12th International Conference on Biomedical Engineering (ICBME)

IFMBE
Proceedings
Volume 12, 2005
December 7 - 10, 2005
SINGAPORE
IFMBE
I n t e r n a t i o n a l F e d e r a t i o n f o r M e d i c a l a n d
The <strong>12th</strong> <strong>International</strong> <strong>Conference</strong> on Biomedical Engineering
ICBME 2005
ISSN: 1727 - 1983
ISBN: 981-05-4572-X
Proceedings of the <strong>International</strong> Federation for Medical and Biological Engineering
© ICBME 2005 & IFMBE
B i o l o g i c a l E n g i n e e r i n g

AN OBSERVATIONAL TRIAL OF AMBULATORY MONITORING OF
ELDERLY PATIENTS
J.R. Boyle*, M.K. Karunanithi*, T.J. Wark*, W. Chan**, C. Colavitti**
*E-Health Research Centre, CSIRO ICT Centre, Brisbane, Australia
**Dept of Geriatric Medicine & Rehabilitation, The Prince Charles Hospital, Brisbane
Abstract: We describe a prospective observational
non-randomised study on the use of ambulatory
monitoring devices designed to record patient
movement. The aim was to evaluate three
accelerometer devices for their efficacy in measuring
patient information relevant for (a) falls assessment
and (b) stroke rehabilitation progress. Fifteen
patients undergoing inpatient rehabilitation (mean
age 67 +/- 18 years) participated in the study.
Patient enrolment was on the basis of high falls risk,
including elderly patients, patients undergoing
rehabilitation following stroke, and patients who
were receiving vasodilators, anti-arrhythmic and
diuretic medication resulting in lowering of blood
pressure.
Over the 95 day trial, there were four falls
experienced by two patients, and hence statistically
insufficient data to develop an algorithm to predict a
fall or falls risk. However the trial indicated that the
devices were able to accurately and reliably capture
data about a rehabilitation patient’s motion at
successive points in time, with minimal discomfort to
the patient. Post trial evaluation by the
rehabilitation team indicates support for integration
of accelerometers into clinical practice, as benefits
were realised in analysing gait as an indicator of
rehabilitation progress and patient movement
classification.
Introduction
The demographic shift of an ageing population is a
global phenomena, without parallel in the history of
humanity [1]. Population ageing slows workforces and
economic growth, and as health expenditure is greatest
for the elderly [2], puts increased demands on health
and aged care systems. Our research addresses two
issues facing an ageing population: falls and
rehabilitation from stroke.
Falls are the leading cause of injury-related
hospitalisation in persons aged 65 years and over, and
account for 4% of all hospital admissions in this age
group [3]. The most serious of fall-related injuries is
fracture of the hip. Elderly people recover slowly from
hip fractures and are vulnerable to postoperative
complications. There have been a tremendous number
of studies evaluating accelerometer-based ambulatory
monitoring systems on elderly population groups, for
purposes such as age or disease determination [4,5],
Justin.Boyle@csiro.au
chronic disease management [6], pain management [7]
and daily activity assessment [8]. Studies that focus
specifically on fall detection have mostly evaluated the
devices on young volunteers or fall simulators, rather
than elderly patients (ie. real data, as in the study by
Tamura et al [9]). Our other area of focus is
rehabilitation following stroke. Survivors of stroke
often constitute the largest group of patients receiving
rehabilitation services in many countries, and it’s
estimated that more than half of stroke survivors have
significant residual physical disability and functional
impairment [10]. In relation to stroke rehabilitation,
accelerometer devices have been used to quantify
activity levels and step counts [11] and measuring
upper-extremity movement [12]. To the best of our
knowledge, the concept presented in this paper of
accelerometer-derived gait smoothness measurements as
a rehabilitation progress indicator has not been
previously presented.
Method
To evaluate the ability of accelerometer devices to
measure patient movement with clinical relevance to
falls assessment and stroke rehabilitation progress, an
observation trial was conducted in a hospital geriatric
ward. Over the 95-day trial, fifteen high falls-risk
patients (as assessed by rehabilitation specialists)
consented to wearing one or two monitoring devices at a
time on a custom belt as shown in Figure 1.
Figure 1: Patient fitted with waist-mounted monitoring
devices
Features and basic signal output of the devices are
summarized in Table 1. Devices A and B were attached
to patients continuously throughout the trial except for
periods of showering or data downloading. Device C
was limited to only 5-6 hours of continuous monitoring
daily due to the 8-hour limit of the battery capacity.

Table 1: Basic characteristics of ambulatory monitoring
devices chosen for the trial
Device Size &
Weight
A 35×53×7
mm
23g
B 90×40×16
mm
60g
C 120×60×10
mm
90g
Sensor
Features
2-axis
accelerometer,
sampling rate
10Hz
2-axis
accelerometer
at 75Hz, 1channel
2
electrodes
ECG circuitry
at 300Hz
2-axis
accelerometer
at 12.5Hz, 2axis
Magnetometer,
1-axis
Gyroscope
Output
Movement/activity
in acceleration (units
in milli g
=9.81/1000ms 2 )
Movement/activity
in acceleration (units
in milli g)
ECG signal in mV
Movement/activity
in acceleration (units
in milli g;
Magnetic field
gauss (Telsa/1000)
Angular rate
(degrees/sec)
In addition to the collection of pre-screening details
(medications, falls history etc), the following were also
recorded on a weekly basis:
• device ID
• position of device attachment
• physiological parameters (blood pressure, heart
rate)
• change in medications
• observed falls/near-falls
• time of device attachment “on” and “off”.
A signal viewing tool was developed to allow for
viewing and understanding the nature of the sensor data
from the three devices, and seeing how the sensors
responded to various known actions such as sitting,
standing, lying, walking, etc. The open-source tool
‘Audacity’ (http://audacity.sourceforge.net/) was
modified to satisfy our requirements for viewing and
annotating biomedical signals. A screenshot of the tool
indicating a patient fall is shown in Figure 3.
The time-of-day is shown along the first track, and
the user can manually annotate by clicking and typing in
the bottom track. Annotations, along with their
respective time indexes, are then written to an ASCII
file as XML-style records, which can then be read by
external programs. Quantitative analysis was
undertaken using the scientific package MATLAB®.
Results
Data from the observational trial demonstrates that
the ambulatory monitoring devices were able to
accurately and reliably capture patient movement data.
However the data did not include a statistically
significant number of patients experiencing a fall. Over
the 95 day trial, there were 4 falls experienced by 2 of
the 15 patients, corresponding to 4 falls per 309 patientdays.
Previous studies by others have found similar low
falls rates in controlled environments, for example, 159
falls per 10,000 patient-days [13] and 0 falls per 403patient
days [14]. The use of statistical analyses with
samples smaller than 10 in size is not recommended
[15] and thus there was insufficient data to develop an
algorithm to predict a fall or falls risk.
In the area of stroke rehabilitation the study was
found to be beneficial from the viewpoints of patient
movement classification and gait analysis as an
indicator of rehabilitation progress.
Previously, there were no measures to identify how
active patients were within the ward in relation to
exercise routines. For example, one physiotherapist
described the difficulties in verifying a patient’s claimed
walk of one hour after lunch. Analysis of monitoring
data indicates exact times, durations, speed and
smoothness of walking episodes. The project has
provided a measure to classify activity and determine
how much of the day is lying, sitting/standing, walking.
This classification is illustrated in a typical daily activity
profile for a patient shown over in Figure 4 (Left).
From the data collected, we can also assess changes in
gait smoothness over time and use this as a quantifiable
indicator for rehabilitation progress. Figure 4 (Right)
indicates 20-second windows of randomly selected
afternoon gait samples for a patient at the
commencement, midway and end of their rehabilitation.
These plots give a basis for rehabilitation progress.
Discussion
This observational trial has indicated that ambulatory
monitoring increases support in diagnoses and
management by having access to information about gait
smoothness and vital signs. Clinicians involved with
this study have been able to quantify for the first time
whether there has been an improvement in gait as a
result of the rehabilitation program and relate this to
care planning. Ambulatory monitoring technology has a
key role in process efficiency improvement and in
contributing to improved health outcomes.
The value of the study was rated highly by post trial
evaluation questionnaire respondents completed by the
rehabilitation specialist, physiotherapist and clinical
nurses (Figure 2).
3 point Rating (1 = lowest, 3 = Highest)
3
2
1
Trial duration
Satisfaction with project
documentation
Satisfaction with training
Post trial Clinical Evaluation
95% confidence intervals across 6 assessors
Level of assistance from EHRC
researchers
Usefulness of weekly Friday
meeting
Criteria
Figure 2 – Post trial clinical evaluation was favourable
Methods of attachment to
patients
Perceived patient satisfaction
Ability to carry our normal
clinical duties
Perceived clinical usefulness of
devices
Overall value of this study

Time track
Anterior/Posterior
Acceleration
Longitudinal
Acceleration
Figure 3: Screenshot of data viewing interface indicating a patient fall; The trial did not include a statistically significant
number of falls
Patient daily profile in early rehab
Patient daily profile in mid rehab
Sitting/standing
walking
Sitting/standing
Figure 4: Benefits were realised in the area of stoke rehabilitation:
(Left) Sample activity profile for a patient over a day and pie-chart of demonstrating the proportions of daily activity at
different stages of rehabilitation;
(Right) Randomly selected afternoon gait samples taken at the start, midway and end of rehab indicating improvements
in gait smoothness.
walking
lying
lying
ECG

Our evaluation at the completion of the study found
several service-related benefits were realised:
1. Improved expert support for clinical decision
making by doctors/ Improved provider access to
evidence-based information at the point of care
2. Improved staff morale, staff satisfaction, greater
involvement of specialist nurses
3. Improved patient self-management
4. Improved patient confidence and satisfaction
5. Improved health services (community expectations,
wider range of services, tailored service, better
utilisation of assets)
6. Improved research and practice
7. Improved health research interactions
Many of these benefits contribute directly to the
achievement of government policy and strategic
objectives.
Although clinical benefits were realised, the
outcomes for patients were not significantly impacted
by the trial. The trial did not involve assessing
ambulatory monitoring as an intervention against
control patients without monitoring. ie. there was no
intervention trialled. Consequently the full potential of
ambulatory monitoring devices has not been assessed by
a technology observational trial. It is recommended that
ambulatory monitoring technologies be further assessed
through randomised controlled trials as opposed to an
observational trial.
Also our project did not involve a centralised
management system for patient data collected during the
trial. Future scenarios could include real-time, remote
monitoring from a home environment, where adverse
patient conditions (eg. a fall) can be transmitted to a
secure database allowing access by clinicians or carers.
Structuring the uploading of adverse patient conditions
to a health record system as an automatic process
provides a flexible, seamless and integrated process of
care, especially useful for elderly patients and those in
rural and remote environments.
Conclusions
The trial has demonstrated the feasibility of
accelerometer-based technologies to measure patient
movement relevant to falls assessment and stroke
rehabilitation. While the trial did not result in the
development of a falls predictor, it did improve clinical
information flow for geriatricians and has potential
clinical value in the management of chronic diseases
(eg. stroke).
Implementation of devices described in this paper is
part of the solution to pressures within health care to
minimise error rates, conduct diagnoses on the bases of
real time patient data, improve efficiency, and reduce
costs. As a result of this review, we conclude that
ambulatory monitoring can play a significant role in the
achievement of health system objectives and there is
value in pursing further development and assessment of
these technologies.
References
[1] World Population Ageing: 1950-2050, Population
Division, DESA, United Nations, 2002
[2] Australian Institute of Health and Welfare, Health
system expenditure on disease and injury in
Australia, 2000–01. AIHW cat. no. HWE 26
Canberra: AIHW (Health and Welfare Expenditure
Series no. 19), 2004
[3] Lord SR, Sherrington C, Menz HB. Falls in older
people: risk factors and strategies for prevention.
Cambridge University Press, 2001
[4] Salarian A, Russmann H, Vingerhoets FJ, et al.,
Gait assessment in Parkinson's disease: toward an
ambulatory system for long-term monitoring, IEEE
Trans Biomed Eng 51(8), 2004, pp. 1434-43
[5] Kavanagh JJ, Barrett RS, Morrison S. Upper body
accelerations during walking in healthy young and
elderly men, Gait Posture 20(3), 2004 , 291-8.
[6] Lovell NH, Celler BG, Basilakis J et al, Managing
Chronic Disease with Home Telecare; A System
Architecture and Case Study, Proc 2nd Joint
EMBS/BMES Conf Houston, IEEE, 2002, pp1896-7
[7] Bussmann JB, van de Laar YM, Neeleman MP, et
al., Ambulatory accelerometry to quantify motor
behaviour in patients after failed back surgery: a
validation study, Pain 74(2-3), 1998, pp 153-61.
[8] Najafi B, Aminian K, Paraschiv-Ionescu A, et al,
Ambulatory system for human motion analysis
using a kinematic sensor: monitoring of daily
physical activity in the elderly, IEEE Trans Biomed
Eng 50(6), 2003, pp.711-23.
[9] Tamura T, Yoshimura T, Horiuchi F, An
ambulatory fall monitor for the elderly, Proc 22 nd
EMBS Conf, Chicago, IEEE, 2000, pp. 2608-2610
[10] Ottenbacher KJ, Jannell S, The results of clinical
trials in stroke rehabilitation research, Arch Neurol.
50(1), 1993, pp. 37-44
[11] Haeuber E, Shaughnessy M, Forrester LW, et al.,
Accelerometer monitoring of home- and
community-based ambulatory activity after stroke,
Arch Phys Med Rehabil 85(12), 2004, pp.1997-
2001
[12] Uswatte G, Miltner WH, Foo B, et al., Objective
measurement of functional upper-extremity
movement using accelerometer recordings
transformed with a threshold filter, Stroke 31 (3),
2000, pp.662-7.
[13] Nyberg L, Gustafson Y, Patient falls in stroke
rehabilitation. A challenge to rehabilitation
strategies, Stroke 26(5), 1995, pp.838-42
[14] Mathie MJ, Coster AC, Lovell NH et al, A pilot
study of long-term monitoring of human
movements in the home using accelerometry, J
Telemed Telecare 10(3), 2004, pp.144-51.
[15] Roscoe J, Fundamental Research Statistics for the
Behavioural Sciences 2nd edition, Holt, Rinehart
and Winston Inc,New York, 1975, pg.184