FIELD OF INVENTIONs

[0001] Embodiments of a present disclosure relates to real-time health monitoring system, and more particularly to a system to predict chronic disease and trauma and a method to operate the same.
BACKGROUND
[0002] Emerging Issues in Global Health is chronic health failure (CHF) and associated trauma. The most common cause of heart failure (HF) is coronary artery disease (CAD) which is increasing in both prevalence and occurrence and is associated with substantial morbidity and mortality. Heart failure (HF) is associated with worsening hemodynamics and increase in vascular resistance and pulmonary congestion. Decrease in pulmonary artery pressure (PAP) and increase in thoracic electrical bioimpedance (TEB) are observed in days and weeks prior to acute decompensation. Worsening HF leads to pulmonary edema, which results in unplanned hospital admission and early deaths. Here, proper prediction of HF deterioration at early stages is required for proper diagnosis.
[0003] The early diagnosis of a plurality of CAD, HF and associated comorbidities require medical devices which enables faster communication among patients and physicians. In addition, sensor technology is also rapidly expanded for monitoring patients. The development of various sensors enabled a variety of measurements to be taken and analysed by a computer to generate useful information. The use of a mobile communication platform such as a mobile phone with one or more biometric sensors have been designed to equip with a heart rate monitor component for detecting heart rate data and an evaluation device for providing fitness information that may be displayed by a display device and derived by a processing unit, embodied for reading in and including supplementary personal data.
[0004] In conventional approach, platform to predict chronic diseases exists which provides an apparatus and method for non-invasive, instantaneous and continuous measurement of a subject's heart rate, respiratory rate, rhythm, volume, systolic and diastolic profiles and other functional and physiological pulmonary metrics for the purposes of detecting cardiac and respiratory trends, events or disturbances related to a pathological condition. Further, the conventional system also includes a data transmission module and a notification module, configured to notify a plurality of users with the transmitted data and a processing module to analyse the physiological data. However, such system is inefficient in predicting the individual’s unique symptoms to predict the disease. Moreover, a real time analysis the physiological data would also enable in detection of any big problems related to the individual using such wearable apparatus.
[0005] Hence, there is a need for an improved system to predict chronic disease and trauma and a method to operate the same and therefore address the aforementioned issues.
BRIEF DESCRIPTION
[0006] In accordance with one embodiment of the disclosure, a system to predict chronic disease and trauma is disclosed. The system includes a wearable smart sensing fabric. The wearable smart sensing fabric includes a set of textile electrodes or, textrodes. The set of textrodes is configured to measure thoracic electrical bioimpedance (TEB) of a user’s body. Here, the set of textrodes comprises at least two first textrodes and at least two second textrodes.
[0007] The wearable smart sensing fabric also includes one or more sensors. The one or more sensors is mechanically coupled to the set of textrodes. The one or more sensors is configured to sense one or more physiological signals generated by the user’s body. The system also includes a health processing subsystem.
[0008] The health processing subsystem includes a retrieving module. The retrieving module is configured to retrieve one or more data of the user in real-time. Here, the one or more data includes at least one of the measured thoracic electrical bioimpedance (TEB) and the one or more physiological signals generated by the user’s body.
[0009] The health processing subsystem also includes processing module. The processing module is operatively coupled to the retrieving module. The processing module is configured to collate the one or more data of the user. The processing module is also configured to analyse the one or more data by an analysing technique.
[0010] The health processing subsystem also includes a prediction module. The prediction module is operatively coupled to the processing module. The prediction module is configured to predict one or more analysed data by a prediction technique. A health memory subsystem is operatively coupled to the health processing subsystem. The health memory subsystem is configured to store retrieved one or more data, the one or more analysed data and the predicted result.
[0011] In accordance with one embodiment of the disclosure, a method to operate a system to predict chronic disease and trauma is provided. The method includes measuring thoracic electrical bioimpedance (TEB) of a user’s body. The method also includes measuring one or more physiological signals generated by the user’s body. The method also includes retrieving one or more data of the user in real-time.
[0012] The method also includes collating the one or more data of the user. The method also includes analysing the one or more data by an analysing technique. The method also includes predicting one or more analysed data by a prediction technique. The method also includes notifying the user about predicted result.
[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 to predict chronic disease and trauma in accordance with an embodiment of the present disclosure;
[0016] FIG. 2 is a schematic representation of a wearable smart sensing fabric in accordance with an embodiment of the present disclosure;
[0017] FIG. 3 is a schematic representation of an exemplary embodiment representing the system to predict chronic disease and trauma of FIG. 1 in accordance of an embodiment of the present disclosure;
[0018] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and
[0019] FIG. 5 is a flowchart representing the steps of a method to operate a system to predict chronic disease and trauma in accordance with an embodiment of the present disclosure.
[0020] 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
[0021] 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 online platform, 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.
[0022] 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, subsystems, elements, structures, components, additional devices, additional subsystems, 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.
[0023] 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.
[0024] 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.
[0025] Embodiments of the present disclosure relate to a system to predict chronic disease and trauma is disclosed. The system includes a wearable smart sensing fabric. The wearable smart sensing fabric includes a set of textrodes. The set of textrodes is configured to measure thoracic electrical bioimpedance (TEB) signals of a user’s body. Here, the set of textrodes comprises at least two first textrodes and at least two second textrodes.
[0026] The wearable smart sensing fabric also includes one or more sensors. The one or more sensors is mechanically coupled to the set of textrodes. The one or more sensors is configured to sense one or more physiological signals generated by the user’s body. The system also includes a health processing subsystem.
[0027] The health processing subsystem includes a retrieving module. The retrieving module is configured to retrieve one or more data of the user in real-time. Here, the one or more data includes at least one of the measured thoracic electrical bioimpedance (TEB) and the one or more physiological signals generated by the user’s body.
[0028] The health processing subsystem also includes processing module. The processing module is operatively coupled to the retrieving module. The processing module is configured to collate the one or more data of the user. The processing module is also configured to analyse the one or more data by an analysing technique.
[0029] The health processing subsystem also includes a prediction module. The prediction module is operatively coupled to the processing module. The prediction module is configured to predict one or more analysed data by a prediction technique. A health memory subsystem is operatively coupled to the health processing subsystem. The health memory subsystem is configured to store retrieved one or more data, the one or more analysed data and the predicted result.
[0030] FIG. 1 is a block diagram representation of a system to predict chronic disease and trauma (10) in accordance with an embodiment of the present disclosure. As used herein, “chronic” refers to an illness persisting for a long time or constantly recurring. As used herein, the term “trauma” refers to physical disturbance of a patient. FIG. 2 is a schematic representation of a wearable smart sensing fabric (70) in accordance with an embodiment of the present disclosure.
[0031] In one embodiment, the wearable smart sensing fabric (70) comprises a vest (120) with electronic components embedded in the vest, for the detection of health conditions. The system (10) includes the wearable smart sensing fabric (70). The wearable smart sensing fabric (70) includes a set of textrodes (100, 110). The set of textrodes (100, 110) is configured to measure thoracic electrical bioimpedance (TEB) signals of a user’s body.
[0032] As used herein, the wearable smart sensing fabric (70), which is also referred as electronic textile, a smart garment, a smart clothing, a smart textile, or a smart fabric is defined as a type of fabric that has modern computer based technology woven into it, by weaving conductive threads or, conductive yarns into the fabric.
[0033] In one specific embodiment, the wearable smart sensing fabric (70) may be composed of a polyester lycra fabric. In another embodiment, the set of textrodes may be composed of a material consisting of conductive Shieldex® “P130+B”, a 2D stretchable, synthetic wrap-knitted silver coated fabric. In another embodiment, the set of the textrodes may be composed of conductive yarn based on stainless steel fibres (30%).
[0034] As used herein, the term “lycra” indicates is a synthetic fibre known for its exceptional elasticity. In such embodiment, “P130+B” is made of 78% polyamide, and 22% elastomer, and coated with 99% conductive silver particles with a surface resistivity ?< 2 ?/sq.
[0035] In another embodiment, the wearable smart sensing fabric (70) may be designed including, but not limited to, as a vest, a shirt, a T-shirt, a jacket or a top (120). In another embodiment, the wearable smart sensing fabric (70) may be designed as a functional garment which may be used for physiological sensing in several disciplines such as sports, firefighting, military and medicine. The user may be a person who may or may not require a continuous medical assistance.
[0036] Furthermore, as used herein, a textrode is defined as a type of conductive electrode made of textile materials to measure electrical signals, which are then transmitted through textile’s conductive paths to an electronic circuit board for further processing. which is fabricated along with the fabric during manufacture. The structure of textile electrodes is usually made of conductive yarns by weaving, knitting, or embroidering processes, by coating or by orienting conductive polymers on non-conductive fabrics. In such embodiments, the set of textrodes (100, 110) knitted or, embroidered in the garment by using an electrochemical cell has lowest contact resistance and may be used to monitor vital signs and other physiological parameters of the user’s body.
[0037] As used herein, the term “thoracic electrical bioimpedance (TEB)” is defined as the measure of fluid accumulation in lungs that is detected by the changes in thoracic bioimpedance, and other hemodynamic parameters such as cardiac output, stroke volume, thoracic fluid content, stroke index etc. of the user’s body.
[0038] Furthermore, the set of textrodes (100, 110) includes at least two first textrodes (100). In one embodiment, the at least two first textrodes (100) may include a first voltage sensing textrode and a first current injecting textrode. In such embodiment, the first voltage sensing textrode may be a positive voltage sensing textrode. The first current injecting textrode may be a positive current injecting textrode.
[0039] In addition, the set of textrodes (100, 110) also includes at least two second textrodes (110). In one embodiment, the at least two second textrodes (110) may include a second voltage sensing textrode and a second current injecting textrode. In such embodiment, the second voltage sensing textrode may be a negative voltage sensing textrode. The second current injecting textrode may be a negative current injecting textrode.
[0040] Furthermore, the set of textrodes (100, 110) is knitted on an upper part of the wearable smart sensing fabric (70). In one embodiment, the upper part of the wearable fabric may correspond to a thoracic region of the user’s body. As used herein, the thoracic region is defined as a region of a human body formed by the sternum, the thoracic vertebrae and the ribs, extending from the neck to the diaphragm, covering the heart and both the lungs.
[0041] Furthermore, the positive current injecting textrode (100) and the negative current injecting textrode (110) may be configured to inject a certain amount of current into the user’s body of the order of milli ampere to micro ampere in order to determine one or more parameters from the user’s body. In such embodiment, the one or more parameters may include differential voltage across the thorax, and the corresponding differential conductance that is the derivative of injecting current with respect to differential voltage obtained during the TEB experiment which may be generated across user’s thoracic region.
[0042] In another embodiment, the wearable smart sensing fabric (70) may further include one or more sensors (90) which may be operatively coupled to the set of textrodes (100, 110). The one or more sensors (90) may be configured to measure one or more physiological signals from the user’s body. The one or more sensors (90) may be used to measure at least one of an electrocardiogram signal, a photoplethysmogram (PPG) signal originated from the user’s body, motion & body temperature and one or more sensors (90) is wired to a IMU sensor to measure motion and body temperature of the user. In one embodiment, the one or more sensors comprises textrodes also.
[0043] Further, the one or more physiological signals which may be sensed by the corresponding one or more sensors may include a heart muscle's electrophysiologic pattern, an inertial state of the user, an oxygen saturation measurement using photoplethysmography (PPG) signal, a respiratory rate using ECG, , a blood pressure using ECG, TEB & PPG signals, a body temperature using IMU sensor and a thoracic impedance using TEB signal. In one embodiment, the one or more physiological signals may be associated to at least one of a coronary artery disease ( CAD), an arrhythmia, a hypertensive heart disease, an ischemic stroke, a chronic obstructive pulmonary disease (COPD), an asthma, a pneumonia, a type 2 diabetes (T2D) and associated trauma disease of the user.
[0044] The system (10) also includes a health processing subsystem (20). The health processing subsystem (20) includes a retrieving module (40). The retrieving module (40) is configured to retrieve one or more data of the user in real-time. In one embodiment, the one or more data includes at least one of the measured thoracic electrical bioimpedance (TEB) and the one or more physiological signals generated by the user’s body. In such embodiment, the wearable smart sensing fabric (70) continuously monitor the condition of the user in real time.
[0045] In one exemplary embodiment, a user may wear the fabric vest with knitted textrodes and embedded processor system (10) anytime during activity. The one or more data may be retrieved during such activity.
[0046] The health processing subsystem (20) also includes processing module (50). The processing module (50) is operatively coupled to the retrieving module (40). The processing module (50) is configured to collate the one or more data of the user. In one embodiment, the one or more data is collated in real time for further use. Here, the data of prior history associated with the user may be manually feed to the processor.
[0047] The processing module (50) is also configured to analyse the one or more data by an analysing technique. In one such embodiment, the analysing technique includes at least one of artificial intelligence technique and a machine learning technique.
[0048] As used herein, “artificial intelligence” refers to sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals, such as visual perception, speech recognition, decision-making, and translation between languages. As used herein, “machine learning” refers to an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
[0049] In one exemplary embodiment, analysis of the one or more data indicates comparing data of the user with previously recorded data of the user or with authorised range of the data as provided by the user’s physician. In such embodiment, the real time analysis enables proper diagnosis of specific health problems.
[0050] The health processing subsystem (20) also includes a prediction module (60). The prediction module (60) is operatively coupled to the processing module (50). The prediction module (50) is configured to predict one or more analysed data by a prediction technique.
[0051] In one such embodiment, the prediction technique includes at least one of artificial intelligence technique and a machine learning technique. In another embodiment, the prediction module (50) predicts result after monitoring previous factors as well as real time factors related to the particular health problem. In one alternative embodiment, manual prediction may be provided by registered users after going through the one or more analysed data. In such embodiment, the registered users may be a third-party or user’s physician.
[0052] The health processing subsystem (20) further comprises a notification module (not shown in FIG. 1). The notification module is operatively coupled to the processing module (50). The notification module is configured to notify the user about predicted result. In one embodiment, the notification may be provided by an electronic mail or a text message.
[0053] A health memory subsystem (30) is operatively coupled to the health processing subsystem (20). The health memory subsystem (30) is configured to store retrieved one or more data, the one or more analysed data and the predicted result. In one embodiment, storing may be performed in a remote storage or a local storage.
[0054] FIG. 3 is a schematic representation of an exemplary embodiment representing the system to predict chronic disease and trauma of FIG. 1 in accordance of an embodiment of the present disclosure. Here, a user X (130) wears a wearable smart sensing fabric for a particular period of time. Here, a set of textrodes and one or more sensors are knitted or, woven or, embroidered in the wearable smart sensing fabric.
[0055] In an exemplary embodiment, measured thoracic electrical bioimpedance (TEB) and the measured physiological signal parameters are retrieved by a retrieving module (40). The physiological data of user X (130) are collated by a processing module (50). Here, the processing module (50) further analyses the retrieved physiological data to establish or ascertain the health problem. For example, if the user X (130) is suffering from pulmonary congestion associated with coronary artery disease (CAD), the processing module (50) will be able to provide the thoracic bioimpedance and corresponding fluid accumulation in the lungs of the user X (130).
[0056] Furthermore, a prediction module (60) enables prediction of such health data. In such exemplary embodiment, the user X health condition will be predicted by the prediction module (60). For such prediction, the prediction module (60) uses machine learning technique.
[0057] After such prediction, the user X (130) will be notified in pre-determined time line through a computing device (80). In such situations, the medical personal associated with user X (130) will also be notified by a notification module (65).
[0058] The retrieving module (40), the processing module (50) and the prediction module (60) in FIG. 3 is substantially equivalent to the retrieving module (40), the processing module (50) and the prediction module (60) of FIG. 1.
[0059] FIG. 4 is a block diagram of a computer or a server (140) in accordance with an embodiment of the present disclosure. The server (140) includes processor(s) (170), and memory (150) coupled to the processor(s) (170).
[0060] The processor(s) (170), 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.
[0061] The memory (150) includes a plurality of modules stored in the form of executable program which instructs the processor (170) to perform the method steps illustrated in Fig 1. The memory (150) has following modules: retrieving module (40), processing module (50) and prediction module (60). The retrieving module (40) is configured to retrieve one or more data of the user in real-time. The processing module (50) is configured to collate the one or more data of the user. The processing module (50) is also configured to analyse the one or more data by an analysing technique. The prediction module (60) is configured to predict one or more analysed data by a prediction technique.
[0062] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, solid state drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (170).
[0063] FIG. 5 is a flowchart representing the steps of a method to operate a system to predict chronic disease and trauma (180) in accordance with an embodiment of the present disclosure. The method includes measurement of thoracic electrical bioimpedance (TEB) of a user’s body in step 190. In one embodiment, measurement of the thoracic electrical bioimpedance (TEB) of the user’s body includes the measurement of the thoracic electrical bioimpedance (TEB) of the user’s body by a wearable smart sensing fabric.
[0064] In another embodiment, measurement of the thoracic electrical bioimpedance (TEB) of the user’s body includes measurement of the thoracic electrical bioimpedance (TEB) of the user’s body by a set of textrodes.
[0065] The method (180) also includes measurement of one or more physiological signals generated by the user’s body in step 200. In one embodiment, measurement of the one or more physiological signals generated by the user’s body includes the measurement of the one or more physiological signals generated by the user’s body by the wearable smart sensing fabric.
[0066] In another embodiment, measurement of the one or more physiological signals generated by the user’s body includes measurement of the one or more physiological signals generated by the user’s body by one or more sensors. In such embodiment, measurement of the one or more physiological signals by the one or more sensors includes knitting, weaving, embroidering the one or more in the wearable smart sensing fabric.
[0067] The method (180) also includes retrieving one or more data of the user in real-time in step 210. In one embodiment, retrieving the one or more data of the user in real-time includes retrieving the one or more data of the user in real-time by a retrieval module.
[0068] The method (180) also includes collating the one or more data of the user in step 220. In one embodiment, collating the one or more data of the user includes collating the one or more data of the user by a processing module. The method (180) also includes analysing the one or more data by an analysing technique in step 230. In one embodiment, analysing the one or more data by the analysing technique includes analysing the one or more data by the processing module.
[0069] The method (180) also includes predicting one or more analysed data by a prediction technique in step 240. In one embodiment, predicting the one or more analysed data by the prediction technique includes predicting the one or more analysed data by a prediction module.
[0070] The method (180) also includes notifying the user about predicted result in step 250. In one embodiment, notifying the user about the predicted result includes notifying the user about the predicted result by a notification module.
[0071] The method (180) further includes storing retrieved one or more data, the one or more analysed data and the predicted result. In one embodiment, storing the retrieved one or more data, the one or more analysed data and the predicted result includes storing the retrieved one or more data, the one or more analysed data and the predicted result by a health memory subsystem. Efficiently the present disclosure predicts the individual’s unique symptoms.
[0072] Present disclosure of a system to predict chronic disease and trauma provides real time analysis the physiological data. Such data would also enable in detection of any serious crisis such as worsening of HF related to the individual using such wearable apparatus. Wearable smart sensing fabric may be developed and manufactured according to user need. Such a wearable smart sensing fabric continuously monitors the health.
[0073] Automatic analysis of the measured readings also enables prediction and early detection of future problems associated with CAD, HF and comorbidities. Present disclosure also enables real time notification to a physician or third-party about the condition of the user wearing the fabric.
[0074] 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.
[0075] 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, 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 dependant 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 to predict chronic disease and trauma (10), comprising:
a wearable smart sensing fabric (70), comprising:
a set of textrodes (100, 110), configured to measure thoracic electrical bioimpedance (TEB) of a user’s body, comprises:
at least two first textrodes (100);
at least two second textrodes (110);
one or more sensors (90) mechanically coupled to the set of textrodes (100, 110), and configured to sense one or more physiological signals generated by the user’s body;
health processing subsystem (20), comprising:
a retrieving module (40) configured to retrieve one or more data of the user in real-time, wherein the one or more data comprises at least one of the measured thoracic electrical bioimpedance (TEB) and the one or more physiological signals generated by the user’s body;
a processing module (50) operatively coupled to the retrieving module (40), and configured to:
collate the one or more data of the user;
analyse the one or more data by an analysing technique;
a prediction module (60) operatively coupled to the processing module (50), and configured to predict one or more analysed data by a prediction technique; and
a health memory subsystem (30) operatively coupled to the health processing subsystem (20), and configured to store retrieved one or more data, the one or more analysed data and the predicted result.
2. The wearable smart sensing fabric (70) as claimed in claim 1, wherein the wearable smart sensing fabric (70) is designed as a vest, a shirt, a T-shirt, a jacket or a top.
3. The wearable smart sensing fabric (70) as claimed in claim 1, wherein the at least two first textrodes (100) comprises a first voltage sensing textrode and a first current injecting textrode and the at least two second textrodes (110) comprises a second voltage sensing textrode and a second current injecting textrode.
4. The wearable smart sensing fabric (70) as claimed in claim 1, wherein the one or more sensors (90) is used to measure at least one of an electrocardiogram or, ECG signal, a photoplethysmogram (PPG) signal, and one or more sensors (90) is wired to a IMU sensor to measure motion and body temperature of the user.
5. The system (10) as claimed in claim 1, further comprises a notification module operatively coupled to the processing module (40), and configured to notify the user about predicted result.
6. A method to operate a system to predict chronic disease and trauma (180), comprising:
measuring, by a wearable smart sensing fabric, thoracic electrical bioimpedance (TEB) of a user’s body (190);
measuring, by the wearable smart sensing fabric, one or more physiological signals generated by the user’s body (200);
retrieving, by a retrieving module, one or more data of the user in real-time, wherein the one or more data comprises at least one of the measured thoracic electrical bioimpedance (TEB) and the one or more physiological signals generated by the user’s body (210);
collating, by a processing module, the one or more data of the user (220);
analysing, by the processing module, the one or more data by an analysing technique (230);
predicting, by a prediction module, one or more analysed data by a prediction technique (240); and
notifying, by a notification module, the user about predicted result (250).
7. The method (180) as claimed in claim 6, measuring the thoracic electrical bioimpedance (TEB) of the user’s body comprises measurement by a set of textrodes.
8. The method (180) as claimed in claim 6, measuring the one or more physiological signals generated by the user’s body comprises measurement by one or more sensors.
9. The method (180) as claimed in claim 6, further storing, by a health memory subsystem, retrieved one or more data, the one or more analysed data and the predicted result.