TOWARD MOBILE HEALTHCARE SERVICES BY USING

EVERYDAY MOBILE PHONES

Akio Sashima, Yutaka Inoue, Takeshi Ikeda

Tomohisa Yamashita, Masayuki Ohta and Koichi Kurumatani

National Institute of Advanced Industrial Science and Technology (AIST)/CREST, JST

2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan

Keywords:

Healthcare, mobile phone, wireless biological sensor, electrocardiograph, skin thermometer, and 3-axis ac-

celerometer.

Abstract:

More than two billions people use mobile phones in the world of today. The mobile phones are not just potable

telephones but portable computers which have WWW browsers with multi-task OS. In this paper, we specif-

ically examine the possibility of mobile healthcare services by using everyday mobile phones. We describe a

prototype system of the mobile healthcare services. It consists of the following components which coopera-

tively work with mobile phones: wireless biological sensors, mobile sensor routers, and sensor middleware.

The service of the system aims to maintain and improve user’s condition by monitoring one’s biological sens-

ing data, such as ECG, skin temperature, and body movement.

1 INTRODUCTION

Aged people have rapidly increased in most of ad-

vanced nations. In such matured societies, mobile

and ubiquitous computing technologies which sup-

port healthcare services are important. In addition,

more than two billions people use mobile phones

in the world of today. As popularity of the mo-

bile phones have increased, healthcare services us-

ing the mobile phones have drawn match attention

from researches (Leijdekkers and Gay, 2006)(Oliver

and Flores-Mangas, 2006). Although mobile services

have been studied in mobile computing for many

years, researches using everyday mobile phones with

sensor technologiesare very few. Recently, several re-

searches which intended to detect user’s contexts by

using sensor devices embedded in a mobile phones

are proposed in the ﬁeld of ubiquitous computing

(Lester et al., 2006)(Kawahara et al., 2007).

This research speciﬁcally examines the possibil-

ity of ubiquitous healthcare service by using every-

day mobile phones. Although current mobile phones

are regarded as portable computers which have vari-

ous computational facilities, they have limited com-

putational powers. Smartphones which have sufﬁ-

cient computational powers are proposed for several

years. However, they have got little popularity for or-

dinary people.

In this paper, we describe a prototype for the

mobile healthcare system by using everyday mobile

phones and wireless biological sensors. To com-

pensate the limited abilities of the everyday mobile

phones, the prototype system consists of the fol-

lowing components which cooperatively work with

mobile phones: wireless biological sensors, mobile

sensor router, and sensor middleware. The system

provides the healthcare service to maintain and im-

prove user’s conditions by monitoring one’s biologi-

cal information, such as heartbeat, posture, and move-

ment. The system uses wearable wireless sensors,

e.g., electrocardiograph, skin thermometer, and 3-axis

accelerometer.

2 HEALTHCARE SERVICES

USING MOBILE PHONES

To develop mobile healthcare systems, we have con-

sidered the following two healthcare scenarios.

Self monitoring services for physical exercises

When people are getting physical exercises, such

as doing aerobics, it is important to monitor

their current physical conditions for keeping

appropriate strengths of the exercises. If their

everyday mobile phones can show the conditions

242

Sashima A., Inoue Y., Ikeda T., Yamashita T., Ohta M. and Kurumatani K. (2008).

TOWARD MOBILE HEALTHCARE SERVICES BY USING EVERYDAY MOBILE PHONES.

In Proceedings of the First International Conference on Health Informatics, pages 242-245

 SciTePress

without special self monitoring devices, they can

easily control the strength of the exercises. Thus,

self monitoring for physical exercises using their

mobile phones is an important mobile healthcare

service.

Remote and self healthcare services for aged people

Aged people and patients of heart diseases require

preparing for unexpected health troubles. Their

physical conditions should be remotely monitored

by their doctors and family members. Thus, mon-

itoring their current physical conditions, such as

abnormality of ECG, is an important healthcare

service using their mobile phones.

Under the vision of the above scenarios, we have

developed a prototype system of the mobile health-

care services. Figure 1 shows an outline of the mobile

healthcare system.

The system monitors user’s physical conditions

using an everyday mobile phone

and a wireless bi-

ological sensor

. To sense electrocardiograph cor-

rectly, it requires to be attached to user’s chest by

sticking electrodes of the sensor on tight with a peel-

off sticker. Once it is attached to user’s chest, it can

detect the inclination and movement of the upper half

of user’s body by the 3-axis accelerometer.

Figure 1: Healthcare services by using everyday mobile

phones.

The system consists of wireless biological sen-

sors, mobile sensor router and sensor middleware,

and provides following facilities: 1) communicating

to a wireless bio sensor via a mobile sensor router at-

tached to user’s mobile phone, 2) analyzing the sensed

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data by cooperating with sensor middleware on a re-

mote server to capture one’s conditions, and 3) pro-

viding personalized information for the user using

GUI on the mobile phone.

Most of mobile phones popularly used in Japan

can execute Java programs downloaded from Web

sites. Thus, service processes in mobile phones are

implemented by multiple threads running on the Java

runtime environment. The processes includes col-

lecting surrounding sensor data via a mobile sensor

router, sending sensed data to a remote sensor mid-

dleware for analyzing them, and graphically showing

analytic results of the sensed data.

The wireless biological sensor for sensing user’s

conditions is a small wearable sensor which integrates

three kinds of built-in biological sensors: electrocar-

diograph, skin thermometer, and 3-axis accelerometer

(see Figure 2).

Figure 2: Wireless bio sensor.

Figure 3 shows a mobile sensor router attached to a

serial port (UART) of the mobile phones. The router

has three main functions: communicating with sensor

networks, reducing the number of sensed data, and

communicating with a mobile phone.

Figure 3: Mobile Sensor Router.

Although the router is able to receive full rates of

sensor data stream from the wireless bio sensor, the

mobile phone is not able to receive such amount of

data stream. Thus, the router sends not raw data re-

ceived from the sensors but reduced data of them at

the router. It repeats the following processes to re-

duce the data:

1. clearing a sample buffer;

2. storing obtained sensing data for a certain period

of time in the sample buffer;

3. calculating a representative value (e.g., average,

maximum, minimum, latest) of the stored data in

the sample buffer;

The size of the sample buffer is conﬁgurable. If

the size is N, the process reduces by one Nth the num-

ber of sensed data.

To analyze sensed data for the healthcare service,

sensor middleware, called SENSORD (Sashima et al.,

2006), performs several signal processing and clas-

siﬁcation processes based on machine learning tech-

niques, such as Discrete Fourier Transform (DFT),

support vector machine (SVM), nearest neighbor

learning. Using such algorithms, the sensed data is

statistically analyzed or classiﬁed to some qualitative

categories. The results of the analysis are used for

the service to create suitable contents, such as HTML

documents to be shown by the mobile phones.

3 USER INTERFACE OF THE

SERVICE

User interface of the prototype system has the follow-

ing three modes: conﬁguration, graph, and monitor.

In the rest of this section, we describe outlines of the

service at the each mode.

3.1 Conﬁguration Mode

In “conﬁguration mode”, users can conﬁgure several

parameters of the services, such as transmission rates

and sensor device id. Considering limited computa-

tional power of a mobile phone, we have decided a

best transmission rate and a type to reduce the data

for each sensor. We have also decided best transmis-

sion rates of the router for each sensor. Table 1 shows

a default conﬁguration of the sensors and the router

for the healthcare service. Notice that all transmis-

sion rates of the router are smaller than the rates of

sensors. This means that the data sent to the mobile

phone is not raw sensor data but reduced sensor data.

In our implementation, the default conﬁguration

shows best results about processing the data. For ex-

ample, when the transmission rates are more than the

conﬁguration values, the response time of the system

is slow down, and showing classiﬁcation results be-

comes delayed for several dozen seconds.

Table 1: Default conﬁguration of the data transmission for

the mobile healthcare service.

Transmission Rate

Sensing

Data

From

Sensor to

Router

From

Router to

Phone

Reduction

Type

Skin temp. 204 Hz 2 Hz latest

ECG. 204 Hz 8 Hz maximum

3-axis acc. 204 Hz 8 Hz average

3.2 Graph Mode

In “graph mode”, the system graphically shows user’s

biological statuses, such as ECG, in real time (see

left side of Figure 4). The user can know their cur-

rent physical conditions graphically, and control the

strength of the exercise. In this mode, the received

data from the router is directly shown on the display

without data processing by the sensor middleware.

Figure 4: GUI of the healthcare service: graph mode (left

side) and monitor mode (right side).

3.3 Monitor Mode

In “monitor mode”, the system shows analytical re-

sults of user’s sensing data. The results include

numerical values representing the user’s conditions,

such as heartbeat. It also categorizes user’s condi-

tions into some qualitative statuses: running, walk-

ing, standing-still, etc. (see left side of Figure 4). The

following analytical results are shown in this mode.

3.3.1 Posture Recognition

This process analyzes the sensed data to categorize

user’s postures into three categories: standing-still,

facing downward, and facing upward. The postures

are determined by calculating inclination of the upper

half of user’s body based on the values of the 3-axis

accelerometer.

3.3.2 Movement Recognition

This process analyzes the sensed data to monitor

user’s movements, such as step speeds. The steps are

recognized by calculating Discrete Fourier Transform

(DFT) of ﬁnite length time series of sensed data of the

y-axis accelerometer. In the current implementation,

the length of the time series is 8 seconds (64 samples).

When the middleware is asked to calculate the current

steps, it retrieves the latest 64 samples from the data

storage of the middleware, and calculate it. Then, it

classiﬁes user’s statuses into three categories: staying,

walking, running based on the results of DFT. When

user’s status is “walking” or “running,” it also shows

an average speed of user’s movements as Steps Per

Minute (SPM). The user can control his/her move-

ments based on the information.

3.3.3 Heartbeat Monitoring

This process analyzes the sensed data to monitor the

user’s current status of heartbeat as beat per minute

(BPM). The heartbeats are calculated by DFT of ﬁ-

nite length time series of sensed data of the electrocar-

diograph. In the current implementation, the length

of the time series is about 16 seconds (128 samples).

When the sensor middleware is asked to calculate the

heart beat, it retrieves the latest 128 samples from the

data storage of the middleware, and calculates it.

As a prototype implementation of the remote

healthcare scenario, emergency messages (email) are

sent to a doctor and family members when the abnor-

mality of the heartbeat and the posture are recognized.

3.3.4 Skin Temperature Monitoring

The biological sensor is able to monitor user’s skin

temperature. If the temperature is lower than 31 ˚C,

the system recognizes that the sensor is not attached

to a human body.

4 CONCLUSIONS

In this paper, we have focused on implementing a pro-

totype system of the mobile healthcare service by us-

ing not smart-phones but mobile phones with attach-

ments, namely mobile sensor routers. Smartphones

are still special devices for limited types of persons,

such as businesspersons. By using the existing mo-

bile phones, we have aimed to enable ordinary people

to always collect and analyze their health information

derived from wireless biological sensors. We have

conﬁrmed that cooperations between the current mo-

bile phones, the mobile sensor routers, and the sensor

middleware is able to provide the mobile healthcare

service for the ordinary people. In future work, we

plan to examine possibility of a general-purpose ser-

vice platform for various mobile healthcare services

by using everyday mobile phones.

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