A Novel Approach for Asthma Prediction - ijarcsse.comijarcsse.com/Before_August_2017/docs/papers/Volume_7/6_June2017/… · Sridevi Women’s Engineering, College, Hyderabad, India

International Journals of Advanced Research in Computer Science and Software Engineering ISSN: 2277-128X (Volume-7, Issue-6)

Research Article

June 2017

© www.ijarcsse.com, All Rights Reserved Page | 254

A Novel Approach for Asthma Prediction Rajesh Kumar VRS

Professor & Head,

Department of Electronics and

Communication Engineering,

Sridevi Women’s Engineering,

College, Hyderabad, India

Rathish Babu TKS Professor,

Department of Computer

Science and Engineering,



Ramasubramanian M

Professor & Head,

Department of Computer

Science and Engineering,



DOI: 10.23956/ijarcsse/V7I6/0147

Abstract—About 334 million people across the globe are suffering from asthma. People with asthma are sensitive to

things which may not bother normal people at all. For example they may feel uncomfortable with increased levels of

smoke, pollen or fog in air. The number of people suffering from asthma has been rising sharply over the years,

pollution being one of the biggest reasons. Though controlling pollution is a broad topic, but preventing oneself from

asthma is an easier way out. It is important to keep track of what triggers asthma attack in a patient, because

symptoms do not occur right after the exposure to the triggering parameters. The delay in attack occurs depending on

how much the person is sensitive to the factor. Thus we try to propose a model of a smart asthma prediction system

using Internet of Things.

Keywords—Asthma, Sensor, Detector, Wearable, Temparature, Laser, Smarthphone.

I. INTRODUCTION

According to Global Burden of Disease Study (GBD) undertaken in 2008-2010, the number of people suffering from

asthma in the world may be as high as 334 million. The worst affected are the aged people between 75-79 years and

adolescence (ages 10-14) in terms of disability and premature death. Asthma is a chronic disease which causes uneasy

breath along with coughing and wheezing. There may various factors which may trigger an asthma attack, which varies

from person to person. Some of the common factors which trigger asthma are:

• Infections

• Food and food additives

• It can happen after an exercise.

• Smoking and chemical fumes

• Sinusities

• Outdoor allergens, such as pollens from grass, trees and weeds

• Indoor allergens, such as pet dander, dust mites and mold.

Here we try to measure and predict when a person is about to have an asthma attack using Internet of things (IoT).

Whenever he or she is about to have an asthma attack, a warning is sent to their smart phone as a notification.

Thus the person moves away to some safe zone free from factors which can trigger the attack. We propose a system

and method which will help an asthma patient to avoid some situations which cause breathing trouble. The following

work has already been done: Smart inhalers detect inhaler use and transmit that data. Smart inhalers contain sensors that

attach to existing inhalers and record when your medication is taken. They are Bluetooth-enabled, so can be paired

wirelessly with a smart device like a phone or tablet or with a computer to allow data to be transferred from the smart

inhaler automatically.

The devices link to smartphones via Bluetooth. Each manufacturer tends to have accompanying smartphone apps,

where you can look at your data or change reminder settings, and these may not be available for every type of

smartphone. The smart inhaler apps that have emerged so far appear to work with both iPhone and Android. In the future,

an app on your smart phone could receive and interpret the data from your smart inhaler and send you health advice and

reminders.

Your data could also be shared with your GP, asthma nurse or hospital team to help tailor care to your needs.

Knowing when and where your symptoms flare up may help identify personal triggers and allow a more individually

tailored self-management plan. When the user takes the inhaler, automatically a signal is sent to the mobile app via BLE

Bluetooth, at the same time by getting the signal the app starts gathering some real time parameter values like the

location by GPS, the body temperature, heart beat etc. using fitbit wearable.

II. RELATED WORK

Alongside its trackers, Fitbit offers a mobile app and website that can be used with or without the Fitbit Tracker,

although owning one is recommended. Users have the ability to log their food, activities, and weight, to track over time

and can set daily and weekly goals for themselves for steps, calories burned and consumed, and distance walked. The

devices also come with a USB dongle, to sync data to the account via Fitbit Connect, as shown in Fig. 1.

dx.doi.org/10.23956/ijarcsse/V7I6/0147

https://www.asthma.org.uk/about/media/news/smart-inhalers-questions-answered/

https://www.asthma.org.uk/about/media/news/smart-inhalers-questions-answered/

https://en.wikipedia.org/wiki/Mobile_app

Rajesh et al., International Journals of Advanced Research in Computer Science and Software Engineering

ISSN: 2277-128X (Volume-7, Issue-6)


Fig. 1 Fitbit wearables

If your tracker is paired to the Fitbit app on a mobile device, it syncs every time you open the app. It can also sync

periodically throughout the day if you have All-Day sync turned on. If you sync your tracker with a computer and Fitbit

Connect, it syncs automatically every 15 to 30 minutes assuming the tracker and computer are in proximity, and the

dongle is plugged in, if applicable.The Fitbit app uses Bluetooth Low Energy (BLE) technology to sync with your Fitbit

tracker. To initiate a sync, just open the Fitbit app on your mobile device.

You must have Bluetooth enabled on your mobile device to sync your tracker with the Fitbit app. If you are using

an Android mobile device, you must also have Bluetooth enabled in the Fitbit app. If your mobile device is in airplane

mode, it will not sync with your tracker until you turn off airplane mode, or until you manually turn on Bluetooth on your

mobile device.

Android devices must have both a BLE radio and software support. BLE is an optional component of Bluetooth 4.0,

meaning not all devices with Bluetooth 4.0 include BLE. Some devices have the hardware but not the software, while

others have both but software bugs prevent it from working. You can alter your daily step goal but the feature is slightly

hidden away. You need to open the Fitbit software and then make sure that you have a tile for steps displayed on the

dashboard. If it isn't already, click the small green menu tap at the top left and tick the Steps box. Then, once it's on the

dashboard, click the small gear icon in the bottom left of that tile.

While there's a lot more to the Fitbit Charge than simple step counting, the pedometer skills obviously play a big

part – so you'll want to make sure your walking is being tracked with as much accuracy as possible. The Fitbit Charge 2

main menu shows your steps taken, floors climbed, distance traveled, calories burned, heart rate and real-time exercise

stats on your wrist. Tapping around the tracker's screen on the actual band or the body of the tracker because it's not a

touchscreen - will bring up the different stats. Pressing the button takes you into specific menus where tapping will show

you more options for each.

If you're still not sure what all the tapping does, here's a cheat sheet:

Heart rate - Shows your current heart rate. Tap to switch to your resting heart rate.

Exercise - Tap to move through your exercise choices, then press and hold the button to start the specific

exercises. Press and hold the button again to end the exercise. Options include run, weights, treadmill, workout,

elliptical, bike and interval workout.

Alarms - Tap to scroll through any alarms you've set. Press and hold the button to disable or enable any alarms.

Stopwatch - Press and hold the button to start the stopwatch. Press the button to stop and resume the stopwatch.

Press and hold the button again to reset the stopwatch.

Relax - Tap to choose a guided breathing session. Press and hold the button to start the session.

If one of your main reasons for buying a new fitness tracker was to lose weight then it's obviously important that you

keep an eye on what you eat as well. The Fitbit app allows you to keep track of what you've eaten by logging your meals

and snacks using its built-in food database. The first step is to make sure your Fitbit knows which wrist it's sitting on.

You can select either dominant or non-dominant from within the app. Wearing your Charge on your dominant arm is

fine, but the hardware needs to know so it can adjust its sensitivity to account for the extra motion it will experience

during everyday wearing.

The next step, literally, is to get the stride-length sorted. Your Charge will guess your average length based on your

height and gender but you can guarantee better accuracy by changing this value under Personal Info on the Fitbit web

portal to your exact length. It's simple to measure your accurate length: go to a running track or somewhere that you

know the exact distance of, count your steps as you walk that distance, and then divide the total distance taken by the

number of steps to get your stride length. Fitbit's MobileRun mode uses GPS data to more accurately track your walks

and runs, and also lets you control your music playlists from inside the app. To use MobileRun, go into the app and select

the Exercise from the home screen. Tap the stopwatch in the top right corner and you'll see a map of your location.

Simply hit Start when you're ready to track your run.




The MobileRun feature uses the GPS connectivity of your smartphone to calculate distance, steps, active minutes

and calories burned, and this data overrides your tracker's recordings.Your weekly goal will be set to 70,000 steps by

default but you can now adjust the daily goal to whatever you so wish. And it doesn't have to be steps; you can change it

so the status lights indicate another value such as distance or calories.

A. Mitsubishi electric sensor

Measuring toxic fumes in the air are often performed by experts and it requires the use of high-quality and costly

equipment. Mitsubishi Electric Corporation announced that it developed a small, high-precision air-quality sensor. This

is the first of its kind to detect all fine particles, even those which are very small to assess.

The new device, which can measure fine air particles no more than 2.5 µm in diameter or PM2.5, can accurately

measure the density or particles as well as other pollutants such as dust and pollen. PM2.5 generates scattered light and

this is measured by the device's double-sided mirror design. It can collect about 1.8 times more scattered light than

traditional single-sided designs. The company's original shape-discrimination algorithm distinguishes between pollen and

dust based on their differences in optical characteristics in the generated scattered light. Particle pollution or particulate

matter (PM) is a mixture of solid particles and liquid droplets in the air. When these particles are smaller than 2.5 µm in

diameter, may pose serious health problems such as respiratory diseases, cardiovascular problems and premature death.

With their small size, they can be inhaled and they can travel deeply into the respiratory tract. Exposure to fine

particles can cause irritation to the eyes, nose, throat and the lungs. For people suffering from an already diagnosed lung

condition like bronchial asthma or chronic obstructive pulmonary disease (COPD), inhalation of fine particles may

aggravate the condition. Mitsubishi Electric has developed a small, high-precision air-quality sensor, claimed to be the

first in the world to detect fine particles measuring no more than 2.5µm in diameter, called PM2.5, as well as pollen and

dust. It also senses the density of particles precisely.

Scattered light from PM2.5 particles is measured with a double-sided mirror design, which is said to collect about

1.8 times more scattered light than conventional single-sided designs. Mitsubishi says its shape-discrimination algorithm

distinguishes between pollen and dust based on the respective differences in the optical characteristics of their scattered

light. Long-term exposure, is associated with problems such as reduced lung function and the development of chronic

bronchitis and even premature death. Short-term exposure to particles (hours or days) can aggravate lung disease, cause

asthma attacks and acute bronchitis, and may also increase susceptibility to respiratory infections. In people with heart

disease, short-term exposure is linked to heart attacks and arrhythmias. Healthy children and adults may experience

temporary minor irritation when particle levels are elevated, as shown in Fig. 2.

Researchers have developed an integrated, wearable system that monitors a user's environment, heart rate and other

physical attributes with the goal of predicting and preventing asthma attacks. The researchers plan to begin testing the

system on a larger subject population this summer. In the energy-efficient smart building, occupancy detection and

localization is an area of increasing interest, as services, such as lighting and ventilation, could be targeted towards

individual occupants instead of an entire room or floor. Also, an increasing quantity and diversity of environmental

sensors are being added to smart buildings to ensure the quality of services provided by the building. The need for

particulate matter (PM) sensors in consumer devices such as air purifiers, is an example where manufacturing advances

have made the sensors much less expensive than laboratory equipment. Beyond their original intended use, air quality,

they can also be used for occupancy monitoring. The work presented in this article proposes to use a low-cost (< 8 USD)

PM sensor to infer the local movement of occupants in a corridor by sensing the resuspension of coarse (≥ 2.5 µm)

particles.

Fig. 2 Mitsubishi electric sensor

B. Health and environmental tracker (het)

The patch has sensors for monitoring movement, heart rate, respiratory rate, the amount of oxygen in the blood, skin

impedance and wheezing in the lungs. The wristband monitors air quality levels like the amount of ozone and volatile

organic compounds in the air and also measure temperature and humidity. The wristband also monitors health data

http://www3.epa.gov/pm/basic.html




movement, heart rate and oxygen in the blood. The data from all of the sensors is sent wirelessly to a computer where

software collects and analyzes it. In testing, the sensors have been accurate and the system has successfully compiled the

data of users and their environment. They are working on making the prediction software now so that soon users could

use the system and pair it with their smartphones, getting alerts about their health and environment as they go through

their day and warnings when an attack is coming. The system, called the Health and Environmental Tracker (HET), is

composed of a suite of new sensor devices and was developed by researchers from the National Science Foundation's

Nanosystems Engineering Research Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies

(ASSIST) at North Carolina State University. According to the Centers for Disease Control and Prevention, asthma

affects more than 24 million people in the United States. Asthma patients currently rely on inhalers to deal with their

symptoms, which can include often-debilitating asthma attacks.

―Our goal was to design a wearable system that could track the wellness of the subjects and in particular provide the

infrastructure to predict asthma attacks, so that the users could take steps to prevent them by changing their activities or

environment," says Alper Bozkurt, the principal investigator of a paper describing the work and an assistant professor of

electrical and computer engineering at NC State. "Preventing an attack could be as simple as going indoors or taking a

break from an exercise routine," says James Dieffenderfer, lead author of the paper and a Ph.D. student in the joint

biomedical engineering program at NC State and the University of North Carolina at Chapel Hill.The HET system

incorporates a host of novel sensing devices, which are incorporated into a wristband and a patch that adheres to the

chest.The patch includes sensors that track a patient's movement, heart rate, respiratory rate, the amount of oxygen in the

blood, skin impedance and wheezing in the lungs.The wristband focuses largely on environmental factors, monitoring

volatile organic compounds and ozone in the air, as well as ambient humidity and temperature. The wristband also

includes additional sensors to monitor motion, heart rate and the amount of oxygen in the blood.

The system also has one non wearable component: a spirometer, which patients breathe into several times a day to

measure lung function. "Right now, people with asthma are asked to use a peak flow meter to measure lung function on a

day-to-day basis," Dieffenderfer says. "That information is used to inform the dosage of prescription drugs used in their

inhalers, as shown in Fig. 3.

Tracker (HET), an integrated, wearable system that monitors a user's environment, heart rate and other physical

attributes with the goal of predicting and preventing asthma attacks.

When the patient goes to the Doctor for consultation the app shows some analytics to help the doctor to diagnose

the asthma problem. Now we are going to extend this work. We have different sensors which sense smoke, fog etc. We

can incorporate these sensors into the device. We can take pictures using street cam and fed to Watson to analyze

whether there are any specific object which affects the persons' health.

Fig.3 An early prototype of the wristband used in the health and environmental

Humidity affects a person health by increasing asthma attack chance. We can use weather report for Humidity. For

some people physical activities like exercising also trigger asthma attack. Thus when the person is running or walking

hard and crossing the normal threshold value of regular walk, we can send a warning notification.

C. Benefits of exercise

Exercising with a heart rate sensor allows you to check how much effort you bring based on your heart rate. The

higher your heart rate, the harder your lungs work, which is proportionately tied to your calorie expenditure. If you have

a heart murmur, you can still exercise using a heart rate sensor, but you will need to take some extra steps to protect your

cardiovascular health.

Exercise is a common trigger for asthma symptoms. Many people with asthma may experience coughing, wheezing,

or chest tightness during or after exercising. However, most people with asthma can successfully participate in

their exercise of choice with proper guidance. ―If your asthma is under good control, you can and should exercise

normally. Exercising (when you have) asthma can help reduce your symptoms, improve your breathing, and reduce

your stress and anxiety," says Rachel Taliercio, DO, a lung and allergy specialist at the Cleveland Clinic, as shown in

Fig. 4.

https://phys.org/tags/heart+rate/

http://www.medicinenet.com/asthma_symptoms_and_signs/symptoms.htm

http://www.medicinenet.com/asthma_overview/article.htm

http://www.medicinenet.com/chronic_cough/article.htm

http://www.medicinenet.com/wheezing/symptoms.htm

http://www.medicinenet.com/asthma_pictures_slideshow/article.htm

http://www.medicinenet.com/exercise_and_fitness_quiz/quiz.htm

http://www.medicinenet.com/asthma_quiz/quiz.htm

http://www.medicinenet.com/lungs_design_and_purpose/article.htm

http://www.medicinenet.com/stress/article.htm

http://www.medicinenet.com/anxiety/article.htm

http://www.medicinenet.com/allergy/article.htm




Fig. 4 Indicating risk of having asthma

―Many people with asthma assume that exercise is bad for them. That causes them to get out of shape, and that's bad

for asthma," says Dr. Taliercio. A review of 19 studies (involving 695 people) on exercises for asthma was published in

the Cochrane Database of Systematic Reviews in 2012. The review found that exercise for asthma is safe, improves heart

and lung fitness, and enhances quality of life. The author's conclusion is that people with asthma should be encouraged to

exercise without worrying that their symptoms will get worse, as shown in Fig. 5.

Fig.5 Sensors help in doing proper exercise

Having asthma means your lungs are more sensitive to things like cold or hot temperatures, dry air, allergens, and

pollution. When you're not exercising, you probably breathe through your nose. Breathing through your nose moistens,

warms, and filters the air you breathe before it gets into your lungs. But while working out, you probably breathe through

your mouth. That can be tough on your lungs and can trigger asthma symptoms

―We tell people with asthma to try to breathe in through the nose and out through the mouth," says Taliercio. It is

better to pick an exercise that is not too difficult for you because trying an exercise that you are not in shape for may also

trigger asthma symptoms. Try to do the exercise you have chosen four or five days a week. Don't push yourself if your

asthma starts to flare. "The best exercise is (one) that causes you to be just slightly out of breath. If you can talk

comfortably while you are exercising, your exercise level is not passing the talk test," says Taliercio. That means you

may not be working hard enough to receive all the benefits of exercising.

D. Traffic detector

We can also alert that person to use mask whenever there is any kind of abnormality in environmental situation such

as a lot of dust is being generated at a construction site which may cause an asthma attack. Often during a journey, a

person may travel to a zone which is harmful to an asthma patient. In such a case, he or she can input the location of

source and destination in smart phone. Using location and internet we can notify the user if a given route is a safe one.

We will have sensors to collect the traffic data, more the traffic more the pollution, as shown in Fig. 6.

There are several different types of traffic sensors available, but three above-ground types have become more

common in recent years: radar, active infrared and laser radar. The technology employed by radar traffic sensors has been

around since World War II, when it helped the military track enemy vessels in the air and at sea. Mimicking that method,

radar traffic sensors deploy a measurable area of microwave energy that is reflected back to the device when a vehicle

passes through it. Active infrared and laser radar sensors operate in a similar manner, using low power infrared energy

and infrared beams to form detection areas. In all three types of devices, the time it takes for the energy to bounce back to

the sensor is compared to data collected in an unobstructed field to determine the size and speed of the vehicle passing

through it. Using a wireless data network the information is immediately transmitted back to a server where it’s

http://www.medicinenet.com/image-collection/lungs_picture/picture.htm




formatted and forwarded to subscribers via the Internet. Today’s technology allows each of these devices to monitor

several lanes of traffic at a time.The design of the wireless sensor network (WSN) used in the proposed traffic light

control system is discussed in detail.

Fig. 6 Sensors to detect traffic

In order to enable system components to communicate, two algorithms that include the traffic system

communication algorithm (TSCA) and traffic signals time manipulation algorithm (TSTMA) were developed. The

successful operation of the control system depends on the interaction of the algorithms with each other as well as with

other system components. The process begins with the traffic WSN, the TSA and the TSTMA and ends by the setting an

effective time on the traffic signals for specific traffic light durations, as shown in Fig. 7.

Fig. 7 Traffic detector

Communication routes between base stations and traffic sensor nodes can be identified and controlled using the

TSCA. A direct routing scheme is used by the TSCA wherein the distribution of all TSNs must be within the range of the

base station. All the vehicles are identified and counted using the traffic sensor nodes (TSN). This information is

periodically transmitted to the base station. Based on the number of TSNs, system operation is segregated into time slots

in which each TSN will operate. The aggregated traffic information is then transferred to the TSTMA in order to fix the

duration of the time for traffic signals dynamically based on the number of vehicles on each traffic signal.

Installation of the traffic sensor nodes into small holes centered in each lane in the roadbed. These holes are

designed such that they are safe, protected from the condition of the roadbed and the environment and do not interfere

with the operations of the TSN. The conventional traffic sensors typically provide volume, occupancy, vehicle presence

and speed data. Vehicle reidentification is an emerging advanced sensor technology that aims to provide more data for

travel information, travel time estimation, vehicle clasification, emission, and origin-destination (OD) estimation

applications.

Based on more than 40 years' experience and with optical sensors in traffic, SICK's solutions are designed for

reliable operation even under severe weather conditions. An extremely wide range of robust and innovative products

in transportation systems, combined with a worldwide service and sales structure have made SICK a market and

technology leader in many fields of the traffic and environmental data acquisition. The transport o f goods and the

movement of people is today optimised to a large degree using control and safety systems. Sensors for measuring

different types of data form a substantial basis for a robust and long-lasting performance of such systems.

1) Laser measurement sensors for getting vehicle contours

The LMS511 or LMS111 laser scanners provide 2D profile data of vehicle passing below for very precise

vehicle classification and vehicle separation. Systems of several laser scanners mounted overhead on one gantry and

synchronised allow for traffic data collection not only in single-lane applications like toll plazas but also in multi-lane-




free-flow (MLFF) traffic. The same laser scanners can also be used for red light and speed enforcement. Furthermore,

laser scanners of this kind, could be used for monitoring dangerous areas at level crossings.

2) Laser measurement systems for vehicle classification

The Traffic Information Collector (TIC) is an integrated traffic measurement system that is based on SICK laser

measurement technology, a technology that is used in thousands of traffic applications worldwide. The TIC102 AC

Pro is especially designed for multi-lane, free-flow applications such as tolling enforcement and traffic management.

The proven TIC102 (traffic information collector) profiling system has been supplemented with an additional laser

scanner for performing axle counting. The extended data record provides increased classification accuracy and

reliably determines and outputs the number of axles. Axle counting works in free-flowing traffic and stop-and-go

situations, day and night. All information can be accessed via a TCP interface or stored on the system and retrieved

for post-processing via FTP. The graphical installation wizard and various self-tests allow easy and economical

commissioning.

The system also functions in stop-and-go situations. Through a highly innovative configuration wizard that

includes a live 3D view of the traffic, installation of a multi-lane gantry is quick and easy. The system auto calibrates

in moving traffic and can be accessed remotely for maintenance. A software trigger increases the detection rate and

reading accuracy of third-party systems such as number plate recognition cameras and DSRC antennas. Additional

information such as vehicle speed and dimension are transmitted over the TCP interface.

3) Light grids for vehicle classification and separation

The sunlight-proof XLG light curtains are used at toll stations on single lanes. The resolution (distance between

two adjacent beams) is typically 30mm. Data from each beam is transmitted via serial interface. From this data, the

vehicle class, front / rear end and the number of axles can be derived easily with high accuracy.

4) Vehicle dimensioning for detecting hazardous loads

While a vehicle passes the profiler, the vehicle outside dimensions are measured with laser scanners. A three-

dimensional model of the vehicle can be produced for further inspection. This allows the user to easily identify

hazards such as load displacement and oversized loads and to take appropriate action. After driving through the

gantries and with the use of critical values, the measurement data is given out on the TCP interface. For safety on our

roads and for the protection of traffic infrastructure, particularly in tunnels, the monitoring of vehicle external

dimensions is becoming increasingly important.

So in such situations the user will receive a warning not to take a route where there is too much smoke produced

due to prolonged traffic jam. Source and Destination path of patients or end user. We can assume that a person travel

from point A to point H (see fig-2.4). We can define this path as an input in our application:

By analyzing regular activities of that person via mobile GPS tracker.

Put manually source and destination path in application.

We are assuming that a person takes a specific path to reach office. Now we place some sensors at some specific

terminal points or some specific places on his way to office. These sensors collect the PM (Particulate matter) levels of

specific places where sensors have been deployed. We can use soot sensors for this purpose, as shown in Fig. 8.

.

Fig. 8 Soot Sensors

Various types of soot sensors, also known as particulate matter or PM sensors are used for the control and

diagnostics of emission systems utilizing diesel particulate filters (DPF). Soot sensors have been developed for two main

types of applications:

Estimation of the amount (mass) of the soot accumulated in a diesel particulate filter, in order to utilize accurate

DPF regeneration strategies.

DPF failure detection which may result in excess PM emissions, to trigger an OBD fault signal.

Even small amounts of inorganic impurities found in real diesel soot samples can create significant signal drift

over time.

Exact composition of fuel and ash is unpredictable (Silica, Calcium, Zinc, Magnesium, etc.)




An accurate estimate of a DPF soot mass allows devising a proper regeneration strategy (how often, when to start or

stop a regeneration), while inaccurate estimates result in unsuitable regeneration timing. If the soot mass is over-

estimated, too-frequent (excessive) regenerations take place, resulting in unnecessary fuel consumption penalty and rapid

system wear-out, amongst other adverse effects. Conversely, under-estimating a DPF soot mass may cause excessive

regeneration exotherms inside a DPF, inducing rapid aging, washcoat loss or DPF deterioration, or even a total DPF

failure. The other area of soot sensor application has been driven by advances in OBD regulations, especially those

adopted by the California ARB/US EPA, as well as by the EU OBD requirements. These OBD regulations demand more

diagnostic measures of the emission system, such as monitoring the DPF filtration efficiency and tailpipe PM emissions.

While US emission standards are expressed in terms of particle mass, European regulations (Euro 5 & Euro VI)

additionally include particle number (PN) limits applicable to diesel and GDI vehicles. Therefore, it may be desirable that

sensors for EU applications be also capable of PN emission monitoring.

Various technology approaches have been employed to devise soot sensors for the above applications—DPF soot

load estimation, DPF failure monitoring and PN emission monitoring. Based on the type of the sensing technology, soot

sensors may be divided into four main types:

Differential pressure (Delta-P)—The DPF soot load is estimated from the increase in the filter pressure drop.

Radio frequency (RF)—The DPF load is calculated based on the absorption of a microwave signal by soot

accumulated in the filter.

Accumulating electrode—A DPF failure is detected by measuring a change of electrical properties of an

electrode due to a time-dependent soot deposition. Resistance is the electrical property most commonly used,

and the corresponding devices are often called resistive electrode sensors or simply resistive sensors.

Commercially-available accumulators: Include risk of warranty issues (false + and -) Questionable if they meet

present requirements. Unlikely to meet requirements beyond 2016. But only option for near term. Real time PMTrac®

sensors show promise in simplicity, resolution, and durability. If the author formed enforcement policy, OBD DPF

failure detection via direct measurement of PM would not be mandated until reliability of sensors was proven.

In a vehicle equipped with a diesel particulate filter, particulate matter (PM) is created during the combustion

process and captured in the vehicle’s exhaust system by the diesel particulate filter. Delphi’s new Particulate Matter

Sensor helps detect leakage in the diesel particulate filter to meet system diagnostic standards. The patented sensor

combines fast response and high sensitivity with simplicity of design and self-diagnostic features. It is an affordable

sensing technology that offers manufacturers a low cost system integration path.

ELISA is a protein based method which involves the proteins being extracted from the sample and added to the

ELISA kit. A Sandwich ELISA is often used for allergen detection. The basic principle for this test is the binding of the

allergenic protein in the sample to the wells in the kit; any unbound proteins are removed. A particular colour is formed

after the addition of a chemical and with Sandwich ELISAs the greater the intensity of the colour, the greater the

concentration of allergen in the sample. Quantification can be made by comparing the intensity of the colour with

standards of known concentrations of allergens.

This technique identifies the presence of the allergenic protein through its molecular mass by measuring the mass to

charge ratio of the ions. This method is not limited to one allergen per analysis.

E. Wireless sensors

Now we have a data base where we have kept the PM level value which is normal for the user. If we see the PM

level has crossed the normal threshold value we will alert him not to take the path. VOCs are categorized as either

methane (CH4) or nonmethane (NMVOCs). NMVOCs depend on the local region. Aromatic NMVOCS like benzene,

toluene and xylene have been suspected to be carcinogenic. They are also asthma triggering substance. Aeroqual makes

VOC sensors which will help us to determine VOC content in air and thus make suitable recommendation to the user.

Apart from these environment parameters we introduce some biometric parameters.

Fig. 9 Between A to H there are various paths where wireless sensors have been placed.




It may happen that the user may unknowingly enter an environment which can trigger asthma attack. In such a

situation, the user will initially have irregular breathing pattern. For such a situation Spire can be used to monitor his

breathing pattern and warn him accordingly to leave the place, as shown in Fig. 9.

F. Pollen detector (ps2 pollen sensor)

This pollen detector detects pollens by the principle of scattering of light. Conventional Pollen counting has been

time-consuming work so far. However, a person can now on spot analyze the presence of pollen particles using pollen

sensors by taking advantage of real time analysis that is available anywhere. It uses one light emitter and two light

receptors to distinguish pollen from other particles on the basis of two factors, "scattered light intensity" and "degree of

polarization". Thus the asthma detection system successfully detects presence of pollen to warn the user, as shown in Fig. 10.

Fig 10. PS2 pollen sensor

Built-in Suction Fan Fan enables constant and larger sampling of air. Pollen count information can be made

available without delay by taking advantage of the real time analysis. Built-in Heater prevents dew Optical unit and

circuit are fully covered with plastic case, and built-in heater keeps the sensor away from dew, as shown in Fig. 11.

Fig. 11 Sensor

G. Humidity sensor

Humidity sensing is very important, especially in the control systems for industrial processes and human comfort.

Hot humid air is heavier and difficult to breathe. Hot air can irritate the airway and lead to inflammation. Again at the

same time, moisture in the air can absorb oxygen. Thus for some asthma patient it is not a problem. However insufficient

moisture can lead to inflammation of airways too. Thus, some people experience asthma symptoms when air is too dry

(during winters). So there is a need for a humidity sensor. CL-M53R- a small structure humidity sensor / SHINYEI

HUMENT serves this purpose. CL-M53R sensor is lead free sensor and environmentally safe. Thus the system also

detects humidity percentage, as shown in Fig. 12 and Fig. 13.

Fig. 12 Humidity Sensor




Fig. 13 Weather detector

Fig. 14 Types of humidity sensors

Electronic type hygrometers or humidity sensors can be broadly divided into two categories: one employs

capacitive sensing principle, while other use resistive effects, as shown in Fig. 14.

1) Sensors based on capacitive effect

Humidity sensors relying on this principle consists of a hygroscopic dielectric material sandwiched between a pair

of electrodes forming a small capacitor. Most capacitive sensors use a plastic or polymer as the dielectric material, with a

typical dielectric constant ranging from 2 to 15. In absence of moisture, the dielectric constant of the hygroscopic

dielectric material and the sensor geometry determine the value of capacitance.

At normal room temperature, the dielectric constant of water vapour has a value of about 80, a value much larger

than the constant of the sensor dielectric material. Therefore, absorption of water vapor by the sensor results in an

increase in sensor capacitance. At equilibrium conditions, the amount of moisture present in a hygroscopic material

depends on both the ambient temperature and the ambient water vapour pressure. This is true also for the hygroscopic

dielectric material used on the sensor.

By definition, relative humidity is a function of both the ambient temperature and water vapor pressure. Therefore

there is a relationship between relative humidity, the amount of moisture present in the sensor, and sensor capacitance.

This relationship governs the operation of a capacitive humidity instrument, as shown in Fig. 15.

On Alumina substrate, lower electrode is formed using gold, platinum or other material. A polymer layer such as

PVA is deposited on the electrode. This layers senses humidity. On top of this polymer film, gold layer is deposited

which acts as top electrode. The top electrode also allows water vapour to pass through it, into the sensing layer. The

vapours enter or leave the hygroscopic sensing layer until the vapour content is in equilibrium with the ambient air or

gas. Thus capacitive type sensor is basically a capacitor with humidity sensitive polymer film as the dielectric.

Fig. 15 Basic structure of capacitive type humidity sensor




2) Sensors based on Resistive effect

Resistive type humidity sensors pick up changes in the resistance value of the sensor element in response to the

change in the humidity, as shown in Fig. 16.

Fig. 16 Basic structure of resistive type humidity sensor from TDK

Thick film conductor of precious metals like gold, ruthenium oxide is printed and calcinatedin the shape of the

comb to form an electrode. Then a polymeric film is applied on the electrode; the film acts as a humidity sensing film

due to the existence of movable ions. Change in impedance occurs due to the change in the number of movable ions.

Humidity sensors based on various materials for both relative and absolute humidity have been reviewed extensively,

including ceramic, semi conducting, and polymer materials. In the majority of publications, there are few papers dealing

with absolute humidity sensors, which have extensive applications in industry.

We reviewed extensively absolute humidity sensors in this chapter, which is unique comparing with other reviews

of humidity sensors. The electrical properties of humidity sensors such as sensitivity, response time, and stability have

been described in details for various materials and a considerable part of the review is focused on the sensing

mechanisms. In addition, preparation and characterization of sensing materials are also described. For absolute humidity

sensors, mirror based dew point sensors and solid-state Al2O3 moisture sensors have been described. Each has its own

advantages. Mirror-based dew-point sensors are more costly in fabrication with better accuracy, while the Al2O3

moisture sensors can be fabricated with low cost and used for moisture control more conveniently. As the major problem

in Al2O3 moisture sensors, long-term instability, has been solved, Al2O3 moisture sensors may have promising future in

industry.

H. Food allergen detector

There are 8 types of food that can cause 90% of the food allergy: milk, eggs, peanuts, tree nuts, fish, shellfish, soy

and wheat. Food allergen detector allows us to detect presence of these components in food by glowing red as an alert. If

the food is safe, then it grows green, as shown in Fig. 17.

This concept is used to feed a warning or safety signal as a notification in smart phone. For example Nima[8] is

such a detector as mentioned above. Results are displayed in front of Nima as well as in sync with an application in the

smart phone of the user, as shown in Fig. 18.

Fig. 17 Nima detector

Fig. 18 Results are displayed in front of Nima as well as in sync with an application in the smart phone of the user.




III. ARCHITECTURE

A. Wireless sensor network architecture

A WSN is composed of a set of nodes that are small as a coin or a credit card and that communicate through

wireless interfaces. WSN have a set of transducers to acquire environmental data and have a microprocessor and a

memory. It can run simple software programs, as shown in Fig. 19.

Fig. 19 Architecture

The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless

sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in

many industrial and consumer applications, such as industrial process monitoring and control, machine health

monitoring, and so on.

The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to

one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with

an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the

sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in

size from that of a shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic

dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of

dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in

corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth.

The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The

propagation technique between the hops of the network can be routing or flooding, as shown in Fig. 20.

Fig. 20 Typical multi-hop wireless sensor network architecture

1) Advantages and disadvantages

Advantages:

Using location and internet we can notify the user if a given route is a safe one. We will have sensors to collect

the traffic data, more the traffic more the pollution.

When the person is running or walking hard and crossing the normal threshold value of regular walk, we can

send a warning notification.

https://en.wikipedia.org/wiki/Radio

https://en.wikipedia.org/wiki/Transceiver

https://en.wikipedia.org/wiki/Antenna_(radio)

https://en.wikipedia.org/wiki/Microcontroller

https://en.wikipedia.org/wiki/Battery_(electricity)

https://en.wikipedia.org/wiki/Embedded_system

https://en.wikipedia.org/wiki/Energy_harvesting

https://en.wikipedia.org/wiki/Sensor_node

https://en.wikipedia.org/wiki/Star_network

https://en.wikipedia.org/wiki/Mesh_networking

https://en.wikipedia.org/wiki/Wireless_mesh_network

https://en.wikipedia.org/wiki/Routing

https://en.wikipedia.org/wiki/Flooding_algorithm




A person can now on spot analyze the presence of pollen particles using pollen sensors by taking advantage of

real time analysis that is available anywhere.

We have different sensors which sense smoke, fog etc.

Disadvantage:

A challenge yet remains to integrate the entire system into one model to make it usable. We have food, pollen

and also humidity detector available, but all these sensors need to be integrated into a single device which will

also incorporate other parameters as we have described in our model.

2) Applications

This pollen detector detects pollens by the principle of scattering of light. Conventional Pollen counting has been

time-consuming work so far. It uses one light emitter and two light receptors to distinguish pollen from other particles on

the basis of two factors, "scattered light intensity" and "degree of polarization". Thus the asthma detection system

successfully detects presence of pollen to warn the user. CL-M53R –a small structure humidity sensor serves this

purpose. CL-M53R sensor is lead free sensor and environmentally safe. Thus the system also detects humidity

percentage. Food allergen detector allows us to detect presence of these components in food by glowing red as an alert. If

the food is safe, then it grows green. This concept is used to feed a warning or safety signal as a notification in smart

phone. For example Nima is such a detector as mentioned above. Results are displayed in front of Nima as well as in

sync with an application in the smart phone of the user. Blood pressure monitoring.

Healthcare Solutions using Smart phones. The use of mobile devices to remotely monitor the health or location of

patients with chronic diseases or conditions has already become a viable option. Mobile device apps can provide public

health surveillance, aid in community data collection, or assist disabled persons with independent living. In one study, a

single-lead electrocardiograph (ECG) was connected to a smartphone to diagnose and follow treatment of patients with

sleep apnea, providing a possible alternative to costly and labor-intensive polysomnography. Sensors attached to

garments that communicate with mobile devices have also been used to remotely monitor and collect medical data

regarding chronically ill elderly patients.

A clinical monitoring system was developed to monitor an entire unit or one bed in intensive care via smartphone; it

displays an alarm, color-coded according to severity, based on patient vital signs. The app iWander for Android was

developed to monitor and track patients with early Alzheimer’s disease who are prone to wandering by using the mobile

device GPS. HanDBase, a HIPAA-compliant relational database software program, can be used on mobile devices to

track hospitalized patients according to their locations, diagnosis, tests, treatments, and billing information. Smartphone

apps have also been used to monitor patients during rehabilitation. For example, a smartphone connected via Bluetooth to

a single-lead ECG device enabled the monitoring of patients in their own neighborhoods when they were unable to reach

traditional hospital-based rehabilitation. Although potentially useful, patient monitoring apps can be limited by factors

such as Internet and GPS reliability, as well as the patient’s ability to use the device. Mobile apps that supplement

medical devices are being developed. One example is iStethoscope, which uses the microphone function of the iPhone to

auscultate and record. While this app isn’t officially intended for use as a medical device, it is significant in that its

existence suggests that mobile devices can eventually replace medical devices. Mobile devices have also been used to

accurately track heart rate and heart-rate variability. In January 2011, MobiSante became the first company to receive

FDA approval for a smartphone-based medical diagnostic tool that uses an ultrasound probe for echocardiography. Work

has also already been initiated to develop ECG recording devices that work with smartphones.

IV. CONCLUSION

We have tried to propose a model of a system which will take into consideration, the most important parameters

before arriving at a conclusion whether to alert the user or not. However a challenge yet remains to integrate the entire

system into one model to make it usable. We have food, pollen and also humidity detector available, but all these sensors

need to be integrated into a single device which will also incorporate other parameters as we have described in our

model. These are some of the parameters which have not been addressed yet. Asthma can be prevented if proper

monitoring of the patient is done on a regular basis, as we all know, prevention is better than cure.

V. FUTURE ENHANCEMENT

Histamine is a chemical which is involved in our immune system, Central Nervous System and also required for

proper digestion. Histamine’s role in our body is to cause an immediate inflammatory response. It acts as an alert in our

immune system, notifying our body of any potential attackers. As a result, asthma attacks are more probable whenever

histamines come into play. Histamine stabilizers can control histamine production in our body, but till date we haven’t

found a histamine tracking system which can keep a watch on the histamine levels in the patient. These are some of the

parameters which have not been addressed yet. We try to propose a model to develop sensors to sense histamine levels in

our body.

REFERENCES [1] Soot sensors for efficient combustion and Low emissions – SootSensII(PDF)

https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source

[2] Pollen Detector http://www.shinyei.co.jp/stc/optical/poln/main_ps2_e.html

https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source

http://www.shinyei.co.jp/stc/optical/poln/main_ps2_e.html




[3] Histamine http://www.mindbodygreen.com/0-11175/everything-youneed- to-know-about-histamine-

intolerance.html

[4] Food Allergy detector http://knegadesign.com/allergen/

https://www.pinterest.com/pin/51650726946424180/?from_navigate=true

[5] J. Höller, V. Tsiatsis, C. Mulligan, S. Karnouskos, S. Avesand, D. Boyle, From Machine-to-Machine to the

Internet of Things: Introduction to a New Age of Intelligence, Amsterdam, The Netherlands:Elsevier, 2014.

[6] G. Kortuem, F. Kawsar, D. Fitton, V. Sundramoorthy, "Smart objects as building blocks for the Internet of

Things", IEEE Internet Comput., vol. 14, pp. 44-51, Jan./Feb. 2010.

[7] K. Romer, B. Ostermaier, F. Mattern, M. Fahrmair, W. Kellerer, "Real-time search for real-world entities: A

survey", Proc. IEEE, vol. 98, no. 11, pp. 1887-1902, Nov. 2010.

[8] Z. Pang, "Technologies and architectures of the Internet-of-Things (IoT) for health and well-being", Jan. 2013.

http://www.mindbodygreen.com/0-11175/everything-youneed-%20to-know-about-histamine-intolerance.html

http://www.mindbodygreen.com/0-11175/everything-youneed-%20to-know-about-histamine-intolerance.html

http://knegadesign.com/allergen/

https://www.pinterest.com/pin/51650726946424180/?from_navigate=true

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A Novel Approach for Asthma Prediction - ijarcsse.comijarcsse.com/Before_August_2017/docs/papers/Volume_7/6_June2017/… · Sridevi Women’s Engineering, College, Hyderabad, India