monitoring health parameters using wireless …...overcome in implementation health care systems...

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MONITORING HEALTH PARAMETERS USING WIRELESS

SENSOR NETWORKS

Jigar Chauhan1, Sachin Bojewar2 1Master of Engineering Student, Vidyalankar Institute of Technology

2Associate Professor, Vidyalankar Institute of Technology

Abstract— Wireless Sensor Network technologies have become a latest research area in health care

industries due to rapid maturity in improving the quality of life of a patient. Wireless Sensor

Networks when work in medical field provide continuous monitoring of vital health parameters

which over a long period of time provide doctors much needed help to make accurate diagnosis and

giving better treatment. In this paper we propose a model which monitors various health parameters

like heart rate (BPM), body temperature, blood pressure monitor (mm Hg) and ECG

(Electrocardiogram) of an individual. The collected data through the system is then transferred over

the internet to a smartphone application of the patient. This data is transferred to the registered

doctors on to their smartphone application. The doctor can then prescribe the medication based on

the data results shown by the system. The designed prototype will reduce the burden on patients to

visit the doctor every time for monitoring of these health parameters.

Keywords— WSN; Heart Rate (BPM); Blood Pressure Monitor; ECG; BPM Algorithm; Raspberry

Pi 3, Arduino Mega.

I. INTRODUCTION

Wireless sensor network (WSN) [4] is a network comprised of autonomous sensor devices which are

deployed in known locations for the purpose of monitoring and collecting data such as sound, radio

signals, temperature, pressure etc. [1] WSNs can be used in many industrial and civilian applications,

including industrial process monitoring and control, healthcare applications, habitat monitoring,

home automation and many others. [2]

Nowadays, health care systems are highly complex. People in need for continuous health care are

increasing day by day. Medical staff faces with more and more challenges. This raises serious

questions in the domain of medical which must be answered in the best possible ways.Problem

solving must include detailed analyses of the current state so as to form functional system which

resolves the satisfying number of issues which are to be faced in future. In medical WSNs can offer

this kind of solution. [3]

Driven by technology advancements in low-power networked systems and sensors in medical

science we have witnessed the emergence of Wireless Sensor Networks. WSNs are distributed

autonomous sensors to monitor physical or environmental conditions such as temperature, pressure,

sound etc. Intelligent Medical Sensor System models are always built using Wireless Sensor

Networks. The primary aim of our system is to gather the information of individual health

parameters based on WSN and to provide physicians with a clear data and readings which can be

used monitor the diagnosis of health parameters through mobile communication. This can be utilized

for individual investigation to help with rolling out conduct improvements, and to share with parental

figures for early detection and treatment. In the meantime such systems are successful and monetary

methods for observing ailments.

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 10; October - 2016 [ISSN: 2455-1457]


II. BACKGROUND OVERVIEW

A. Traditional Approach

Wireless sensor network (WSN) [4] is a network comprised of autonomous sensor devices which are

usually deployed in known locations for the purpose of monitoring and collecting data such as radio

signals, sound, temperature, etc. Becoming mature enough to be used for improving the quality of

life, wireless sensor network technologies are considered as one of the key research areas in

computer science and healthcare application industries. The pervasive healthcare systems provide

rich contextual information and alerting mechanisms against odd conditions with continuous

monitoring. This minimizes the need for caregivers and helps the chronically ill and elderly to

survive an independent life. Currently, wireless sensor networks deployment is starting to be

deployed at an accelerated pace. It is reasonable to expect that the world will benefit from services of

Wireless sensor networks with technological access.

While considering Health care application using wireless sensor networks several challenges also

needs to be considered such as low power, limited computation, material constraints, and continuous

operation, robustness and fault tolerance, scalability, security and interference and regulatory

requirements. Our system would try to overcome these performance metrics which needs to be

overcome in implementation health care systems using WSN.

Health car monitoring systems using Wireless Sensor Networks consists of physiological monitors,

which are custom-built sensors, patient worn motes that sample, and pass the data through a wireless

network. Moreover there is also one backend server which stores the data medical data and presents

them to authenticated GUI clients only. The main challenge here is mobile communication where

clients don’t have the flexibility to communicate through smart hand held devices and have to be

location dependent for gaining treatment from doctor thereby visiting him every time for medication.

Hereby we propose a system which overcomes this drawback and allows clients to send their data to

doctors through mobile communication and by using system insights two major factors of such as

time and location dependencies are saved.

B. Drawbacks of Existing System

Existing System has the problems of using single functionality specific machines to monitor various

health parameters of the user. These machines are huge and bulky in size as well as it is also not able

to communicate the sensed or captured data to other devices using mobile communication.

C. The Proposed System

Proposed System presents a distributed set of sensors which will mimic the work of individual

elements by sensing the data captured by them.



Figure 1. Block Diagram

Proposed system highlights use of different wireless sensors to demonstrate how to check various

health parameters of the user using various sensors like Pulse Sensor, Blood Pressure Sensor,

Temperature Sensor and ECG sensor which we will use to monitor heart rate of the person. All the

sensors are connected to a Raspberry Pi 3 [11] credit card-sized single board computer which

features an ARM compatible CPU and on chip GPU (Graphics Processing Unit) with the CPU speed

ranges from 700 MHz to 1.2 GHz along with an onboard memory of 1GB RAM. It also features an

RJ 45 Ethernet Port and Pi 3 also has onboard WiFi 802.11n and Bluetooth. The given system also

has additional components such as ADC to convert the analog pulse to digital value. To generate

ECG of an individual AD 8232 Heart Rate Monitor is used. To calculate Blood Pressure of an

individual system uses BMP 180 Barometric Pressure Sensor along with its handcuff, pipe

attachments and Electronic Valve which is used to control the air flow to the pressure sensor which

resides inside the pressure container. Next, to measure the temperature of an individual system uses

M513 MLX90615 Humidity & Temperature Sensor. All the sensors collect their individual data and

give to Pi 3 for processing. Health parameter ranges are defined well in advance to determine

whether the collected data is correct or not. The channel bandwidth provided by the system model

depends on the speed of the mobile node connected to the internet. Here we make the mobile node as

our WiFi router and send data to the both the android applications using the mobile node data which

is provided by telecommunication partner. Let’s discuss the proposed system working components

individually.



III. SYSTEM OVERVIEW

1. Press button on the device

2. Red light glows indicating processing the report, checking body and generating numbers for report

3. Green light glows when data is uploaded to server

4. We can open app to check the report

5. Patient can view report and wait for the doctors feedback

6. Doctor when opens the app get list of submitted report

7. Doctor will send the prescription

8. When patient opens the app he gets the prescription

A. Pulse Sensor

The given proposed system uses Pulse Sensor. [12] Pulse Sensor is a well-designed plug and play

heart rate sensor for Arduino as well as Pi. The sensor clips on to a fingertip or earlobe and plugs

right in to Pi 3 with some jumper cables. The front of the sensor is the pretty side with the Heart

logo. This is the side that makes contact with the skin. On the front you see a small round hole,

which is where the LED shines through from the back, and there is also a little square just under the

LED. The square is an ambient light sensor, exactly like the one used in cellphones, tablets, and

laptops, to adjust the screen brightness in different light conditions. The LED shines light into the

fingertip or earlobe, or other capillary tissue, and sensor reads the light that bounces back. The back

of the sensor is where the rest of the parts are mounted. After the code is run, we see pin 13 blink on

Arduino [9] in time with your heartbeat when user holds the sensor on his fingertip. Spot pressure on

the pulse sensor will give nice and clean data and signals to Pi 3. Here we define a normal resting

heart rate for adults which range from 60 to 100 BPM. The collected data through the pulse sensor is

given to Arduino which we use it as analog to digital convertor because our pulse sensor generates

waveform for heartbeat. Arduino is used here because it has the ability to convert Analog waveform

into Digital value and then processed into BPM algorithm to generate values for heartbeats for every

user. This heartbeat is then stored in Pi’s OS and then transferred to a remote server over the internet

and directly transferred to user Smartphone application so that he can view his real time heartbeats

and then if this data is not in the resting range mentioned above it will get automatically transferred

to registered physicians for diagnosis and treatment.

B. BPM Algorithm

1. Fill the data from sensor in an array for T seconds.

2. Find minimum value and maximum in that array suppose A and B.

3. Map values in array A to some negative value –x and B equivalent positive value +x and all middle

value directly proportional.

4. Increment counter C for every time the value in array changes from negative to positive.

5. BMP = (C/T)*60.

C. Temperature Sensing Unit

This module integrates M513 MLX90615 Digital Infrared Temperature sensor and other required

components on Pi 3. The sensor includes a resistive-type humidity measurement component, an NTC

(Negative Temperature Coefficient) temperature measurement component and a high-performance 8-

bit microcontroller inside, and provides calibrated digital output when connected to Pi 3. This sensor

has high reliability and long-term stability. The system accepts data from MLX90615 sensor through

Pi 3 and gives it to user’s smartphone application for diagnosis. The average normal body temperature

is 98.6°F (37°C). The range defined in our system for the body temperature to be normal is from 97°F

(36.1°C) to 99°F (37.2°C). The temperature over 100.4°F (38°C) is the quoted abnormal human body

temperature for the system; it means the user is suffering from fever caused by infection or illness.



D. Pressure Sensing Unit

In the given proposed system we are measuring Blood Pressure of the user using The BMP180

Breakout barometric pressure sensor [13] with an I2C (“Wire”) interface. This Pressure sensing unit

consists of various other components which are responsible to calculate the systolic (maximum) and

diastolic (minimum) blood pressure of the user. This unit comprises of one pressure container which

contains pressure sensor which is used to monitor pressure in our system. Second component is Air

pump which is used to fill the handcuff; it is an inflatable strip tied around the hand of the patient for

whom we want to calculate blood pressure, when wrapped around hand and inflated it will stop the

blood flow in hand.

Pressure from handcuff will be induced inside the pressure container and the pressure inside the

pressure container and handcuff will be said that will be calculated by bmp180 sensor inside the

pressure container. Third component is air pump, it consist of centrifugal pump which forces air

inside the handcuff to inflate it. Fourth component is pulse sensor (heart rate sensor), just like doctor

uses his stethoscope to listen the beats from hand’s veins; we are using this sensor which will digitally

detect beats. We will fill air inside the handcuff till a point when the pulse sensor don’t show pulse in

its output, this is when we get the Systolic pressure [13] by reading value from BMP180 sensor inside

the pressure container. When we get the pressure reading, we have to deflate the handcuff. Fifth

component is a solenoid valve, it will open the air flow hole by which air will escape and the handcuff

will deflate and blood flows normally again and we get the pulse from pulse sensor. We will continue

to deflate the handcuff by turning on solenoid valve, till we don’t get pulse reading and stop that is

when we read Diastolic pressure [13] from pressure sensor BMP180. These readings are then

transferred to user smartphone application via Pi 3. If the blood pressure of the user is above or below

the normal resting range of approximately 120/80 mm Hg then necessary action can be taken by the

doctor as this data will be sent to doctor’s smartphone from user application after getting the alert to

the user.

E. ECG Sensing Unit

In the given system we generate a heart rate graph of the user using ADS1292R ECG/Respiration

Breakout kit. This kit consists of breakout board for the TI ADS1292R Analog front-end IC for ECG

and respiration measurement will help you measure both ECG patterns and respiration. The kit when

connected to Pi 3 follows the following steps of execution:

We connect the probes to body on 3 places as specified in Fig. 4. The program reads analog values

from the probes, which are minute fluctuations in our body’s electric current. IC will amplify and

digitally sample the readings. The IC samples the data and normalizes it; it filters out all the outlier

from the digital values. All the values are stored inside an array which is displayed as an image and

saved in one .png image. The values in the array are directly equivalent to the pulses from ECG graph

the program stores the image in a directory that is later on uploaded to server using HTTP Request.

IV. RESULTS AND ANALYSIS

The proposed system when used is operated in two perspectives:

4.1 User Side Android Application:

In user side, application scenario is just collecting data from different sensors all together and

showing it on smart health application specially designed to view the measured parameters. If any

measured parameter falls in abnormal range as discussed in section III, it will automatically get

displayed in the form of an alert and will be sent to registered doctors in the vicinity. Given figure

below is the demonstration of Patient side Android Application:



Figure 2. Patient Dashboard -1 Figure 3. Patient Dashboard – 2

4.2 Doctor Side Android Application:

When the doctor logs in on to his application, he will be directly able to see the reports and data sent

by every individual patient along with their current and previous reports and statistics. The doctor

will be also able to view every patient’s medical history on his smartphone application. So based on

the previous medical records and current diagnosis the doctor can then immediately prescribe the

medication in acknowledgement to the patients data. This prescription would be immediately visible

to patient along with doctor’s registration number and other valid credential details. Given figure

below displays the application.



Figure 4. Doctor Dashboard

4.3 Temperature Result Analysis:

In this section we compare the results of temperature sensor generated by our kit along with Hicks

digital thermometer. Here in the first table we are showing outputs obtained from Hicks Digital

thermometer.

Patient Names Specifications

Saloni S 97.9°F

Bhakti C 97.6°F

Rekha C 92.5°F

Mukesh C 97.1°F

Jigar C 97.2°F

Priya A 97.3°F

Table1. Output Parameters calculate using Hicks Digital Thermometer

Table2. Output Parameters calculate using our Kit

Patient Names Specifications

Saloni S 94.6°F

Bhakti C 93.2°F

Rekha C 91.6°F

Mukesh C 93.6°F

Jigar C 94.1°F

Priya A 95.3°F



Hicks Digital Thermometers was used in axilla area in contact with the skin. Hicks thermometer

takes nearly a minute to capture the body temperature in the axilla area whereas the time required by

our kit is just 2 seconds. The infrared temperature sensor throws a strong beam of light on the object

when the object is hovered over the sensor. Our kits accuracy is ±3 degree F. Given graph below

shows the comparison between both the entities.

Figure 5. Temperature Analysis Graph

4.4 Blood Pressure Results Analysis

In this section we compare the results of pulse sensor and pressure sensing unit along with Omron

M6 Comfort Digital Automatic Blood Pressure machine. Here in the first table we demonstrate the

outputs obtained from Omron M6 machine.

Patient Names Systolic mmHg Diastolic mmHg BPM

Saloni S 109 71 90

Bhakti C 103 66 79

Rekha C 142 99 74

Mukesh C 133 69 72

Jigar C 125 84 89

Priya A 123 60 107

Table 3.Specifications calculated by OMRON M6 Comfot Blood Pressure Machine

OMRON M6 Comfort states their accuracy as ±5 mmHg in their device specification manual.

In the next section we calculate the blood pressure and heart rates of the above mentioned subjects

using our kit. The kit takes a total of 1 minute in calculating all the vital parameters along with the

output to be sent to smart phone android application. On the other hand OMRON M6 takes 30

seconds to measure only blood pressure and pulse rate.

While comparing accuracy for measuring blood pressure our system provides ±10 mmHg based on

the human movement constraint consideration.



Patient Names Systolic mmHg Diastolic mmHg BPM

Saloni S 136 86 90

Bhakti C 121 81 82

Rekha C 140 93 75

Mukesh C 117 76 73

Jigar C 127 82 90

Priya A 116 78 97

Table 4.Specifications calculated by our Kit

Now we show the comparison graphs between Omron M6 and our kit.

Figure 6. Systolic Analysis Graph

Next we show Diastolic Analysis Graph.

Figure 7. Diastolic Analysis Graph



Figure 8. Beats Per Minute Analysis Graph

4.5 ECG Results Analysis

For analyzing and comparing Electrocardiogram generated by our system, we state the fact that our

system generated ECG is 3 pin connector 1 lead ECG which generates ECG Heart Rate graph only

as the sensor which we have used in our system is not medically proven for ECG analysis whereas

the other ECG graph generated for comparison is produced using portable BPL ECG machine which

is 12 pin connector 3 lead ECG; medically accurate and proven for diagnosing strokes and accurate

heart rate and as a result we just show here comparison of two graphs. The only advantage our

system possesses is that the generated ECG would be easily viewable on mobile devices.

Next we show the comparative outputs of ECG generated by using two different devices.

Figure 9. BPL Machine generated ECG Graph



Figure 10. Beats Per Minute Analysis Graph

V. CONCLUSION & FUTURE SCOPE

In this project, we were able to implement the MHWSN prototype system for the tracking of vital

health parameters. Our implementation is able to successfully calculate ECG, beats per minute,

temperature and blood pressure of the user in two modes. Firstly normal mode where patient is not

suffering any chronic disease and he isn’t critical; here the system would only once calculate all the

parameters when the kit is turned ON. Secondly the critical mode where in the patient is bed ridden

and he needs continuous monitoring; here the system needs a kick start only for the first time then it

would automatically calculate parameters every (2/5/10/15) minutes as specified by the doctor while

assigning kit to the patient. Lastly all the parameters are easily fetched to users handheld smart phone

device.

The calculation of medical health parameters using Wireless Sensor Networks is not a new idea, but

rather this paper concentrates on calculating various parameters like Heartbeats, ECG, and Blood

Pressure Monitoring altogether in a single calibrated kit which poses to the user as a single system

when interfaced with android smart phone application providing higher usability both to doctors as

well as patients. The system will eliminate the problems observed in the manual and conventional

machine based monitoring system as the real time data monitoring demand increases because of rise

of chronic diseases which will vary person to person. With the right information at the right time, the

sensor based medical system can help medical patient to easily track and monitor their health record.

The system will curb the menace of visiting doctor every time for diagnosis and will help registered

patients of the doctors to gain treatment effectively.

In the future, we might want to add security enhancement to the kit as the data traversed between

patient and the doctor could be encrypted and it maintains confidentiality of the reports generated

and communicated between doctor and the patient.



The average accuracy of our system right now in terms of monitoring all the health parameters is

±15. We aim to achieve ±0 accuracy in future by introducing or replacing new hardware components

which are small in size and it would help in effectively making this kit more small and compact. We

see the future of this project as one wearable device in coming years.

REFERENCES 1. Security Architecture for At-Home Medical Care Using Wireless Sensor Network, Soufiene Ben Othman,

Abdelbasset Trad, Habib Youssef. UR PRINCE,ISITcom, Hammam Sousse, University of Sousse, Tunisia. Date of

Conference 4-8 August 2014. Page(s): 304-309. ISBN: 978-1-4799-7324-8. Conference location: Nicosia. INSPEC

Accession Number: 14618916. DOI: 10.1109/IWCMC.2014.6906374. Publisher: IEEE.

2. M. C. Vuran, I. F. Akylidiz, Wireless Sensor Networks.Wiley, 2010. ISBN: 978-0-470-03601-3 516 pages. July

2010.

3. Wireless Sensor Networks for Healthcare, JeongGil Ko, Chenyang Lu, Mani B. Srivastava John A. Stankovic,

Andreas Terzis, Matt Welsh. Published in: Proceedings of the IEEE (Volume:98, Issue:11). Page(s):1947-1960.

ISSN: 0018-9219. Date of Publication: 13 September 2010. Date of current version:18 October 2010. INSPEC

Accession Number: 11588456. Issue date: Nov. 2010. DOI: 10.1109/JPROC.2010.2065210. Sponsored by:IEEE.

Published by:IEEE.

4. A Survey on Ambient Intelligence in Healthcare, By Acampora, G. : Sch. of Sci. \& Technol., Nottingham Trent

Univ., Nottingham, UK. Cook, D.J. ; Rashidi, P. ; Vasilakos, A.V. Published in : Proceedings of the IEEE

(Volume:101,Issue: 12). Page(s): 2470-2494. ISSN: 0018-9219. Date of Publication: 13 August 2013. Date of

current version: 19 November 2013. INSPEC Accession Number: 13914461.Issue date: Dec. 2013. DOI:

10.1109/JPROC.2013.2262913. Sponsored by: IEEE. Published by: IEEE.

5. SECURITY ARCHITECTURE FOR AT-HOME MEDICAL CARE USING BODY SENSOR

NETWORK,S.S.Mohanavalli, Sheila Anand, International Journal of Ad hoc, Sensor & Ubiquitous Computing

(IJASUC) Vol.2, No.1, March 2011.

6. Sensor Networks for Medical Care, Victor Shnayder, Bor-rong Chen, Konrad Lorincz, Thaddeus R. F. Fulford-

Jones, and Matt Welsh, Harvard University

7. Wireless Sensor Networks for medical care services By Sghaier, N. TINC Group, Univ. of Paris-Est Creteil Val de

Marne (UPEC), Vitry sur Seine, France. Mellouk, A. ; Augustin, B. ; Amirat, Y. ; Marty, J. ; Khoussa, M.E.A. ;

Abid, A. ; Zitouni, R. Published in: Wireless Communications and Mobile Computing Conference (IWCMC), 2011

7th International. Date of Conference: 4-8 July 2011. Page(s): 571-576. Conference location: Istanbul. Print ISBN:

978-1-4244-9539-9. INSPEC Accession Number: 12179154. DOI: 10.1109/IWCMC.2011.5982596. Publisher:

IEEE

8. Smys S, Bala GJ. Efficient self-organized backbone formation in mobile ad hoc networks (MANETs). Computers &

Electrical Engineering. 2012.

9. Learn about Arduino, URL: https://www.arduino.cc/

10. S.Park and S.Jayaraman, “Enhancing Quality of life through Wearable Technology”, IEEE Engineering and Biology

Magazine, May-June 2003, vol.22 pp 41-48.

11. Raspberry Pi - Teach, Learn, and Make with Raspberry Pi https://www.raspberrypi.org/

12. https://pulse-sensor.googlecode.com/files/PulseSensorAmpedGettingStartedGuide.pdf

13. BMP 180 Barometric Pressure Sensor https://learn.sparkfun.com/tutorials/bmp180-barometric-pressure-

sensorhookup.

https://www.arduino.cc/

https://www.raspberrypi.org/

https://pulse-sensor.googlecode.com/files/PulseSensorAmpedGettingStartedGuide.pdf

https://learn.sparkfun.com/tutorials/bmp180-barometric-pressure-sensorhookup

https://learn.sparkfun.com/tutorials/bmp180-barometric-pressure-sensorhookup

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