=Paper=
{{Paper
|id=Vol-2544/paper2
|storemode=property
|title=IoT Based Smart Health Care
|pdfUrl=https://ceur-ws.org/Vol-2544/paper2.pdf
|volume=Vol-2544
|authors=Rashbir Singh
|dblpUrl=https://dblp.org/rec/conf/irehi/Singh18
}}
==IoT Based Smart Health Care==
https://ceur-ws.org/Vol-2544/paper2.pdf
IoT based smart health care
Rashbir Singh1
1Department of Information Technology, Amity University Uttar Pradesh, Noida, India, rashbits@gmail.com
Keywords: Internet of Things, Arduino, Micro- day to day objects like beds and clothing. This
Controller, Android, Ultrasonic sensor, EEG, ECG, EMG, research work proposes a model for smart
Temperature Sensor, Capnography, Antimony electrode healthcare system for personal, hospital and
sensor, Piezoelectric sensor.
general use which is easy to operate and
implement and can be utilized both by urban
Abstract
and rural area people. The motive of this
research work is to convert a regular bed into a
This paper proposes a model for smart
smart self-managing health bed and a smart
healthcare and real-time management
healthcare clothing system to monitor your body
technology can be utilized both by hospitals,
and help one with a better understanding of
ambulance and even at normal day to day
one's own body. The proposed solution will
activity tracker and require no technical or
provide health support and will reduce one's
medical knowledge to start with. As per the
expenses over health and help hospitals with a
survey, about 40% of worlds total deaths due to
better understanding of patients data which is
any disease can be prevented if an earlier
collected daily 24X7. This can potentially
diagnosis is made. People tend to avoid health
reduce initial delay due to various tests the
and health care practices now either it is due to
doctor has to perform on patient before
the busy schedule or lack of money. So, the
operating on the patient hence the patient can be
research work focuses on incorporating
admitted for treatment which less to no delay
technology into people life without disturbing
and doctors can start operating as soon as
their daily routine and does not require septate
possible and can also be used as a daily body
time to use. This technology is powered by IoT
checker which can be used to create your health
and uses many biosensors to give real-time
database and informing the nearest hospital and
solutions, prescribe medication, earlier detection
relatives in case of an emergency. As there are
of diseases, give a better understanding of
still rural areas in India which do not have
patients current health and past health and
access to hospital, about 80% of Indian
significantly reduces medical expenses and by
population still do not have proper access to
having more information about patient health
health-related facilities and healthcare treatment
doctors can operate and treat the patient with a
so this proposal can help them to a great extent
better approach. This technology works when
to manage health at their homes in a cost-
the user sleeps and learn when the user performs
effective manner and utilize it when so ever they
day to day task with the help of machine
feel. It can be used as a smart hospital beds,
learning.
smart house beds or even smart ambulance bed
with a health monitoring clothes which can save
1 Introduction
many life with technologies like EEG
(electroencephalogram), ECG
Due to the increase in diseases, there is a drastic
(electrocardiogram), EKG (Electrocardiogram),
increase in demand for health care and health
Temperature sensor, skin quality sensor
care support facilities. Health is wealth and
pressure sensor and many other biomedical
people nowadays are ready to spend an
sensors powered by the advance technology of
enormous amount of money without even
IoT(Internet of Things), hence connecting a
giving a second thought but the need is not to
regular bed to the internet and detect several
spend in millions but to develop smart solutions
health related issues and reducing overall cost of
to health-related problems. This can be achieved
with the help of the combination of IoT with our
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
IREHI 2018 : 2nd IEEE International Rural and Elderly Health Informatics Conference
health and time of treatment of health which can compared to fourteenth in 2000 all around the
make health treatments approachable for all. world.
Lower respiratory diseases and infections
2 Literature Survey continued to be the most deadly contagious
condition, creating three million losses around
The literature survey is consist of the analysis of the world in the year 2016 alone. The death rate
deaths due to delayed diseases detection and due to diarrhoeal conditions reduced over nearly
multiple reasons regarding death in humans. one million between the year 2000 and 2016 but
Of the fifty-seven million deaths worldwide still managed to affect two million lives in the
in the year 2016, more than half (54%) were due year2016. Likewise, the deaths due to
to the top ten reasons. Ischaemic heart disease tuberculosis declined around the same time still
and stroke are the world’s greatest top reason is among one of the top ten reasons for the death
behind the death, considering as a combined of two million. As of now HIV/AIDS is no
fifteen million deaths in the year 2016. These longer reason of deaths and is not in the world’s
conditions have persisted to be the major cause list of top ten agents of death, causing deaths of
of death all around the world in the last fifteen one million humans in the year 2016 as related
years. The chronic obstructive pulmonary to 1.5 million in the year 2000.
disease took nearly three million lives in the
year 2016, while lung carcinoma (along with
trachea and bronchus cancers) caused two
million deaths. Diabetes alone took two million
humans lives in the year 2016, up from less than
one million in the year 2000. Losses due to
dementias have been doubled in the year
between 2000 and 2016, and hence is the fifth
leading cause of deaths in the year 2016
Figure 1. Top 10 Causes of deaths in year 2016 around the world [1]
Road injuries took almost 1.4 million humans may incorporate shortness of breath, pain in the
lives in the year 2016 and from that nearly jaw or neck area, or even fatigue.
three-quarters i.e 74% of what remained men Some illnesses or conditions could or could
and boys. Figure 1. shows the top 10 Causes of not be analyzed because their symptoms and
deaths in the year 2016 around the world. signs may be similar to that of other medical
Similarly, Late determination of diseases conditions. For example, failure in the detection
remains one of the usual medical failures. It of chronic obstructive pulmonary disease
might occur if the doctor fails in the diagnoses (COPD) in the first exhibition of symptoms
of a patient correctly and it takes a prolonged could result in quicker progression. This
duration of time than exacted for a reliable condition has indications that evolve with time
diagnosis to be made. When a Late into some serious conditions. Signs such as a
determination of diseases occurs, the valuable cough, chest pain and shortness of breath may
medication time is wasted. In some be similar to symptoms of other conditions, and
circumstances, this can create added difficulties in some cases, may lead to a delayed diagnosis.
for the patient, prolonged healing period, with There are many other types of ailments that can
additional medical debts and even can result in lead to an unwanted condition or progression if
loss of life. not properly detected in a timely manner. [2]
The late determination of diseases can happen The leading causes of death in the world are
450
400
350
Deaths
300
observed
Number of deaths
250
200
150 Potentially
preventable
100 deaths
50
0
Heart Cancer Respiration Brain Injury
Causes of deaths
Figure 2. Potentially Preventable Deaths from the Five Leading Causes of Death[4]
due to several reasons. Doctor negligence, cancer, chronic lower respiratory diseases, heart
complicated medical history, incomplete patient disease, stroke. Together they accounted for
information or simply due to testing errors all sixty-three percent of all deaths in the year 2010
can contribute to a late determination of alone. According to the report, in CDC ’s
diseases. journal, Morbidity, and Mortality, analyzed
The failure to determine the cause of disease premature deaths from each cause for each state
immediately, and thus treat immediately, heart from the year 2008 to the year 2010.
disease may lead to serious outcomes such as The study suggests that:
cardiac arrest. Both men and women can
undergo chest pains leading up to or directly at Thirty-four percent of premature deaths
the point of a heart attack. However, other caused due to heart diseases, prolonging
symptoms may indicate a heart attack. These about 92,000 lives
Twenty-one percent of premature deaths generates rate data of the heartbeat. This device
caused due to cancer, prolonging about is being used for the cardiovascular health
84,500 lives analysis by the medical assistant and trained
Thirty-nine percent of premature deaths doctors.
due to the condition of chronic lower Electrodes of an ECG Sensor consist of
respiratory diseases, prolonging about three pins which are connected by a thirty
29,000 lives inches ling cable which makes It easy for ECG
Thirty-three percent of premature deaths sensor to connect and communicate with the
caused due to stroke, prolonging about microcontroller placed at the waist, pocket or
17,000 lives different location on the body. In addition to
Thirty-nine percent of premature deaths this, the cable is a plug-in male sound plug
occurred due to the unintentional injuries which makes the cable to easy to remove or
in the body, prolonging about 37,000 connect it into the amplification board. The
lives[4] sensor assembled on an arm pulse and a leg
pulse. All of every sensory electrode of the ECG
3 Proposed System has methods to be assembled on the body.
This research work uses the AD8232
The proposed system consists of the following module which has nine connections from the IC
modules. The description of each module is that are being used to solder wires, pins and
given below. other connectors as well. The pins are namely -
GND, LO+, OUTPUT, LO-, 3.3V, SDN which
3.1 EEG (electroencephalogram) sensor provides required pins for operating and
monitoring with the microcontroller board. It
An electroencephalogram (EEG) is a used to also provides three pins namely as - RA which
discover inconveniences related to an electric is for the Right Arm, LA which is for the Left
movement of the brain. Arm, and RL which is for the Right Leg and
An EEG tracks and insights, mind wave fashion. pins to attach and use for custom sensors.
Little metallic plates(electrodes) with small Moreover, there is an LED indicator light that
electric wires are placed above the head of the will pulsate as according to the rhythm of a
user, the amplifier amplifies the electromagnetic heartbeat.
brain waves and captures and plot a real-time
graph of the electric movements inside the 3.3 EMG (Electromyography) sensor
brain. The graph is shown which show us the
events and impacts happened inside the brain. A The EMG sensor which measures the muscle
persons day to day activity and his interest activation on to the concept of electric potential
creates an electric brainwaves into a and is called as electromyography (EMG) and is
recognizable pattern. Through an EEG, traditionally been used in the field of research in
therapeutic specialists can scan and anticipate the medical profession and for the diagnosis of
the seizures and different issues and can give the neuromuscular dysfunctions. Though, with the
recommendation changes in lifestyle/medication development of ever smaller and smaller yet
to help the person. powerful integrated circuits and
microcontrollers, making it possible for the
3.2 ECG (electrocardiogram) sensor EMG circuits and sensors to find their way into
the field of Prosthetics, Robotics, and other
An ECG Sensors with electrodes attaches right control systems.
above to the chest area in order to detect every This research work uses Myoware Muscle
produced heartbeat. The electrodes of ECG sensor (AT-04-001), which is suitable for
sensor then convert every produced version of producing raw electric EMG signal which is
heartbeat into a raw electric signal. ECG Analog output signal and can be analyzed with
Sensors are very less in weight and slim while the microcontroller based application, the
having the capability to accurately measures Myoware Muscle sensor which is designed for a
continuous heartbeat produced by the heart and reliable EMG output and has low power
consumption. It operates with the single power Calibrated directly in degree Celsius
supply ranging from +2.9V to +5.7V with (Centigrade)
protection for polarity reversal and provides an Rated for range between −55˚ to +150˚C
additional feature with this sensor which is that Suitable for applications which requires
the user can easily adjust the sensitivity gain of remote access
the electrodes. These sensors are suitable for the Is lower in cost due to wafer-level
purpose of the wearable device and are the trimming
compact ones, hence it is easy to handle and Operates in voltes between the range of
measure the muscle activation signal. 4Vs to 30Vs
Self-heating factor is low
3.4 Antimony electrode sensor ±1/4˚C of typical nonlinearity
Antimony electrodes are medically useful as The LM35 can be attached easily in the same
because they are low in cost and have a simple way as that of other integrated circuit based
construction as there is no glass part to break. temperature sensors. It can be placed or
There is only a resistance which is of few established on a surface and its temperature
hundred ohms between an antimony pH range will be around 0.01˚C of the surface
electrode and the reference electrode so if the temperature.
voltage is generated it would be easy to record it This assumes that the ambient air temperature
with the simple low-impedance recorder which around it is just about the same as that if surface
is connected to the microcontroller. temperature and if the air temperature is much
Antimony is a unique metal with the lower or higher than that of the surface
characteristic of a direct relationship between temperature then the actual temperature of the
pH and its measured potential. The voltage or LM35 would be at a mean temperature between
electric potential difference developed between the surface temperature and that of the air
that in antimony and a copper or copper sulfate temperature.
reference electrode which varies between
approximately ranging from 0.1 volts to as high 3.6 Capnography
as 0.7 volts due to variations in the pH.
Waveform capnography represents the amount
3.5 Temperature Sensor of carbon dioxide (CO2) in exhaled air, which
assesses ventilation. It consists of a number and
A temperature sensor IC can operate over the a graph. The number is capnometry, which is
nominal IC temperature range of -55°C to the partial pressure of CO2 detected at the end
+150°C. This sensor is consists of a material of exhalation. This is end-tidal CO2 (ETCO2)
that performs the operation according to which is normally 35-45 mm Hg. The
temperature to vary the resistance. This change capnograph is the waveform that shows how
of resistance is sensed by the circuit and it much CO2 is present at each phase of the
calculates temperature. When the voltage respiratory cycle, and it normally has a
increases when the temperature also rises. We rectangular shape. Capnography also measures
can see this operation by using a diode. and displays respiratory rate. Changes in
This research work is using LM35 as the respiratory rate and tidal volume are displayed
body temperature monitoring sensor which will immediately as changes in the waveform and
be connected to the microcontroller. The LM35 ETCO2.
temperature sensors series are precise In people with healthy lungs, the brain
integrated-circuit temperature sensors, whose responds to changes in CO2 levels in the
voltage output is linearly proportional to that of bloodstream to control ventilation. We assess
Celsius temperature. The LM35 temperature this by observing chest rise and fall, assessing
sensor operates between the range of -55˚C to respiratory effort, counting respiratory rate, and
+120˚C. The following are the features of LM35 listening to breathing sounds. ETCO2 adds an
Temperature Sensor: objective measurement to those findings. The
patient’s respiratory rate should increase as CO2 operations that are transverse effective,
rises, and decrease as CO2 falls. transducer effect, and shear effect.
In this research work, we used the
If a patient has slow or shallow respirations, and piezoelectric sensor to detect where the user is
a high ETCO2 reading, this tells us that applying more body weight to give a better
Figure 3. Proposed Model Architecture
ventilation is not effectively eliminating CO2 understanding on ones applied body weight
(hypercarbia) and that the brain is not pressure while doing daily tasks like walking,
responding appropriately to CO2 changes. This sitting and sleeping and detect the pattern and
may be caused by an overdose, head injury, or suggest remedies to improve the one's sense of
seizure by measuring the end-tidal CO2 Figure 4(a). Front View
(ETCO2, the level of carbon dioxide released at
the end of applied body weight pressure.
expiration) through a sealed mask, EMS Moreover to use piezoelectric sensor while
technicians can receive an "early warning" of a sleeping which can be placed inside the bed to
patients worsening condition. detect ones overnight movement while sleeping,
With accurate and instantaneous CO2 posture and body weight distribution. Which
measurements required for Capnography, the will be used to adjust the bed temperature
COZIR high-speed wide range CO2 sensor required according to what is suggested by the
seemed like the perfect solution to the problem. doctors. So, as to maximize the health benefits.
All the above technologies will be combined
3.7 Piezoelectric sensor into a common processing unit of a
microcontroller which will be continuously
The piezoelectric effect is used by the capturing all the outputs from different sensors
piezoelectric sensor. This sensor measures the and storing it on the database while connecting
change in force, temperature, acceleration, over an MQTT client. While remedies will be
pressure, and strain and is hence used in provided in real time with different means like
conversion into an electrical charge. The using oxygen tanks, heating elements, air
piezoelectric sensor shows three main conditions, air purifies and massage pads. So, it
Figure 4(b). Back View
can not only collect data of one's body while the Antimony electrode sensor
person is sleeping, sitting, walking and carrying Temperature Sensor
out day to day activities but also it will provide Capnography
the real-time solution to the abnormality in a Piezoelectric sensor
human body. This research work propose a
wearable smart clothing which can be used by Various components of the proposed model
user to detect ones heart health (EKG), brain monitor different parts of human body and the
health (EEG), muscles condition (EMG), sweat output from different components when
quality and its PH (Antimony electrode sensor), analyzed in combination provides a detailed
Respiration (Capnography) and his body weight information about the human user like:
distribution and centre of gravity shift
(piezoelectric) which will be placed in both Ones sleep pattern
clothing and bed. Ones body natural centre of gravity
The bed will be equipped with heating Variation of body temperature with time
elements to control the temperature of the bed Ones lungs capacity
only in the area where the user is sleeping which
will be sensed with the help of piezoelectric. A
small oxygen tank will be placed over the
headrest area if user CO2 exhaled during
respiration is detected to be abnormal in order to
provide the required amount of oxygen supply,
while the user behaving choices either to use
oxygen tanks, air purifier or both. If stress is
detected with the help of EEG then various
remedies like playing meditation sound like
alpha music will be taken with the help of
speakers attached to the bed. Small massaging
motors inside the bed will help is muscle relief.
The wearable clothing with the heating element
will help in managing healthy body temperature
which is required for the body to function
properly and will maximize physical and mental
output.
As being powered with technology of Amount of oxygen absorption capability
IoT(Internet of Things) it will not only be
providing remedies in physical world in real
time but also the data collected will be user
specific and will be used by doctors for better
understanding of patients health as the data is
collected everyday and stored it can be used in
early disease detecting like cancer, high or low
blood pressure, distress, respiratory diseases,
low Na+ ions level in body fluid, fever, etc.
4 Methodology
In this research work main seven components
are being used i.e.
EEG
EKG
EMG
Quality of ones sweat
Acidity and basicity of fluids
Mental health
Stress and hypertension
Doctors cannot gain much information
with the short-term test to know all the physical
and mental health issues before operating on the
patient. But the model proposed by this research
work can help doctors with a better
understanding of patients health and his medical
background and can provide the best suitable
treatment to the patient. Hence resulting in
elongation on one's life and disease free healthy
life. This proposed model can even reduce
medical expenses by using a machine learning
model based on the K-NN algorithm to suggest
user with best remedies for small health-related
issues like cold, headache, cough etc and can
judge the acuteness of the fever which can help
Figure 4(c). Left View the user to know the severity of the disease.
The model is based on a single controller and
multiple sensor methods where several sensors
are connected to a single microcontroller and
are dependent on the microcontroller to supply
sensors with power, process the output from
sensors use decision tree to provide real time
solutions and collect and upload the received
data onto the database. The microcontroller is
powered by 5Vs supply and various other
sensors are connected to the different I/O pins of
the micro controller and ground of each sensor
is connected to the common microcontroller
ground. As being so low in power consumption
it requires a low input electric supply. And can
be attached to various objects as desired by the
user. In this model the IoT based mechanism
is attached to two day to day objects making
them smart i.e.
Wearable clothing (thin and comfortable
vest and pants)
Beds (Room beds, ambulance beds or
hospital beds)
Figure 4(d). Right View
A wearable smart full body cloth will be having
different components placed at various locations
Bodies immunity and resistance to
in order to monitor different parts.
diseases Figure 4 shows the wearable smart health
Cardiovascular health management clothing which is displaying where
Muscular health will different sensors be placed and their
significance in those positions. Figure 4 shows provide the detailed information about persons
four views from different angles i.e. muscles and pressure applied to carry out day to
day activities.
Figure 4(a) shows depiction of All the above sensors and temperature
placement of sensors and heating management system is fully controlled with the
element from front. help of an android application and data received
Figure 4(b) shows depiction of is with the help of the microcontroller. The
placement of sensors and heating heating pad will connect to the ground for the
element from back. ground wire and digital input pin of the
microcontroller so the functionality can be
Figure 4(c) shows depiction of controlled over android application on can be
placement of sensors and heating automated with a click. Following the similar
element from left.Figure 4(d) shows mechanism, all the sensors ground will be
depiction of placement of sensors and connected to the common ground of the
heating element from right. microcontroller and for the sensors.
ECG is consist of three electrode RA (Right
Figure 4(a) shows green straight line above arm), LA (Left arm) and RL (Right leg)
chest and stomach area and above arm are that microcontroller 3.3V -will be used as a power
is showing the placement of insulated Teflon supply for the ECG module, whereas L0+ and
wire or heating pad to heat the coating when the L0- pin is connected to microcontroller digital
temperature detected is too low. The pin and output of ECG module will be
temperature sensor along with CO2 can be seen connected to the analog. For EEG this research
around the sky blue portion of the neck is. work uses neurosky mindwave with two
Whereas two black dot/spheres can be seen electrodes and connect the ground of EEG with
on the forehead area and ear lobe area. These the ground of microcontroller while using
are EEG sensors which are two small electrodes digital pins as transmission and receiving pins.
connected to the brain region to detect brains And the piezoelectric material is used to judge
different alpha, beta, gamma, theta and delta the amount of pressure according to the
brain waves generated and amplify them to electricity generated when the piezoelectric
record users brain activities. material in under pressure. EMG sensors are
A blue colour object can be seen around the plages above main muscles groups like bicep,
area above where there is the heart, it is an ECG tricep, shoulder, chest, abdomen, thighs, calves
sensor to monitor cardiovascular health. Then etc.
white spears can be seen in figure 4(a), All the collected data is then transmitted over
figure4(b), figure 4(c) and figure 4(d) which are MQTT broker where the microcontroller act as
an MQTT publisher, publishing the data to the
MQTT broker and MQTT receiver is android
application and online cloud database.
5 Capability
A practical implementation of EEG was taken
with the help of neurosky mind wave to
measure patients mental state. EEG detects
different alpha, beta, theta and gamma brain
waves along with users concentration and
meditation level. The headgear has one
electrode for frontal lobe and one electrode for
Figure 5.EEG based stress management and detection an ear. As soon as the electrodes are at their
with alpha music to calm the mind desired places the EEG begin sensing brain data
and then that data is used to make the prediction
nothing but combination of EMG and in out K-NN based classification model.
piezoelectric material sensor around the body to
Five classifications are being made i.e.
Eyes open
Eyes close
Equation 1
Relaxed
Equation 2
Excited
Not excited
5.1 Data acquiring
The training dataset is from reference [R.G.
Andrzejak et all, 2001]. The dataset has five Figure 6(a). Training Dataset
sections divided as dataset A discussed as Z,
dataset B discussed as O, dataset C discussed as
N, dataset D discussed as F and dataset E
discussed as S each containing set of EEG
fragments with the recording of the
electromagnetic movement of a healthy person
for 23.6 Sec. Dataset A and dataset B have are
having data related to EEG chronicles from
healthy volunteers with categorization as eye
open and eyes close, individually.
The second dataset characterised in reference
[Dharmawan, Z., 2007] is included healthy
people who volunteered and analysed under
EEG to collect the data as playing particular PC
computer games with a class as excited, not
excited and relaxed on the basis of the various Figure 6(b). Test Dataset
different values of the alpha, beta, theta and
gamma.
5.2 Data extraction
Ambiguous data are removed and the data is
polished/refined to get more refined information
for unique groups differently. The element
utilized here is categorized underneath the
graph. The trapezoidal rule can be used to find
the area under the graph which is formed in the
dataset from the first source which is in raw
graphical form. On numerical examination, the
trapezoidal governs (likewise alluded to as the
trapezoid control or trapezium run) is a strategy
for approximating the particular imperative Figure 7. Prediction made using K-NN
understood as by approximating the region
The equation 1 shows the differentiation with underneath the diagram of the element f(x) as a
the upper bound as (b) and lowers bound as (a) trapezoid and computing its locale.
The working of the trapezoidal rule can be
With this, we can derive the different values for
different waves. It takes after that the district of Gamma
the recurrence groups (delta, theta, alpha, beta) Theta
is ascertained for every EEG section. Alpha
Beta
5.3 Training data and Test Data
Test dataset is consist of 100 datasets which are
unclassified and prediction is made for those
The acquired data is then divided into two parts 100 datasets.
i.e.
Training Dataset 5.4 Accuracy and prediction of model
Test Dataset
Figure 7 shows the prediction made by the K-
Training data set is used for teaching purposes NN model using 5 nearest neighbours i.e K = 5.
of the system while the test dataset is used for Figure 8 shows the accuracy level which is
the purpose of testing the prediction accuracy of 97.50% for the particular model and on the basis
the model. Training dataset is consist of 800 of prediction a pie chart and bar is obtained as
classified data as Eyes open, Eyes Close, shown in figure 9(a) and figure 9(b) showing
Relaxed, excited and Not Excited. Figure 6(a) the mental state of the user during real-time
shows the snap of training dataset and figure testing where
6(b) shows the snap of test dataset. Attributes
Figure 8. K-NN prediction model with 97.50% accuracy
Red is for Not excited
Blue is for Excited
Yellow is for relax
Figure 9(a). Prediction Pie Chart
Green is for Eyes closed
Sea blue is for Eyes open
6 Conclusion and Future Scope
This system is successful in provided automated
health benefits at home with high accuracy and
reducing the expenses on overall health care,
generating data of persons mental and physical
health at each moment and successful in early
detecting of diseases and can save many people
from injury or even deaths. It is easy to use and
provide health care support even when the user
is sleeping. With accuracy as high as 97.50% it
Figure 9(a). Prediction Bar Graph can completely revolutionize people’s idea
about healthcare and management.
that were used for making prediction were
Research paper only proposes wearable and [13] McNally, R. J., & Eke, M. (1996). Anxiety
bed smart health care but can be used in cars, sensitivity, suffocation fear, and breath-holding
duration as predictors of response to carbon
ambulances for patients health test while en dioxide challenge. Journal of Abnormal
route hospital, or can be used as a point of Psychology, 105(1), 146.
treatment in small hospitals in absence of [14] Wu, Q., Zhang, X., Tian, B., Zhang, H., Yu, Y.,
doctors. Applying technologies like artificial & Wang, M. (2017, June). Wearable sweat
intelligence and machine learning alongside the detector device design for health monitoring and
clinical diagnosis. In IOP Conference Series:
proposed model can help people on masses. Earth and Environmental Science (Vol. 69, No.
1, p. 012137). IOP Publishing.
References [15] Computation of Power, Energy Information and
Commuincation (ICCPEIC), 2016 International
[1] World Health Organization, W. H., & World Conference on (pp. 200-203). IEEE.
Health Organization. (2014). The top 10 causes [16] Dharmawan, Z. (2007). Analysis of computer
of death. games player stress level using EEG data. Master
[2] https://www.gacovinolake.com/practice-areas/ of Science Thesis Report, Faculty of Electrical
medical-malpractice/injury-related-to-delayed- Engineering, Mathematics and Computer
diagnosis/ fetched on 13-june-2018 Science, Delft University of Technology,
[3] Lu, T. C., Tsai, C. L., Lee, C. C., Ko, P. C., Yen, Netherlands.
Z. S., Yuan, A., ... & Chen, W. J. (2006). [17] Dimitriadis, S. I., Sun, Y. U., Kwok, K.,
Preventable deaths in patients admitted from Laskaris, N. A., Thakor, N., & Bezerianos, A.
emergency department. Emergency medicine (2015). Cognitive workload assessment based on
journal, 23(6), 452-455. the tensorial treatment of EEG estimates of
[4] Centers for Disease Control and Prevention. cross-frequency phase interactions. Annals of
(2014). Up to 40 percent of annual deaths from biomedical engineering, 43(4), 977-989
each of five leading US causes are preventable. [18] Fahrion, S. L., Walters, E. D., Coyne, L., &
Atlanta, GA: Centers for Disease Control and Allen, T. (1992). Alterations in EEG Amplitude,
Prevention. Personality Factors, and Brain Electrical
[5] Prentice, J. C., & Pizer, S. D. (2007). Delayed Mapping after Alpha‐Theta BrainwaveTheta Brainwave
access to health care and mortality. Health Training: A Controlled Case Study of an
services research, 42(2), 644-662. Alcoholic in Recovery. Alcoholism: Clinical and
[6] R., Singh. (2018). PROSTHETIC ARM Experimental Research, 16(3), 547-552.
CONTROLLED USING MIND-WAVES [19] Andrzejak, R. G., Lehnertz, K., Mormann, F.,
CALLED MIND-YOUR-ARM. International Rieke, C., David, P., & Elger, C. E. (2001).
Journal of Advanced Computational Indications of nonlinear deterministic and finite-
Engineering and Networking,6(1), 7-13. dimensional structures in time series of brain
Retrieved June 12, 2018, from electrical activity: Dependence on recording
http://www.iraj.in/journal/journal_file/journal_p region and brain state. Physical Review E, 64(6),
df/3-434-15216282087-13.pdf 061907.
[7] Opekun, A. R., Smith, J. L., & Graham, D. Y. [20] 2012 IEEE Symposium on (pp. 297-301). IEEE.
(1990). Antimony electrodes. Digestive diseases [21] Park, K. S., Choi, H., Lee, K. J., Lee, J. Y., An,
and sciences, 35(8), 950-955. K. O., & Kim, E. J. (2011). Patterns of
[8] https://www.elprocus.com/temperature-sensors- electroencephalography (EEG) change against
types-working-operation/ fetched on 13-June- stress through noise and memorization test.
2018 International Journal of Medicine and Medical
[9] Wu, R. J., Wu, J. G., Yu, M. R., Tsai, T. K., & Sciences, 3(14), 381-389.
Yeh, C. T. (2008). Promotive effect of CNT on [22] Peniston, E. G., & Kulkosky, P. J. (1991).
Co3O4–SnO2 in a semiconductor-type CO Alpha-theta brainwave neurofeedback for
sensor working at room temperature. Sensors Vietnam veterans with combat-related post-
and Actuators B: Chemical, 131(1), 306-312. traumatic stress disorder. Medical
[10] https://www.ems1.com/ems-products/ Psychotherapy, 4(1), 47-60.
Capnography/articles/2163495-5-things-to- [23] Petrantonakis, P. C., & Hadjileontiadis, L. J.
know-about-capnography/ etched on 13-June- (2010). Emotion recognition from EEG using
2018 higher order crossings. IEEE Transactions on
[11] O'Flaherty, D., & O'Flaherty, D. (1994). Information Technology in Biomedicine, 14(2),
Capnography. London: BMJ. 186-197.
[12] Woods, S. W., Charney, D. S., Goodman, W. K., [24] Prashant Lahane, Amit Vaidya, Chetan Umale,
& Heninger, G. R. (1988). Carbon dioxide— Shubham Shirude, Akshay Raut. 2016,Real Time
induced anxiety: behavioral, physiologic, and System to Detect Human Stress Using EEG
biochemical effects of carbon dioxide in patients Signals, IJIRCCE.Vol. 4, Issue 4
with panic disorders and healthy subjects. [25] Ratey, J.J., Mikkelsen, E., Sorgi, P., Zuckerman,
Archives of General Psychiatry, 45(1), 43-52. S., Polakoff, S., Bemporad, J., Bick, P., &
Kadish, W. (1987). Autism: The treatment of
aggressive behaviors. Journal of Clinical
Pharmacology, 7, 35-41.
[26] Rösler, O., & Suendermann, D. (2013). A first
step towards eye state prediction using eeg. Proc.
of the AIHLS.
[27] Sabitha, A. S., Mehrotra, D., & Bansal, A.
(2014). Enhanced learning by extending
metadata of learning objects with knowledge
objects. International Journal of Education and
Learning, 3(1), 1-12.
[28] Sabitha, S., Mehrotra, D., & Bansal, A. (2014).
A data mining approach to improve re-
accessibility and delivery of learning knowledge
objects. Interdisciplinary Journal of E-Learning
and Learning Objects, 10, 247-268.
[29] Saeed, S. M. U., Anwar, S. M., Majid, M., &
Bhatti, A. M. (2015, December). Psychological
stress measurement using low cost single
channel EEG headset. In Signal Processing and
Information Technology (ISSPIT), 2015 IEEE
International Symposium on (pp. 581-585).
IEEE..
[30] Saidatul, A., Paulraj, M. P., Yaacob, S., & Nasir,
N. M. (2011, March). Automated system for
stress evaluation based on EEG signal: A
prospective review. In Signal Processing and its
Applications (CSPA), 2011 IEEE 7th
International Colloquium on (pp. 167-171).
IEEE.
[31] Singh, S., & Bansal, D. (2014). Design and
development of BCI for online acquisition,
monitoring and digital processing of EEG
waveforms. International Journal of Biomedical
Engineering and Technology, 16(4), 359-373.
[32] Ssang-Hee, S. (2011). Stress and EEG.
[33] Harris, D. M., Liu, T., & Bush, J. W. (2015). A
low-cost, precise piezoelectric droplet-on-
demand generator. Experiments in Fluids, 56(4),
83.
[34] Zhao, J., & Zhu, S. C. (2013). Design of
Obstacle Avoidance Robot Car Based on
Arduino Microcontroller [J]. Automation &
Instrumentation, 5(002).
[35] Medina-Santiago, A., Camas-Anzueto, J. L.,
Vazquez-Feijoo, J. A., Hernández-de León, H.
R., & Mota-Grajales, R. (2014). Neural control
system in obstacle avoidance in mobile robots
using ultrasonic sensors. Journal of applied
research and technology, 12(1), 104-110.
[36] Warren, J. D., Adams, J., & Molle, H. (2011).
Arduino for robotics. In Arduino robotics (pp.
51-82). Apress, Berkeley, CA.
[37] Bhagat, K., Deshmukh, S., Dhonde, S., & Ghag,
S. (2016). Obstacle Avoidance Robot.
International Journal of Science, Engineering
and Technology Research (IJSETR), 5(2), 439-
442.
[38] Singh, Rashbir et all, "TECHEYEFOR
VISUALLY IMPAIRED USING INTERNET
OF THINGS", 2016, pp. 833-840.