Description:
1. A system (100) for facilitating chronic kidney disease patient with remote monitoring comprising:
characterized in that,
a non-invasive patch (140) positioned between a needle insertion point on an arteriovenous fistula to protect a skin of a patient (185), wherein the arteriovenous fistula is a surgically created connection between an artery and a vein utilized for a dialysis procedure, wherein the non-invasive patch (140) is configured to:
capture and analyse data pertaining to a health of the patient (185);
monitor continuously a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure; and
detect any compromise and a dysfunction in a vital health parameter of the patient (185) in real time;
a plurality of sensors comprising a light sensor (145), an audio sensor (150), and a temperature sensor (155), wherein the plurality of sensors are configured to capture a plurality of data and monitor the plurality of vital health parameters of the patient (185); and
a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (180) to control bidirectional communications among a plurality of modules comprising :
a prediction module (110) is configured to :
predict an early diagnosis of a chronic kidney disease in the patient (185) based on an assessment of the vital health parameters; and
estimate a probability of a timeframe for the patient (185) to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch (140);
a collection module (115) configured to:
allow an admin to add the new patient (185) and a doctor (190) and receive a plurality of details required for a registration process of the new patient (185) and the doctor (190) ;
enable the doctor (190) to input a plurality of patient (185) care instructions comprising a treatment plan and a medical test;
store the patient (185) care instructions in a database (170); and
collect automatically the plurality of vital health parameters of the patient (185) comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things;
a monitoring module (120) operatively coupled to the collection module (115) , wherein the monitoring module (120) is configured to :
analyse the plurality of vital health parameters of the patient (185);
update the doctor (190) about the vital health parameters of the patient (185) in real time comprising a health symptom of the patient (185); and
enable the doctor (190) to remotely monitor the health symptom;
an output module (125) operatively coupled to the monitoring module (120), wherein the output module (125) is configured to :
send automatically multiple reminders to the patient (185) about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication ;
notify the doctor (190) about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient (185); and
assign a risk score to the patient (185) by analysing the plurality of vital health parameters and help the doctors (190) by minimizing an advent of emergency and hospitalization ; and
a communication module (130) operatively coupled to the output module (125) , wherein the communication module (130) is configured to provide a medical advice by the doctor (190) to the patient (185).

2. The system (100) as claimed in claim 1, where in the non -invasive patch (140) comprises:
a light emitting diode (160) ,wherein the light emitting diode (160) is utilized for visual indication; and
a microphone (165) is utilized for auditory signal detection and to enable real-time monitoring and feedback of the patient (185).

3. The system (100) as claimed in claim 1, wherein the non-invasive patch (140) is adapted to provide a comprehensive physiological monitoring and detection of subtle changes in condition of the patient (185).

4. The system (100) as claimed in claim 1, wherein the non-invasive patch (140) is utilized to provide a continuous monitoring of the vital health parameters comprising , blood oxygen levels, heart rate, and kidney function to assess the patient (185) status during the dialysis procedure.

5. The system (100) as claimed in claim 1, wherein the output module (125) is configured to inform the doctor (190) with insights into the patient’s (185) condition and adapts to save consultation time and improve operational efficiency of the doctor (190).

6. The system (100) as claimed in claim 1, wherein the communication module (130) is configured to utilize a communication device to deliver advice from the doctor (190) to the patient (185) receiving a chronic kidney disease treatment.

7. The system (100) as claimed in claim 1, comprises a patient interface (420) configured to capture data related to a health condition of the patient (185).

8. The system (100) as claimed in claim 1, comprises a cloud-based platform (400), wherein the cloud-based platform (400) is configured to receive and store the captured data from the patient interface (420).

9. The system (100) as claimed in claim 1, comprises a healthcare provider interface in the form of a doctor interface (410), wherein the doctor interface (410) is adapted to utilize stored data from the cloud-based platform (400) and made available to a healthcare provider for analysis and review.

10. The system (100) as claimed in claim 1, wherein the system (100) utilizes a closed loop real time feedback network between the doctor (190) and the patient (185) to adapt and modify the treatment plan.

11. A method (200) for facilitating chronic kidney disease patient with remote monitoring comprising:

capturing and analysing, by a non- invasive patch, data pertaining to a health of the patient; (205)
monitoring continuously, by the non-invasive patch, a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure ; (210)
detecting, by the non-invasive patch, any compromise and a dysfunction in a vital health parameter of the patient in real time ; (215)
capturing , by a plurality of sensors, a plurality of data of the patient ; (220)
monitoring, by the plurality of sensors, a plurality of vital health parameters of the patient; (225)
predicting, by a prediction module, an early diagnosis of a chronic kidney disease in the patient based on an assessment of the vital health parameters; (230)
estimating, by the prediction module, a probability of a timeframe for the patient to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch; (235)
allowing, by a collection module, an admin to add the new patient and a doctor and receive a plurality of details required for a registration process of the new patient and the doctor ; (240)
enabling, by the collection module, the doctor to input a plurality of patient care instructions comprising a treatment plan and a medical test; (245)
storing, by the collection module, the patient care instructions in a database; (250)
collecting automatically, by the collection module, the plurality of vital health parameters of the patient comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things; (255)
analysing, by a monitoring module, the plurality of vital health parameters of the patient; (260)
updating, by the monitoring module, the doctor about the vital health parameters of the patient in real time comprising a health symptom of the patient; (265)
enabling, by the monitoring module, the doctor to remotely monitor the health symptom; (270)
sending automatically, by an output module, multiple reminders to the patient about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication; (275)
notifying, by the output module, the doctor about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient; (280)
assigning, by the output module, a risk score to the patient by analysing the plurality of vital health parameters and help the doctors by minimizing an advent of emergency and hospitalization; (285) and
providing, by a communication module, a medical advice by the doctor to the patient . (290)

Dated this 30th day of April 2025
Signature

Manish Kumar
Patent Agent (IN/PA-5059)
Agent for the Applicant
, Claims:FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to the field of wearable medical device with healthcare technologies and more particularly a system for facilitating chronic kidney disease patient with remote monitoring and a method thereof.
BACKGROUND
[0002] The kidney disease refers to a condition that impairs kidney function, preventing them from effectively filtering waste and maintaining fluid balance. The kidney disease ,if left untreated, acute kidney disease can gradually lead to chronic kidney disease. The chronic kidney disease is a long-term condition where kidney function deteriorates over time.

[0003] The chronic kidney disease is a progressive condition characterized by the gradual loss of kidney function over time. The kidneys play a crucial role in filtering waste products and excess fluids from the blood, maintaining electrolyte balance, and regulating blood pressure. The chronic kidney disease can result from various underlying conditions, including diabetes, hypertension, glomerulonephritis, and polycystic kidney disease. The chronic kidney disease is often asymptomatic in its early stages, which makes early diagnosis challenging. When chronic kidney disease progresses to end-stage renal disease, kidney function is severely compromised, and patients may require a dialysis procedure or a kidney transplant to survive.

[0004] Traditionally, the existing methods for treatment of chronic kidney disease includes managing underlying conditions and slowing disease progression. The existing medications including angiotensin-converting enzyme inhibitors and angiotensin receptor blockers are commonly prescribed to control blood pressure and reduce proteinuria, a sign of kidney damage. Additionally, in advanced stages of chronic kidney disease , the dialysis procedure is adapted , wherein the dialysis includes haemodialysis and peritoneal dialysis. The peritoneal dialysis is used to artificially filter waste products and fluids from the blood. Furthermore, a kidney transplantation is another option for those with end-stage renal disease, providing a long-term solution but requiring suitable donors and immunosuppressive therapy to prevent organ rejection.

[0005] Although there is notable progress in integrating modern scientific technology into healthcare for various general parameters and chronic kidney disease healthcare, there remains a significant gap in the application of wearable devices specifically for nephrology monitoring and rehabilitation. Firstly, the dialysis procedure, while lifesaving, comes with numerous challenges, including the need for frequent hospital visits, significant lifestyle disruptions, and potential complications including infections, blood clots, and vascular access issues. Additionally, the kidney transplants are limited by donor availability, and patients must endure long waiting times and manage the risks associated with immunosuppressive drugs. In terms of medication, while the patient can help manage chronic kidney disease, they do not restore kidney function. The significant reverse damage leads to a need for more radical treatments as the disease advances. Furthermore, the treatment of chronic kidney disease includes frequent dialysis sessions and potential kidney transplantation, can lead to a significant financial burden due to high medical costs and long-term care needs. The patient and their caretaker often face expenses related to medications, hospital visits, and post-transplant care, resulting in substantial financial strain.

[0006] In response to these challenges, there exists a need to address these challenges and develop an IoT-based solution specifically tailored for chronic kidney disease monitoring. Firstly ,the advancements in the chronic kidney disease treatment are focused on improving early detection, slowing progression, and potentially restoring kidney function. Additionally, the utilization of artificial intelligence procedure and machine learning are being explored to enhance early detection and personalized treatment plans. These future developments, combined with innovations in drug therapies, could significantly improve outcomes and quality of life for the chronic kidney disease patients.

[0007] Hence, there is a need for an improved a system for facilitating chronic kidney disease patient with remote monitoring and a method thereof which addresses the aforementioned issue(s).

OBJECTIVES OF THE INVENTION
[0008] The primary objective of the invention is to provide a system and method to facilitate the treatment of chronic kidney disease and transplant patients by incorporating advanced monitoring and predictive capabilities. The system includes a plurality of modules including a specialized module adapted to analyse a wide range of health parameters and predicts the time frame for the patient to progress to end-stage renal disease. The system by utilizing this predictive feature, proactively informs both the patient and healthcare professionals about the patient's health status. Additionally, a communication interface is utilized to generate timely outputs for the patient and guide them through treatment actions including medication schedules. Furthermore, the system alerts the doctors about the criticality of the patient's condition, improving operational efficiency by enabling doctors to prioritize consultations based on the patient’s health status.

[0009] Another objective of the invention is to utilize a smart, flexible, non-invasive patch encased in silicone for continuous monitoring of vascular access sites in dialysis patients. The patch is adapted to be worn between the cannulation sites of the arteriovenous fistula or graft for a specific time frame and ensures non-intrusive surveillance. The patch detects early signs of vascular dysfunction or complications, reducing hospitalization risks. The patch integrates a plurality of sensors, light emitting diodes, and photodetectors. These sensors work across various spectral bands to provide a comprehensive assessment of the patient's physiological condition.

[0010] Yet another objective of the invention is to offer a holistic approach to the patient care by continuously monitoring a plurality of vital parameters of the patient before and after dialysis procedure including urea, creatinine, potassium, sodium, haemoglobin, blood sugar, blood pressure, red blood cells count, platelet count, albumin levels, and heart rate. This continuous data stream allows for the early detection of subtle changes in the patient's condition, enabling timely intervention. The integration of the patch with real-time health monitoring not only enhances patient safety but also improves clinical decision-making.

BRIEF DESCRIPTION
[0011] In accordance with an embodiment of the present disclosure, a system for facilitating chronic kidney disease patient with remote monitoring is provided. The system includes a non-invasive patch positioned between a needle insertion point on an arteriovenous fistula to protect a skin of a patient, wherein the arteriovenous fistula is a surgically created connection between an artery and a vein utilized for a dialysis procedure, wherein the non-invasive patch is configured to capture and analyse data pertaining to a health of the patient. Additionally, the non-invasive patch is configured to monitor continuously a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure. Furthermore, the non-invasive patch is configured to detect any compromise and a dysfunction in a vital health parameter of the patient in real time. Additionally, the system includes a plurality of sensors comprising a light sensor , an audio sensor, and a temperature sensor, wherein the plurality of sensors are configured to capture a plurality of data to monitor the plurality of vital health parameters of the patient. Furthermore, the system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a prediction module configured to predict an early diagnosis of a chronic kidney disease in the patient based on an assessment of the vital health parameters. Additionally, the prediction module is configured to estimate a probability of a timeframe for the patient to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch. The processing subsystem includes a collection module configured to allow an admin to add the new patient and a doctor and receive a plurality of details required for a registration process of the new patient and the doctor. Additionally , the collection module is configured to enable the doctor to input a plurality of patient care instructions comprising a treatment plan and a medical test. Furthermore, the collection module is configured to store the patient care instructions in a database. Moreover, the collection module is configured to collect automatically the plurality of vital health parameters of the patient comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things. The processing subsystem also includes a monitoring module operatively coupled to the collection module, wherein the monitoring module is configured to analyse the plurality of vital health parameters of the patient. Additionally, the monitoring module is also configured to update the doctor about the vital health parameters of the patient in real time comprising a health symptom of the patient. Furthermore, the monitoring module is configured to enable the doctor to remotely monitor the health symptom. The processing subsystem also includes an output module operatively coupled to the monitoring module, wherein the output module is configured to send automatically multiple reminders to the patient about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication. Additionally, the output module is configured to notify the doctor about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient. Furthermore, the output module is configured to assign a risk score to the patient by analysing the plurality of vital health parameters and help the doctors by minimizing an advent of emergency and hospitalization. The processing subsystem also includes a communication module operatively coupled to the output module, wherein the communication module is configured to provide a medical advice by the doctor to the patient.

[0012] In accordance with another embodiment of the present disclosure, a method for facilitating chronic kidney disease patient with remote monitoring is provided. The method includes capturing and analysing, by a non- invasive patch, data pertaining to a health of the patient. The method includes monitoring continuously, by the non-invasive patch, a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure. The method includes detecting, by the non-invasive patch, any compromise and a dysfunction in a vital health parameter of the patient in real time. The method includes capturing , by a plurality of sensors, a plurality of data of the patient. The method includes monitoring, by the plurality of sensors, a plurality of vital health parameters of the patient. The method includes predicting, by a prediction module, an early diagnosis of a chronic kidney disease in the patient based on an assessment of the vital health parameters. The method includes estimating, by the prediction module, a probability of a timeframe for the patient to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch. The method includes allowing, by a collection module, an admin to add the new patient and a doctor and receive a plurality of details required for a registration process of the new patient and the doctor. The method includes enabling, by the collection module, the doctor to input a plurality of patient care instructions comprising a treatment plan and a medical test. The method includes storing, by the collection module, the patient care instructions in a database. The method includes collecting automatically, by the collection module, the plurality of vital health parameters of the patient comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things. The method includes analysing, by a monitoring module, the plurality of vital health parameters of the patient. The method includes updating, by the monitoring module, the doctor about the vital health parameters of the patient in real time comprising a health symptom of the patient. The method includes enabling, by the monitoring module, the doctor to remotely monitor the health symptom. The method includes sending automatically, by an output module, multiple reminders to the patient about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication. The method includes notifying, by the output module, the doctor about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient. The method includes assigning, by the output module, a risk score to the patient by analysing the plurality of vital health parameters and help the doctors by minimizing an advent of emergency and hospitalization. The method includes providing, by a communication module, a medical advice by the doctor to the patient.

[0013] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0015] FIG. 1 is a block diagram representation of a system for facilitating chronic kidney disease patient with remote monitoring in accordance with an embodiment of the present disclosure;

[0016] FIG. 2 is a block diagram of a computer or a server of FIG. 1 in accordance with an embodiment of the present disclosure;

[0017] FIG. 3 is a schematic diagram depicting cloud visualisation interface includes a patient interface, a doctor interface, and a caregiver interface of FIG. 1, in accordance with another embodiment of the present disclosure;

[0018] FIG. 4 is an exemplary representation of the closed loop control system between the patient and the doctor of the system for facilitating chronic kidney disease patient with remote monitoring of FIG. 1, in accordance with yet another embodiment of the present disclosure ;

[0019] FIG.5(a) illustrates a flow chart representing the steps involved in a method for facilitating chronic kidney disease patient with remote monitoring in accordance with an embodiment of the present disclosure; and

[0020] FIG.5(b) illustrates continued steps of the method of FIG.5(a) in accordance with an embodiment of the present disclosure.

[0021] 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 DESCRIPTION
[0022] For 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.

[0023] 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 listed or 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.

[0024] 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.

[0025] 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.

[0026] In accordance with an embodiment of the present disclosure, a system for facilitating chronic kidney disease patient with remote monitoring is provided. The system includes a non-invasive patch positioned between a needle insertion point on an arteriovenous fistula to protect a skin of a patient, wherein the arteriovenous fistula is a surgically created connection between an artery and a vein utilized for a dialysis procedure, wherein the non-invasive patch is configured to capture and analyse data pertaining to a health of the patient. Additionally, the non-invasive patch is configured to monitor continuously a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure. Furthermore, the non-invasive patch is configured to detect any compromise and a dysfunction in a vital health parameter of the patient in real time. Additionally, the system includes a plurality of sensors comprising a light sensor , an audio sensor, and a temperature sensor, wherein the plurality of sensors are configured to capture a plurality of data to monitor the plurality of vital health parameters of the patient. Furthermore, the system includes a processing subsystem hosted on a server, wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a prediction module configured to predict an early diagnosis of a chronic kidney disease in the patient based on an assessment of the vital health parameters. Additionally, the prediction module is configured to estimate a probability of a timeframe for the patient to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch. The processing subsystem includes a collection module configured to allow an admin to add the new patient and a doctor and receive a plurality of details required for a registration process of the new patient and the doctor. Additionally , the collection module is configured to enable the doctor to input a plurality of patient care instructions comprising a treatment plan and a medical test. Furthermore, the collection module is configured to store the patient care instructions in a database. Moreover, the collection module is configured to collect automatically the plurality of vital health parameters of the patient comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things. The processing subsystem also includes a monitoring module operatively coupled to the collection module, wherein the monitoring module is configured to analyse the plurality of vital health parameters of the patient. Additionally, the monitoring module is also configured to update the doctor about the vital health parameters of the patient in real time comprising a health symptom of the patient. Furthermore, the monitoring module is configured to enable the doctor to remotely monitor the health symptom. The processing subsystem also includes an output module operatively coupled to the monitoring module, wherein the output module is configured to send automatically multiple reminders to the patient about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication. Additionally , the output module is configured to notify the doctor about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient. Furthermore, the output module is configured to assign a risk score to the patient by analysing the plurality of vital health parameters and help the doctors by minimizing an advent of emergency and hospitalization. The processing subsystem also includes a communication module operatively coupled to the output module, wherein the communication module is configured to provide a medical advice by the doctor to the patient.

[0027] FIG. 1 is a block diagram representation of a system for facilitating chronic kidney disease patient with remote monitoring in accordance with an embodiment of the present disclosure. The system (100) includes a non-invasive patch (140) positioned between a needle insertion point on an arteriovenous fistula to protect a skin of a patient (185), wherein the arteriovenous fistula is a surgically created connection between an artery and a vein utilized for a dialysis procedure. The non- invasive patch (140) is a silicone-based patch designed to be worn between cannulation sites of an arteriovenous fistula or graft. Additionally, the non-invasive (140) is adapted to securely cover the area for a specific time frame. In one embodiment, this time frame lasts for seven days. The non- invasive patch (140) is designed to be flexible, ensuring comfort while safeguarding the site from infection or irritation during the dialysis procedure.

[0028] The non-invasive patch (140) is configured to capture and analyse data pertaining to a health of the patient (185). The non-invasive patch (140) is designed to be worn on the patient's (185) skin, where it captures a plurality of physiological data including heart rate, and temperature. This data is then analysed to monitor the patient's (185) health in real-time. Additionally, the non-invasive patch (140) is optimized to do the analysis of a plurality of health parameters and assist the doctor (190) for timely intervention. This timely intervention helps the doctor (190) to prevent complications including low blood pressure, hypokalaemia, hyperkalaemia and the like which sometimes lead to a life-threatening condition including cardiac attack.

[0029] Additionally, the non-invasive patch (140) is configured to monitor continuously a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure. The non-invasive patch (140) is designed to detect and track key indicators including blood flow, pressure, or signs of infection at the access site, helping ensure the site remains functional and free from complications. The non-invasive patch (140) by monitoring these parameters in real-time provides an alert to healthcare providers or patients (185). The prompt alert mitigates the urge of exigency to potential issues, allowing for prompt intervention and improving the safety and efficiency of the dialysis procedure.

[0030] Additionally, the non-invasive patch (140) is configured to detect any compromise and a dysfunction in a vital health parameter of the patient (185) in real time. The non-invasive patch (140) is adapted to detect any compromise and impairment positioned at medical access points including catheters and grafts and helps identify early issues, preventing complications. This is done through regular monitoring, assessments, and diagnostic tools to ensure proper function and timely intervention. Additionally, the non-invasive patch (140) detects dysfunction in real time utilizing continuous monitoring and initiates a prompt action from the doctor and healthcare professionals.

[0031] In one embodiment, the non-invasive patch (140) comprises a light emitting diode (160) ,wherein the light emitting diode (160) is utilized for visual indication. The light emitting diode (160) is integrated into the non-invasive patch (140) to provide visual cues or alerts. The light emitting diode (160) when triggered, emits light in specific colours and patterns and capture the data.

[0032] In one embodiment, the non-invasive patch (140) comprises a microphone (165) is utilized for auditory signal detection and to enable real-time monitoring and feedback of the patient (185). The microphone (165) captures auditory signals, including sound coming from a fistula for real-time monitoring. This auditory data is used for an immediate feedback and monitoring of the patient’s (185) health status.

[0033] The system (100) includes a plurality of sensors comprising a light sensor (145), an audio sensor (150), and a temperature sensor (155), wherein the plurality of sensors are configured to capture a plurality of data and monitor the plurality of vital health parameters of the patient (185). The light sensor (145) is adapted to detect blood oxygen levels, and the audio sensor (150) is utilized to monitor sounds including heartbeats. The temperature sensor (155) tracks body temperature of the patient (185). The plurality of sensors are adapted to work together to capture real-time data and help to assess the patient’s (185) health and detect potential issues.

[0034] The system (100) includes a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (180) to control bidirectional communications among a plurality of modules. In one embodiment, the server (108) may include a cloud-based server. In another embodiment, parts of the server (108) may be a local server coupled to a patient (185) registration details via a user interface. In one example, the network (180) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (180) 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 (180) 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 (180) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and/or enhanced data for global evolution (EDGE) network.

[0035] The plurality of modules includes a prediction module (110), a collection module (115), a monitoring module (120), an output module (125), and a communication module (130).

[0036] The prediction module (110) is configured to predict an early diagnosis of a chronic kidney disease in the patient (185) based on an assessment of the vital health parameters. The prediction module (110) analyses the patient's (185) vital health parameters, including blood pressure, kidney function markers, and other relevant data. Additionally, the prediction module (110) utilizes machine learning for predictive analysis of the vital health parameters of the patient (185) and identifies patterns and risk factors associated with chronic kidney disease. Furthermore, the prediction module (110) based on these insights provides an early diagnosis prediction for the chronic kidney disease in the patient (185).

[0037] Additionally, the prediction module (110) is configured to estimate a probability of a timeframe for the patient (185) to reach an end stage of the renal health utilizing the internet of things. The prediction module (110) by integrating internet of things enabled devices to monitor real-time health data, tracks the plurality of vital health parameters and kidney function metrics. The internet of things utilizes a statistical model to estimate the probability of the time frame for the patient (185) to reach an end stage renal disease. The statistical model utilizes the vital health parameters data for the prediction. This continuous data flow enables the prediction module (110) to analyse trends and forecast disease progression in the patient (185) from a stage including chronic kidney disease one to chronic kidney disease four, wherein the chronic kidney disease one is predicted as initial stage and the chronic kidney disease four referred as the end stage of the disease progression. Additionally, the prediction from the prediction module (110) is fed into the non-invasive smart patch (140). The prediction from the prediction module (110) is analysed by the doctor (190) and based on a patient’s (185) kidney function including chronic kidney disease one to four, the patient (185) is advised to wear the non-invasive smart patch (140).

[0038] The collection module (115) is configured to allow an admin to add the new patient (185) and a doctor (190) and receive a plurality of details required for a registration process of the new patient (185) and the doctor (190). The collection module (110) allows an admin to register a new patient (185) and a new doctor (190) by collecting necessary details including personal information, medical history, and credentials. These details are then processed to complete the registration and enable access to the healthcare infrastructure.

[0039] Additionally, the collection module (115) is configured to enable the doctor (190) to input a plurality of patient (185) care instructions comprising a treatment plan and a medical test. The doctors (190) are adapted to accesses a digital platform where they can input patient (185) care instructions, including treatment plans and medical tests, through predefined fields. The collection module (115) is adapted to provide options to select and customize specific treatments and tests based on the patient's (185) health condition. Once entered, the data is saved to the patient's (185) profile for continuous monitoring and follow-up.

[0040] Additionally, the collection module (115) is adapted to store the patient (185) care instructions provided by the doctor (190) and health care professionals in a database (170). This database (170) storage ensures that the instructions are easily accessible and retrievable when needed. The data will be securely stored in the database (170) for future reference and updates.

[0041] Additionally, the collection module (115) is configured to collect automatically the plurality of vital health parameters of the patient (185) comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things. The collection module (115) is adapted to collect the plurality of vital health parameters automatically utilizing the internet of things in devices including wearable sensors and medical monitors. These devices are used to monitor blood pressure, glucose, and creatinine levels. These devices wirelessly transmit the data to a central platform through internet connectivity. The collected data is then processed and monitored in real-time for health tracking.

[0042] The monitoring module (120) is operatively coupled to the collection module (115). The monitoring module (120) is configured to analyse the plurality of vital health parameters of the patient (185). The monitoring module (120) process the collected health data utilizing a statistical model adapted to identify patient’s (185) vital health parameters trends and detect abnormalities. The plurality of vital health parameters includes evaluating multiple health metrics including blood pressure, glucose, and creatinine levels to assess a patient's (185) overall health. This helps to detect potential health issues early by identifying abnormal trends and risks.

[0043] Additionally, the monitoring module (120) is configured to update the doctor (190) about the vital health parameters of the patient (185) in real time comprising a health symptom of the patient (185). The real-time updates are sent to the doctor (190) via a connected interface utilized to display the patient's (185) vital health parameters and symptoms. This ensures the doctor (190) to monitor the patient's (185) condition continuously and take timely actions.

[0044] Additionally, the monitoring module (120) is configured to enable the doctor (190) to remotely monitor the health symptom of the patient (185). The monitoring module (120) allows the doctor (190) to access real-time data from the internet of thing utilized devices and display the patient's (185) health symptoms and parameters remotely. This enables continuous monitoring and timely medical intervention by the doctors (190) and healthcare professionals from any location. The remote monitoring increases specialist accessibility for the patients (185).

[0045] The output module (125) is operatively coupled to the monitoring module (120). The output module (125) is configured to send automatically multiple reminders to the patient (185) about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication. The output module (125) is adapted to send automatic reminders to the patient (185) about multiple treatment actions. The output module (125) is set up to schedule notifications for each treatment action, including medication times. Additionally, the output module (125) stores a plurality of treatment details including date, time, medication in the database (170). The output module (125) utilizes an automated message interface including a short message service, email reminders, wherein the automated message are sent at specified time and ensure that the patient (185) is alerted for each treatment action.

[0046] Additionally, the output module (125) is configured to notify the doctor (190) about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient (185). The output module (125) is adapted to notify the doctor (190) about a non-compliant action by the patient (185) and monitors the patient's (185) adherence to treatment actions. Additionally, the output module(125) detects the non-compliance, if patient (185) misses and fails to follow the treatment actions. The system (100) then automatically sends the notification email, message to the doctor (190), providing details of the non-compliant action.

[0047] Additionally, the output module (125) is configured to assign a risk score to the patient (185) by analysing the plurality of vital health parameters and help the doctors (190) by minimizing an advent of emergency and hospitalization. The output module (125) analyses the patient's (185) vital health parameters including blood pressure, and heart rate to assess their current health status. The output module (125) based on the analysis calculates a risk score, indicating the likelihood of adverse health events. This risk score helps doctors (190) proactively manage the patient's (185) care, reducing the chances of emergencies and hospitalizations.

[0048] In one embodiment, the output module (125) is configured to inform the doctor (190) with insights into the patient’s (185) condition and adapts to save consultation time and improve operational efficiency of the doctor (190). The output module (125) collects and analyses the patient's (185) health data, generating key insights into their condition. These insights are summarized and sent to the doctor (190), providing a clear overview of a health status of the patient (185). This allows the doctor (190) to quickly assess the situation, saving consultation time and improving operational efficiency.

[0049] The communication module (130) is operatively coupled to the output module (125) . The communication module (130) is configured to provide a medical advice by the doctor (190) to the patient (185) utilizing secure channels, including message and email. The communication module (130) ensures that the advice is delivered promptly and securely, facilitating clear communication between the doctor (190) and patient (185).

[0050] In one embodiment , the communication module (130) is configured to utilize a communication device to deliver advice from the doctor (190) to the patient (185) receiving a chronic kidney disease treatment. The communication device enables the doctor (190) to deliver advice to patient (185) utilizing the secure digital platforms . Examples of the communication device includes, but is not limited to, smartphones, laptops, or medical communication tools with internet access can be used for the purpose. The communication device helps communicating some advice for the patient (185) undergoing the chronic kidney disease treatment by a concerned physician doctor (190) through the doctor (190) mobile or desktop to patient's (185) mobile device. A communication interface is adapted to connect various specialty doctors (190), wherein the various speciality doctors includes cardiologists, nephrologists, endocrinologists, psychologists , clinical specialists dietician, and physiotherapists. The various speciality doctors (190) allows communication through text, audio, and images to exchange and adjust treatment plans for the patient (185). The patient’s (185) ailments is done based on analysed data, including physiological, symptoms, dialysis, eating habits, mental condition, and activity.

[0051] In one embodiment, the non-invasive patch (140) is adapted to provide a comprehensive physiological monitoring and detection of subtle changes in condition of the patient (185). The system (100) continuously monitors the patient's (185) physiological data, including heart rate, blood pressure, and respiration. It analyses these parameters to detect even subtle changes in the patient's condition, enabling early intervention and personalized care.

[0052] In one embodiment, the non-invasive patch (140) is utilized to provide a continuous monitoring of the vital health parameters comprising , blood oxygen levels, heart rate, and kidney function to assess the patient (185) status during the dialysis procedure.

[0053] Further, the processing subsystem (105) is configured to store data in a database (170) to control data integrity and prevent security breaches. The database (170) is a structured collection of data organized to facilitate efficient access, management, and updating. It serves as a central repository for storing and retrieving information, enabling applications to store, retrieve, and manipulate data easily. The database (170) can range from simple flat file systems to complex relational databases like Structured query language (SQL), which use tables to store data in rows and columns. They are crucial in modern applications for maintaining data integrity, ensuring scalability, and supporting transactions. Common database management systems include MySQL, Oracle, and MongoDB, each offering unique features suited to different use cases and scale requirements.

[0054] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (300) includes processor(s) (330), and memory (310) operatively coupled to the bus (320). The processor(s) (330), 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.

[0055] The memory (310) includes several subsystems stored in the form of executable program which instructs the processor (330) to perform the method steps illustrated in FIG. 1. The memory (310) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: a prediction module (110), a collection module (115), a monitoring module (120), an output module (125), and a communication module (130).

[0056] The bus (320) 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 (320) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (320), 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.

[0057] For instance, in a non-limiting example, consider a patient (185) with chronic kidney disease undergoing regular dialysis through an arteriovenous fistula. A non-invasive patch (140) placed over the needle insertion point on the fistula continuously monitors the patient’s (185) health. The non-invasive patch (140), integrated with sensors for light (145), audio (150), and temperature (155), collects data on vital health parameters like blood pressure, glucose levels, and creatinine. This data is sent to a processing subsystem (105) hosted on a server (108), where a prediction module (110) uses the collected data to assess the likelihood of disease progression, estimating when the patient (185) may reach end-stage renal failure, wherein the prediction is fed into the non-invasive smart patch (140). The collection module (115) registers patient (185) information and treatment plans. It also tracks the patient’s (185) health metrics and alerts healthcare providers in real time if any issues arise, including compromised vascular access. The monitoring module (120) provides doctors (190) with updated data, enabling remote care and allowing them to adjust treatment plans accordingly. Additionally, the system (100) automatically sends reminders to the patient (185) to take medications and updates the doctor (190) on the patient's (185) compliance. The output module (125) assigns a risk score to help doctors (190) minimize emergencies and hospitalizations. Ultimately, the system (100) ensures proactive, remote monitoring, providing timely interventions and improving the patient’s (185) quality of care by reducing hospital visits.

[0058] FIG. 3 is a schematic diagram depicting cloud visualisation interface includes a patient interface, a doctor interface, and a caregiver interface of FIG. 1, in accordance with another embodiment of the present disclosure. The system for facilitating chronic kidney disease patient with remote monitoring includes a doctor interface (410), a patient interface (420), and a caregiver interface (430), all integrated through internet of things and cloud (400) computing. An artificial intelligence technology is utilized to process the data from sensor networks for accurate measurements. The patient (185) data is stored in a cloud (400)-based database (170) and regularly updated with new health parameters in the patient's (185) profile via the doctor interface (410). Based on the updated information, the doctor (190) provide instructions and updates for the treatment plan, which are recorded in the interface through text messages or comments on medical records. These updates are synchronized in real-time with the patient interface (420) and caregiver interface (430), ensuring the patient (185) and caregivers are notified of any changes. This cloud (400) interconnection allows seamless medical preparation, treatment adjustments, training and reduce risks like crash dialysis and improving patient (185) outcomes.

[0059] In one embodiment, the system (100) includes a patient interface (420) designed to collect data about the patient's (185) health condition. The patient interface (420) gathers relevant health metrics, including vital signs, symptoms, and activity levels. The collected data is then processed to monitor and manage the patient's (185) health effectively.

[0060] In one embodiment, the system (100) includes a cloud-based platform (400), wherein the cloud-based platform (400) is configured to receive and store the captured data from the patient interface (420). The cloud-based platform (400) stores the captured health data securely in the cloud (400) for easy access and management. Additionally, the cloud-based platform (400) enables real-time updates and centralized data storage, ensuring effective monitoring and analysis of the patient's (185) health condition. In one example the cloud-based platform (400) can also be called as kifayati hub.

[0061] In one embodiment, the system (100) includes a healthcare provider interface in the form of a doctor interface (410), wherein the doctor interface (410) is adapted to utilize stored data from the cloud-based platform (400) and made available to a healthcare provider for analysis and review.

[0062] FIG. 4 is an exemplary representation of the closed loop control system between the patient and the doctor of the system for facilitating chronic kidney disease patient with remote monitoring FIG 1, in accordance with yet another embodiment of the present disclosure. The system (100) functionality is resembled utilizing a closed loop control system. The closed loop control system includes the patient (185), and the doctor (190) connected in a feedback network of the controller. The plurality of sensors is applied as an input to the closed loop control system and the vital health parameters as an output. The feedback provided by the doctor (190) is the feedback utilized in the closed loop control system and the difference between the plurality of sensor data and the doctor’s feedback is utilized as net output to pass through the controller and the patient (185). In the closed-loop control system, the controller processes feedback data to adjust device parameters and maintain desired outcomes. The controller is adapted to continuously monitors the closed loop control system and make real-time corrections to ensure accurate performance in medical devices. The feedback in the closed-loop control system allows real-time monitoring and adjustments to ensure the closed loop control system stays on target with desired outcomes. The feedback enhances accuracy and reliability by correcting deviations from the set parameters, improving overall system performance.

[0063] In one embodiment , the system (100) utilizes a closed loop real time feedback network between the doctor (190) and the patient (185) to adapt and modify the treatment plan. The feedback network is utilized to a modification in the treatment plan provided by the doctor (190) based on the updated situation of the patient (185).

[0064] FIG.5(a) illustrates a flow chart representing the steps involved in a method for facilitating chronic kidney disease patient with remote monitoring in accordance with an embodiment of the present disclosure. FIG.5(b) illustrates continued steps of the method of FIG.5(a) in accordance with an embodiment of the present disclosure. The method (200) includes capturing and analysing, by a non- invasive patch, data pertaining to a health of the patient, to provide a real-time and accurate insights into the patient's health status in the step (205).

[0065] The method (200) also includes monitoring continuously, by the non-invasive patch, a vascular access site, wherein the vascular access site is a location on a body where a plurality of blood vessels are accessed for the dialysis procedure in the step (210). The non-invasive patch continuously monitors the vascular access site, where blood vessels are accessed for dialysis. It tracks key health parameters to ensure the site remains functional and free of complications during the procedure

[0066] The method (200) also includes detecting, by the non-invasive patch, any compromise and a dysfunction in a vital health parameter of the patient in real time in the step (215). The non-invasive patch detects any compromise or dysfunction in a vital health parameter of the patient in real-time. It continuously monitors for abnormalities, alerting healthcare providers to potential issues as they occur.

[0067] The method (200) also includes capturing ,by a plurality of sensors, a plurality of data of the patient in the step (220). The plurality of sensors capture various health data from the patient, such as vital signs and physiological parameters. This data is collected continuously to monitor the patient's condition and ensure accurate health tracking.

[0068] The method (200) also includes monitoring, by the plurality of sensors, a plurality of vital health parameters of the patient in the step (225). The plurality of sensors monitors multiple vital health parameters of the patient, such as heart rate, blood pressure, and oxygen levels. This continuous monitoring helps track the patient’s overall health and detect any potential issues.

[0069] The method (200) also includes predicting, by a prediction module, an early diagnosis of a chronic kidney disease in the patient based on an assessment of the vital health parameters in the step (230). The prediction module analyses the patient’s vital health parameters to predict the early onset of chronic kidney disease. This helps in identifying potential risks early, enabling proactive treatment and management of the condition.

[0070] The method (200) also includes estimating, by the prediction module, a probability of a timeframe for the patient to reach an end stage of the renal health utilizing the internet of things, wherein the prediction is fed into the non-invasive smart patch in the step (235). The prediction module uses data from IoT-enabled devices to estimate the probability of the timeframe for the patient to reach the end stage of renal health. By continuously monitoring vital health parameters, it provides insights into disease progression and potential outcome.

[0071] The method (200) also includes allowing, by a collection module, an admin to add the new patient and a doctor and receive a plurality of details required for a registration process of the new patient and the doctor in the step (240).

[0072] The method (200) also includes enabling, by the collection module, the doctor to input a plurality of patient care instructions comprising a treatment plan and a medical test in the step (245). The collection module allows the doctor to input patient care instructions, including a detailed treatment plan and medical test requirements. This information is stored and accessible for further reference and implementation in the patient's care.

[0073] The method (200) also includes storing, by the collection module, the patient care instructions in a database in the step (250). The collection module stores the patient care instructions, including treatment plans and medical tests, in a secure database. This ensures the data is organized, easily accessible, and available for future reference by healthcare providers.

[0074] The method (200) also includes collecting automatically, by the collection module, the plurality of vital health parameters of the patient comprising a blood pressure, a glucose, and creatinine level utilizing an internet of things in the step (255).

[0075] The method (200) also includes analyse, by a monitoring module, the plurality of vital health parameters of the patient in the step (260). The monitoring module analyses the patient’s vital health parameters, such as heart rate, blood pressure, and oxygen levels, to assess their current health status. It processes the data in real-time to identify any abnormalities or trends. This analysis helps in early detection of potential health issues and supports timely medical intervention.

[0076] The method (200) also includes updating, by the monitoring module, the doctor about the vital health parameters of the patient in real time comprising a health symptom of the patient in the step (265).

[0077] The method (200) also includes enabling, by the monitoring module, the doctor to remotely monitor the health symptom in the step (270). The monitoring module enables the doctor to remotely track the patient’s health symptoms in real-time. This allows the doctor to assess the patient’s condition and make timely treatment adjustments without needing in-person visits.

[0078] The method (200) also includes sending automatically, by an output module, multiple reminders to the patient about a plurality of treatment actions, wherein the plurality of treatment actions comprises date and time to take a medication in the step (275).

[0079] The method (200) also includes notifying, by the output module, the doctor about an outcome of the plurality of treatment actions comprising a non-compliant action by the patient in the step (280). The output module detects non-compliance in the patient's treatment actions and automatically notifies the doctor. This is done through alerts or messages, providing details of the non-compliant action for timely intervention.

[0080] The method (200) also includes assigning, by the output module, a risk score to the patient by analysing the plurality of vital health parameters and help the doctors by minimizing an advent of emergency and hospitalization in the step (285).

[0081] The method (200) also includes providing, by a communication module, a medical advice by the doctor to the patient in the step (290). The communication module enables the doctor to send medical advice directly to the patient through secure channels. This can include messages, instructions, or guidance to assist the patient in managing their health condition.

[0082] Various embodiments of the system for facilitating chronic kidney disease patient with remote monitoring and a method thereof described above enables various advantages. By using a non-invasive patch (140) placed over the arteriovenous fistula, the system (100) continuously monitors vital health parameters, such as blood pressure, glucose, and creatinine levels, without the need for frequent hospital visits. This data is sent to a processing subsystem (105) hosted on a server (108), where a prediction module (110) can foresee disease progression and estimate the probability of the time frame for end-stage renal failure, allowing doctors (190) to adjust treatment plans accordingly. Additionally, the system (100) includes a collection module (115) adapted to register patient (185) information and treatment plans. It also tracks the patient’s (185) health metrics and alerts healthcare providers in real time if any issues arise, including compromised vascular access. The monitoring module (120) provides doctors (190) with updated data, enabling remote care and allowing them to adjust treatment plans accordingly. This real-time data collection, facilitated by sensors for light (145), audio (150), and temperature (155), allows doctors (190) to remotely track the patient’s (185) health and quickly detect any issues with vascular access or kidney function. The processing subsystem (105), which collects and analyses this data, enables timely intervention by the healthcare provider, reducing the risk of complications and hospitalizations. Additionally, the system (100) sends automatic reminders to the patient (185) for medication adherence and alerts doctors if the patient (185) is non-compliant. The risk scoring feature helps doctors (190) assess the patient's (185) health and minimize emergencies, ultimately improving patient (185) outcomes. This seamless communication between patients (185) and healthcare providers ensures that patients (185) receive continuous care and management, leading to improved quality of life for chronic kidney disease patients (185).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.