Using Embedded Sensors in Smartphones to Monitor and Detect

Early Symptoms of Exercise-induced Asthma

Chinazunwa Uwaoma and Gunjan Mansingh

Department of Computing, The University of the West Indies, Kingston, Jamaica

Keywords: Mobile Computing, Sensor Fusion, Health Monitoring, Smartphone, Asthma, Context Recognition.

Abstract: This paper describes work in progress on integrated design architecture for monitoring and detecting early

symptoms of asthma attack using smartphone as sensors’ platform for data capturing, processing,

presentation and feedback. We present an application scenario of exercise-induced asthma where a patient

wears a smartphone equipped with built-in sensors which are capable of providing clinical data and context

on detection of any anomaly in the monitored vital signs. Our design architecture extends the functionality

of “Nine-degree of Freedom” (9-DoF) sensor fusion model and context recognition using expert system

frameworks. The design centers on the idea of creating a simple and portable asthma monitoring system that

is able to detect asthma vital signs, perform signal analysis and context generation; and also send

information to other mobile devices worn by caregivers and physicians. This approach removes the need to

have external monitoring sensors patched on the user’s body, thereby enhancing the usability and reliability

of the system in providing timely information on the state of a patient’s health.

1 INTRODUCTION

Asthma is of one the long-term respiratory

conditions that require real-time and continuous

monitoring, as an attack could occur anytime and

anywhere. The ailment has considerable social and

economic impact on the life of an individual sufferer

and the society (Braman, 2006). The growing

concern to reduce the cost of healthcare utilization

due to asthma, the burden of personal management

of the disease and the workload both on the patients

and healthcare providers, has necessitated the

development of asthma healthcare monitoring and

delivery systems.

Combination of mobile computing technologies

and wireless body sensor networks presents

opportunities for unobstructive monitoring of

patient’s clinical data and seamless communication

of the monitored data to healthcare professionals.

Recent studies have focused on utilizing the

potentials of intelligent mobile devices and wireless

body sensors for data acquisition, signal processing

and context recognition in health applications (Seto

et al., 2009; Zhou and Zhang 2011). However, many

heterogeneous wireless body sensors used for health

monitoring do not always provide precise

information on the sensed signals. Oberoi (2011)

argues that these sensors come with different

Operating Systems as well as proprietary network

and middleware protocols, which makes it difficult

to have a single sensing system that can

comprehensively monitor and detect events of

interest. Furthermore, the inability of patients to

choose and setup different sensors and monitors; and

also, difficulty in analyzing the circumstances

surrounding the sensed data may result in delayed

communication and uncertainty of data presented to

healthcare professionals. A design platform that will

allow integration of multiple sensor modalities and

implementation of context recognition frameworks

on a Smartphone could help overcome these issues.

As mobile computing evolves, mobile phones are

being turned into high performance computer

systems which are handy, discreet and pocket fit

devices (Fernandes and Afonso, 2011). Modern

smartphones come readily equipped with advanced

sensors for detection and recognition of events,

internet access, improved processing and memory

units as well as flexible communication system and

connectivity. Weghorn (2013) notes that these

intelligent mobile devices also run open software

system (e.g. Android) with better User Interface

features compared to stand-alone sensors and

monitors which use ‘closed’ system, specialized

145

Uwaoma C. and Mansingh G..

Using Embedded Sensors in Smartphones to Monitor and Detect Early Symptoms of Exercise-induced Asthma.

DOI: 10.5220/0004806901450150

In Proceedings of the 3rd International Conference on Sensor Networks (SENSORNETS-2014), pages 145-150

ISBN: 978-989-758-001-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

hardware and also, bulky in size. The enhanced

capabilities provided in smartphones allow for

development and use of third-party software such as

asthma monitoring applications. Premised on the

above considerations, our design objective centers

on integrated system architecture that allows the use

of personal mobile devices to monitor, detect and

alert early signs of asthma attack.

2 RELATED WORK

Research has focused on the use of advance

information and communications technologies to

improve overall asthma management and control

ranging from electronic peak-flowmetry and asthma

diaries through asthma web-based tools to mobile

phone applications (Seto et al., 2009). Seto et al.

(2009) discuss several approaches that have been

used in the design of asthma e-health systems. These

solution methods however, are not quite sufficient to

provide comprehensive monitoring of asthma patient

without the inclusion of lung sound monitoring.

A new paradigm in asthma management and

control is monitoring wheeze rate which involves

signal analysis of asthma breath sounds. Mobile

phones are capable of recording breath sounds and

performing analysis on the recorded signal

(Anderson et al., 2001; Zhou and Zhang

2011).Wheeze is one the frequent symptoms

experienced by patients and doctors have described

it as a clinical predictor of asthma exacerbation .

Wheeze detection and analysis provides medical

doctors with critical information on how to adjust

treatment of patients with asthma condition. There

are several computer algorithms which can detect

and evaluate percentage of wheeze on normal

breath. Wisniewski and Zielinski (2010) provide

detailed review on these algorithms. Nonetheless,

acoustic signal analysis is only one of many tasks in

preventing asthma attack. There is need to measure

and analyze other vital signs including

environmental effects as well as patient’s level of

activity, in order to provide accurate and reliable

information in controlling asthma exacerbation.

Leveraging the potentials of the internal sensors,

Smartphones can be configured to provide reliable

and timely medical intervention for patients dealing

with asthma crises. Smartphones are able to

correlate patterns in the user’s movement using

embedded sensors like GPS, gyroscope and

accelerometer. They can also recognize sudden

changes in bodily position and decide on the severity

of patient’s condition by evaluating sensors’

measurements (Kiss, 2011). The embedded

microphone can record patient’s breath sound, while

ambient data such as air pressure, humidity and

temperature can be captured by barometer,

hygrometer and thermometer in the smartphone, to

provided context on detected events.

2.1 Monitoring Exercise-Induced

Asthma

Exercised-Induced Asthma (EIA) is triggered by

exercise and physical exertion. Asthmatics with

chronic conditions manifest signs of asthma attack

during exercise. However, there are many people

without asthma who develop symptoms only during

rigorous exercises like sporting activities. An

important characteristic feature of asthma condition

is the hypersensitivity of the airways to

environmental factors. McFadden and Gilbert (1994)

observe that persons with EIA have airways that are

very sensitive to changes in temperature, humidity,

and altitude. Common symptoms of EIA are wheeze

and shortness of breath. Besides wheeze and

difficulty in breathing, another vital sign of EIA

attack is change in patients’ posture. Persons

experiencing asthma attack tend to lean forward in

an effort to get sufficient air into the lungs, as shown

in the presentation (Signs of a Pending Asthma

Attack n.d).

There are preventive measures against EIA

attack such as inhaled medications and Peak–

Expiratory Flow (PEF) measurements (Casa et al.,

2012). However, asthma patients at times find it

tasking to adhere to prescribed medications and

action plans which results in poor management and

control of the condition. Hence the need for a

personal assistive monitoring tool to alert patients

and their care givers on signs of pending asthma

attack as the patients engage in rigorous physical

activities. Figure 1 depicts how a smartphone could

be worn to monitor asthma vital signs during

exercise.

Figure 1: Exercising with Smartphone strapped on the

neck to monitor asthma vital signs (Source: Google

Image).

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2.2 Sensor Fusion and Context

Recognition

A new development in sensor applications is multi-

dimensional sensing which enhances the user’s

experience through sensor fusion. Sensor fusion is a

technique that uses special filtering algorithms to

compensate limitations of individual sensors in order

to generate a desired output.

Integrating inputs from

multiple sensors using sensor fusion allows for more

accurate and reliable sensing, which consequently

produces much higher levels of recognition for

appropriate response. Pattern-matching algorithms

can identify and correlate events that will assist with

delivering awareness to the user or the monitoring

system. Through training and experience, the

monitoring system adapts to changing circumstances

and responses based on the generated context (Lee et

al., 2008).

The importance of context awareness has

continued to gain wider recognition in healthcare

monitoring applications. By combining sensor

fusion and context recognition techniques, we can

develop smart systems that can help manage patients

with chronic conditions such as asthma; and alert the

patient or the healthcare providers of any anomaly.

3 THE EXTENDED 9-DOF

SENSOR FUSION MODEL

Our design methodology builds on the platform of

sensor fusion solution that combines triaxial-

accelerometer, triaxial-gyroscope and triaxial-

magnetometer.

These 3D-sensors have the capability of

performing basic sensing (Ristic, 2012). An

accelerometer measures linear motion, a gyroscope

defines orientation while the magnetometer provides

direction sensing. Their capabilities notwithstanding,

these sensors are characterized by certain limitations

that affect accuracy in applications. However, sensor

fusion can be used to overcome the shortcomings of

individual sensors. It eliminates deficiencies in

separate devices by using intelligent algorithms and

unique filtering techniques to produce synthesized

and more sophisticated output; thus satisfying the

impression that “The whole is greater than the sum

of its parts” (Karimi, 2013).

An important feature of 9-DoF sensor fusion

solution is its flexibility to accommodate several

sensors. More sensors can be added on need basis

and this automatically transforms the sensor fusion

solution to an m-DoF solution where ‘m’ implies

‘multiple’ (see Table 1).

The design of the proposed asthma monitoring

system includes activity and motion sensors,

location sensor, ambient temperature and humidity

sensors, air pressure sensor, and an embedded

MEMS microphone for recording and monitoring

asthma wheeze rate. The design is based on m-DoF

solution (see Figure 2). The model allows two data

paths to be used in processing the sensed data

(Ristic,

2012).

1. Pass Through data path - sends raw data

directly to the application.

2. Sensor Fusion path - allows initial raw data to

be processed and synthesized into a smart

output.

In our design, the monitored wheeze signal will

require a separate application for analysis and need

not to go through the sensor fusion path. Also data

from ambient sensors may have to be sent ‘raw’ for

analysis. This scenario, thus, makes the model

suitable for the proposed design.

Table 1: m-DoF Sensor Fusion Models.

m-DoF Sensors Mobile Platforms

6-DoF

3D-gyroscope, 3D-

compass

Android, Linux

and Windows

9-DoF

3D-gyroscope, 3D-

compass, 3D-

accelerometer

Android, Linux

and Windows

12-DoF

3D- gyroscope, 3D-

compass, 3D-

accelerometer,

Barometer,

Thermometer, ALS

Windows and

Android

4 INTEGRATED DESIGN

ARCHITECTURE

The integrated system architecture consists of three

main layers: Data Acquisition Layer, Data

Processing Layer, and Data Analysis Layer (see

Figure 3). Implementing m-DoF sensor fusion

algorithms at the Data Acquisition Layer allows

multi-modal data collection. Data Processing

components enable extraction and representation of

important features of the data while Data Analysis

components provide analysis and description of

context data sets.

UsingEmbeddedSensorsinSmartphonestoMonitorandDetectEarlySymptomsofExercise-inducedAsthma

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Figure 2: An extended 9-DoF sensor fusion model for asthma monitoring system using embedded sensors in Smartphones

(Ristic, 2012).

Figure 3: Layered architecture of the asthma monitoring

system.

5 DESCRIPTION OF THE

ARCHITECTURAL

COMPONENTS

In this section, we explain the functionality of the

integrated system architecture.

5.1 Data Collection and Signal

Processing

Embedded sensors in the Smartphone collect signals

and quantities of interest - physiological, activity,

ambient and location data. Sensed data can be

distorted due to noise, interference, or other

environmental factors; hence the need for signal

processing to obtain meaningful data. Readers and

Filters algorithms are data processing components

that can interface with the sensors and perform

signal cleansing on the recorded data respectively.

However, these components may not be used for the

fused data since we are applying the m-DoF sensor

fusion solution that performs the two tasks on the

firmware at the Runtime (Ristic, 2012).

5.2 Data Fusion

The described Sensor Fusion model in section 3

provides flexibility for certain aspects of the sensed

signals to be combined to extract desired features

while other data “pass through” as calibrated direct

input to the application processor as shown in Figure

2. With exercise-induced asthma in view, candidate

parameters for fusion are linear motion, gravity,

orientation and direction; derivable from

accelerometer, gyroscope and compass sensors.

Combining these features gives better description of

patient’s level of activity in terms of motion and

position of the body. To obtain these key features,

the accelerometer compensates for gravity loss in

gyroscope which provides the orientation estimates,

while the compass corrects the heading error.

5.3 Wheeze Detection

Wheeze detection systems are designed based on the

idea and principles of stethoscope. Previous designs

involve the use of external sensitive microphones

placed on the chest to record lung sound or on the

neck to record tracheal sound (Sen and Kahya 2006).

However, our design uses an embedded MEMS

microphone located at the mouthpiece of the

Smartphone.

Sovijarvi et al. (as cited in Wisniewski &

Zielinski 2010) note that asthma wheeze signal has a

dominant frequency usually above 100 Hz and with

duration of about 80ms to 100ms. The breath sounds

is pre-processed by applying High pass filters to

remove noise or other low-frequency signals that

may manifest similar morphologies. Characteristic

features used for determining the presence of

Output 2

Sensor 1

Sensor 2

Sensor m

Output1

Output 1

Output 2

Alerts

Diagnosis

Feedback

Alarm

Context/

Knowledge

Generation

Raw Data

Pre-processed Data

Fused Data

Pass

Through

Sensor

Fusion

USER INTERFACE

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wheeze include the tone, location, duration and

number of wheeze in a breath cycle.

Frequency domain algorithms are considered to

be more efficient for wheeze detection and

classification. However time-domain analysis using

classification features like Kurtosis and Renyi

entropy will be faster for real-time applications; and

also, computationally effective for low-power

mobile devices like smartphones.

5.3.1 Kurtosis

Kurtosis (1) shows the degree of peakedness of a

probability distribution for a random variable X and

it is defined as:

k =















(1)

Kurtosis value k around or larger than 3 indicates

normal distribution for non-wheeze signals while a

value less than 3 shows that the breath sound

contains wheezes and thus would have a uniform or

sub-gaussian distribution (Aydore et al., 2009).

5.3.2 Renyi Entropy

This feature, also known as generalized Shannon

entropy is a measure of uncertainty of signals and

uniformity of distributions (2). For random variable

X, Renyi entropy is defined as:

















log 









)

(2)

Study in (Aydore et al., 2009) shows that wheeze

signal distribution has higher degree of uniformity

when compared to non-wheeze signal.

By applying filter algorithms, the signal features

are extracted into the temporary facts/ context

database for further classification.

5.4 Context Generation, Analysis and

Classification

It is important to select algorithms that will allow the

system to generate intelligent information on the

reported event or sensed data. Thus, we consider

classification method based on decision rules

obtained from medical experts and literature, as has

been used in previous studies (Aydore et al., 2009;

Basilakis et al., 2010; Borgohain and Sanyal, 2012;

Bae et al., 2013). Rule-based classifiers label records

or facts using a collection of “condition-action” rules

defined as follows;

Rule: (condition) → l, where ‘condition’ is a

conjunction of attributes and l is the class label.

Processed data from different sensor modes can be

grouped into four categories namely: Physiological

data, Activity data, Ambient data and Location data.

For the purpose of analysis however, we use only

two categories as summarized in Table 2. In the

exemplary classification and analysis of the sensed

data, we illustrate how sensor fusion technique and

rule-based expert system can be combined to handle

multi-parameter sensing and context recognition.

Table 2: Classification of Processed Sensor Data.

Category A: Physiological Data

Sensor Measurement

Duratio

Kurtosis

(k)

Class

Label

MEMS

Micro-

phone

Breath Sound

Signal

≥ 80ms < 3 Wheeze

< 80ms ≥ 3

Non-

wheeze

Category B: Activity Data

Sensor

(Fused)

Measure-

ment

Motion Orientatio

Class

Label

Accelero-

meter,

Gyroscope

and

Compass

Activity

Level

Fast Upright Very

active

Slow Inclined Active

None Inclined Non-

active

The generated context is annotated using JSON-

based description. The annotations can be displayed

directly to the Smartphone console or stored

temporarily in the context database for subsequent

analysis. Context or situational analysis and pattern

recognition can be performed by using Rete

Algorithm (Figure 4) to combine decisions and

confidence levels from experts in the application

domain.

Figure 4: Rete Decision Tree - Network for Context

Recognition.

Knowledge inferred from context analysis

provides classes of services: alerts, diagnosis,

feedback and alarm. The information can be stored

as patient’s health history on the SD card or shared

with healthcare providers for further diagnosis and

treatment. Alarm and Alert messages are sent in the

event of emergency or detection of any anomaly on

the monitored signals.

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6 CONCLUDING REMARKS AND

FUTURE WORK

The emerging IC MEMS sensory technology in

Smartphones defines a new path in healthcare

monitoring. Benefits of using mobile phone as

multi-parameter sensing device include the ability of

an asthma patient to carry an accurate all-in-one

monitor anywhere; and the ability to make baseline

measurements at anytime thereby generating a

database that could allow for improved detection of

disease state and control.

Our emphasis has been on sensor fusion and

context modelling given that data fusion and context

awareness are critical for optimal performance of

any health monitoring system. Whereas data fusion

provides accurate value, context recognition

provides knowledge on what to do with the data.

The sensor fusion requires substantial MCU power

which may not be fully provided by Smartphones

given the limited power source for these devices.

Using a dedicated sensor processor may be an

efficient way of performing sensor data computing.

Optimal performance and cost however, are major

considerations for independent sensor processor.

The wheeze signal also, needs to be analyzed in a

separate application. We are investigating how the

application can run concurrently with data fusion in

order to have a synchronized output.

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