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

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 th

Annals of Biomedical Engineering, Vol 34, No 4, April 2006 (
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

CLIODHNAN´ISCANAILL,1 SHEILACAREW,2 PIERREBARRALON,3 NORBERTNOURY,3

DECLANLYONS,2and GERARDM LYONS1

1Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick,

National Technological Park, Limerick, Ireland;2Clinical Age Assessment Unit, Mid Western Regional Hospital,

Limerick, Ireland; and3Laboratoire TIMC-IMAG, Facult´e de M´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: Cliodhna.NiScanaill@ul.ie

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).49In 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) This problem is compounded further 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.29Telemonitoring 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

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.19classified

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 not place

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.36Mobility levels are sensitive to changes in health and psychological status.4A 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 inhabitants27 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 (Table1) 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 Clark8in 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

TABLE 1 Sensors employed in health smart homes.

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 tiles37 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

activity type Magnetic switches 4,42,54–56 Switches used in doorframes,

cupboard and fridges to detect movement or activity type

Active infrared sensors7 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 Council26

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 1994.4This

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 Trust42,47 health smart

home (Fig.4)42also 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 Sixsmith47 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 generation15 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 device with 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.7developed 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.3designed 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 (HIS2) (Fig 5),34

de-scribed by Virone et al.,54–56to 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 HIS2 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 HIS2 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) and3(b)), among others, to determine the 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

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 clothing10,32,38 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 PDA10,19 or microcontroller

device Data logging and data forwarding systems are those,

which simply acquire data from the sensors and log these for

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 using the GSM network,30,32or indirectly via a base station, using POTS or the GSM network,21,46A 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).5,25Accelerometer-based systems have been shown to accurately measure both

A Review of Approaches to Mobility Telemonitoring 553

dynamic and static activities in both long11,22 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.28,45For this 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,12,22,23,25,59sacrum43or chest28,31to

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,11,20,53typically

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)2is 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 project32 and the VivoMetrics Lifeshirt
R10,57

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 Lifeshirt10 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

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 systems5,12,22,23,25,46,59are 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 However data 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 of data

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
device (Fig.R 8),18 described by Sarela

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

capa-ble of measuring both activity and posture, using a single bi-axial or tri-bi-axial accelerometer-unit located at the person’s

center of gravity (i.e waist or sacrum) Mathie et al.25used

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’s25wearable 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 system22

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, PERSiMON41 (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.53demonstrated 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.11,20and validated in a long-term clinical trial of elderly people This configura-tion was found to have a detecconfigura-tion accuracy of 96%, when

compared to the observed data N´ı Scanaill et al.30adopted 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

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,12,59which 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 Costa16developed 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 Cameron14incorporated 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 HIS2 health smart home.13,33,56 The sen-sor continuously detected physical activity, posture, body vibrations and falls Ambient sensors in the HIS2 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