Authors: Yu-Wei Liu, Yeh-Liang Hsu (2014-01-15); recommended: Yeh-Liang Hsu
Note: This paper was presented in 2013 IEEE International Conference on
Systems, Man, and Cybernetics (SMC), pp 1466-1470, Manchester.
Development of a bed-centered telehealth system based on a
Abstract—Given the rapid increase in the aging population and the decline in
birth rate, there is a growing demand for healthcare services. For the elderly
who are living at home or in nursing homes, the bed is an integral part of
their daily lives. The monitoring of physical activities in bed can provide
valuable information of the status of an elderly person. This
paper presents a Bed-Centered Telehealth System (BCTS), which uses the bed as
the center of health data collection of telehealth systems implemented in homes
and nursing homes. The core sensor of the BCTS is a soft motion-sensing
mattress, WhizPAD. WhizPAD collects signals of physical
activities in bed, which can be classified into events such as on/off bed,
sleep posture, pressure distribution, movement counts, and respiration rate.
The BCTS facilitates bed-related real-time monitoring, service reminders for
caregivers, and a history of the user’s data. Integrated with
information and communication systems, caregivers can maintain awareness of
elderly persons’ daily activities and needs by using their mobile devices to
access the WhizPAD and further
provide the necessary care for them.
Keywords: motion-sensing; telehealth
Given the rapid
increase in the aging population and the decline in birth rate, there is a
growing demand for healthcare services, including medical support, behavior
assistance, daily care, etc. For the elderly who are living at home or in
nursing homes, the bed is an integral part of their daily lives. They often
spend a long time lying in bed at home for rest and sleep. In nursing homes,
the bed is often used as a unit for care service management. Therefore, the
monitoring of physical activities in bed provides valuable information of the
status of an elderly person.
Many care systems have been developed based
on activities detected in bed, for example, detection of bed-exit and fall
events [Yonezawa et al. 2005, Ogawa et al. 2008, Bruyneel et al. 2011],
recognition of sleep pattern and quality [Watanabe et al. 2005, Choi et al.
2007, Cheng et al. 2008, Migliorini et al. 2010], and the monitoring of
obstructive sleep apnea syndrome (OSAS) [Watanabe et al. 2010, Bruyneel et
al.2013]. In such systems, motion sensing in bed, or bed actigraphy, is often
the core technique.
is deﬁned as the measurement of movement in bed. Various types of noninvasive
and unrestrained sensing techniques have been implemented for this purpose.
Load cells or force sensors are the most common sensing components used to
detect body movements in bed. Nishida et al.  presented the idea of a
robotic bed, which is equipped with 221 pressure sensors for monitoring
respiration and body position. Van Der Loos et al.  proposed a system
called SleepSmart™, composed of a mattress pad with 54 force-sensitive
resistors and 54 resistive temperature devices, to estimate body center of mass
and index of restlessness. Many pad-based solutions have been proposed.
Erkinjuntti et al.  presented a design of the static charge-sensitive bed
(SCSB) for long-term monitoring of respiration, heart rate, and body movements.
Kaartinen et al.  used the SCSB method to determine the relation between
movements in bed and sleep quality. Watanabe et al.  designed a
pneumatics-based system for sleep monitoring. A thin, air-sealed cushion is placed
under the bed mattress of the user, and the small movements attributable to
human automatic vital functions are measured as changes in pressure using a
pressure sensor. These systems implemented sensors into the bed, an approach
whose complexity and cost may limit their practical use.
sensing techniques have been developed to provide unobtrusive monitoring of
vital parameters and physical activities. Cheng et al.  proposed a
portable device for telemonitoring physical activities to evaluate body
movements with quantitative measurement and to recognize sleep pattern and
quality. Carvalho et al.  developed textile and polymers applications
(cushions, mattresses, and mattresses overlays) able to monitor and control the
pressure in the body’s areas that are in contact with the support surfaces.
Peltokangas et al.  proposed an integrated system that uses eight
embroidered textile electrodes attached laterally to a bed sheet for measuring
bipolar contact electrocardiography (ECG) from multiple channels. The
textile-based sensing techniques should have greater potential to facilitate
long-term monitoring with lower disturbance or discomfort.
Many of these
motion-sensing techniques can extract signals of physical
activities in bed in an unobtrusive way. However, how
to adapt these techniques to be viable for the home or nursing home remains a
presents the Bed-Centered Telehealth System (BCTS), which is based on a
commercialized motion-sensing mattress WhizPAD
and is designed to be used at home or in a nursing home. Instead of creating a
brand new telehealth system for home users, the design concept of BCTS is to
integrate telehealth functions into something that already exists in the home,
namely the bed.
The core sensor
of the BCTS is a soft motion-sensing mattress, WhizPAD, developed for unobtrusive sensing of physical activities
in the bed [Liu et al. 2012]. Instead of adding sensing components into the
bed, in WhizPAD the mattress itself
becomes a sensor using textile-based sensing techniques. WhizPAD collects signals of physical activities in bed, which can
be classified into events such as on/off bed, sleep posture, pressure
distribution, movement counts, and respiration rate. By being integrated with information
and communication systems, the BCTS can provide telehealth functions including
real-time sleep monitoring, care service reminder, and historical data record.
describes the design of the motion-sensing mattress, WhizPAD. Section 3 explores the application of the Bed-Centered
Telehealth System in the home, and Section 4 considers the use of the BCTS in a
nursing home. Finally, Section 5 discusses possible future extensions of the
BCTS and concludes the paper.
Design of the motion sensing mattress, WhizPAD
WhizPAD is a thin mattress
pad made of memory foam and conductive textile materials. WhizPAD is designed into a mattress with motion sensing capability
using the same material and fabrication process of the bedding manufacturer, so
that the mattress is comfortable, flexible in use, easy to install, and low
WhizPAD is in
a sandwich structure of two pieces of foam, each 6~10 mm in thickness, on which
conductive fiber is knitted in a special pattern in the “sensing area,” with
pieces of conductive foam in between. As shown in Figure 1, the average
resistance of 10 tests of a 20 cm × 20 cm sensing area decreases monotonically
with applied pressure in the range of 500-3,500 Pa (the pressure caused by the
presence of an adult). The special elastic foam provided by the bedding
manufacture has passed the fatigue test of 30,000 pressure cycles. Figure 2
shows a possible layout of the mattress, with three horizontal sensing areas for
detecting movements of the upper limbs, hip, and lower limbs, and three
vertical areas for detecting movements of the trunk.
In order to enhance the comfort and
decrease the occurrence of bedsores, WhizPAD
integrates with the body-shaped memory foam that is atop the sensing layer. The
hardness and elasticity of the memory form changes with body temperature, which
helps to decrease the stress (<32 mm Hg) applied on the skin. Highly
responsive foam is used in the bottom layer of WhizPAD, so that the sensing performance of WhizPAD is not affected by the type of material of the base
mattress on which it is placed.
Figure 1. Relationship between the applied
pressure and resistance of sensing units
Figure 2. A possible layout of WhizPAD
WhizPAD is connected to a
bedside data processor for signal processing and data transmission. The bedside
data processor integrates the microchip Atmega644p, a 6-channel A/D converter,
real-time clocks, micro SD storage, ZigBee transmission module, and Internet
network module. The sampling rate of the signals from WhizPAD is set at 10 Hz. Given the algorithms implemented in
Atmega644p, the signals collected by WhizPAD
from physical activities in bed can be used to detect the following five
events: on/off bed, sleep posture, pressure distribution, movement counts, and
respiration rate. The sensing data and events can be transmitted through ZigBee
transmission module, or stored in the SD card, which can be accessed via the
Internet upon request.
Figure 3 shows
the signals of physical activities in bed collected by WhizPAD from a 60 kg silica gel model and a 80 kg male participant.
In Figure 3(b), the respiration pattern can be seen clearly from signals
collected by WhizPAD, while in Figure
3(a), the signals obtained from a dead weight put on the bed appear to be
Figure 3. Signals of physical activities in bed
collected by WhizPAD
Table 1 shows
the specifications of the WhizPAD.
The BCTS can be used at home or at a nursing home. The application scenarios
are described in the following sections.
Table 1. Specifications of the WhizPAD
188 cm × 90 cm × 3.5
voltage / current
DC 5V / 1 mA
0 ~ 50 °C /
30 to 80%, No
500 ~ 3500 (N/m2)
40 ~ 140 (Ohm)
Home application of the BCTS
Figure 4 shows
the communication structure of the BCTS home application scenario. WhizPAD is put on the bed of the older
adult in the home environment. The bedside processor is plugged directly into a
home router for Internet connection. No special setup of the bedside processor
is required. Remote caregivers can access the bedside processor via the
Internet to browse real-time and historical data from the WhizPAD App on their mobile devices.
Dynamic IPs are
often used in the home environment. As shown in Figure 4, the bedside processor
is scheduled to report its present IP address to a pairing system periodically.
After registering into the pairing system, the WhizPAD App obtains the current IP address of the bedside processor
from the pairing system when requesting data. In the meantime, the pairing
system also facilitates connection with social network platforms such as
Facebook, for reporting historical readings and events to specific caregivers.
Figure 4. Communication structure of the Bed-Centered
Telehealth System in home application
Figure 5 shows
the user interface of the WhizPAD
App, which can be downloaded from online App stores such as Google Play. For
real-time sleep monitoring, the WhizPAD
App displays on/off bed status, sleep posture, number of movements in bed in
the past 1 minute, and the time of the last movement. The user can also browse
historical data from the WhizPAD App
in either graphical or text format.
Figure 5. The display interface of the WhizPAD App
Nursing home application of the BCTS
The BCTS has
been implemented in a nursing home in Taiwan [Liu et al. 2011]. Figure 6 shows
the communication structure of the BCTS in such a setting. The bedside
processor that accompanies a WhizPAD
serves as an end device of a Zigbee wireless sensor network established in the
nursing home. The monitoring data for each resident is transmitted directly to
the server (which also serves as the coordinator of the Zigbee wireless sensor
network) at the nursing station. Intermediate Zigbee routers can be deployed if
the distance between end devices and the coordinator exceeds the design limit.
received from the bedside processor will be displayed on the information board
at the nursing station to facilitate real-time monitoring and alerts, service
reminders, and augmenting the historical data record. Figure 7 shows the main
interface of the information board. The information helps the nursing staff to
keep aware of whether a resident is lying on the bed, as well as when to turn
the resident’s body over or pat his/her back for disabled residents who cannot
leave beds. The nursing staff can also query the data for a particular resident
from their mobile devices. Physical activities in bed and classified events are
stored in the historical database and could be used not only in the management
of the particular resident but for administrative purposes such as ensuring
that adequate staff is on duty.
Figure 6. The Bed-Centered Telehealth System in a nursing
Figure 7. The information board at the nursing
Figure 8 shows sample
historical monitoring data for two conditions of resident. Figure 8(a) shows
historical monitoring data for a typical day of a healthy resident, including
the on/off bed status (red line) and the number of movements in bed per minute (blue
line). These records can indicate how well a resident sleeps. Figure 8(b) shows
historical monitoring data for a typical day of a disabled resident who cannot
leave the bed. There are intense physical activities in bed in regular periods.
These activities are actually the care service of body turning over, to relieve
the pressure and prevent complications such as bedsores. This monitoring data
can be used as service record of the nursing staff for management purpose.
Figure 8. The historical monitoring data for (a) a
healthy elderly person, and (b) a disabled elderly person
Discussion and conclusion
This paper described
a Bed-Centered Telehealth System (BCTS), which uses the bed as the center of
health data collection of telehealth systems implemented in homes and nursing
homes. The core sensor of the BCTS is a soft motion sensing mattress, WhizPAD. The BCTS facilitates
bed-related real-time monitoring (on/off bed status, sleep posture, body
movements), service reminder, and historical data record. Caregivers can also
use mobile devices to access the data collected by WhizPAD.
The BCTS has the
potential to be extended for broader applications, including the following:
(1) Sleep quality monitoring in the home environment
The polysomnographic (PSG) examination is
the standard procedure for research into and clinical diagnosis of sleep
disorders. However, it carries high equipment cost and can be operated only by
a professional. By employing an algorithm designed for sleep monitoring, WhizPAD could detect the most important indicators
of sleep quality, such as frequency of turning over and duration of on-bed
status. It can help not only to evaluate sleep quality but to record a person’s
sleeping history as a reference for further diagnosis.
(2) Shaping a perfect sleep environment
Sleep can be affected by the immediate
environment, including lighting, noise, and temperature. KNX is the worldwide
standard communication protocol for all applications in home and building
control. A centralized BCTS could be integrated with KNX to form a building
automation application that could control appliances according to sleep status
detected by the BCTS.
(3) More sensors for activity of daily living (ADL) monitoring
Activities of daily living (ADLs) refers
to tasks that are required for personal self-care and independent living, such
as eating, dressing, cooking, drinking, and taking medicine [Katz et al. 1963].
The performance of daily activities has been widely used in clinical and
research fields as a measure of disability, or functional status of elderly
people. If additional sensors for ADL monitoring and event algorithms were
integrated with the bedside device of the BCTS, the BCTS could extend telehealth
care from a smart bed to a smart architecture.
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