sleeping situation monitoring system in ubiquitous environments
TRANSCRIPT
ORIGINAL ARTICLE
Sleeping situation monitoring system in ubiquitous environments
Chang-Won Jeong • Su-Chong Joo •
Young Sik Jeong
Received: 29 August 2011 / Accepted: 3 January 2012
� Springer-Verlag London Limited 2012
Abstract In recent years, because of the development of
ubiquitous technology in health care, research is actively
progress. We describe a sleeping situation monitoring
system, created to support home healthcare services. We
discuss the method we used to develop the system and how
to use the sleep activity monitor to support home health
care. Information about the sleeping situation is collected
from motion detection, sound, and vibration sensors. And
this information is based on real-time processing, we used
the TMO (Time-trigger and Message-trigger Object)
schema and TMOSM (TMO Support Middleware) into the
development software environment of the healthcare
application. To verify the practical use of sleeping situation
information as recorded by the system discussed in this
paper, we relate an example of the monitoring of a sleeping
situation using our system, and we describe the results of
an experimental evaluation.
1 Introduction
Recently, the healthcare research is very important to the
activities in ubiquitous computing environments. Espe-
cially, healthcare service area is changed to home for the
elderly or patients. When creating systems for use in home-
based health care and wellness management, extensibility
and personalization are very important [1]. Sleep is extre-
mely important, and almost one-third of the human lifetime
is spent sleeping. Sleep deprivation due to sleep-related
disorders or other factors can cause severe physical effects,
cognitive impairments, and mental health complications
[2]. Polysomnography, a standard medical approach for
sleep monitoring and objective sleep quality measurement,
is usually conducted in a hospital where multiple physio-
logic variables are recorded while subjects sleep [3].
Typically, approaches to sleep monitoring have relied
on experts or trained carers who manually rate a person’s
sleep, on the basis of either objective measurements in a
hospital setting or interviews with the patient or his rela-
tives. This is very expensive and inconvenient, since it
involves data collection in hospital settings that are unfa-
miliar to patients.
To overcome these problems, we suggest a new sleeping
situation monitoring system. This system provides sleep
situation information and an alarm in case of emergency.
Our study addresses the monitoring of sleep in patients
with sleep disorders rather than the risks associated with
sleep disorders or methods for coping with emergency
situations.
However, this system can contribute to health care and
healthcare research through provision of context informa-
tion and emergency alerts to researchers and carers.
In this paper, we describe a software structure based on
the distributed object group framework (DOGF) that inte-
grates sleeping situation information. We used a TMO
(Time-trigger and Message-trigger Object) scheme to
implement the components of the software application and
TMOSM (TMO Support Middleware) to process interac-
tions between distributed components. Finally, we show
the structure of the system and the software application that
is based on it.
C.-W. Jeong � S.-C. Joo (&) � Y. S. Jeong
Department of Computer Engineering, Wonkwang University,
344-2 Shinyong-Dong, Iksan, Chon-Buk 570-749, South Korea
e-mail: [email protected]
C.-W. Jeong
e-mail: [email protected]
Y. S. Jeong
e-mail: [email protected]
123
Pers Ubiquit Comput
DOI 10.1007/s00779-012-0570-x
The paper is organized as follows: the next section
describes related work by other researchers; Sect. 3 pre-
sents the sleeping situation sensing system and the inter-
action of software components; Sect. 4 describes the
software application and its features and abilities; and the
last section describes the conclusion and plans for future
work.
2 Related work
There have been earlier attempts to create sleep monitoring
systems based on sensors. One such system, the bed tem-
perature monitoring system, uses the temperature distri-
bution in a patient’s bed to monitor body movement during
sleep [4]. This system consists of temperature sensors, a
solid-state recorder, and a personal computer. It is effective
for long-term temperature monitoring without patient dis-
comfort. However, the time required for calculation is
much longer than that required for sampling.
Gaddam et al. [5] proposed an intelligent bed sensing
system. This system is based on FlexiForce force sensors,
which are ultrathin, flexible, non-obtrusive printed circuits.
One force sensor is placed under each leg of the bed. The
transient responses from the 4 sensors are compared to
provide an estimate of sleep quality. However, it is difficult
to use the analysis for determining sleep quality, because
only changes in weight are analyzed. Additionally, when
there is more than 1 occupant of the bed, individuals cannot
be identified using this system.
The Biometric Research Center at Seoul National Uni-
versity proposed unconstrained sleep monitoring using an
air mattress [6]. This system detects the minute movements
resulting from the patient’s respiration and heart beat
without attaching any electrodes or sensors to the patient’s
body surface. The system measures air pressure changes in
the air mattress to calculate movement. However, because
it is based on measuring heart rate and breathing intervals,
it is difficult to analyze the wave that results from the
sensing data.
Biswas et al. [1] proposed a framework for an extensible
architecture for sleep and substantiated it with a simple
prototype system for monitoring sleep activity patterns.
This system consists of Bluetooth-enabled, wrist-worn
3-axis accelerometer sensors for measuring activity and a
gateway device (Bluetooth-enabled PDA phone/PC) to
relay sensor data to a remote site. The system uses an
architecture framework to which new sensors, sensing
modalities, or hardware devices can be readily incorporated
in an incremental manner. This allows the system to be
personalized and customized to suit the needs of the patient.
Alexander et al. [7] created an early illness warning
system to be used by an interdisciplinary team comprised
of clinicians and engineers at an independent living facil-
ity. This system consists of algorithms that analyze resident
activity patterns obtained from sensors embedded in resi-
dents’ apartments. Intending to establish links between
sleeping patterns, activity levels, and undesired health
events, these researchers put a system of ambient sensors
such as bed pressure sensors, gait sensors (pressure mats),
video cameras, and stove sensors (to prevent fire hazards)
in several apartments. A bed sensor in this system detects
presence in bed as well as restlessness, breathing, and pulse
while sleeping.
Scanaill et al. [8] described an accelerometer-based
mobility tele-monitoring device in a smart home environ-
ment. The accelerometer in this device is able to classify
different activities such as lying, sitting, standing, and
walking. It collects statistics of each of these activities and
periodically sends summaries to a server via SMS mes-
sages. The data published were from a testing phase that
examined the usability and effectiveness of the design prior
to actual deployment in elderly peoples’ homes. However,
this system does not consider activity that occurs during
sleep.
Many researchers [9–13] have suggested alternate sys-
tems for monitoring sleep disorders instead of expensive
polysomnography. Additionally, several generic system
architectures have already been developed to support patient
monitoring applications. We suggest a system similar to that
created by Scanaill et al. [8]; however, we focus on moni-
toring patient sleeping situations in a healthcare context.
Also, it can be customized to suit the needs of patient, carer
or nurse, and doctor.
Also, most approaches at charting sleep disorders have
restricted their focus to the bed. However, our proposed
system is not restricted to the bed and can be applied
anywhere. For our system, we used DOGF as the archi-
tectural framework [14–18]. This framework allows for the
development of healthcare applications based on applica-
tion logic and the use of different collections of devices and
sensors. To design and implement application components,
we used a TMO scheme [11].
3 Sleeping situation monitoring system
Our sleeping situation monitoring system provides context
information and sensing data through a device manager.
We describe the architecture of proposed system and
interaction of component based on it.
3.1 Proposed system architecture
The system architecture we used allowed us to manage
supporting objects by grouping them into domains for the
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DOGF. Sensors were thus integrated into the system as
supporting objects. To collect and share information in this
environment, we used a TMO scheme and TMOSM [19] in
the development of the software application.
The overall architecture of our proposed system is
illustrated in Fig. 1 and is organized into 6 layers. It sup-
ports a logical single-view system environment by grouping
the layers. The group manager API supports the execution
of the appropriate application service on the uppermost
layer by using the input information obtained from indi-
vidual or grouped physical sensors on the lower layers as a
distributed platform. The sleeping situation information
collected from various sensors and devices is integrated by
the Device Manager. The Device Manager receives the data
as a stream or at discrete intervals. The sleeping situation
information database consists of the sensor node classifi-
cations, collected sensor data, and a user profile. The user
profile includes the health, service information, security
access information, and view information for supporting
service applications.
The architecture of our system can configure new groups
dynamically by integrating physical sensors on the dis-
tributed platform and distributed applications on the upper
layer. We used an adapted TMOSMS as the middleware of
interaction between distributed applications.
Table 1 shows the system architecture services provided
by the components of DOGF and the supporting services of
sleeping situation sensing system that improve the existing
framework.
3.2 The interaction of components
The components of the sleeping situation monitoring
application service are defined by the TMO scheme. We
used the TMOSM to process interactions between distrib-
uted components. Figure 2 describes the interaction of the
components for monitoring application.
In the sleeping situation monitoring application, Sleep-
ing_info_TMO collects data from the sensors, including
motion detection, sound, proximity, and vibration sensors.
Info_provied_TMO displays information about the col-
lected data from Sleeping_info_TMO. Setting_info_TMO
manages sensor data settings. The notification service is
based on this settings information. The service_info_TMO
provides information about emergency situations.
Figure 3 shows the initial data structure for storing sen-
sor data obtained from sleeping situation sensing modules.
The sensor data stored in this structure provide information
about the status of residents and their sleep activity.
In Fig. 4, the form of a message packet is contained in the
header of the message packet. If it is a request message, the
header contains the word ‘‘REQUEST.’’ If it is a response,
the header contains the word ‘‘REPLY.’’ In message packets,
‘‘|’’ delimiters are used to separate the name of the service
component, functions to perform, values and other configu-
ration options, and ‘‘END’’ marks the end of the packet.
Each message packet is an application service. Each
request sent to a TMO component to process will be accepted
through a Query Listener. Request messages are analyzed by
Fig. 1 The system architecture
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Table 1 Supporting services of the sleeping situation sensing system
Component Core supporting services Description
Group
manager
Object group supporting service Grouping service of devices/sensors and distributed objects
Information
repository
Information management service Management service for distributed objects and collected data from sensors
Security Information access right and use to service
object for alarm service
The information collected by sensors is arranged according to users security
demands and user profile information is used for checking authority of each
service object
Dynamic
binder
Dynamic access service Dynamic access for replicated resources according to the binding algorithm
Context
provider
Sleeping situation monitoring service Remote monitoring service by context information extracted from context
provider
Fig. 2 TMO class diagram for
monitoring the application
Fig. 3 The message packet
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the Query Analyzer. The separator ‘‘|’’ is placed between the
Query Parser, the Module Name, Action, and Option.
Response messages are configured by adding the appropriate
collected data, depending on the request message.
4 Implementation of the application service
In this section, we describe the physical environment for
the sleeping situation sensing system and show the moni-
toring of GUI results.
4.1 The physical environment for application service
We suggest the physical system architecture as shown in
Fig. 5 for implementation of the sleeping situation sensing
system described in this paper. This system consists of a
3-tier architecture: home, hospital, or medical institution
environment, server system, and end user systems (pager,
smart phone).
Device for sleeping situation monitoring is placed in the
home, hospital, or medical institution. This device is used
to gather sleeping situation information from motion
detection, sound, proximity, and vibration sensors as in
Fig. 6.
Sound sensors detect breathing, snoring, and crying
during sleep. It provides information regarding the inten-
sity of breathing and snoring. Motion detection, proximity,
and vibration sensors are also included to detect resident
Fig. 4 A process message
Fig. 5 The physical system
architecture
Fig. 6 Device for sleeping situation monitoring
Pers Ubiquit Comput
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movements. This allows detection of any abnormalities
during sleep using the movements and sounds made by
patients. Using a monitoring system server, we can monitor
the resident’s location and status in real time using the
sleeping situation sensing information collected by the
sleeping situation monitoring sensors.
Table 2 shows the classification of sleeping situation
sensors. We defined normal and abnormal results for each
sensor class [7, 20]. These definitions were applied to the
application service.
As seen in Table 2, the application service provides
information regarding the status of the sleeping situation
that is based on data from related sensors. Table 3 contains
the settings used for determining sleep status.
For data collection and medical analysis, we used the
real data collected over 24 h. A more reliable system could
be created if the settings were based on the results of
medical examinations.
4.2 Executing results of application service
We developed a software application that is built on top of
the system architecture, and we enabled monitoring GUI’s
on a server. Figure 7 shows the results of an SMS message
Table 2 The classification of
sleeping situation and related
sensors
Status Sleeping situation Related sensors
Normal state On bed Motion detection sensor, vibration sensor, proximity sensor
During sleep Motion detection sensor, vibration sensor, sound sensor, proximity
sensor
Abnormal
state
Apnea Sound sensor
Seizure,
convulsions
Motion detection sensor, vibration sensor, proximity sensor
Crying, snoring Sound sensor
Bed departure Motion detection sensor, vibration sensor, proximity sensor
Table 3 The sensor settings and status information
Sensor Setting value Status information
Motion detection 0 or 1 Sleeping(1) time
(10:00 p.m.–07:00 a.m.)
Bed departure(0)
Proximity sensor [15 cm Notification of abnormal state
Sound [25 dbe Notification of abnormal state
Vibration [10 Hz Notification of abnormal state
Fig. 7 Result of sleeping
situation monitoring
Pers Ubiquit Comput
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about a patient’s sleeping situation. This application ser-
vice was applied to the patient’s in wards.
The monitoring server GUI is shown collecting data from
4 sensors while a person sleeps on the bed. The symbol �
indicates patient lists and location information, and `
indicates the sleeping situation information resulting from
analysis of sensing data at ˆ. In this figure, an abnormal
state and bed departure are shown. The settings for each
sensor are shown at ´. Not only is the sleeping situation
information sent to the monitoring server every hour, but
notification information is also sent to the caregiver.
Figure 8 presents the sleeping situation information on
smartphone provided to notifications of abnormal status.
The application provides correct information according
to the predefined classifications of sleeping situations.
5 Conclusions
With the improvement of u-healthcare technology, it is
possible to enhance the quality of life of elderly people and
their families. Most approaches to sleep monitoring have
relied on experts or trained carers who manually rate a
person’s sleep. As this system is very expensive, we sug-
gest a sleeping situation monitoring system for supporting
home healthcare services. We designed a system and
described how to use sleep activity monitoring to support
home healthcare services. In order to deal with the system
requirements, we based our system on the DOGF. To
implement the various components of this application, we
used a TMO scheme and TMOSM for the interactions
between distributed components. Finally, we showed the
system environment and the applications based on it.
We plan to create an intelligent algorithm for effectively
compiling and relating medical information and will work
on improving the functionality of our system. We also plan
to perform field tests to evaluate the performance of the
sleeping situation sensing system.
Acknowledgments This paper was supported by Wonkwang uni-
versity in 2012.
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