Annals of Biomedical Engineering, Vol. 34, No. 4, April 2006 (
C
2006) pp. 547–563
DOI: 10.1007/s10439-005-9068-2
A Review of Approaches to Mobility Telemonitoring of the Elderly
in Their Living Environment
CLIODHNA N
´
I SCANAILL,
1
SHEILA CAREW,
2
PIERRE BARRALON,
3
NORBERT NOURY,
3
DECLAN LYONS,
2
and GERARD M. LYONS
1
1
Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick,
National Technological Park, Limerick, Ireland;
2
Clinical Age Assessment Unit, Mid Western Regional Hospital,
Limerick, Ireland; and
3
Laboratoire TIMC-IMAG, Facult
´
edeM
´

ISDN Integrated Services Digital Network
LAN Local Area Network
PDA Personal Digital Assistant
POTS Plain Old Telephone System
PSTN Public Switched Telephone Network
Address correspondence to Cliodhna N
´
ı Scanaill, Biomedical Elec-
tronics Laboratory, Department of Electronic and Computer Engineering,
University of Limerick, National Technological Park, Limerick, Ireland.
Electronic mail: [email protected]
RF Radio Frequency
SMS Short Message Service
WLAN Wireless Local Area Network
WPAN Wireless Personal Area Network
INTRODUCTION
The western world is experiencing a so-called “greying
population” (Fig. 1).
49
In 2001, 17% of the European Union
(EU) was over 65 and it is estimated that by the year 2035
this figure will have reached 33%. This demographic trend
is already posing many social and economic problems as
the care ratio (the ratio of the number of persons aged
between 16 and 65 to those aged 65 and over) is in decline.
This trend suggests that there will be less people to care for
elderly, as well as a decreased ratio of tax paying workers
(who fund the health services) to elderly people (using the
health services). Thisproblem is compoundedfurther by the
fact that elderly place proportionally greater demands on

´
I SCANAILL et al.
FIGURE 1. Growth of the UK population as a percentage of the total UK population. (Office of Health Economics, 2006, reproduced
with permission.)
effective, patient-centered, timely, efficient, and location-
independent monitoring; thus, fulfilling the six key aims
for improvement of healthcare as proposed by the Institute
of Medicine, Washington, DC.
9
Telemonitoring has become increasingly popular in re-
cent years due to rapid advances in both sensor and telecom-
munication technology. Low-cost, unobtrusive, telemoni-
toring systems have been made possible by a reduction
in the size and cost of monitoring sensors and record-
ing/transmitting hardware. These hardware developments
coupled with the many wired (PSTN, LAN, and ISDN) and
wireless (RF, WLAN, and GSM) telecommunications op-
tions now available, has lead to the development of a variety
of telemonitoring applications. Korhonen et al.
19
classified
telemonitoring applications into two models—the wellness
& disease management model and the independent living
& remote monitoring model. Applications covered by the
wellness & disease management model are those in which
the user actively participates in the measurement and mon-
itoring of their condition and the medical personnel play
a supporting role. An example of this model is a diabetes
management system, in which the user is responsible for
measuring and uploading their blood sugar levels to a cen-

as home telemedicine (estimated at €30 per patient per
day), were employed by the health services.
51
Existing
methods for mobility measurement include observation,
clinical tests, physiological measurements, diaries and
questionnaires, and sensor-based measurements. Diaries
and questionnaires require a high level of user compliance
and are retrospective and subjective. Observational and
clinometric measurements are usually carried out over
short periods of time in artificial clinical environments,
rely heavily on the administrator’s subjectivity and may
be prone to the “white coat” phenomenon. Physiological
A Review of Approaches to Mobility Telemonitoring 549
FIGURE 2. Estimated hospital and community health services expenditure by age group, in pound per person, in England 2002/3.
(Office of Health Economics, 2006, reproduced with permission.)
techniques, though objective, have a high cost per
measurement.
Long-term, sensor-based measurements taken in a per-
son’s natural home environment provide a clearer picture of
the person’s mobility than a short period of monitoring in
an unnatural clinical setting. By monitoring and recording
a patients’ health over long periods, telemonitoring has the
potential to allow an elderly person to live independently
in their own home, make more efficient use of a carer’s
time, and produce objective data on a patient’s status for
clinicians.
REMOTE MOBILITY MONITORING
OF THE ELDERLY
Health Smart Homes

health smart home systems often have little or no access to
the subject’s biomechanical parameters, and must therefore
measure mobility and/or location indirectly using environ-
mental sensors (Table 1). These methods range from simply
detecting the subject’s location and recording the time spent
there, to measuring the time of travel from one place to
another by the subject.
Early activity monitoring systems in health smart homes
used pressure sensors to identify location. The EMMA (En-
vironmental Monitor/Movement Alarm) system, described
by Clark
8
in 1979, detected movement using pressure mats
(Fig. 3(a))
50
under the carpets and a vibration detector on
the bed. These passive sensors raised an alert unless the
550 N
´
I SCANAILL et al.
TABLE 1. Sensors employed in health smart homes.
Sensor type Sensor description
Pressure sensors
50
An unobtrusive pad placed
under a mattress or chair to
detect if the bed or chair is in
use
Pressure mat
26,50

detect movement or activity
type
Active infrared sensors
7
Sensors, consisting of an
infrared emitter and receptor
and placed in a doorway to
estimate size and direction
through doorway
Optical/ultrasonic system
3
Measure gait speed and
direction as subject passes
through doorway
user reset a clock device. Edinburgh District Council
26
also employed both pressure mats and infrared sensors
(Fig. 3(b))
50
to monitor activity in their sheltered housing
scheme, thus saving their wardens time and effort.
The first telemonitoring health smart home to measure
mobility was presented by Celler et al. in 1994.
4
This sys-
tem determined a subject’s absence/presence in a room by
recording the movements between each room using mag-
netic switches placed in the doors, infrared (IR) sensors
identified the specific area of the room in which the sub-
ject was present, and generic sound sensors detected the

47
the majority of alerts
raised were false positives, 76% of the subjects thought
FIGURE 4. Layout of house monitored by Anchor Trust\BT
Lifestyle monitoring system. (Porteus and Brownsell, 2006, re-
produced with permission.)
A Review of Approaches to Mobility Telemonitoring 551
the sensitivity was just right. Two subjects fell during the
trial but both these subjects used their community alarms
before the system had sufficient time to recognize the
situation.
There were several implementation issues in this system.
BT had to develop a control box due to the unavailability
of a suitable commercial product. It was also necessary to
add an additional telephone line to each dwelling solely
for the control box. The authors raised the topic of PIR
conflicts, noting that it is possible for two or more PIR
sensors to be active at the same time. It was also noticed
that curtains blowing in the wind caused PIR conflicts. The
authors found the development of an algorithm, to distin-
guish between an alarming situation and a minor deviation
was more difficult than they had originally expected but
this distinction became easier to make as more lifestyle
data were collected.
Perry et al.
40
described a third generation
15
telecare
system, The Millennium Home, which has built on the

tored subject lived alone and had repetitive and identifiable
habits. Chan et al. also used this approach in a later system,
6
where IR movement detectors measured the night activities
of elderly subjects suffering from Alzheimer’s disease. This
system was tested for short term (16 subjects monitored for
an average of 4 nights) and long term durations (1 subject
monitored for 13 consecutive nights) and good agreement
was found between the system and observations made by
the nursing staff. However, the authors had difficulties with
the IR sensors and noted that they could not detect fast
movement or more than a single person in the room. The
imprecise boundaries of the IR sensors was also an issue in
this system, as the possibility of two or more sensors being
active at the same time made the timing of certain events,
such as going to bed, difficult.
Cameron et al.
3
designed a health smart home that mea-
sured mobility and gait speed along with other parameters,
to determine the risk of falling in elderly patients. PIR sen-
sors were also used in this system to quantify motion within
each room. The authors developed an optical/ultrasonic
system to measure gait speed and direction as the sub-
ject passed through each doorway. In the next evolution
of this system Doughty and Cameron,
14
recognizing the
importance of accurate mobility and fall data in fall risk
calculation, replaced the ambient fall detection sensors with

health smart home initially communicated
with a local server using an Ethernet link. In the next evo-
lution of the system a PSTN line was used to transfer data
to a remote server. However, this method proved costly as
the link was continually running. The HIS
2
health smart
home now collects the data locally and emails this data, as
an attachment, to the remote server every day. This method
is also used to alert the remote server in emergency cases.
The Tunstall Group,
50
in the UK, provides commercial
health smart home solutions for the remote monitoring of
elderly patients by using PIRs, door-, bed-, and chair-usage
sensors (Figs. 3(a) and 3(b)), amongothers, to determinethe
activity level and type of the monitored subject. A gateway
unit, placed in the person’s house, stores information from
these sensors and downloads it via a telephone line to a
central database and an alert is generated if an alarming
trend is detected. The carer can review the patient’s data
using the Internet and determine what action, if any, is
required. Tunstall also have a facility for the carer to request
552 N
´
I SCANAILL et al.
FIGURE 5. The HIS
2
smart home. (Nourg
et al.;

or microcontroller
device. Data logging and data forwarding systems are those,
which simply acquiredata from the sensors and log thesefor
offline analysis or forward these directly to a local analysis
station. These systems are best suited to cases where the
increased processing power of a PC is required to complete
complex analysis.
Wearables designed for telemonitoring applications
must have the capability to transfer their data, for long-
term storage and analysis, to a remote monitoring center.
Data can be transmitted directly from the wearable to the
monitoring center usingthe GSM network,
30,32
or indirectly
via a base station, using POTS or the GSM network,
21,46
A
portable GSM modem consumes more energy than a local
transmission unit but it allows “anytime anywhere” location
independent monitoring of a patient. Indirect methods place
a range restriction on the monitored subject, as the subject
has to be near the base station for the recorded data to be
transmitted to the remote monitoring center via the POTS
or GSM network.
Wearable Sensors
Wearable sensors have the ability to measure mobility
directly. Pedometers, foot-switches and heart rate measure-
ments (calculated by R-R interval counters) can measure a
person’s level of dynamic activity and energy expenditure
however they do not provide information on the person’s

systems.
28,45
Forthis reason most mobility, gait, and posture
wearable applications are accelerometer and/or gyroscope
based. However, there is little consensus as to the optimal
placement and amount of sensors required to obtain suffi-
cient results; with some authors preferring a single sensor
unit worn at the waist,
12,22,23,25,59
sacrum
43
or chest
28,31
to
multiple sensors distributed on the body.
11,20,30,53
Data Logging Wearables
Data logging systems have the advantage of being able
to monitor the subject regardless of their location. The dis-
advantage of data logging systems is that the subject’s mo-
bility patterns cannot be analyzed between uploads. If an
alarming trend occurs between uploads it will not be dis-
covered until that data is uploaded and analyzed on the pc.
This problem will become more significant as improving
memory technology increases the time between uploads.
Non-telemonitoring data logging systems,
11,20,53
typically
used in a clinical setting, require a skilled user to upload
the data and perform complex offline analysis. Telemon-

Wearable systems integrated into clothing, such as the
VTAMN project
32
and the VivoMetrics Lifeshirt
R
10,57
products, can be worn discreetly under clothing. The pro-
cess of donning and doffing multiple sensors is simpli-
fied by integrating these sensors into clothing. Clothing-
based wearables also ensure correct sensor placement. The
Lifeshirt
10
is a lightweight, comfortable, washable shirt
containing numerous embedded sensors. It measures over
30 cardiopulmonary parameters, and it’s 3-axis accelerom-
eter records the subject’s posture and activity level. The
sensors are attached, using secure connectors, to PDA
device. The data is saved to a flash memory card and
can be analyzed locally using VivoLogic software or up-
loaded via the Internet and processed by staff at the
Data Center who will generate a summary report for the
subject.
The VTAMN smart cloth (Fig. 7)
32
measures several
parameters of daily living, including activity, using sen-
sors incorporated into the garment. The activity-measuring
module of the VTAMN project is based on a 3-axis ac-
celerometer, worn under the subject’s armpit. The data from
this module is processed by embedded software and can

Simple accelerometer-based activity monitors, known
as actigraphs, can be worn at the wrist,
46
waist, or foot
to monitor mobility and are usually a single-axis devices
that simply distinguish between activity and inactivity in
order to estimate energy expenditure, sleep patterns, and
circadian rhythm. While actigraphs were originally local
data logging systems that required manual uploading ofdata
to a PC, an evolution of these devices are data forwarding
systems such as the Vivago device described by Sarela,
46
which can generate an alarm in emergency cases.
The Vivago
R

device (Fig. 8),
18
described by Sarela
et al.
46
in 2003, is a wrist-worn device with a manual
alarm button and inbuilt movement measurement, capa-
ble of distinguishing between activity and inactivity. The
Vivago system continually monitors the user’s activity pat-
terns in their home by forwarding data from the wrist unit
to the base station. The base station generates an automated
alarm if an alarming period of inactivity is detected. The
base station is typically connected to the server using the
PSTN, or using a GSM modem if the PSTN is not available.

(Chronic Obstructive Pulmonary Disease). The device was
initially placed at the sacrum, but during testing, subjects
complained of difficulty attaching the device and discom-
fort when sitting with the device attached. It was decided to
place the device on the hipbone to improve comfort. How-
ever, the authors noted that this placement was more likely
to be affected by artifact than placement at the sacrum, and
that some distortion of the output signal occurred as the
device was not aligned symmetrically (left-right) on the pa-
tient. Data were sampled at 40 Hz and forwarded over a RF
link to a PC. All parameters in the system were calculated
twice a minute, and summarized information was uploaded
to a central server each night. Like all data forwarding sys-
tems, this system was unable to monitor the subject when
they were outside of the range of the RF link. This system
implemented telemonitoring by uploading data to a central
server every night. At the same conference, Celler et al.
5
described the “Home Telecare System” which combined
Mathie’s
25
wearable system, with a fixed workstation (for
ECG, BP and temperature measurements) and ambient sen-
sors (light, temperature, humidity). Data from the wearable
element was collected by a local PC, compressed and trans-
mitted during the night to a remote server. Measurements
A Review of Approaches to Mobility Telemonitoring 555
taken using the fixed workstation were transmitted to the
central server immediately following collection. Passwords
were used to control the level of access each user had to the

terfaced to an RF data acquisition unit. Sensor data can
be continuously forwarded from the wearable to the base
unit for two days before recharging the batteries on the
wearable unit. Processing and storage occur predominantly
in the base station PC. Trend and summary data is generated
by database software resident on the base station PC. The
PC uploads data to a central recording facility every day
or in response to an emergency. This data can be accessed
remotely by authorized medical staff using a web browser.
Data Processing Wearables
Data processing wearables consume more power than
other types of wearable systems but they can provide real-
time feedback to a user and do not require large amounts
of data storage, as the raw data are typically summarized in
real-time before storage or transmission. The use of sum-
marized data also reduces costs by lowering the upload time
to the server.
CSIRO have developed a data processing mobility mon-
itoring system, PERSiMON
41
(Fig. 9),
41
which measures
heart rate, respiration rate, movement and activity. The non-
contact PERSiMON unit is held in the pocket of an under-
garment vest. The 3 accelerometers in the unit are analyzed
to measure movement, long-term activity trends and to de-
tect falls. Sensor data are processed in the wearable unit
in order to produce summaries, and to detect and record
FIGURE 9. CSIRO PERSiMON unit. (CSIRO, 2006, reproduced

previous hour, is sent from the data acquisition unit every
hour to a remote monitoring and analysis server. This sys-
tem was tested in short-term conditions on healthy subjects
and showed an average detection accuracy of over 99%.
Prado et al.
43,44
developed a WPAN-based (Wireless
Personal Area Network) system that is capable of moni-
toring posture and movement of the subject 24 h a day,
inside and outside of the home. This system utilizes an
intelligent accelerometer unit (IAU), capable of 2 months
of autonomous use and which is fixed to the skin at the
height of the sacrum using an impermeable patch. The IAU
(diameter 50 mm, thickness 5 mm) consists of two dual-
axis accelerometers, a PIC microcontroller and a 3 V Li-Ion
supply. It can reset itself and inform the WPAN server when
556 N
´
I SCANAILL et al.
FIGURE 10. Remote mobility monitoring using the GSM network.
it detects hardware failure. The WPAN server includes an
alarm button, a display to show the state of the IAU, and an
optical/acoustic signal to confirm transmission to a remote
unit. Low power ISM-band FSK RF transmission was used
to communicate within the WPAN and a Bluetooth link
was used to transfer data between the WPAN server and
the remote access unit (RAU). Several alternatives were
explored for the transmission of data from the RAU to the
telecare center,
44

itoring health smart home with a wearable fall detection
element. The wearable element consists of pressure pads
in the shoes to count steps, tilt sensors to detect transfers,
and shock sensors to detect falls. The health smart home
element indirectly monitored location using sound sensors,
and switches on the lights and television. The following
year Doughty and Cameron
14
incorporated a wearable fall
detector into their already developed fall risk health smart
home, to improve the accuracy of their fall detection system.
The combination wearable/health smart home system de-
signed by Noury et al. also used a wearable sensor to detect
posture and movement after a fall but used ambient sensors
(magnetic switches and IR sensors) to monitor location.
Activity monitoring using wearables in a health smart
home environment provides more accurate data than mon-
itoring with ambient sensors alone. Virone et al. described
an ambulatory actimetry sensor in several of the papers
describing the HIS
2
health smart home.
13,33,56
The sen-
sor continuously detected physical activity, posture, body
vibrations and falls. Ambient sensors in the HIS
2
home
provided data on the patient’s circadian activity.
DISCUSSION

range
continuity
Smart Home
0
0.5
1
1.5
2
2.5
3
power
volume
user input
cost
communicationbiomechanical
ubiquity
range
continuity
Data Logging
0
0. 5
1
1. 5
2
2. 5
3
power
volume
user input
cost

cost
communicationbiomechanical
ubiquity
range
continuity
Combination System
Power Volume
User
Input Cost Bandwidth Range Ubiquity
Bio-
mechanical Continuity
3 = low
1.5 =
Low
3 =
No
1.5 =
Low 1.5 = l ow
3 =
outside 3 = Yes 1.5 = Yes 3 = Yes
2 =
med.
1 =
normal
2 =
med.
1 =
med. 1 = med.
2 =
around

3
IR sensor and optical/ultrasonic sensors in doorway ISM band RF No
Celler
4
IR sensors, magnetic contacts Power-line Yes, PSTN to server
Chan
et al
.
6,7
Active and passive IR sensors, magnetic contact sensors BUS Yes, PSTN
Porteus and Brownsell
42
;Perry
40
Passive IR, magnetic switches RF Yes, PSTN to server
Noury
54–56
IR sensors, magnetic contacts. Audio sensors CAN Yes, email to server
Tunstall group
50
Passive IR sensors, door-, bed-, and chair-occupancy
sensors, magnetic contacts
ISM band RF Yes, PSTN
558 N
´
I SCANAILL et al.
measure mobility by determining the location of the sub-
ject and recording their interactions in that location as well
as the time spent there.
Health smart homes are highly suitable for housebound

are
an effective solution but they are not suitable for those with
dementia who may forget to don the tag. A solution based
on ambient sensors, such as the active IR sensors placed
in the doorways in Chan’s health smart home,
7
or smart
footstep identification mats used by Orr and Abowd
37
in
the Georgia Tech Aware House, is preferable because it is
less invasive than wearable ID tags and requires no action
on the part of the user to operate.
The topic of PIR conflicts was raised by several au-
thors. Each PIR sensor should monitor a certain activity,
for example if the person is in bed. If a subject is identified
by the “in bed” PIR sensors and the “in bedroom area”
PIR sensor at the same time the system will not be able
to identify the subject’s activity properly. This issue can
be overcome by careful placement of sensors or intelligent
decision-making software.
35
Careful IR sensor placement is
also required to avoid false detections caused by nearbyheat
sources.
Health smart homes cannot monitor a person while they
are outside of the home environment. A wearable element
would be required to measure the person’s mobility outside
of the home environment but then the system would have
to be reclassified as a combination system. Smart home

and RAU
X.25 protocol
Wilson and Dadd
12,59
3-axis accelerometer Data forwarding RF Internet transfer
Noury
et al
.
32
(VTAMN) 3-axis accelerometer, worn
under armpit
Data processing None (direct transmission
to remote centre)
GSM
Sarela
46
Sensor capable of
distinguishing between
activity and inactivity
Data forwarding RF PSTN or GSM
CSIRO PERSiMON
41
3 accelerometers Data Processing Bluetooth PSTN or GPRS
N
´
ı Scanaill
30
Two uni-axial accelerometers,
placed at the chest and thigh
Data Processing None (direct transmission

additional relevant parameters.
Data forwarding systems, such as the Vivago system de-
scribed by Sarela et al.,
46
allow real-time complex analysis
of mobility data on a local PC. They are typically smaller
than their data logging and data processing counterparts, as
they use a miniature transmission module instead of storage
or processing modules. A range/power-consumption trade
off is made when selecting the data transmission module
for a data forwarding system and the technology with the
lowest power consumption is usually selected at the ex-
pense of a wide-ranged technology. As a result, once the
subject is out of range of the base station, the subject’s data
are not received by the base station and are therefore not
analyzed. These wireless technologies include Bluetooth,
WLAN and ISM. Low-power Bluetooth (0.3 mA in standby
mode and 30 mA during sustained data transmissions) has
a range of 10 m, making it ideal for Personal Area Net-
works (PAN) or communicating with a base station placed
centrally in a small apartment. Higher power Bluetooth has
a range of up to 100 m. WLAN is a more mature network
technology than Bluetooth, and has a longer range (up to
300 m outdoors) however it is bulkier and does require more
power than Bluetooth to operate. However, the increasing
use of Bluetooth and WLANin consumer electronics makes
the data forwarding systems, based on these technologies,
susceptible to data “fog”. The European Telecommunica-
tions Standards Institute (ETSI) has allocated the 869 MHz
(ISM) frequency band for both narrow and broadbandsocial

wearable devices.
Combination Systems
General mobility is one of the four parameters noted by
Celler et al.
4
to be most sensitive to changes in health, and is
therefore a very useful parameter to measure. Health smart
home developers appear to have recognized that simple
and accurate mobility measurement is not feasible using
ambient sensors alone (Table 4) and instead, like Doughty
and Cameron, have adapted their pure health smart home
systems
3
to include a wearable element for more accurate
fall and mobility measurement.
14
Local communication in
combination systems encompasses both a wireless element
from the wearable, and a wired or wireless element from the
health smart home sensors. Data from the wearable sensors
must be forwarded wirelessly in real-time to be processed
in tandem with the real-time data from the ambient sensors
on a local PC. The ambient sensors may use any wired or
wireless communication method appropriate for local data
TABLE 4. Summary of combination systems discussed.
Author Sensor description Classification
Local data
transmission Telemonitoring
Doughty and Cameron
3,14

3. Limited access to biomechanical parameters
3. Does not require user input to operate it (suited to
persons with dementia)
4. Problems with IR sensors as shown by Chan
6
Wearable system 1. Direct access to biomechanical parameters 1. Design limitations in form factor, power
consumption, processing power, communications,
and durability of materials
2. Data logging and data processing wearables
measure mobility regardless of location
3. Technological advances leading to reduced size,
weight and cost of systems
2. Bulky systems are indiscrete
3. Data forwarding systems cannot monitor person
outside of range of base station
4. User must control system (recharge, switch on/off,
don/doff)
Combination system 1. Monitoring inside and outside of the home 1. Combines disadvantage of wearable and health
smart home systems2. Combines advantages of wearable and health
smart home systems
transmission in a health smart home. Telemonitoring in
combination systems is achieved using the same transmis-
sion techniques used in health smart homes. This implies
that telemonitoring in most combination systems is via a
PSTN line. Although, the PSTN line may be replaced by
wireless GSM transmission, in future health smart home
and combination systems.
If a health smart home-based mobility system requires
that some aspects of the system should also be worn, then it
would seems that a complete wearable solution would be a

impede their daily activities or force them into a fixed life
pattern.
39
The cost of telemonitoring may dissuade many
elderly people, who only have their pension. However, in
many countries, this cost may be partially or fully funded by
the health services or social services, or private insurance
companies. Commercial telemonitoring systems can also
be purchased by adults with elderly relatives, to provide
the purchaser and the monitored person with reassurance
that their condition is being monitored. Functionally, a sys-
tem must be accurate, reliable, and have continuous ac-
cess to an alarm center. An inaccurate system which raises
false alarms wastes valuable healthcare resources; can lead
to a lack of confidence in the system’s ability; and will
eventually annoy both the client and responder.
14
On the
other hand, a system that fails to recognize an alarming
situation may put a person’s life in danger.
58
Unreliable
systems are very problematic for two reasons—first, they
require constant maintenance, which will deter elderly pa-
tients and second, there is an increased risk of missing
alarming situations while the system is broken. Alarming
situations can also be missed if the communication link
to the remote alarm center is not available when required.
Automated detection of alarm conditions, based on individ-
ually configurable alarm thresholds is necessary as a subject

only to the rich, thus enforcing the “digital divide”. The
motivation of the patient and prescribing clinician to use
telemonitoring should also be questioned—is telemonitor-
ing in the patient’s best interest, or would they receive better
care in a clinical environment? Is it safe for the candidate
to live independently? Is there a possibility of the candidate
becoming over-dependent on the technology to an extent
that they do not report an illness to their clinician, but wait
for the system to report the illness on their behalf? Unfor-
tunately, methods for assessing socio-ethical implications
of health technology are relatively undeveloped and even
fewer mechanisms exist to take actions based on the re-
sults of such evaluations.
52
The decision-making process
for selecting a telemonitoring system should be similar to
the decision-making process used when selecting a therapy.
The clinician examines the advantages and disadvantages
of employing telemonitoring, and also examines the advan-
tages and disadvantages of not employing telemonitoring,
which is slightly different.
CONCLUSION
Mobility telemonitoring is a growing area, which en-
ables the subjective monitoring of the health status of el-
derly people living independently in their own homes. It
provides the clinician with continuous quantitative data that
can indicate an improvement or deterioration in a patient’s
condition. Telemonitoring also reduces the cost of provid-
ing care to elderly subjects by moving care from the tra-
ditional hospital/nursing home setting into the home, thus

the Irish Research Council for Science, Engineering and
Technology under the Embark Initiative.
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