Chapter 5. Extending the bed-centered telehealth system
describes the extension of the bed-centered telehealth system. By adding
sensors into the bed-centered telehealth system, the system could provide more
valuable information of the elderly people in daily lives for remote
5.1 Integrating sensors of activities of daily living (ADL)
The ageing process of the elderly results in declined
functional status and decreased mobility, which affects their level of
self-care and independence. Functional status can be defined as the ability to
perform activities necessary to ensure well-being, and it can be assessed by
examining the ability to carry out various activities of daily living (ADL)
[Heikkinen, 1998]. Limitations in basic ADL and instrumental ADL (IADL) are
strongly associated with less mobility [Shimada et al., 2010]. Katz et al.
identified 6 basic ADL items associated with one’s living independence:
bathing, dressing, toileting, transfer, continence and feeding [Katz et al.,
1970; 1976]. Physical activity is regarded as any bodily movement produced by
skeletal muscles and results in energy expenditure [Caspersen et al., 1985].
With rapid advances in sensors and telecommunication
technologies, human physical activities can be monitored by means of sensors.
Celler et al.  proposed the concept of implementing sensor networks in a
home environment to monitor functional status of the elderly living alone. For
measuring ADL, passive infrared sensors (PIR) and switches are commonly used to
detect the occupancy of human in space (home) and location transfer. Electricity
sensors can be used to detect the use of home appliances [Franco et al., 2008].
Motion sensors can also be used to measure human activities.
The ADL sensor introduction and power-save
Yang et al.  developed an activity monitoring system
for mobility and functional ability telemonitoring for elderly people living at
home. This system is also integrated with the Decentralized Home Telehealth
System (DHTS) for home use. The sensors are including the temperature,
humidity, Passive infrared (PIR) and CT (current transformer) sensing
functions. It is distributed in several locations of interests in a home
environment for detecting home ADL of the elderly. Figure 3-1 shows the ADL
Figure 5-1. The ADL
sensors (Right hand side: temperature, humidity and
passive infrared sensor; left hand side: current
In this study, these ADL sensors are integrated with the bed-centered
telehealth system for mobility and functional ability tele-monitoring of
elderly people. The ADL sensor equipped with a microchip, Zigbee transmission
module and sensing components powered by AC adapter or battery (+4.8V). For extending the life of battery,
a power-saving circuit and sleep-mode procedure is designed for the Microchip
of ADL sensors. The main target is to decrease the meaningless energy consumption
by the sensor. As shown in Figure 5-2, the ADL sensor will send the sensor ID
number to the bedside data processor (BDP) when it is started. When the PIR sensor
is in motionless state for more than 1 minute, the microchip of the sensor will
go into sleep mode, and the power of Zigbee module is also shut off (Pin4 is
low); if the PIR sensor is activated, the Zigbee module will be turn on (Pin4
is High) first, and the sensing data will be transmitted to the BDP by UART (Universal
Asynchronous Receiver/Transmitter). In this power saving
design, the static working current of ADL sensor is lower than 1mA, and the
dynamic working current is 67mA. The estimated battery life is about 2,400
hours (100 days) if the ADL sensor is powered by battery in 2,400mAH.
Figure 5-2. The design
of power-saving circuit and sleep-mode procedure for ADL
Integrating the ADL sensor with bed-centered
Figure 5-3 shows the system structure of the extended BCTS.
The BCTS structure is extended based on the original WhizPAD system. The sensing data collected will be
transmitted to the BDP of BCTS though Zigbee wireless
data transmission. The temperature and humility data will be transmitted to BDP
every 1 min, and the PIR and CT sensing data will be transmitted to BDP if the
sensor is triggered. All the
ADL sensing data will be stored in the SD card for data storage and management.
The remote caregivers can also access the BDP to browse the ADL monitoring
data and receive abnormal event alert from mobile devices.
Figure 5-3. The system
structure of extended bed centered telehealth system
By typing a
series of IP address and the specific string of the ADL sensor on the web page,
the real-time data and historical record can be displayed. Figure 5-4 shows the
result of real-time monitoring data from ADL sensors displaying on the web page
when the BDP was accessed. The user interface of ADL
sensors is integrated with the WhizPAD App. The interface displays real time monitoring data of temperature, humidity,
body activities of specific location and appliance use situation, as shown in Figure
5-5. The real time data refreshes
every 6 second, and the total
count of the real time data of
body activities and appliance usage will be displayed every 10 minutes. The historical data can also be shown in graphic. Figure 5-6 shows the historical data of TV usage
all day. The sensor title can be edited by the users in this user interface
according to the location of ADL sensors.
Figure 5-4. The result
of real-time monitoring data from ADL sensors displaying on the web page
Figure 5-5. The real time monitoring
data of ADL sensors displaying on the user interface of App.
Figure 5-6. The historical data of TV usage all day
Using simple, low-cost and less diverse sensors modality,
the passive infrared (PIR) and the current transformer (CT) were adopted in the
home ADL sensors. The PIRs can detect active movement related to mobility and
the CTs can detect the usage of electrical home appliances of particular
interest associated with daily activities. Based on the bed-centered telehealth
system, the home ADL monitoring system was installed in a real home environment
for long-term monitoring the ADLs of an elder person living alone. The subject
was not aware of the system operation and thus this system did not interfere
with the daily livings of the elderly subject. The subject did not report any
discomfort brought by the system. The ZigBee WSN technology offers reliable ADL
data delivery and it also simplifies complex instrument setup in a home
environment. Compounding the above advantages, this system can be suitable for
5.2 Integrating the motion sensing carpet, WhizCarpet
of strength contributes to impaired mobility and increases the risk of falls. Approximately 28-35% of people aged of 65 and
over fall each year increasing to 32-42% for those over 70 years of age [World
Health Organization, 2008]. Twenty to 30 percent of those who fall suffer
injuries that reduce mobility and independence and increase the risk of
premature death. In addition, depression, fear of falling, and other
psychological problems are common consequences of repeated falls [De Ruyter et
activities and fall events of the elderly people is the one of important
technologies developed for ambient assisted living (AAL). Many sensing
technologies been developed for monitoring activities and fall events for home
users, including the accelerometry-based wearable sensor, RFID sensing
technology, passive infrared (PIR) sensors,
and camera application in image and voice recognition etc. Figure 5-7 shows the
accelerometry-based wearable device. By detecting the change of acceleration,
the wearable device is used to detect activities and fall event of users [Klingbeil
et al., 2008; Yang et al., 2009]. Kaushik et al. proposed a method which setups
PIR sensors in home environment for monitoring the activities and revealing
changing trends in their health status [Kaushik et al., 2006]. Petrushin et al.
 presented the multiple camera indoor surveillance project, which is
devoted to using multiple cameras, agent-based technology and knowledge-based
techniques to identify and track people and summarize their activities.
Figure 5-7. The
accelerometry-based wearable device [Klingbeil et al.,
integrated sensing components into floor for long-term monitoring with minimal
disturbance or discomfort in these years. Liau et al.  proposed the
sensory floor integrating load cell units for determining inhabitants'
locations and tracking their movements. Morgado et al.  presented the sensory
floor equipped resistive pressure sensors for detecting footsteps of users at
home, as shown in Figure 5-8. Through a careful consideration of user
preferences, the sensing carpet is developed for easy installation and use for
home users. Savio et al.  presented an innovative approach of embedding
electronics in textiles and interweaving them inside a carpet for footstep tracking.
This sensor is terminated to the I/O of a 16-bit microcontroller that has 4
UART’s. A regular mesh network connecting to computer is formed by interconnecting
the UART’s of 120 such constructions with a separation gap of 5 cm between the
sensors. Each sensor has own number and X/Y-coordinates. When the sensor is
activated, the location of sensor can be display on the computer interface to
show the user location at home, as shown in Figure 5-9.
Figure 5-8. The sensory
floor equipped resistive pressure sensors[…]
Figure 5-9. The smart carpet
for footstep tracking[…]
The development of motion sensing carpet,
with the same manufacturer, SEDA Company, a motion sensing carpet named WhizCarpet
is developed for tele-monitoring of mobility level, indoor locations and fall
events in an unobtrusive way for older adults in the home environment [Chang et
al., 2014]. The motion sensing carpet, WhizCarpet,
is developing for mobility, fall event and
localization tele-monitoring of elderly people. In this study, WhizCarpet
is integrated with bed-centered telehealth system for
composed of 30cm×30cm “puzzle floor mat” modular units, which can be assembled
freely into any size and shape according to the setup of the home environment,
as shown in Figure 5-10. Instead of adding sensing components to the carpet,
the puzzle floor mat unit itself is designed into a motion sensor. The working
principle is similar to that of a membrane switch. Once a WhizCarpet unit is
under pressure, the top and bottom layers make contact with each other.
Different pressure will create different contact quality and therefore
generates different resistance. Figure 5-11 shows the relationship between
applied pressure and output voltage of WhizCarpet.
Figure 5-10. The motion
sensing carpet, WhizCarpet
Figure 5-11. The relationship
between applied pressure and output voltage of WhizCarpet
The unit of
WhizCarpet imbeds a microprocessor as the brain for data collection, analysis
and transmission. When the unit is under pressure, the unit will transmit the
sensing data to the master of WhizCarpet units. The master of WhizCarpet units
connects to a microprocessor (Arduino Mega 2560) and Zigbee data transmission
module for data communication. The sensing data could
be transmitted, analyzed and stored in bedside data processor of WhizPAD for
remote application. The two main features of WhizCarpet are described below:
Using the I2C (Inter - Integrated Circuit) as the data communication
method, the units of WhizCarpet can share the data among them. A unit has to be
the master to collect the data from units as slaves, and can also request to
the slaves. Figure 5-12 shows the I2C data communication. I²C uses
only two bidirectional open-drain lines, Serial Data Line (SDA) and Serial
Clock Line (SCL), pulled up with resistors. The I²C bus speed is the 100
Figure 5-12. The I²C
Figure 5-13 shows the I²C bus connection of WhizCarpet units. Each
unit uses the same power from a DC adapter (5V/2A). The red unit is the master
in whole system, and blue units are slaves. When the unit connects each
together, the I²C Bus (yellow line) of WhizCarpet can be built. The number and
location of WhizCarpet units can be adjusted according to the setup of the home
Figure 5-13. The I²C
bus connection of WhizCarpet units
The auto-mapping algorithm is developed for no-specific connection
of WhizCarpet units according to the Breadth-First Search [Thomas, et al. 2001]
and First In - First Out [Robert L. Kruse & Alexander J. Ryba. 1998] algorithms.
Figure 5-13 shows the description of Breadth-First Search algorithm.
Node 1 is the first node to search if any neighbor node exists. As Node 2 and 3
are found, they replace Node 1 to search new nodes. Node 2 does not find any
new node, so the search process of Node 2 ends; Node 3 found Node 4, so the
Node 4 turns to find the new one. The search process stops as there no new node
appears, and the map of all nodes can be found.
A WhizCarpet unit represents a node and has a connector on the each
side. Applying Breadth-First Search in auto-mapping, the procedure starts from
the master unit and follows the search sequence to search new units. The search
sequence is from the upside, right side, downside, to left side. The new unit found
will be asked to search for the next unit. The process will be repeated until
there is no new unit found. Therefore, all units and their corresponding
locations can be found. Figure 5-14 shows the mapping process of WhizCarpet
units. The unit has its own number written in microprocessor. Starting from the
unit 1 (master), the unit 4 is the first unit found, and unit 7 is the second
unit found. According to the “First In - First Out” algorithm, unit 4 is used to
find the next units, then unit 7 is used next. In the second round, unit 4 can
find unit 6 and 9, and unit 7 can find unit 2. This process repeats until the
last unit is found. Finally, these units found will form an array to represent
the numbers and locations of units. A zero in the array means there is no
existing unit. The size of the array is not limited, and depends on the number
Figure 5-13. The
description of Breadth-First Search algorithm
Figure 5-14. The
mapping process of WhizCarpet units
Integrating the WhizCarpet with bed-centered
Figure 5-3 shows the BCTS structure integrating with
WhizCarpet, which is the same with integrating ADL sensors. By integrating the
Zigbee data transmission module, WhizCarpet system can be integrated with the bedside
data processor of WhizPAD for the tele-monitoring of localization, mobility and
fall event. The sensing data will be transmitted to the
BDP as WhizCarpet is
activated. Real-time and historical data will be
stored in the SD card for data storage and management. Remote caregivers can
also access the BDP to browse the
monitoring data of WhizCarpet and receive abnormal event alarm by mobile
As the power of
WhizCarpet system is turned on, the system will start the
mapping processing automatically. By typing a series of
IP address and the specific string of WhizCarpet system on the web page, the result of mapping can be display on the web
page of BDP as shown in
Figure 5-15; Figure 5-16(a) shows the result of units under no applied pressure on web page.
Figure 5-16(b) shows the result of pressure distribution when the units are under applied pressure on web page.
Figure 5-15. The result
of mapping process on web page
Figure 5-16 (a). The
result of units under no applied pressure on web page
(b). The result of pressure distribution when the units are under applied
pressure on web page
shows the mapping result and activated units of WhizCarpet on the user interface of the App. If there is applied pressure on
units, units on interface will become red; if there is no applied pressure, units
are blue. The interface will displays the units which is under pressure, and
refresh every 2 seconds. In
this pilot development, the fall event is determined according to the applied pressure distribution on WhizCarpet units.
When the light pressure (< 0.4 kg/cm2) extends on large, continuous
areas (> 3 units), the fall event is determined. Figure
5-18 shows the real-time monitoring of mobility and fall event on App.
Figure 5-17. Activated units of WhizCarpet on App
Figure 5-18. The
real-time monitoring of mobility and fall event on App
In this pilot
application, WhizCarpet is a puzzle floor mat which is
capable of motion sensing. Considering user acceptance,
WhizCarpet is unobtrusive, easy to use, low cost, and be a natural part of the
home environment. Integrating with the bed-centered system, WhizCarpet has
greater potential to facilitate long-term monitoring of mobility monitoring and
fall event. WhizCarpet is now in commercialization process.
R. L., 1998. The role of physical activity in healthy ageing. World Health Organization- Ageing
and Helth Programme.
H., Ishizaki, T., Kato, M., Morimoto, A., Tamate, A., Uchiyama, Y., Yasumura,
S., 2010. “How often and how far do frail elderly people need to go outdoors to
maintain functional capacity?” Archives
of Gerontology and Geriatrics, Vol. 50, pp. 140-146.
S., Akpom, C., 1976. “A measure of primary sociobiological functions”, International
Journal of Health Service, vol. 6, no. 3, pp. 493-508.
S., Downs, T. D., Cash, H. R., Grotz, R. C., 1970. “Progress in development of
the index of ADL”, The Gerontologist, pp. 20-30.
C.J., Powell, K.E., Christenson, G.M., 1985. “Physical activity, exercise and
physical fitness: Definitions and distinctions for health-related research,” Public Health Rep, Vol. 110, pp.
B.G., Lovell, B.G., Earnshaw W. A., Ilsar E. D., Betbeder-Matibet L., 1994.
“Development of an integrated system for remote monitoring of health status of
the elderly at home,” Biomedical
Engineering, Application, Basis, Communications, Vol. 6:6, pp. 919-926.
G.C., Gallay, F., Berenguer, M., Mourrain, C., Couturier, P., 2008.
“Non-invasive monitoring of activities of daily living of elderly people at
home- a pilot study of the usage of domestic appliances,” Journal of Telemedicine and Telecare,
Vol. 14, pp.231-235.
C. C., Hsu, Y. L., 2012. “ Journal
of Clinical Gerontology and Geriatrics, Vol.
3, No. 2, pp. 97-104.
Organization, 2008. “WHO Global Report on
Falls Prevention in Older Age”.
De Ruyter, B.,
Pelgrim, E., 2007. “Ambient Assisted Living
research in CareLab,” ACM Interactions, Vol.
Celler B.G., 2006. “Characterization of
Passive Infrared Sensors For Monitoring Occupancy Pattern,” in 28th Annual
International Conference of the IEEE Engineering in Medicine and Biology
V. A. Petrushin, G.
Wei, A. V. Gershman., 2006. “Multiple-camera
people localization in an indoor environment,” Knowledge and Information Systems,
vol. 10, no. 2, pp. 229-241.
Liau, W.H., Wu C.L.,
Fu L.C., 2008. “Inhabitants Tracking System in
a Cluttered Home Environment Via Floor Load Sensors,” Automation Science and
Engineering, IEEE Transactions on Volume 5, pp. 10-20.
Morgado, J.M.A., Konig,
A., 2012. “Low-power concept and prototype of
distributed resistive pressure sensor array for smart floor and surfaces in
intelligent environments,” Systems, Signals and Devices (SSD), 2012 9th
International Multi-Conference on, pp. 1-6.
Savio, D., Ludwig,
T., 2007. “Smart carpet: A footstep tracking
interface,” In: Proceedings of the 21st International Conference on Advanced
Information Networking and Applications Workshops, Vol. 2, pp. 754-760.
Chang, K. W., Hsu,
Y. L., Lin, C. K., Liu, Y. W., Chang, W. Y., 2014. “Development of a Motion Sensing Carpet for Multiple
Interactive Applications,” Gerontechnology, 13(2).