DESC:EARLIEST PRIORITY DATE:
This Application claims priority from a Provisional patent application filed in India having Patent Application No. 202141048003, filed on October 21, 2021, and titled “SYSTEM AND METHOD FOR HEALTHPARAMETERS MONITORING”.
FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to the field of healthcare devices, and more particularly, a system and a method for monitoring health parameters of a patient using artificial intelligence.
BACKGROUND
[0002] Monitoring patients helps to have more control over their care. Specifically, monitoring various health parameters of a patient has been an important aspect of hospital patient care, especially for patients with diseases at advanced stages, suffering from severe trauma or when recovered from a traumatic condition. Additionally, outpatient monitoring of various physiological conditions is being increasingly used for evaluation of patient health conditions as well as early detection and treatment of many chronic diseases such as heart diseases, diabetes, and the like.
[0003] There are several types of patient monitoring devices that are used to monitor patients for example, continuous glucose monitor, blood pressure monitors and heart rate monitors. The patient monitoring devices may also be used along with other remote technology tools such as videoconferencing and telemedicine. Typically, the patient monitoring devices are attached to the patient’s body, like the chest, back, hand and neck which makes it difficult for the patient to move around freely. Several attempts have also been made to develop systems to improve a patient’s comfort, freedom and privacy by decreasing the number of devices directly or indirectly attached to the patient.
[0004] Currently, medical software providers have been developing a plethora of systems that facilitate electronic data storage and management to enable healthcare providers to be in compliance with increased regulations. A patient's electronic health records can provide a longitudinal electronic record of patient health information gathered during one or more encounters in a care delivery setting, which can include information such as patient demographics, medications, vital signs, medical history, laboratory test results, and radiology reports, etc. The electronic health records can also be used to provide decision support, quality management, and outcomes reporting. However, there is absence of such systems that integrates the real time monitoring capability of wireless sensors worn by a patient with the data storage and processing capabilities afforded by electronic health records management systems for personalized monitoring and clinical decision support, improving accuracy in diagnosis and validating treatment options proposed by physicians.
[0005] Hence, there is a need for an improved system and method for monitoring health parameters of a patient using artificial intelligence which addresses the aforementioned issue(s).
BRIEF DESCRIPTION
[0006] In accordance with an embodiment of the present disclosure, a system for monitoring health parameters of a patient using artificial intelligence is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a sensor node module comprising a plurality of sensor nodes wherein the plurality of sensor nodes is configured to steadily capture corresponding health parameters of a patient wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient thereby providing mobility of the patient. The processing subsystem also includes a controller module operatively coupled to the sensor node module and positioned at a predefined distance from the sensor node module wherein the controller module is configured to: collect the health parameters from the sensor node module wherein the health parameters are in the form of analog format, convert the health parameters in analog format into corresponding health parameters in a digital format and remove unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters. Further, the processing subsystem includes a low-power radio frequency modem module operatively coupled to the controller module wherein the low-power radio frequency modem module is configured to receive the health parameters in digital format from the controller module. Furthermore, the processing subsystem includes an artificial intelligence module operatively coupled to the low-power radio frequency modem module wherein the artificial intelligence module is configured to: receive the health parameters in digital format from the low-power radio frequency modem module, identify the plurality of corresponding sensor nodes associated to each of the health parameters, convert the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters and compare the health parameters with corresponding threshold values for characterizing a physiological condition of the patient. Moreover, the processing subsystem includes a display module operatively coupled to the artificial intelligence module wherein the display module is configured to: display the health parameters in a graphical and numeral format obtained from the waveforms to a user accountable to monitor the health of the patient remotely based on the health parameters and generate a report at a periodical interval based on the health parameters of the patient to the user. The processing subsystem also includes an alert module to alert the user at the occurrence of a disorder in any one of the health parameters.
[0007] In accordance with another embodiment of the present disclosure, a method for monitoring health parameters of a patient using artificial intelligence is provided. The method includes capturing, by a sensor node module of a processing subsystem, the health parameters of a patient wherein the sensor node module comprises a plurality of sensor nodes wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient thereby providing mobility of the patient. The method also includes collecting, by a controller module of the processing subsystem, the health parameters in the form of analog format from the sensor node module. The method includes converting, by a controller module of the processing subsystem, the health parameters in analog format into corresponding health parameters in a digital format. The method includes removing, by a controller module, unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters. The method includes receiving, by a low-power radio frequency modem module, the health parameters in digital format from the controller module. The method includes receiving the health parameters in digital format from the low-power radio frequency modem module, identifying the plurality of sensor nodes corresponding to each of the health parameters; converting the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters and comparing the health parameters with corresponding threshold values for characterizing a physiological condition of the patient, by an artificial intelligence module. Further, the method includes transmitting, by a wireless communication module, the one or more waveforms from the artificial intelligence module to a network. Furthermore, the method includes displaying, by a display module, the health parameters in a graphical and numeral format obtained from the waveforms to a user accountable to monitor the health of the patient remotely based on the health parameters. Moreover, the method includes generating, by a display module, a report at a periodical interval based on the health parameters of the patient to the user.
[0008] 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
[0009] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0010] FIG. 1 is a block diagram representation of a system for monitoring health parameters in a patient using Artificial Intelligence in accordance with an embodiment of the present disclosure;
[0011] FIG. 2 is a block diagram representation of a sensor node module of FIG. 1 in accordance with an embodiment of the present disclosure;
[0012] FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure;
[0013] FIG. 4 (a) illustrates a flow chart representing the steps involved in a method for monitoring health parameters in a patient using Artificial Intelligence in accordance with an embodiment of the present disclosure; and
[0014] FIG. 4 (b) illustrates continued steps of the method of FIG. 4 (a) in accordance with an embodiment of the present disclosure.
[0015] 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
[0016] 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.
[0017] 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.
[0018] 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.
[0019] 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.
[0020] Embodiments of the present disclosure relate to a system and method for monitoring health parameters of a patient using Artificial Intelligence. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a sensor node module comprising a plurality of sensor nodes wherein the plurality of sensor nodes is configured to steadily capture corresponding health parameters of a patient wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient thereby providing mobility of the patient. The processing subsystem also includes a controller module operatively coupled to the sensor node module and positioned at a predefined distance from the sensor node module wherein the controller module is configured to: collect the health parameters from the sensor node module wherein the health parameters are in the form of analog format, convert the health parameters in analog format into corresponding health parameters in a digital format and remove unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters. Further, the processing subsystem includes a low-power radio frequency modem module operatively coupled to the controller module wherein the low-power radio frequency modem module is configured to receive the health parameters in digital format from the controller module. Furthermore, the processing subsystem includes an artificial intelligence module operatively coupled to the low-power radio frequency modem module wherein the artificial intelligence module is configured to: receive the health parameters in digital format from the low-power radio frequency modem module, identify the plurality of corresponding sensor nodes associated to each of the health parameters, convert the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters and compare the health parameters with corresponding threshold values for characterizing a physiological condition of the patient. Moreover, the processing subsystem includes a display module operatively coupled to the artificial intelligence module wherein the display module is configured to: display the health parameters in a graphical and numeral format obtained from the waveforms to a user accountable to monitor the health of the patient remotely based on the health parameters and generate a report at a periodical interval based on the health parameters of the patient to the user. The processing subsystem also includes an alert module to alert the user at the occurrence of a disorder in any one of the health parameters.
[0021] FIG. 1 is a block diagram representation of a system for monitoring health parameters in a patient using Artificial Intelligence in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108). 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 user device (not shown in FIG.1). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. In one example, the network (115) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (115) 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 (115) 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 (115) 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.
[0022] Further, the processing subsystem (105) is a low power wireless device used for monitoring the health parameters of the patient (150). In one embodiment, the wireless device is an Internet of Things (IoT) device.
[0023] The system (100) includes a sensor node module (120) operatively coupled to the processing subsystem (105) and configured to steadily capture corresponding health parameters of a patient (150). The plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient (150) thereby providing mobility of the patient (150). Typically, a sensor node is a device that can gather sensor data from the environment, process the gathered data, and communicate with other nodes. The basic components of sensor nodes include a microcontroller, transceiver, external memory, power source, and one or more sensors. Sensors are thus a type of an application-specific device. Each sensor installation may have its own set of financial constraints, measurement objectives, site thresholds, or other application-specific considerations that influence sensor network architecture. These application-specific characteristics make it difficult to find a scalable solution that can be utilized across multiple industries and marketplaces. For example, it is acknowledged that a scalable system should be adaptable to new types of sensor applications with minimal modification or redeployment of a wireless sensor network. As additional sensors and application features are rolled out across an already existing sensor network architecture, a scalable system like this would drastically minimize installation and maintenance costs. Examples of sensor nodes include electrocardiogram (ECG) sensors, electroencephalogram (EEG) sensors, temperature sensors, and the like. An ECG sensor, for example, is a visual representation of the electric current created by the heart muscle during a heartbeat. It provides information on the patient's status as well as the heart's performance. An EEG sensor is generally placed on the patient’s head and detects the brain’s activity.
[0024] The system (100) also includes a controller module (125) operatively coupled to the sensor node module (120) and positioned at a predefined distance from the sensor node module (120). In one embodiment, the predefined distance may be 20 meters from the location of the patient (150). The controller module is configured to collect the health parameters from the sensor node module. The health parameters are in the form of an analog format. Typically, microcontrollers are self-contained computers that are integrated into a microchip. They enable connectivity and control in any device that can connect to the internet. Microcontrollers are designed to execute certain functions and can be integrated, including industrial equipment, warehouse inventory items, wearable gadgets, and home appliances. In various applications, distinct types of microcontrollers are employed. For example, 8-bit microcontrollers are used to perform logic and arithmetic operations. 16-bit microcontrollers are used to perform functions with greater accuracy and speed. 32-bit microcontrollers are used to perform arithmetic and logical operations commonly used in home appliances and medical devices.
[0025] Typically, the information from the sensor node module normally represents an analog signal is referred to as analog data. Analog data comes in a variety of forms. All these data, however, are transformed into voltage and current by sensors. A thermal couple, for example, converts temperature into voltage, which a meter then converts back into visual information in degrees. Temperature is one example of an analog data. The voltage that reflects temperature is analog data as well. The controller module is therefore configured to convert the health parameters in analog format into corresponding health parameters pertaining to a digital format. Typically, the amount of data generated from the sensor node module is huge. Continuous unorganized data is generated by the sensor node module and the unorganized data is to be converted into organized data so that the insights from the analog format are retrieved. Digital data is valuable for a variety of analyses, including forecasting that can be utilized for future prediction. Further, the controller module is configured to remove unwanted noise from the health parameters in digital format. The unwanted noise is removed with the aid of one or more filters. Typically, unwanted noise is an undesired signal that interferes with the original signal and effects the original signal. In one embodiment, the undesired signal is removed using various filters. For example, low pass filter, high pass filter, band pass filter and the like.
[0026] The system (100) also includes a low-power radio frequency modem module (130) operatively coupled to the controller module (125). The low-power radio frequency modem module (130) is configured to receive the health parameters in the digital format. Typically, a radio frequency module is a device that allows two devices to transmit and receive the data within the range. It is a frequently used embedded system to communicate with other devices wirelessly. In one embodiment, the wireless communication is through radio frequency transmission. For example, the range of a low-power radio frequency modem is 435Mhz.
[0027] The system (100) further includes an artificial intelligence module (135) operatively coupled to the low-power radio frequency module (130). The artificial intelligence module (135) is configured to receive the health parameters in digital format from the low-power radio frequency modem module (130). The artificial intelligence module (135) is also configured to identify the plurality of corresponding sensor nodes associated to each of the health parameters. Typically, the artificial intelligence module (135) identifies the data sent by the sensor. For example, the artificial intelligence module (135) uses classification algorithms to classify the datasets. Typically, the data sets are the health parameters of a patient (150). In one embodiment, the artificial intelligence module (135) classifies each node data separately. The artificial intelligence module (135) is further configured to convert the health parameters into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters. In most cases, the artificial intelligence module (135) uses the patient's datasets and translates them into acceptable and understandable waveforms. For example, it displays the ECG waveform, EEG waveform, temperature value, and so on. Furthermore, the artificial intelligence module is configured to compare the health parameters with corresponding threshold values for characterizing a physiological condition of the patient (150). In most cases, the artificial intelligence module (135) assigns a threshold to each node for comparison. For example, the artificial intelligence module may define temperature threshold as 102 degrees. The artificial intelligence module (135) now compares the threshold value to the temperature of the patient (150). Typically, artificial intelligence is the study of embedding intelligence in machines, allowing them to perform tasks. AI-powered systems are continuously evolving in terms of application, adaption, processing speed, and capabilities. Machines are becoming capable of performing non-routine activities. AI is simply about 'choosing' the right decision at the right time. By developing a classification function and an attribute identification function from specified training data sets, the artificial intelligence system enhances identification processing time and accuracy. The attribute identification function in some embodiments may be a convolutional neural network (CNN).
[0028] The system (100) includes a display module (140) operatively coupled to the artificial intelligence module (135). The display module is configured to display the health parameters in a graphical and numeral format obtained from the waveforms to a user (155) accountable to monitor the health of the patient (150) remotely based on the health parameters. The display module is also configured to generate a report at a periodical interval based on the health parameters of the patient (150) to the user (155). Typically, the display module uses visualization tools to display the insights present in the data regarding patient health. Data visualization tools are software applications that display information in a visual format, such as a graph, chart, or heat map, for the purpose of data analysis. Such tools make it easier to understand and operate with huge amounts of data. The visualization tools are UI-based applications, PowerBI, Thing speak, Grafana, tableau and the like.
[0029] Furthermore, the system (100) includes an alert module (145). The alert module (145) is configured to alert the user (155) at the occurrence of a disorder in any one of health parameters. As each sensor node is assigned a threshold value by the artificial intelligence module (135). If the patient values exceeds the threshold, an emergency alert is sent to the user (155).
[0030] The system (100) also includes a patient (150) who is receiving medical care. The patient (150) can be located in a hospital or at home. Several sensor nodes are attached at specific positions on the patient (150) to collect respective health parameters. and the sensor nodes are wireless and are configured to collect data at periodic intervals when the patient is within the predetermined distance.
[0031] Further, the system (100) includes a concerned user (155). The concerned user (155) is a person who is accountable to monitor the health of the patient (150). Further, the concerned user (155) receives the data from the network regarding health parameters of the patient (150) and also receives alerts if the health parameters exceed the threshold value.
[0032] Furthermore, the system (100) includes a database (160). The data from all sensor nodes will be stored in the database (160). Sensor nodes typically generate huge amounts of data, such as streaming data and time series data. The database (160) is required for effective data management.
[0033] It is to be noted that the system may comprise, but is not limited to, a mobile phone, desktop computer, portable digital assistant (PDA), smart phone, tablet, ultra-book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronic system, or any other communication device that a user (155) may use. In some embodiments, the system may comprise a display module (not shown) to display information (for example, in the form of user interfaces). In further embodiments, the system may comprise one or more of touch screens, accelerometers, gyroscopes, cameras, microphones, global positioning system (GPS) devices, and so forth.
[0034] In one embodiment, the various functional components of the system may reside on a single computer, or they may be distributed across several computers in various arrangements. The various components of the system may, furthermore, access one or more databases, and each of the various components of the system may be in communication with one another. Further, while the components of FIG. 1 are discussed in the singular sense, it will be appreciated that in other embodiments multiple instances of the components may be employed.
[0035] FIG. 2 is a block diagram representation of a sensor node module of FIG. 1 in accordance with an embodiment of the present disclosure. The sensor node module (120) includes an Electrocardiogram sensor (220), an Electroencephalogram sensor (225), a Hand Proximity sensor (230), a Temperature sensor (235), a 3D Accelerator sensor (240) and a Blood Oxygen Monitor (245).
[0036] Wireless ECG measurement is used to monitor the bio-potential electrical activity of the patient’s heart. Typically, the Electrocardiogram sensor (220) collects cardiac signals from the patient’s body and wirelessly transfers them to the controller module (125). In one embodiment, the Electrocardiogram sensor (220) uses a plurality of electrodes on the precordial area of the patient’s body to obtain the precordial lead signals.
[0037] The electroencephalogram (EEG) sensor (225) records the electrical activity with distinct patterns of the brain which depicts waveforms of varied frequency and amplitude as measured in voltage. EEG waveforms are categorized based on their frequency, amplitude, and location on the scalp.
[0038] The hand proximity sensor (230) is positioned anywhere on the patient body to detect the patient in a room.
[0039] The temperature sensor (235) captures the temperature of the patient’s body. Temperature is sensed either directly or indirectly between the sensing element and the object. In one embodiment, temperature anomalies such as unusual patterns of rises and falls are detected, and an alert message is sent to the concerned user (155).
[0040] The 3D accelerator sensor (240) captures movements in 3dimensional to determine the device's placement on the patient's body.
[0041] The blood oxygen monitor (245) monitors the amount of oxygen carried by red blood cells. People with chronic health issues need to monitor their blood oxygen levels frequently. Asthma, heart disease, and chronic obstructive lung disease lead to serious health issues. The oxygen saturation level is a measurement of the amount of oxygen in your blood.
[0042] It should be noted that the sensor node module (120) may include other suitable sensor nodes and should not be limited to the aforementioned sensor nodes.
[0043] FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0044] The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: a sensor node module (120), a controller module (125), a low-power radio frequency modem module (130), an artificial intelligence module (135), a display module (140) and an alert module (145).
[0045] The sensor node module (120) comprising a plurality of sensor nodes wherein the plurality of sensor nodes is configured to steadily capture corresponding health parameters of a patient (150) wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient (150) thereby providing mobility of the patient (150). The processing subsystem (105) also includes a controller module (125) operatively coupled to the sensor node module (120) and positioned at a predefined distance from the sensor node module (120) wherein the controller module (125) is configured to: collect the health parameters from the sensor node module wherein the health parameters are in the form of analog format, convert the health parameters in analog format into corresponding health parameters in a digital format and remove unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters. Further, the processing subsystem (105) includes a low-power radio frequency modem module (130) operatively coupled to the controller module (125) wherein the low-power radio frequency modem module (130) is configured to receive the health parameters in digital format from the controller module (125). Furthermore, the processing subsystem (105) includes an artificial intelligence module (135) operatively coupled to the low-power radio frequency modem module (130) wherein the artificial intelligence module (135) is configured to: receive the health parameters in digital format from the low-power radio frequency modem module, identify the plurality of corresponding sensor nodes associated to each of the health parameters, convert the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters and compare the health parameters with corresponding threshold values for characterizing a physiological condition of the patient (150). Moreover, the processing subsystem (105) includes a display module (140) operatively coupled to the artificial intelligence module (135) wherein the display module (140) is configured to: display the health parameters in a graphical and numeral format obtained from the waveforms to a user (155) accountable to monitor the health of the patient (150) remotely based on the health parameters and generate a report at a periodical interval of time based on the health parameters of the patient (150) to the user (155). The processing subsystem (105) also includes an alert module (145) to alert the user (155) at the occurrence of a disorder in any one of the health parameters.
[0046] The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.
[0047] FIG. 4 (a) illustrates a flow chart representing the steps involved in a method (300) for monitoring health parameters in a patient using Artificial Intelligence in accordance with an embodiment of the present disclosure. FIG. 4 (b) illustrates continued steps of the method (300) of FIG. 4 (a) in accordance with an embodiment of the present disclosure.
[0048] The method (300) includes capturing the health parameters of a patient by a sensor node module of a processing subsystem in step (305). The health parameters may be defined as vital signals/ signs that are measurements of the human body’s most basic functions. The vital signs can define a current physical functioning of the human body that can indicate acute and chronic conditions in patients. Vital signs provide critical information that is 'vital' for life, and so they are called vital signs. Typically, there is a range of numbers provided as the norm for each vital sign. These normal ranges include heartbeat as 60-100 beats per minute, respirations as 12-18 breaths per minute, blood pressure as 90/60 to 120/80 and temperature as 97.8 to 99.1 degrees Fahrenheit or 38 to 38.5 degrees Celsius. It is to be noted that these ranges are the norms for the adult population. A health care worker will also look at the normal levels for the patient. Some patients' norms vary slightly from the norms listed above. For example, a softball player may have a slower heart rate. This is because of the athletic conditioning the softball player has achieved. The heart is a muscle, and it has learned to function at a lower rate. Vital signs can provide the information needed by the health care professional to care for the needs of the patient. The alteration of vital signs in a patient can indicate an acute or chronic medical problem. The more off the norm the vital signs are usually indicates a sicker patient.
[0049] The sensor node module includes a plurality of sensor nodes that are typically placed at specific positions on the body of a patient, for example, chest, hand, neck and back. In one embodiment, the sensor nodes can be affixed to the patient directly (such as by adhesives) or to the patient’s clothing. It must be noted that the plurality of sensor nodes is wireless. The wireless sensor nodes facilitate the patient to move around freely at their place of location (for example, hospital, clinic or home). Typically, the plurality of sensor nodes is used to detect the vital signs and convert them into electrical signals. Examples of the sensor nodes include, but is not limited to, electrocardiogram sensor, electroencephalogram sensor, hand proximity sensor, temperature sensor, 3D accelerator sensor, blood oxygen monitor and pressure sensor.
[0050] In one embodiment, the plurality of sensor nodes can be any device that senses angles and can include a wide range of sensing technologies, such as mercury filled insulative containers with electrical contacts, magnetic sensors, optical sensors and the like. More sophisticated sensors can be used that provide a signal indicative of the actual angle of orientation of the patient, as opposed to the angle merely exceeding a threshold value, which may provide early warning of the patient deteriorating.
[0051] The method (300) includes collecting the health parameters in the form of analog format from the sensor node module in step (310). The sensor node module collects the health parameters from the plurality of sensor nodes simultaneously. Typically, an analog format has continuous electrical signals carrying information. These signals are continuous in both values and time. Specific to the ongoing discussion, the health parameters obtained from the plurality of sensor nodes are characterized in the analog format.
[0052] The method (300) includes converting the health parameters in analog format into corresponding health parameters in a digital format in step (315). It is a requirement of the method disclosed herein to convert the analog format of the health parameters into a digital format. Typically, the digital format carries information in non-continuous electrical signals. It is to be noted that the analog format of the health parameters is suitable for digital electronics such as computers and mobiles.
[0053] Further, the conversion of the health parameters from the analog format to the digital format may take place in two steps namely sampling and quantization. However, any other suitable method may be applied for the said conversion.
[0054] The method (300) includes removing unwanted noise from the health parameters in digital format in step (320). The unwanted noise (such as Poisson, speckle, blurred, Gaussian Noise and the like) may be caused by several factors such as electricity, heat, sensor illumination levels and the like. Further, the unwanted noise may refer to unwanted behaviors within the data of the health parameters that provide a low signal-to-noise ratio. Therefore, the need for removing the unwanted noise is essential and may be done with the aid of one or more filters such as Weiner filter, Gaussian filter, Median filter and the like. In one embodiment, the unwanted noise may also be removed by any other suitable method and should not be limited to the said filters.
[0055] The method (300) includes receiving the health parameters in digital format by a low-power radio frequency modem module from the controller module in step (325). The radio frequency modem module is typically a small electronic device that is used to receive and transmit radio signals between the controller module to the artificial intelligence module.
[0056] The method (300) includes receiving the health parameters in digital format by an artificial intelligence module from the low-power radio frequency modem module in step (330).
[0057] It is to be noted that the artificial intelligence module is configured with an artificial intelligence algorithm. Examples of the artificial intelligence algorithm includes, but are not limited to, a Deep Neural Network (DNN), Convolutional Neural Network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN) and Deep Q-Networks.
[0058] The method (300) includes identify the plurality of sensor nodes corresponding to each of the health parameters by the artificial intelligence module in step (335). As mentioned earlier, the controller module is adapted to collect the health parameters simultaneously from the plurality of sensor nodes. In such a scenario, it is essential to identify a particular sensor node and its corresponding reading of the health parameter. Therefore, the artificial intelligence module is accountable to arrange all the health parameters corresponding to their respective sensor node.
[0059] The method (300) includes converting the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters in step (340). The waveforms are usually a graphical representation of a signal in the shape of a wave that indicates its characteristics, such as frequency and amplitude. Specifically, the waveforms such as an electrocardiogram (ECG) or an electroencephalogram (EEG) are widely utilized in physiological examinations, physiological research, electronic medical records, healthcare information and other areas in the clinical field.
[0060] The method (300) includes comparing the health parameters with corresponding threshold values for characterizing a physiological condition of the patient in step (345). Typically, each health parameter is associated with a corresponding threshold value. The fundamental health parameters are body temperature, pulse rate, respiration rate, blood oxygen saturation and blood pressure. The crossing of the threshold values of the health parameters indicates the degree of abnormalities or disorders in the patient. As a result, a caretaker of the patient may identify the existence of an acute medical condition or the mark of a chronic disease.
[0061] The method (300) includes transmitting the one or more waveforms from the artificial intelligence module to a network in step (350). The artificial intelligence module is adapted to accumulate all the health parameters data from the plurality of sensor nodes and uploads it to the cloud through Wi-Fi or Bluetooth. In one embodiment, the user can access the health parameters from the network.
[0062] The method (300) includes displaying the health parameters in a graphical and numeral format obtained from the waveforms to a user accountable to monitor the health of the patient remotely based on the health parameters in step (355). The user may be a doctor, a caretaker, a medical worker or a family member of the patient. The patient may be in a hospital, clinic or at home. Typically, the health parameters are transmitted to a user device operated by the user.
[0063] The method (300) includes generating a report at a periodical interval based on the health parameters of the patient to the user in step (360). The reports can assist in timely diagnosis.
[0064] The method includes alerting the user at the occurrence of a disorder in any one of the health parameters in step (365). The alerts are typically transmitted when a negative condition is occurring in the patient. It then actuates an alarm circuitry for local and/or remote alarms. Soft alarms may also be used to report adverse trends before an emergency condition arises. All alarms may interact with the user in the said location (hospital, clinic or home).
[0065] Various embodiments of the system and method to monitor health parameters using artificial intelligence described above enable various advantages. The use of wireless sensor nodes aids the patient to move around freely and excludes the need of the patient to remain in a particular position throughout the monitoring process. Further, the low-power radio frequency modem module communicates with a unique self-developed protocol that is adapted to receive data from different sensor nodes. Further, the method disclosed herein resolves the problem of monitoring patients from home after a serious illness recovery. It also helps in monitoring the post trauma care patients and alerts if there any disorders.
[0066] It is to be noted that the aforementioned method and system may be applied to different applications area, such as to track health of an athlete and the like.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. ,CLAIMS:1. A system (100) for monitoring health parameters of a patient (150) using Artificial Intelligence comprising:
a processing subsystem (105) hosted on a server (108) wherein the processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules comprising:
a sensor node module (120) comprising a plurality of sensor nodes wherein the plurality of sensor nodes is configured to steadily capture corresponding health parameters of a patient (150) wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient (150) thereby providing mobility of the patient (150);
a controller module (125) operatively coupled to the sensor node module (120) and positioned at a predefined distance from the sensor node module (120) wherein the controller module (125) is configured to:
collect the health parameters from the sensor node module (120) wherein the health parameters are in the form of analog format;
convert the health parameters in analog format into corresponding health parameters in a digital format; and
remove unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters;
a low-power radio frequency modem module (130) operatively coupled to the controller module (125) wherein the low-power radio frequency modem module (130) is configured to receive the health parameters in digital format from the controller module (125);
an artificial intelligence module (135) operatively coupled to the low-power radio frequency modem module (130) wherein the artificial intelligence module (135) is configured to:
receive the health parameters in digital format from the low-power radio frequency modem module (130);
identify the plurality of corresponding sensor nodes associated to each of the health parameters;
convert the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters; and
compare the health parameters with corresponding threshold values for characterizing a physiological condition of the patient (150);
a display module (140) operatively coupled to the artificial intelligence module (135) wherein the display module (140) is configured to:
display the health parameters in a graphical and numeral format obtained from the waveforms to a user (155) accountable to monitor the health of the patient (150) remotely based on the health parameters; and
generate a report at a periodical interval based on the health parameters of the patient (150) to the user (155).

2. The system as claimed in claim 1 wherein the plurality of sensor nodes is adapted to communicate simultaneously at the same time to the controller module (125).

3. The system as claimed in claim 1 wherein the one or more waveforms from the artificial intelligence module (135) is transmitted via a wireless communication to a network (115).

4. The system as claimed in claim 1 wherein the plurality of sensor nodes comprises nodes of electrocardiogram, electroencephalogram, oxygen saturation, temperature and pressure.

5. The system as claimed in claim 1 wherein the artificial intelligence module (135) accumulates the health parameters for a predetermined period before uploading to the network (115).

6. The system as claimed in claim 1 comprising an alert module (145) configured to alert the user at the occurrence of a disorder in any one of the health parameters.

7. The system as claimed in claim 3 wherein the wireless communication comprises one of a Bluetooth module, a wireless fidelity module, ZigBee, IR and ultrasonic.

8. The system as claimed in claim 1 wherein the user (155) fetches the health parameters from the network (115) for live monitoring at periodical intervals.

9. The system as claimed in claim 1 comprising a database (160) to store the plurality of health parameters, threshold values of the health parameters and reports corresponding to the patient (150).

10. A method (300) for monitoring health parameters of a patient using Artificial Intelligence comprising:
capturing, by a sensor node module of a processing subsystem, the health parameters of a patient wherein the sensor node module comprises a plurality of sensor nodes wherein the plurality of sensor nodes are wireless sensor nodes placed at appropriate positions on the patient thereby providing mobility of the patient; (305)
collecting, by a controller module of the processing subsystem, the health parameters in the form of analog format from the sensor node module; (310)
converting, by a controller module of the processing subsystem, the health parameters in analog format into corresponding health parameters in a digital format; (315)
removing, by a controller module, unwanted noise from the health parameters in digital format wherein the unwanted noise is removed with the aid of one or more filters; (320)
receiving, by a low-power radio frequency modem module, the health parameters in digital format from the controller module; (325)
receiving, by an artificial intelligence module, the health parameters in digital format from the low-power radio frequency modem module; (330)
identifying, by an artificial intelligence module, the plurality of sensor nodes corresponding to each of the health parameters; (335)
converting, by an artificial intelligence module, the health parameters in digital format into corresponding waveforms upon identifying the plurality of corresponding sensor nodes associated to each of the health parameters; (340)
comparing, by an artificial intelligence module, the health parameters with corresponding threshold values for characterizing a physiological condition of the patient; (345)
transmitting, by a wireless communication module, the one or more waveforms from the artificial intelligence module to a network; (350)
displaying, by a display module, the health parameters in a graphical and numeral format obtained from the waveforms to a user accountable to monitor the health of the patient remotely based on the health parameters; (355)
generating, by the display module, a report at a periodical interval based on the health parameters of the patient to the user; (360) and
alerting, by the display module, the user at the occurrence of a disorder in any one of the health parameters. (365)

Dated this 20th day of October 2022

Signature

Jinsu Abraham
Patent Agent (IN/PA-3267)
Agent for the Applicant