures that the interventions are relevant and effective, leading to improved patient outcomes.
[041] Finally, both the real-time alerts and personalized health recommendations are communicated to healthcare providers. By delivering timely information and targeted guidance, the system empowers healthcare professionals to make informed decisions, take proactive actions, and provide enhanced care for their patients. The integration of IoMT devices, adaptive machine learning, real-time alerts, and personalized recommendations collectively creates a comprehensive and responsive health monitoring system that aims to improve the quality of patient care and outcomes.
[042] FIG. 2 illustrates a flowchart depicting the data collection, processing, and analysis workflow of the system, in accordance with an embodiment of the present invention. The figure represents the workflow of a health parameter monitoring system using IoMT devices and an adaptive machine learning model. This process provides a step-by-step approach to collecting, processing, analyzing, and using health data to deliver real-time insights and personalized health recommendations, ultimately improving patient care.
[043] The workflow begins with the Start Health Parameter Monitoring step, which initiates the monitoring process. Once initiated, the system proceeds to Collect Real-time Health Parameters using IoMT Devices. This involves using various IoMT devices, such as wearable sensors and smart home devices, to continuously collect important health parameters, including heart rate, blood pressure, glucose levels, and physical activity. The collection occurs in real-time, allowing the system to have up-to-date information about the patient's health status.
[044] After data collection, the system moves to the Preprocess Health Parameters step. The collected data often contains noise or inconsistencies that need to be corrected before analysis. The preprocessing stage ensures that the health data is clean and standardized, which is crucial for accurate interpretation. Following this, the system further proceeds to Filter Noise and Detect Anomalies. This step involves filtering out unnecessary information and identifying potential anomalies that may indicate a health issue. Detecting anomalies early allows the system to flag potential problems for further analysis or action.
[045] The preprocessed data is then Transmitted to the Adaptive ML Model, where it undergoes detailed analysis. The Adaptive Machine Learning Model analyzes the health parameters to derive insights about the patient's current health status. The adaptive nature of the machine learning model means that it continuously learns from incoming data, adjusting its parameters to provide increasingly personalized insights. This adaptability is particularly important in healthcare, as individual health conditions can vary significantly over time.
[046] Based on the analysis, the system Generates Insights into the Patient's Health Condition. These insights are used to understand patterns in the patient's health data and to identify potential areas of concern. In cases where an anomaly is detected, the system also Generates Real-time Alerts for Anomalies to notify healthcare providers. These real-time alerts allow for timely intervention, helping healthcare professionals address potential health issues before they become critical.
[047] Once the insights have been generated, the machine learning model is updated using Real-time Data for Personalization. This means that the model continuously improves based on new health data, providing more accurate and relevant insights as it learns from the patient's evolving condition. This continuous update mechanism ensures that the recommendations provided by the system are tailored to the patient's specific health needs.
[048] Subsequently, the system Provides Personalized Health Recommendations based on the analysis and insights. These recommendations are customized according to the patient's health data, lifestyle, and history, providing guidance that is specific to the individual. By offering personalized health recommendations, the system helps patients manage their health proactively and effectively.
[049] Finally, the workflow concludes with the End Monitoring Process, indicating that the health monitoring session has been completed. This end stage could either indicate the completion of a specific monitoring period or the successful resolution of an anomaly. The entire workflow ensures that the patient's health data is continuously monitored, analyzed, and used to provide actionable insights and recommendations, leading to better overall healthcare management. This detailed process is crucial for chronic disease management and preventive healthcare, as it helps in early detection, timely intervention, and the delivery of personalized care.
[050] The present invention offers several significant advantages over conventional health monitoring systems. One major advantage is the ability of the system to provide real-time health monitoring and personalized recommendations. Unlike static wearable devices that merely collect data, the integration of adaptive machine learning models ensures that the health monitoring process evolves in response to the patient's changing health conditions. This real-time adaptability provides a level of personalization that is crucial for effective healthcare management, particularly for patients with chronic conditions where health status can vary significantly.
[051] Another advantage is the interoperability of the IoMT devices. The system enables seamless integration of various wearable sensors, smart home devices, and other connected medical instruments. This interoperability ensures that data from different devices can be consolidated and analyzed holistically, providing healthcare providers with a comprehensive view of the patient's health. This consolidated analysis enables healthcare providers to make better-informed decisions, thereby improving patient outcomes.
[052] The system also significantly enhances the reliability and quality of health data through its preprocessing step. By filtering out noise and detecting anomalies before the data is fed into the machine learning model, the system ensures that only high-quality data is used for health analysis. This preprocessing step reduces the chances of incorrect assessments and provides more accurate insights, contributing to better patient care and reducing the likelihood of unnecessary medical interventions.
[053] The adaptive machine learning model is another critical advantage of the present invention. Unlike traditional machine learning models that rely on static training data, the adaptive model continuously learns from real-time inputs and adjusts its parameters accordingly. This dynamic nature allows the model to stay relevant as the patient's health condition evolves, leading to more accurate predictions and effective personalized health recommendations. This adaptability is particularly beneficial in chronic disease management, where the patient's condition may fluctuate over time.
[054] Furthermore, the system includes a mechanism for generating real-time alerts when health anomalies are detected. These alerts provide an added layer of safety, enabling healthcare providers to intervene promptly in the event of potential health risks. The ability to generate real-time alerts ensures that medical intervention can occur before a health issue becomes critical, which is particularly important for vulnerable patients who may require continuous monitoring.
[055] The present invention also addresses issues related to data privacy and security. By incorporating secure wireless communication and privacy measures, the system ensures that sensitive patient information is protected during data collection, transmission, and storage. This focus on data security not only protects patient confidentiality but also helps in building trust between patients and healthcare providers, encouraging greater adoption of the system.
[056] Overall, the present invention provides an effective, adaptable, and secure solution for health parameter monitoring. Its ability to provide real-time insights, personalized recommendations, and timely alerts makes it a valuable tool for proactive health management. By leveraging the power of IoMT devices and adaptive machine learning, the invention bridges the gap between data collection and actionable healthcare insights, thereby enhancing the quality of patient care and improving health outcomes.
[057] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. A system for health parameter monitoring using IoMT devices, comprising:
a. a network of interconnected IoMT devices configured to collect health parameters from a patient in real-time;
b. an adaptive machine learning model configured to analyze the health parameters and provide insights into the patient's health status;
c. wherein the adaptive machine learning model is continuously updated based on real-time data to provide personalized health recommendations; and
d. a secure wireless communication network for transmitting the collected health parameters from the IoMT devices to a central processing unit.
2. The system as claimed in claim 1, wherein the health parameters include heart rate, blood pressure, glucose levels, physical activity, and oxygen saturation levels.
3. The system as claimed in claim 1, wherein the adaptive machine learning model is configured to detect health anomalies in the collected health parameters and generate real-time alerts for healthcare providers.
4. The system as claimed in claim 1, wherein the system further comprises a central processing unit configured to preprocess the health parameters by filtering noise and detecting data anomalies before inputting the data into the machine learning model.
5. The system as claimed in claim 1, wherein the system is capable of integrating and ensuring interoperability among multiple IoMT devices, enabling the consolidation of data from wearable sensors, smart home devices, and other connected medical instruments.
6. A method for monitoring health parameters using IoMT devices and adaptive machine learning models, comprising:
a. collecting health parameters from a patient in real-time using a plurality of IoMT devices;
b. preprocessing the collected health parameters to remove noise and detect anomalies;
c. transmitting the preprocessed data to an adaptive machine learning model;
d. analyzing the preprocessed health parameters using the adaptive machine learning model to generate insights into the patient's health condition; and
e. continuously updating the adaptive machine learning model based on real-time data to provide personalized health recommendations.
7. The method as claimed in claim 6, wherein the collected health parameters include heart rate, blood pressure, glucose levels, physical activity, and oxygen saturation levels.
8. The method as claimed in claim 6, further comprising the step of generating real-time alerts for healthcare providers upon detecting health anomalies in the patient's health parameters.
9. The method as claimed in claim 6, wherein the preprocessing step further comprises filtering out noise and performing anomaly detection to ensure the quality and reliability of the collected health parameters.
10. The method as claimed in claim 6, wherein the adaptive machine learning model dynamically adjusts its parameters to reflect changes in the patient's health condition, thereby improving the accuracy of health monitoring and personalized recommendations.

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