The present invention relates to monitoring devices. More specifically, the present
invention relates to a wearable health monitoring device for remotely diagnosing patients.
Background
[0002] The background description includes information that may be useful in understanding
the present invention. It is not an admission that any of the information provided herein is prior art or
relevant to the presently claimed invention, or that any publication specifically or implicitly referenced
is prior art.
[0003] Seamless patient health monitoring has been enabled by the healthcare industries
inclination towards provisioning comprehensive virtual solution in a safe ambit of operation. Teleconsultations have transcended across perception obstacles, proving to be solution to crisis situations.
Amelioration of services is brought about by shared experiences of the patients. In compliance with
HIPAA, government stakeholders ensure that the confidence level do not face a plummet, maintaining
adequate transparency in the policies & execution of the federal law. Sanctions imposed on these portals
keep them under surveillance & protect consumer rights under the yardstick of law. Healthcare motives
accomplished today have the potential of transforming goals of tomorrow. There won’t be a gigantic
demand & supply deficit whereby privileging cost-effective healthcare solution to whomsoever needs
it.
[0004] Wearable devices form a special sub-class of ambulatory monitoring devices. As a
group, these have the added advantage that more continuous data can be collected and data can be
collected in the subject’s more natural environment. Holter monitors for ECG and EEG monitoring are
among the first and most frequently used wearable devices. Current devices are limited, however, in
that they are strictly data acquisition devices. Data is typically retrieved and analyzed post recording
3
session. When the devices are wirelessly enabled, they are also in the field of wireless sensor networks
(WSNs). WSNs are comprised of one or more sensor nodes and a system controller. Sensor nodes
include a computing platform with wireless communication capabilities and one or more sensor devices.
The system controller provides a data sync point for the collection and extraction of data, System
configuration capabilities, and may include an interface for the end user of the wireless sensor network.
The system controller may be referred to as a personal server, network coordinator, or personal area
network (PAN) coordinator. The system controller may provide visual, audible or other signals to the
user in response to certain events. Sanctity of doctor- patient relationship has not been compromised,
primarily due to real time consultations. Diagnostic competencies of medical professionals transmitted
far across, has made life easier revealing ameliorated vistas of healthcare amenities. User does not have
to bother about cues & appointments & can acquire services from the best in the business.
[0005] Therefore, in consideration of the aforementioned facts, a system is proposed to
monitor the health of a patient/subject remotely. There is a vast scope of improvement as far as the idea
of the invention is concerned. Referring to the prior art, the inconsistency in the values of the health
vital parameters has been subjected to investigation and analysis to rectify the same. However, the
proposed system aims to improve on one or more grievances persisting with the system and can be
modified and updated, in accordance with the needs of present and future.
[0006] All publications herein are incorporated by reference to the same extent as if each
individual publication or patent application were specifically and individually indicated to be
incorporated by reference. Where a definition or use of a term in an incorporated reference is
inconsistent or contrary to the definition of that term provided herein, the definition of that term
provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties
such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments
of the invention are to be understood as being modified in some instances by the term “about.”
Accordingly, in some embodiments, the numerical parameters set forth in the written description and
4
attached claims are approximations that can vary depending upon the desired properties sought to be
obtained by a particular embodiment. In some embodiments, the numerical parameters should be
construed in light of the number of reported significant digits and by applying ordinary rounding
techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of
some embodiments of the invention are approximations, the numerical values set forth in the specific
examples are reported as precisely as practicable. The numerical values presented in some embodiments
of the invention may contain certain errors necessarily resulting from the standard deviation found in
their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning
of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as
used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly
dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand
method of referring individually to each separate value falling within the range. Unless otherwise
indicated herein, each individual value is incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in any suitable order unless otherwise
indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or
exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended
merely to better illuminate the invention and does not pose a limitation on the scope of the invention
otherwise claimed. No language in the specification should be construed as indicating any non-claimed
element essential to the practice of the invention.
[00010] Groupings of alternative elements or embodiments of the invention disclosed herein
are not to be construed as limitations. Each group member can be referred to and claimed individually
or in any combination with other members of the group or other elements found herein. One or more
members of a group can be included in, or deleted from, a group for reasons of convenience and/or
patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain
5
the group as modified thus fulfilling the written description of all Markush groups used in the appended
claims.
Objects of the Invention
[00011] An object of the present disclosure is to overcome one or more drawbacks associated
with conventional mechanisms.
[00012] An object of the proposed system can be the automation of the process of health
monitoring.
[00013] An object of the proposed system can be the maintenance of health record of one or
more patients on a daily basis.
[00014] An object of the proposed system can be to alert the medical professionals/doctors if
one or more health monitoring parameters show irregularity or anomaly.
[00015] An object of the proposed system can be portability and compatibility.
[00016] An object of the proposed system can be to address one or more impending issues visà-vis conventional mode of health monitoring.
[00017] An object of the proposed system can be to provide an alternative to conventional
mode of health monitoring.
[00018] An object of the proposed system can be to include systemic and systematic practices
in order to assure health monitoring.
[00019] An object of the proposed system can be to expedite the process of health monitoring.
[00020] An object of the proposed system can be to provide a cost and time effective health
monitoring.
Summary
6
[00021] The present invention relates to monitoring devices. More specifically, the present
invention relates to a wearable health monitoring device for remotely diagnosing patients.
[00022] In an aspect, the present invention provides a system to monitor the body vital
parameters, the system comprising: a wearable device is arranged to be worn by a user, wherein the
wearable device comprising: an internet of things (IoT) enabled sensing unit comprising: a cardiac
sensor is arranged to determine the cardiac parameters selected from a heart rate, a blood pressure, and
an electrocardiogram; a temperature sensor is arranged to determine a body temperature of the user; an
electromyography (EMG) sensor is arranged to determine a muscle tone of the user; and a posture
determination sensor is arranged to determine a posture of the user; and a platform comprises: a weight
sensor is arranged to determine weight of the user; and a height determination sensor is arranged to
determine a height of the user; and a computing device is arranged to: receive the cardiac parameters,
the body temperature, the muscle tone, the posture of the user, the weight of the user and the height of
the user; and create a graphical representation, which depict the body vital parameters, based on the
received the body temperature, the muscle tone,
[00023] In an embodiment, the computing device computes a level of (BMI).
[00024] In an embodiment, the posture determination sensor is selected from an accelerometer
and a gyroscope.
[00025] In an embodiment, the computing device creates a health database to store the body
temperature, the muscle tone, the posture of the user, the weight of the user and the height of the user.
[00026] In an embodiment, the computing device creates a replica of the created heath
database on a remote server.
[00027] In an embodiment, the computing device utilizes a machine learning technique to
determine a list of corrective measures to improve the BMI.
7
[00028] In an embodiment, the machine learning technique is selected from Decision Tree,
Support Vector Machines (SVM), Multilayer Perceptron, Naive Bayes, and k-Nearest Neighbor (kNN).
[00029] Various objects, features, aspects and advantages of the inventive subject matter will
become more apparent from the following detailed description of preferred embodiments, along with
the accompanying drawing figures in which like numerals represent like components.
Brief Description of the Drawings
[00030] FIG. 1 illustrates showcasing an architectural paradigm of a system to monitor the
body vital parameters of a user, in accordance with an embodiment of the present disclosure.
[00031] FIG. 2 illustrates an architectural setup of functional units, in accordance with the
embodiments of the present disclosure.
[00032] FIG. 3 illustrates the generated graph of the body vital parameters of the user, in
accordance with an embodiment of the present invention.
[00033] FIG. 4 illustrates Referring to the computing device which can be arranged to compute
a level of body mass index (BMI), in accordance with an embodiment of the present invention.
[00034] FIG. 5 illustrates a screenshot of an exemplary login page of a web-based application
in order to access one or more functional operations of system on the computing device, in accordance
with an embodiment of the present invention.
[00035] FIG. 6 illustrates showcasing a screenshot of a homepage of the web-based
application, post an authentication access to the user, in accordance with an embodiment of the present
invention.
[00036] FIG. 7 illustrates depicting a screenshot of web-based application accessed on the
computing device with one or more connected embodiments of system, in accordance with an
embodiment of the present invention.
8
[00037] FIG. 8 illustrates depicting a screenshot of web-based application illustrating a result
of the sensing unit, in accordance with an embodiment of the present invention.
Detailed Description
[00038] The following discussion provides many example embodiments of the inventive
subject matter. Although each embodiment represents a single combination of inventive elements, the
inventive subject matter is considered to include all possible combinations of the disclosed elements.
Thus, if one embodiment comprises elements A, B, and C, and a second embodiment comprises
elements B and D, then the inventive subject matter is also considered to include other remaining
combinations of A, B, C, or D, even if not explicitly disclosed.
[00039] The present invention relates to monitoring devices. More specifically, the present
invention relates to a wearable health monitoring device for remotely diagnosing patients.
[00040] According to illustration made in figure 1, showcasing an architectural paradigm of a
system 100 to monitor the body vital parameters of a user 112. The phrase “body vital parameters” may
correspond to, but not limited to, one or more numerical values vis-à-vis a vital sign (body temperature,
pulse rate and other known examples). For example, one or more numerical representation of the “body
vital parameters” can exemplarily aid/assist one or more medical professional in a health monitoring
and diagnosis of a patient. The body vital parameters can include one or more numerical values such as
a normal blood pressure (b.p.) can be represented as a systolic value of less than 120 and a diastolic of
less than 80 (120/80). An elevated b.p. can be represented as the systolic value of 120 to 129 and
diastolic value can be less than 80.
[00041] In an exemplary embodiment, the system 100 can comprise a sensing unit 102, a
platform 104, a network interface 106, a computing device 108, a server 110 and other known elements
thereof. For example, the sensing unit 102 can be arranged in form of a wearable monitoring device,
such as but not restricted to an electronic garment/equipment/apparatus/assembly/gadget worn by the
user 112. Each sensor of the sensing unit 102 can be arranged in a vicinity of an organ to measure one
9
or more body vital parameters (such as a cardiac sensor 102a can be arranged on a left side of a chest
of the user 112 and the like).
[00042] In an exemplary embodiment, the network interface 106 can be arranged to
functionally or operationally link the elements of system 100, with each other. Non-limiting examples
of network interface 106 may include a short-range communication network and/or long-range
communication network. The short-range communication network may include WiFI, Bluetooth low
energy (BLE), Zigbee, and the like. The long-range communication network may include Local Area
Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), a cloud computing
platform, a data center, Internet of Things (IoT), light fidelity (LiFi) etc. The embodiments of proposed
disclosure work well with any or a combination of aforementioned networks. The network includes
any or a combination of wired or wireless communication mechanisms that can be performed through
various computer networking protocols. The computer networking protocol may include Asynchronous
Transfer Mode (ATM), Transmission Control Protocol/Internet Protocol (TCP/IP), Ethernet
management, Simple Mail Transfer Protocol (SMTP); and security, such as Secure Shell (SSH),
Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP) and User Datagram Protocol (UDP).
Moreover, any other suitable protocols using voice, video, data, or combinations thereof, can also be
employed. For transmission of data Wi-Fi (Node MCU) or Bluetooth (HC-05) module can also be used.
[00043] In an embodiment, the sensing unit 102 can comprise a plurality of sensors such as,
but not restricted to, a cardiac sensor 102a, a temperature sensor 102b, an electromyography (EMG)
sensor 102c, a posture determination sensor 102d and other known elements thereof. For example, one
or more sensors of the sensing unit 102 can comprise an output screen to show/display one or more
body vital parameters numerical value. The sensing unit 102 can sense one or more body vital
parameters and can exemplarily be based on a contact-based sensing (in a direct contact with the body
of the user 112) and a contactless (no direct contact with the body of the user 112) sensing. The
positioning/arrangement of each of elements the sensing unit 102, may vary from the illustration made
in figure 1. The other known elements can include an ECG/ Heart Rate sensor AD8232 deployed
10
to monitor an electrical activity of the heart of the user 112. Similarly, for measuring saturation of
peripheral Oxygen (SpO2), a MAX 30102 sensor can be deployed peripheral capillary oxygen
saturation which calculates the quantity. The SpO2 sensor can detect oxygen present in blood of the
user 112, which can correspond to proportion of oxygenated hemoglobin compared to a total quantity
of hemoglobin (oxygenated and non-oxygenated blood). A normal range for SpO2 can lie between 95-
100%. If the value plummets below 95%, can indicate of poorly oxygenated blood (hypoxia).
[00044] In an embodiment, the cardiac sensor 102a can be arranged to determine one or more
cardiac parameters such as but not restricted to a heart rate, a blood pressure, an electrocardiogram and
other known examples thereof. For example, the heart rate can be represented as beats per minute
(b.p.m) which can indicate the number of times a heart of the user 112, beats in a minute. Similarly, the
blood pressure (b.p.) can indicate pressure of a circulating blood, exerted on one or more wall of one or
more blood vessel. Electrocardiogram (E.C.G.) can measure an electrical activity of a heartbeat.
[00045] In an embodiment, the temperature sensor 102b can be arranged to determine a body
temperature of the user 112. For example, the temperature sensor 102b can be in form a digital
thermometer such as, but not restricted to a MAX30205 temperature sensor 102b which can convert a
measured temperature into a digital form by deploying an analog to digital converter (ADC sigmadelta). The temperature sensor 102 can be an inter-integrated (I2C) compatible, 2 wire serial interface
with an operating voltage range of 2.7-3.3 volts, low supply current 600µA.
[00046] In an embodiment, the electromyography (EMG) sensor 102c can be to determine a
muscle tone of the user 112. For example, the EMG sensor 102c can sense one or more movement of
one or more muscle of the user 112. The EMG sensor 102c can be based on a principle such that, but
not restricted to a muscle contraction which can lead to a generation of an electrical activity which can
propagate through one or more adjacent tissue and bone. Non limiting example EMG sensor 102c can
be a Myoware Muscle sensor SEN-13723 to detect an electrical activity produced by one or more
skeletal muscles. An operating voltage for the EMG sensor 102c can be in a range of 3.1-5 volts. The
EMG sensor 102c can detect one or more muscle disorder, inflammation caused by injury, nerve
11
disorder and in various other applications. The EMG sensor 102c can measure one or more contraction
per minute of one or more muscle (CPM) in an exemplary range of 0-60 CPM and a muscle signal range
is 0-5 volts.
[00047] In an embodiment, the posture determination sensor 102d can be arranged to
determine a posture of the user 112. The term “posture” as used herein can relate to, but not limited to
a position of a body of the user 112 in a space, which can indicate one or more orientation the body in
relation to an environment and a base of a support. In other words, the term “posture” can also relate to
a spatial distribution of one or more skeletal segments. Non limiting example of the posture
determination sensor 102d can be, but not restricted to an accelerometer, gyroscope and other known
variants thereof. For example, the posture determination sensor 102d can help in a determination of a
position/orientation of the user 112 such as, but not limited to a prone (lying face down), a supine (lying
face up), a left lateral (left side body position during lying down), a right lateral (Right side position)
and other known examples thereof. The posture determination sensor 102d can also be deployed in a
detection of one or more condition associated with a user 112, such as but not restricted to a dizziness,
palpitation, aback spasm, and other known conditions due to an inappropriate posture.
[00048] In an embodiment, a platform 104 can comprise a height determination sensor 104a
and a weight sensor 104b, in accordance with the embodiments of present disclosure. For instance, the
height determination sensor 104a can give a digital output of a height of the user 112 such as but not
restricted to, output in feet and inches or centimeters and other known examples thereof (5’ 9” or 5ft. 9
in. /175.26 cm). Similarly, the weight sensor 104b can give a digital output of a weight of the user 112
such as but not restricted to output in Kilograms (Kg) or pounds (lbs). The height determination sensor
104a and the weight sensor 104b, respectively, can be integrated/ detached with the platform 104 and
the positioning/arrangement of each of these sensors can vary from the illustration made in figure 1.
[00049] In an embodiment, the height determination sensor 104a can be arranged to determine
height of the user 112. For example, the height determination sensor 104a can comprise a scale in feet
and inches or centimeters. The user 112 can align with the scale in order to determine the height of the
12
user 112. The term “height” as used herein, may correspond to, but not limited to a vertical measurement
of a length of the user 112, such as from head to foot. Non limiting example of the height determination
sensor 104a can be HC-SR04, SRF04 and other known examples thereof.
[00050] In an embodiment, the weight sensor 104b can be arranged to determine a weight of
the user 112. For example, the weight sensor 104b (load cell/ RGB-d sensor) can convert the weight of
the user 112 into an equivalent electrical signal. The user can exemplarily stand on a surface of the
weight sensor 104b to determine the weight. The term “weight” as used herein, may relate to, but not
restricted to a force of gravity on the user 112 and may be defined as the mass times the acceleration
due to gravity, w = mg, wherein w stands for the weight of the user 112, m for mass of the user 112 and
g can be acceleration due to gravity.
[00051] In an embodiment, the computing device 108 can be arranged to perform a plurality
of functional operations of the system 100 such that, but restricted to a reception of data from the sensing
unit 102 over the network interface 106, a data analysis of the received data, computation of a body
mass index (BMI), generation of a graphical representation of the result post the analysis of the received
data and other known examples thereof. For example, an exemplary graphical user interface (GUI),
which may be hosted by the computing device 108, can display the graphical rendition of determined
of body vital parameters of the user 112.
[00052] Referring to the computing device 108, which can be arranged to create a health
database to store the body temperature, the muscle tone, the posture of the user, the weight of the user
and the height of the user 112. For instance, in an exemplary tabulation 1, as illustrated below,
showcasing a section of health database wherein row (1) can represent an exemplary entitlement of the
health database for a visual identification of the same. The computing device 108 can also create a
replica (copy) of the created heath database on a server 110 (The server 110 can comprise a one or more
microprocessor (a reduced instruction set) and a storage device (RAM, ROM) to store the health
database replica generated by the computing device 108). In another embodiment, an exemplary
deployment of a controller to digitally convert an analog signal and further process with an amplifier
13
for lowering a noise in a circuit. Non limiting examples can be, but not restricted to an Arduino Uno.
ATmega328P can be a microcontroller used in Arduino with 14 digital I/O pins, 6 analog input pins
and 16 MHZ quartz crystal.
[00053] In an exemplary tabulation 1, as illustrated below, showcasing a section of health
database wherein row (1) can represent an exemplary entitlement of the health database for a visual
identification of the same. Cell (2),(3),(4),(5),(6),(7),(8), (9), (10), (11), as per the illustrations in table
1, can be arranged to divulge the pre-stored one or more body vital parameters of one or more user 112,
such as, but not limited to a serial number (S.No.), a name of the user 112, an age of the patient, an
average body temperature, a heart rate in beats per minute (BPM), a SpO2 level, a body posture, a
height represented as feet(ft.) and inches (in.), electrocardiogram signals (volts), muscle signals (volts)
and other known elements, respectively. For example, unique identification code/patient ID (U.I.C),
geographical location of the treatment (G.L.O.T.), physician identification (P.I.), hospitalization details
(H.D.), medical complications (M.COMP.) and other relevant information associated with of one or
more patients, respectively. The one or more information associated with the body vital parameters of
the user 112 can be chronologically arranged/stored/updated/accessed or retrieved from the health
database. An exemplary arrangement of rows and column can comprise similar information, as
illustrated in table 1.
S. No.
(2)
NAME
(3)
AGE
(4)
BODY
TEMPERA- -
TURE (0F) (5)
HEART
RATE
(BPM) (6)
SpO2
LEVEL
(7)
BODY
POSTURE (8)
HEIGHT (FT.
& IN.) (9)
ELECTROCARDIO -GRAM
SIGNAL
(VOLTS) (10)
MUSCLE
SIGNAL
(VOLTS)
(11)
1 J. DOE 24 98.9 85 97% Sitting 5’5” 4.00 3.00
2 D. WILL 25 98.8 75 97% Sitting 6’1” 4.31 4.21
3 M. BILL 25 98.6 71 98% Supine 5’7” 4.59 4.55
HEALTH DATABASE (1)
14
4 K. SON 30 98.7 88 100% Sitting 5’6” 4.22 4.72
Table 1
[00054] Throughout the present disclosure, the term “computing device” and/or “electronic
device” relates to a device, including but are not limited to, a cellular phone, a smart phone, a personal
digital assistant (PDA), a handheld device, a wireless modem, a laptop, a mobile terminal, a user
terminal, a subscriber unit, a wearable computer, a wearable computing device, a smart watch and other
known variants thereof. The computing device 108 may include a casing, a memory, a processor, a
network interface card, a microphone, a speaker, a keypad, and a display.
[00055] According to an illustration made in figure 2, can represent an architectural setup 200
of one or more functional units. The architecture 200 can comprise a data reception unit 202, a data
analysis unit 204, a graph generation unit 206, an alert relay unit 208 and other known elements thereof.
A person ordinarily skilled in art would prefer that the aforementioned functional elements/units can be
stored in a exemplary memory for any duration and can be executed or functionally performed
sequentially or selectively, collectively or individually by the exemplary processor of the computing
device 108.
[00056] In an embodiment, the data reception unit 202 can be arranged to receive the cardiac
parameters, the body temperature, the muscle tone, the posture, the weight, the height, and other
detected information associated with the user 112 body vital parameters, from the one or more arranged
sensors, respectively, over the network interface 106.
[00057] In an embodiment, the data analysis unit 204 can be arranged to perform a detailed
assessment/evaluation/estimation of one or more received information of the data reception unit 202.
The processor can cause the execution of the data analysis unit 204 and can exemplarily employ a
machine learning technique which can be selected from Decision Tree, Support Vector Machines
(SVM), Multilayer Perceptron, Naive Bayes, and k-Nearest Neighbor (k-NN), to perform one or more
data analysis operation such as data extraction, calculation/estimation/computation, pattern evaluation,
result generation, generation of test and training set and other known examples thereof.
15
[00058] In an embodiment, the graph generation unit 206 can be arranged to generate a
graphical representation to depict the body vital parameters of the user 112, based on the analysis of
received one or more information from the sensing unit 102 (102a, 102b, 102c, 102d and the like) and
platform 104 (104a, 104b etc). For example, an exemplary illustration of the generated graph of the
body vital parameters of the user 112 can be represented by a figure 3. The generated graph can
exemplarily comprise one or more axis (X, Y) with relevant information “labeled” on each axis. The
graphical representation can divulge information not restricted to pulse rate (beats/minute), respiration
rate (cycles/minute), temperature (0C) and other relevant information thereof, labeled on a Y-axis and
a time (minutes /hours/ days) on an X-axis.
[00059] In an embodiment, the alert relay unit 208 can be arranged to transmit a notification
to one or more computing device 108. For example, the notification can transmit the generated graphical
representation to the computing device 108. The user 112 or the patient can be exemplarily notified via
an acoustic signal on the exemplary reception of the notification/alert on the computing device 108. The
user 112 or the patient may be asked to enter a private key, deemed to be known specifically by an
authenticated/trusted user in order to deter a malicious access to the vital body parameters.
[00060] Referring to the computing device 108 which can be arranged to compute a level of
body mass index (BMI), as illustrated in figure 4. The term “BMI” as used herein can relate to, but not
limited to a standardized ratio of weight to height, and can be used to indicate health of the user 112.
For instance, a BMI of 25.1 can be computed for the exemplary user 112, aged 40 with a height of 5ft
8in / 173cm tall, and a current weight of 75.0kg, by the processor of the computing device 108. The
user 112 can exemplarily enter one or more relevant information on the computing device 108 through
an input device (touch screen, keyboard) to compute a level of BMI
[00061] The system 100 can also be implemented as a decentralized ledger. The functional
units of architecture 200 can also be alternatively executed as, but not limited to example illustrated
herein. For example, Firstly, each received body vital parameters from the one or more sensors can be
optionally split into one or more individual information (e.g., a lab results document can contain several
16
types of blood analyses, a set of images can be split into individual images, etc.). Aggregation (of the
same type of data such as glycemic control or blood pressure control). Given a finite set of healthrelated categories, the information (or the records if no split is performed) can be grouped by their
respective categories. Example categories include blood analyses, MRI images, medical reports,
medical prescriptions, family history, and health habits. The splitting can be performed based on a
metadata (data within data) of the information, UMLS based annotations, or by trained categorizers.
[00062] In some embodiments, clustering methods may be used to group the information.
Within each group, the information may be further grouped into Subclasses (e.g., for lab results into
glycemic controls, lipid controls, or medical prescriptions into prescriptions of amoxicillin, metoprolol,
etc.). In each group and where possible, the acts, are sorted by timeline. Optionally, free text based
medical reports can be parsed and searched for medical concepts and related numerical entities extracted
(e.g., keeping only statistics, or extreme values). This includes filtering the records to identify the most
salient information. Human can be known to absorb a lot of visual information in a very short time
frame (50% of the cerebral cortex is for vision). To assist a practitioner/ medical professional, presenting
the information can be graphically rather than textually allows the healthcare provider to absorb the
information quickly. Information graphics (e.g., graphical displays, plots, charts) can thus be used to
show statistics or evolution of these values over time. Similarly, for grouped acts, clickable visual icons
may be based on corresponding act types (e.g., a red drop icon for blood test results). Optionally, if
reference values can be available, the microprocessor can highlight values that are outside these
reference values, as per tabulations 2 and 3. The table 2 can comprise one or more conditions (normal,
fever, hypothermia), in cell (1) associated with the health of the user 112 with the corresponding
reference values of the temperature, in cell (2), respectively. Similarly, table 3 illustrates one or more
ages of the user 112, in cell (3) with a corresponding heart rate in BPM, in cell (4), respectively.
CONDITION (1) TEMPERATURE REFERENCE VALUE (2)
Normal 36.5-37.50C (97.7-99.50F)
17
Fever >37.50C (99.5 or 100.90F)
Hypothermia <35.0 0C (95.0 0F)
Table 2
AGE IN YEAR (3) BEATS/MINUTE (4)
Normal Resting rate (Adults) 60-100
40 90-153
45 88-149
50 85-145
55 83-140
60 80-136
65 78-132
70 75-128
Table 3
[00063] According to the illustration made in figure 5, representing a screenshot of an
exemplary login page of a web-based application in order to access one or more functional operations
of system 100 on the computing device 108, linked through the network interface 106. The login page
may require one or more authorization/authentication credential of the user 112 to grant an access. The
login page may also divulge one or more tabs or sections illustrating, but not restricted to “ABOUT
US”, “GUEST”, “CONTACT US”, a logo, a slogan and other relevant information thereof.
[00064] According to the illustration made in figure 6, showcasing a screenshot of a homepage
of the web-based application, post an authentication access to the user 112. The homepage can divulge
a plurality of information such as, but not restricted to “a username”, a user profile, a list of
past/scheduled appointments, a dashboard, one or more connected device (USB drive, sensing unit 102,
platform 104 and the like)
[00065] According to illustration made in figure 7, depicting a screenshot of web-based
application accessed on the computing device 108 with one or more connected embodiments of system
100, through the network interface 106. The screenshot may divulge one or more information such as,
but not limited to a MAC address or I.P address of the connected embodiments of system 100 or an
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exemplary turning on of a Bluetooth device arranged with the computing device 108, caused by the
web-based application.
[00066] According to illustration made in figure 8, depicting a screenshot of web-based
application illustrating a result of the sensing unit 102, such as but not restricted to a b.p., b.p.m, a date,
a time, an icon of the connected element of system 100, a search tab and other relevant information
thereof. In another embodiment, one or more detected values of body vital parameters sensed by sensing
unit 102 and platform 104 can be obtained on a screen of an Arduino serial monitor and data can be
saved on the server 110, for further processing.
[00067] A person ordinarily skilled in art would appreciate that one or more variants of the
above disclosed and other features and functions, or alternatives thereof, may be combined into many
other one or more system 100 or applications. Various presently unforeseen or unanticipated
alternatives, modifications, variations or improvements therein may be subsequently made by those
skilled in the art which can also be intended to be encompassed by the following claims. Throughout
the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’
includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing
(CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word
(VLIW) microprocessor, or any other type of processing circuit.
[00068] In an aspect, any or a combination of machine learning mechanisms such as decision
tree learning, Bayesian network, deep learning, random forest, supervised vector machines,
reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression,
Support Vector Machines, Linear Discriminant Analysis, K-Nearest Neighbours, Decision Trees,
Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression,
Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical
Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest
Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis
Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA,
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Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again),
Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be
employed to learn sensor/hardware components.
[00069] The term “non-transitory storage device” or “storage” or “memory,” as used herein
relates to a random access memory, read only memory and variants thereof, in which a computer can
store data or software for any duration.
[00070] The foregoing description of the specific embodiments will so fully reveal the general
nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or
adapt for various applications such specific embodiments without departing from the generic concept,
and, therefore, such adaptations and modifications should and are intended to be comprehended within
the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the
phraseology or terminology employed herein is for the purpose of description and not of limitation.
Therefore, while the embodiments herein have been described in terms of preferred embodiments, those
skilled in the art will recognize that the embodiments herein can be practiced with modification within
the spirit and scope of the embodiments as described herein.
[00071] It should be apparent to those skilled in the art that many more modifications besides
those already described are possible without departing from the inventive concepts herein. The
inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims.
Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the
broadest possible manner consistent with the context. In particular, the terms “comprises” and
“comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive
manner, indicating that the referenced elements, components, or steps may be present, or utilized, or
combined with other elements, components, or steps that are not expressly referenced. Where the
specification claims refer to at least one of something selected from the group consisting of A, B, C ….
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and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B
plus N, etc.

Advantages of the Invention
[00072] An advantage of the present disclosure is to overcome one or more drawbacks
associated with conventional mechanisms.
[00073] An advantage of the proposed system can be the automation of the process of health
monitoring.
[00074] An advantage of the proposed system can be the maintenance of health record of one
or more patients on a daily basis.
[00075] An advantage of the proposed system can be to alert the medical professionals/doctors
if one or more health monitoring parameters show irregularity or anomaly.
[00076] An advantage of the proposed system can be system portability and compatibility.
[00077] An advantage of the proposed system can be to address one or more impending issues
vis-à-vis conventional mode of health monitoring.
[00078] An advantage of the proposed system can be to provide an alternative to conventional
mode of health monitoring.
[00079] An advantage of the proposed system can be to include systemic and systematic
practices in order to assure health monitoring.
[00080] An advantage of the proposed system can be to expedite the process of health
monitoring.
[00081] An advantage of the proposed system can be to provide a cost and time effective
health monitoring.

Claims
I/We claim:
1. A system to monitor the body vital parameters, the system comprising:
a wearable device is arranged to be worn by a user, wherein the wearable device
comprising:
an internet of things (IoT) enabled sensing unit comprising:
a cardiac sensor is arranged to determine the cardiac
parameters selected from a heart rate, a blood pressure, and
an electrocardiogram;
a temperature sensor is arranged to determine a body
temperature of the user;
an electromyography (EMG) sensor is arranged to determine
a muscle tone of the user; and
a posture determination sensor is arranged to determine a
posture of the user; and
a platform comprises:
a weight sensor is arranged to determine weight of the user; and
a height determination sensor is arranged to determine a height of the
user; and
a computing device is arranged to:
receive the cardiac parameters, the body temperature, the muscle tone,
the posture of the user, the weight of the user and the height of the user;
and
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create a graphical representation, which depict the body vital
parameters, based on the received the body temperature, the muscle
tone,
2. The system of claim 1, wherein the computing device computes a level of (BMI).
3. The system of claim 1, wherein the posture determination sensor is selected from an
accelerometer and a gyroscope.
4. The system of claim 1, wherein the computing device creates a health database to store the body
temperature, the muscle tone, the posture of the user, the weight of the user and the height of
the user.
5. The system of claim 4, wherein the computing device creates a replica of the created heath
database on a remote server.
6. The system of claim 2, wherein the computing device utilizes a machine learning technique to
determine a list of corrective measures to improve the BMI.
7. The system of claim 2, wherein the machine learning technique is selected from Decision Tree,
Support Vector Machines (SVM), Multilayer Perceptron, Naive Bayes, and k-Nearest Neighbor
(k-NN).