A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment potx

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
´

edecine, 38706, La Tronche Cedex, France
(Received 10 May 2005; accepted 8 December 2005; published online: 21 March 2006)
Abstract—Rapid technological advances have prompted the de-
velopment of a wide range of telemonitoring systems to enable
the prevention, early diagnosis and management, of chronic con-
ditions. Remote monitoring can reduce the amount of recurring
admissions to hospital, facilitate more efficient clinical visits with
objective results, and may reduce the length of a hospital stay for
individuals who are living at home. Telemonitoring can also be
applied on a long-term basis to elderly persons to detect gradual
deterioration in their health status, which may imply a reduction
in their ability to live independently. Mobility is a good indicator
of health status and thus by monitoring mobility, clinicians may
assess the health status of elderly persons. This article reviews
the architecture of health smart home, wearable, and combina-
tion systems for the remote monitoring of the mobility of elderly
persons as a mechanism of assessing the health status of elderly
persons while in their own living environment.
Keywords—Activity, Remote, Review, Health smart home,
Wearable, Telemedicine.
ABBREVIATIONS
ANN Artificial Neural Network
BP Blood Pressure
BUS Binary Unit System
CAN Controller Area Network
ECG Electrocardiogram
GPRS General Packet Radio Service
GSM Global System for Mobile communications
IR Infrared
PIR Passive InfraRed

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:
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

health services than any other population grouping, outside
of newborn babies (Fig. 2).
49
Healthcare delivery meth-
ods will need to be adapted to meet the challenges posed
by this aging population and to care for this group while
constrained by limited resources, but maintaining the same
high standards. It is generally expected that the use of tech-
nology will be required to create an efficient healthcare
delivery system.
9
One such technology, telemonitoring, can be used to
monitor elderly and chronically ill patients in their own
community, which has been shown to be their preferred set-
ting.
29
Telemonitoring can lead to a significant reduction in
healthcare costs by avoiding unnecessary hospitalization,
and ensuring that those who need urgent care receive it
in a more timely fashion. Long-term telemonitoring pro-
vides clinically useful trend data that can allow physicians
to make informed decisions, to monitor deterioration in
chronic conditions, or to assess the response of a patient to a
treatment. Telemonitoring has the potential to provide safe,
547
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2006 Biomedical Engineering Society
548 N

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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-

tral monitoring center. This model is best suited to those
who are willing and technologically able to measure their
health status and respond to any feedback received. The in-
dependent living & remote monitoring model does notplace
any such technological demands on the user. In this model,
it is the medical personnel who monitors the patient’s con-
dition and receives the necessary feedback. Health smart
home systems and many wearable systems are examples of
this model.
The relationship between health status and mobility
is well recognized. Increased mobility improves stamina
and muscle strength, and can improve psychological
well-being and quality of life by increasing the person’s
ability to perform a greater range of activities of daily
living.
36
Mobility levels are sensitive to changes in health
and psychological status.
4
A person’s mobility refers to the
amount of time he/she is involved in dynamic activities,
such as walking or running, as well as the amount of time
spent in the static activities of sitting, standing and lying.
Objective mobility data can be used to monitor health,
to assess the relevance of certain medical treatments and
to determine the quality of life of a patient. The need for
expensive residential care (estimated at €100 per patient per
day), home visits (estimated at €74 per patient per day), or
prolonged stays in hospital (estimated at €820 per patient
per day) could be decreased if monitoring techniques, such

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

Smart homes are developed to monitor the interaction
between users and their home environment. This is achieved
by distributing a number of ambient sensors throughout
the subject’s living environment. The data gathered by the
smart home sensors can be used to intelligently adapt the
environment in the home for its inhabitants
27
or can be
studied for the purposes of health monitoring. In Health
Smart Homes,
34
the acquired data is used to build a pro-
file of the functional health status of the inhabitant. The
monitored person’s behavior is then checked for deviations
from their “normal” behavior, which can indicate deterio-
ration in the patient’s health. Smart home systems passively
monitor their occupants all day everyday, thus requiring no
action on the part of the user to operate. A large number
of parameters can be monitored in a health smart home,
by employing a variety of sensors and the processing ca-
pabilities of a local PC. Health smart home sensors, placed
throughout the house, have fewer restrictions (size, weight,
and power) than wearable sensors (which are placed on the
person) thus simplifying overall system design. However,
health smart homes cannot monitor a subject outside of the
home setting, and have difficulties distinguishing between
the monitored subject and other people in the home.
Health smart homes provide a complete picture of a
subject’s health status, by monitoring the subject’s mobil-
ity and their interactions with their environment. However,

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
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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

An unobtrusive pad placed
under a mat to detect
movement
Smart tiles
37
Footstep detection tiles, which
can identify a subject and the
direction in which they are
walking
Passive infrared
sensors
3,4,34,42,54–56
Detects movement by
responding at any heat
variations. Can be used in
broad mode to detect
presence in a room or in
narrow mode to detect
presence in an area. But
there is a possibility of false
alarms due to heat sources or
wind blowing curtains
Sound sensors
54
Sensors used to determine
activity type
Magnetic switches
4,42,54–56
Switches used in doorframes,
cupboard and fridges to

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

activity type. Data from the sensors were collected using
power-line communication and automatically transmitted,
via the telephone network, to a monitoring and supervisory
canter.
The British Telecom/Anchor Trust
42,47
health smart
home (Fig. 4)
42
also used passive IR sensors and magnetic
switches to monitor activity. Radio transmission was used
to transfer data between the sensors and the system control
box, thus reducing the amount of cabling in the house and
FIGURE 3. Smart home sensors (a) pressure mats and (b) pas-
sive infrared sensors. (Tunstall Group Ltd., 2006, reproduced
with permission.)
making the system easier to install and remove. The data
were time-stamped and stored on the system control box
and then forwarded to the BT Laboratories every 30 min
using the PSTN. All data were processed at the BT Labora-
tories. If an alarming situation was detected, an automated
call was made to the monitored home. The monitored sub-
ject could indicate that there was no problem by answering
the call and pressing the number “1”. If they pressed the
number “2” or didn’t answer the call a nominated contact
was notified.
This system monitored 11 males and 11 females, aged
between 60 and 84, and gathered 5,000 days of lifestyle
data during trials. The system generated 60 alert calls, and
although according to Sixsmith

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

work of the second generation Anchor Trust/BT telecare
project. Like it’s predecessor, the Millennium Home was
designed to support “a cognitively fit and able-bodied user”
and detect any deviations from their normal healthy circa-
dian activities using health smart home sensors. However,
the Millennium home provides the resident with the op-
portunity to communicate with the Millennium Home sys-
tem using a variety of home–human (computer-activated
telephone, loudspeakers, television/monitor screen) and
human–home (telephone, remote-control devicewith a tele-
vision/monitor, limited voice recognition) context-sensitive
interfaces, which were not available in the Anchor Trust/BT
home. These interfaces provide a quick and easy method for
the user to cancel false alarms, or to raise an alarm quickly,
thus improving on the preceding system.
Chan et al.
7
developed a system, which not only detected
a subject’s absence/presence in a particular room, but also
measured their mobility in kilometers. Active IR detectors
and magnetic switches were placed in each doorframe to
determine the subject’s direction through the doors and to
estimate their size for identification purposes. Passive IR
sensors mounted on the ceiling formed circles of diameter
2.2 m on the floor and detected any heat variations caused
by human movement within and between these circles. A
binary unit system (BUS) linked the sensors and the local
PC. An artificial neural network (ANN) monitored the sub-
ject’s mobility data for deviations from their usual pattern.
This system was based on the assumptions that the moni-

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

wearable sensors.
Noury et al.
33
designed the Health Integrated health
Smart Home Information System (HIS
2
) (Fig. 5),
34
de-
scribed by Virone et al.,
54–56
to monitor the activity phases
within a patient’s home environment using location sen-
sors. Data from magnetic switches and IR sensors placed
in doorframes were transmitted via a CAN network to the
local PC, where the number of minutes spent in each room
per hour was calculated. Measured data were compared to
statistically expected data each hour. The CAN network
requires only a single telephone cable to transfer data from
multiple sensors to the local PC, thus reducing the amount
of cabling required for a health smart home. CAN networks
have sophisticated error detection and the ability to operate
even when a network node is defective. In the absence of
a clinical evaluation, a simulator was developed to simu-
late 70 days of data and test the ability of the system to
store large amounts of data and to manipulate these data to
produce results.
55
The HIS
2

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
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FIGURE 5. The HIS
2
smart home. (Nourg
et al.;

c

2003 IEEE).
a current status report for the client by SMS messaging, in
order to provide the carer with peace of mind.
Wearable Systems
Overview
Wearable systems are designed to be worn during nor-
mal daily activity to continually measure biomechanical
and physiological data regardless of subject location. Wear-
able sensors can be integrated into clothing
10,32,38
and
jewelry,
1,46
or worn as wearable devices in their own
right.
5,22,23,25,30,45
Wearable sensors are attached to the
subject they are monitoring and can therefore measure
physiological/biomechanical parameters which may not be
measurable using ambient sensors. However, the design
of wearables is complicated by size, weight, and power
consumption requirements.
19
Wearable systems can be classified by their data col-
lection methods—data processing, data logging, and data
forwarding. Data processing wearable systems include a
processing element such as a PDA
10,19

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

static activities. Accelerometer and gyroscope-based wear-
ables can be used to distinguish between individual static
postures and dynamic activity. Magnetometers have also
been used in combination with accelerometers to assess the
giratory movements.
31
Accelerometry is low-cost, flexible, and accurate method
for the analysis of posture and movement,
24
with applica-
tions in fall detection, gait analysis, and monitoring of a
variety of pathological conditions, such as COPD (Chronic
Obstructive Pulmonary Disease).
5,25
Accelerometer-based
systems have been shown to accurately measure both
A Review of Approaches to Mobility Telemonitoring 553
dynamic and static activities in both long
11,22
and short-
term situations.
30
Accelerometers operate by measuring
acceleration along each axis of the device and can therefore
detect static postures by measuring the acceleration due to
gravity, and detect motion by measuring the corresponding
dynamic acceleration. Gyroscopes measure the Coriolis ac-
celeration from rotational angular velocity. They can there-
fore measure transitions between postures and are often
used to compliment accelerometers in mobility monitoring

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-

itoring data logging systems,
2,32,57
used by elderly sub-
jects in their own homes, include simplified data upload
mechanisms and automated data analysis and transmis-
sion to increase their suitability for non-technically-minded
users.
The BodyMedia SenseWear (Fig. 6)
2
is such a telemon-
itoring data logging system. It is worn on the upper arm
and is capable of storing up to 14 days of continuous data
from its dual-axis accelerometer, galvanic skin response
sensor and heat sensors. The SenseWear can form a Body
Area Network (BAN) with other commercial physiological
monitors, such as heart rate monitors, to supplement its
analysis. The data can be uploaded to the local PC using a
USB cable or can be uploaded wirelessly using the wireless
communicator module. The associated desktop application,
InnerView, retrieves lifestyle data, including energy expen-
diture, physical activity, and number of steps, from the
SenseWear unit. Data from the SenseWear unit can trans-
mitted, via an Internet server, to a health or fitness expert
for remote monitoring of the subject’s health status. A carer
can be notified by SMS message if an alarming trend has
been detected. The SenseWear unit can also operate as a
data forwarding device, which wirelessly streams data to
the local PC for immediate analysis.
FIGURE 6. SenseWear armband. (BodyMedia Inc., 2005, pre-
produced with permission).

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

distinguish between activity, a fall, and standing, lying, and
bending postures. The VTAM shirt can connect to a remote
call center using the GSM network if it detects an alarm-
ing situation. Data can also be transmitted, via the GSM
network, from the activity-measuring module to a remote
PC, where it is analyzed using further mobility-detection
algorithms.
554 N
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I SCANAILL et al.
FIGURE 7. The VTAMN shirt, an example of a wearable system
integrated into clothing. (Noury
et al.
,
c

2004 IEEE).
Data Forwarding Wearables
Data forwarding systems
5,12,22,23,25,46,59
are used when
the weight of the wearable system is a key factor, as a data
storage or a data processing unit can be replaced by a minia-
ture transmitter.Howeverdata forwarding wearables, which
typically use RF, Bluetooth, or WLAN, are range-limited,
and therefore the data from the subject is not recorded when
the subject is outside the range of the receiver. This makes
data forwarding systems suitable for housebound subjects
but not necessarily those who are independent and have the
ability to move outside of the house.

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.

The gateway server then transmits the alert, as voice or text
FIGURE 8. IST Vivago wrist unit. (IST OY, 2006,
19
reproduced
with permission).
messages, to the appropriate care personnel. Activity data
can be remotely monitored using specially designed soft-
ware. This system was evaluated, over three months, on 83
elderly people living at home or in assisted living facilities.
Subjects were actively encouraged to wear the device and
skin conductivity data, measured by the wrist units, showed
that the subjects were within monitoring range (20–30 m)
of the base unit for 94% of the time and user compliance
was high.
Mathie et al.,
22,23,25
Wilson et al.,
12,59
and Prado
et al.
43,44
have each designed more complex systems, capa-
ble of measuring both activity and posture, using a single bi-
axial or tri-axial accelerometer-unit located at the person’s
center of gravity (i.e. waist or sacrum). Mathie et al.
25
used
a single, waist mounted, tri-axial accelerometer to mea-
sure mobility, energy expenditure, gait and fall incidence in
patients with CHF (Congestive Heart Failure) and COPD

(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

patient’s data on the server. A web interface to the server
was provided for the clinicians to observe the patients mo-
bility trends. Easy access to the server was necessary for
clinicians to monitor mobility trends because automated
trend detection and automated summary reports were not
implemented in this system. A pilot study of this system
22
was carried out with six subjects, aged between 80 and
86, over a period of 13 weeks. The wearable system was
housed in a case (71 mm × 50 mm × 18 mm), which
could be clipped to a belt. Healthy subjects, who were
likely to still be in their own homes at the end of trial, were
selected for this study; consequently, the health status of
the subjects remained unchanged throughout the study. A
high rate of compliance (88%) was measured, which was
attributed by the authors to the simplicity of the system, its
unobtrusiveness (subjects forgot they were wearing it), and
the computer-generated reminders to wear the system. The
high rate of compliance and positive user feedback suggest
that the system is suitable for long-term continuous use.
The CSIRO “Hospital without Walls” project described
by Wilson et al.
59
and Dadd et al.,
12
monitors vital signs
from patients in their homes using a wearable ultra low-
power radio system and a base station located in the home.
The wearable module contains a tri-axial accelerometer,
and a rubber electrode system for detecting heartbeats, in-

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

with permission).
details of an event. A voice channel is activated in the case
of an alarm to reduce the incidence of false positives. The
data is transmitted by Bluetooth, to a base station in the
home, from where it is uploaded to a remote monitoring
center. If the subject carries a Bluetooth and GPRS enabled
mobile phone they will be monitored, regardless of their
location, provided GSM coverage is available.
Veltink et al.
53
demonstrated a dual sensor configuration,
where uni-axial accelerometers are placed on the trunk and
thigh to measure mobility. Veltink’s configuration has been
has been adapted by Culhane et al.
11,20
and validated in a
long-term clinical trial of elderly people. This configura-
tion was found to have a detection accuracy of 96%, when
compared to the observed data. N
´
ı Scanaill et al.
30
adopted
this accelerometer configuration, which requires only two
data channels to distinguish between different postures and
dynamic activities, for a wearable telemonitoring system
(Fig. 10). A wearable data acquisition unit processed the
data from the chest and thigh accelerometers every second
to determine the subject’s posture. A SMS (Short Message
Service) message, summarizing the subject’s posture for the

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

including POTS, GSM, ISDN, and X.25
protocol. The X.25 protocol was chosen for cost-efficiency,
security reasons, ubiquitous access (especially in rural ar-
eas), development time, and ease of use.
Combination Wearable/Health Smart Home Systems
Health smart home systems developers have recently
been integrating wearable sensors into their systems in or-
der to make more accurate physiological and biomechanical
measurements. These systems combine the physiological
and location-independent monitoring advantages of wear-
ables with the less severe design constraints of a health
smart home. Combination wearable/health smart home sys-
tems are those, which used both wearable and health smart
home sensors to measure mobility. Systems, such as the
Hospital without Walls project,
12,59
which monitors mobil-
ity using a wearable, and uses ambient sensors to make
non-mobility measurements (such as weight, and blood
pressure) are not considered as combination systems for
the purposes of this review.
Fall detection using only ambient sensors is compli-
cated as there is no direct access to the subject who is
falling. This makes it difficult to distinguish between a
subject falling and a heavy object being dropped. If a fall
is properly recognized using the ambient sensors the sys-
tem has to decide if it is a recoverable fall or if an alarm
must be raised. Doughty and Costa
16
developed a telemon-

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

Smart Homes
Health smart homes, wearables, and combination
systems monitor mobility using a variety of sensor and
A Review of Approaches to Mobility Telemonitoring 557













0
0.5
1
1.5
2
2.5
3
power
volume
user input
cost
communicationbiomechanical
ubiquity

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

communicationbiomechanical
ubiquity
range
continuity
Data Forwarding
0
0.5
1
1.5
2
2.5
power
volume
user input
cost
communicationbiomechanical
ubiquity
range
continuity
Data Processing
0
0.5
1
1.5
2
2.5
3
power
volume
user input

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

1 =
indirectly
1 =
high
0.5 =
Bulky
1 =
High
0.5 =
High 0.5 = high
1 =
Inside 1 = No 0.5 = No 1 = no
FIGURE 11. Graphical comparisons of the different approaches to mobility telemonitoring and the associated rating scale.
communication technologies. However, no method is better
than the other in all respects (Fig. 11).
Health smart homes (summarized in Table 2), by their
nature monitor a wide range of factors and while there are
many examples of health smart home systems only a few
implement mobility monitoring as part of their functions.
This may be because mobility is a difficult quantity to mea-
sure simply using ambient sensors. Passive infrared sen-
sors and switches placed in doorframes are the most com-
mon sensors applied to measure mobility. These sensors
TABLE 2. Summary of health smart home systems discussed.
Author Sensor description
Local data
transmission
Telemonitoring in
system?
Cameron

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

elderly who are living alone as the health smart home ap-
proach eliminates the need for daily donning and doffing
of the monitoring equipment. These systems would be the
preferred option for persons with dementia, as the user
would not need to remember to don the equipment. How-
ever, health smart home systems have several disadvantages
including the requirement to identify the monitored subject
from others in the home (Biometrics signature), IR conflicts
and the inability to monitor the subject outside of the home
environment.
Monitoring the activity of an elderly person in a smart
home is relatively simple if the person is living alone, as all
the detected activity can be attributed to that person. How-
ever, the health smart home must have the ability to identify
the monitored subject, and distinguish between their loca-
tion and the location of others if the monitored person is
living with others or regularly receives visitors. This can
be achieved using video recognition, audio recognition,
54
height recognition,
7
wearable id tags
48
or footstep analy-
sis.
37
Video recognition and audio recognition may be seen
as intrusive. Electronic identification tags, such as those in
the Elite Care nursing home, described by Stanford,
48

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

systems are therefore not suited to monitoring the mobility
levels of active persons who are frequently and irregularly,
outside of the home.
Wearables
Wearable systems (Table 3) are typically based on ac-
celerometers, gyroscopes or a combination of these. The
distinguishing features in mobility monitoring wearables
are the particular configurations of these sensors and the
data acquisition methods employed. In the past, wearable
systems were bulky and heavy and consumed excessive
power, however the size and power consumption of both
sensors and processing units have decreased significantly
in recent years, enabling the development of smaller, more
discrete systems. Wearable systems are not suited to those
who are not mentally or physically able to operate them, as
most wearable require a limited amount of user interaction
to maintain and operate them.
TABLE 3. Summary of wearable systems discussed.
Author Sensor description Classification Local data transmission Telemonitoring
Mathie and Celler
5,22,23,25
Waist-mounted tri-axial
accelerometer
Data forwarding RF Internet transfer
Prado
43,44
Sacrum-mounted 4-axis
accelerometer unit
Data Processing RF within WPAN and
Bluetooth from WPAN

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

to remote centre)
SMS
messaging
BodyMedia SenseWear
2
Dual axis accelerometers worn
on the upper arm
Data logging (data
forwarding optional)
USB or wireless upload to
PC
Internet transfer
VivoMetrics LifeShirt
10,57
3-axis accelerometer,
integrated into vest
Data logging Flash card, manually
uploaded to PC
Internet transfer
A Review of Approaches to Mobility Telemonitoring 559
Telemonitoring data logging systems allow the person
to be monitored regardless of their location and allow com-
plex analysis to be performed off-line using the processing
power of a PC. The expanding storage capabilities of mod-
ern data logging systems suggest that the period between
data uploads will increase. An excessive period between
uploads is to be avoided as a worrying trend which occurs
within this period may be missed. Rather, the increased data
storage capability should be used to improve the quality of
data, by increasing thesampling frequencyor by monitoring

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

alarms and telecare. Telecare applications, which use this
frequency, will be secure,reliableand free from interference
from non-telecare applications.
Data processing wearables are becoming more common
due to the rapid development of PDA and microproces-
sor technology. The smaller form factors of these units
allow them to be carried for prolonged periods without
causing discomfort to the user. The capability of these
processors to deal with larger numbers of inputs and to
perform more complex calculations than before has lead to
the development of more sophisticated multi-sensor mon-
itoring solutions. Personal Area Networks, and its wire-
less equivalent WPAN, are now emerging in the wearable
sensor domain as a result. The ability of data processing
wearables to interface with wireless communication mod-
ules such as mobile phones or WLAN modules, to trans-
mit processed data to remote servers, is also beginning to
be explored as a possibility in recent years
43,44
and when
combined with the improved processing ability of PDAs
and processors, may lead to the removal of the PC as an
intermediate stage in future mobility monitoring systems.
PAN- and WPAN-based systems, with the ability to plug-
and-play new sensors or third-party devices into the exist-
ing monitoring system, increase the flexibility of wearable
systems and enable easy upgrading and maintenance of
the systems. Therefore, the advantages that once attracted
people to health smart homes (discretion, multi-parameter
measurement, and ease-of-use) are now also available in

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

Sensors selected according to patients
needs, including pressure pads in
shoes, tilt, shock, and optical sensors
Data forwarding ISM band RF Yes, PSTN
Noury
13,33,56
IR and magnetic contact switches.
Wearable accelerometer and tilt sensor
Data forwarding RF Yes, email to remote
server
Costa and Doughty
16
IR sensor and optical/ultrasonic sensors in
doorway. Wearable fall detector
Data forwarding ISM band RF Yes, “telecommunication
channel”
560 N
´
I SCANAILL et al.
TABLE 5. Advantages and disadvantage of different approaches to mobility telemonitoring of the elderly.
Monitoring system Advantages Disadvantages
Health smart home
System
1. Less severe design (power consumption, form
factor, processing power, and communications
means) limitations
1. Need for user identification if multiple persons are
present
2. Cannot monitor outside of the home environment
2. Person does not have to wear electronics

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

better approach. Requiring the user to wear some elements
of the system and also interact with the health smart home to
measure mobility imposes the disadvantages of both types
of systems on the user. One, they are restricted to the home
when mobility isbeing measured; and two, they are required
to remember to don the wearable unit for their mobility to
be measured (Table 5). Though, it gives the possibility to
the person to doff the wearable for a while and still be
monitored by the smart home.
Practical, Functional and Ethical Issues
Elderly people wish to remain living in their own homes
for as long as possible provided they are safe. Technology,
and in particular telemedicine, has a role to play in achiev-
ing this goal by reassuring the person that their condition
is being monitored. However, several practical, functional,
and ethical issues need to be addressed to promote the use
of telemonitoring and to ensure long-term patient compli-
ance. Practical issues include ease of use, discretion, cost,
and the ability to perform daily activities unimpeded. Is-
sues regarding ease of use can be resolved by automating
system functions to reduce patient-system interaction and
by clearly explaining the system to the user. Elderly people
will be encouraged to use a telemonitoring system if they
feel the system benefits them. They have been found
39
to
reject indiscrete systems that indicate to others that the per-
son is being monitored as they fear they label them as old
and dependent. Elderly people will also reject any systems
which due to their size, communication methods or location

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

may not recognize a slow deterioration in their situation
or their ability to raise an alarm may be compromised.
Overall, the ability to detect a worrying trend and raise
an appropriate alarm is very important to elderly people
39
A Review of Approaches to Mobility Telemonitoring 561
who fear they will remain unattended in the event of an
accident.
The increasing use of telemonitoring to support inde-
pendent living inside and outside of the home inevitably
raises many ethical questions regarding privacy, cost and
motivation. A patient’s right to confidentiality must be
respected in any aspect of healthcare, telemonitoring in-
cluded. At the patient side, the intrusiveness of long-term
analysis of the patient in his/her own private life should
be minimized as much as possible. Data encryption and
secure methods should be applied to ensure confidentiality
of data during transfers over the network. At the monitoring
end, access to data should be restricted using a hierarchi-
cal password system. Telemonitoring is a cheaper option
than hospitalization, clinical visits, or home help,
51
but the
decision to telemonitor a person should not be purely on
economic grounds. Although a majority of elderly people
would prefer to remain in their own homes, the minority
who would prefer to be cared for outside of their home
should not be denied this opportunity, simply because there
is a cheaper telemonitoring option available. Conversely,
there is a concern that telemonitoring would be available

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

making more efficient use of healthcare resources. It im-
proves the quality of life of the monitored person and their
carer, as they can continue with their daily lives, reassured
that if an alarming trend occurs it will be detected and acted
upon early.
There is a wide range of solutions for the telemonitoring
of mobility of elderly in their living environment, vary-
ing in both sensor type and communication method. As
technology advances on both these fronts even more per-
mutations will undoubtedly be developed. This variety is to
be welcomed, as each system solves a particular problem.
Health smart homes are suited to housebound people who
are either unwilling or incapable of operating a monitor-
ing system. Wearable data forwarding systems, the lightest
wearable option, are suited to the frail and housebound as
they analyze the data in real-time and can raise immediate
alerts. Data-logging wearables are suitable for monitoring
multi-parameter, long-term trends of healthy elderly sub-
jects, who regularly leave their homes. However they are
not suited to real-time alarm detection because they require
the user to upload the data to a PC before an alarming trend
is detected. Automated data processing wearables require
little user interaction and are suited to monitoring mobility
of people who leave their houses regularly but would ben-
efit from real-time alarm detection. Finally, combination
systems are best suited to those who require the quantity of
data provided by a health smart home but also the accuracy
of physiological measurements provided by a wearable.
ACKNOWLEDGMENTS
The authors would like to acknowledge the funding from

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