WRIST-WORN FALL DETECTION DEVICE
Development and Preliminary Evaluation
Mattia Bertschi and Leopoldo Rossini
Swiss Center for Electronics and Microtechnology, Jaquet-Droz 1, Neuchâtel, Switzerland
Keywords: Elderly, Monitoring, Portable device, Wearable device, Automatic fall detection, Accelerometers.
Abstract: Falls are the most important cause of accidents for elderly people and often result in serious physical and
psychological consequences. The rapid growth of the elderly population increases the magnitude of the
problem as well as the generated costs. In order to take care of old people living by themselves or in care
centres and to reduce the consequences of a fall, various technological solutions have been studied, however
none led to a commercial product fulfilling user requirements. In this work we present an automatic fall
detector in the form of a wrist watch which could lead to better life conditions for the elderly. Our device
implements functionalities such as wireless communication, automatic fall detection, manual alarm
triggering, data storage, and simple user interface. Even though the wrist is probably the most difficult
measurement location on the body to discern a fall event, the proposed detection algorithm shows
encouraging results (90% sensitivity, 97% specificity) with the signals of our database.
1 INTRODUCTION
Falls are the most widespread domestic accidents
among the elderly. Their consequences often give
rise to impairments to the health and lifestyle of the
victims (Pérolle et al., 2004). In many cases,
physical after-effects and other injuries are direct
consequences of these accidents and result in
significant medical costs.
Furthermore, it frequently happens that elderly
people who have previously experienced a fall fear a
new fall and sink gradually into inactivity and social
isolation. The reduction in their mobility leads
progressively to an increase in the risk of a fall
(Doughty et al., 2000). Hence, given the growing
part of the elderly people in our modern societies,
the socio-economic impact that this self-imposed
isolation may have should not be neglected.
The most widespread solution for limiting the
apprehension of a fall is provided by social alarms
consisting of portable devices. These are generally
equipped with an alarm triggering button and
endowed with telecommunication means suitable for
alerting the care centre. Nevertheless, because of the
fall, the person may not be able to actuate the button
and to trigger the alarm (unconsciousness, state of
shock, broken arm, etc.).
To alleviate this drawback, autonomous fall
detectors have been developed and are capable of
triggering an alarm automatically without any
intervention of the victim and transferring this
information to a remote site (Doughty et al., 2000).
These autonomous detectors operate essentially on
two principles. The detector is either sensitive to the
person's appearance or impact on its environment
and is based on video (CCD or IR camera) or
vibratory type sensors (acoustic or piezoelectric
layers on the ground) placed in the usual
surroundings of the subject. The benefit of these
devices is that they do not have to be worn. Instead,
they are fixed and integrated in a given spot and
cannot be moved easily when the person changes
location. Moreover, in the case of a video sensor the
person will have the impression of being supervised
and feel inconvenienced. The major drawback of
acoustic based sensors is that they are surface
dependent, while those based on vibration are fragile
and expensive. The detector can also be worn by the
person and thus detect a fall directly as soon as it
occurs, triggering an immediate alarm. In this case,
the information provided by inclinometers,
gyroscopes or accelerometers is exploited. These
devices are generally compact, inexpensive, fairly
non-obtrusive, easy to use, and can be worn at
various body locations.
368
Bertschi M. and Rossini L. (2009).
WRIST-WORN FALL DETECTION DEVICE - Development and Preliminary Evaluation.
In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 368-371
DOI: 10.5220/0001540903680371
Copyright
c
SciTePress
Devices worn close to the centre of gravity
(Depeursinge et al., 2001) are the most reliable ones,
but also the least convenient to wear on a daily basis,
in particular while performing common daily
activities. A device having the form of a wristwatch
would be well tolerated in all situations, despite the
challenge to detect a fall due to changes of position
and accelerations that the wrist experiences during
everyday actions (Degen et al., 2003). Furthermore,
the inclination measurement of the forearm cannot
give reliable information about the person's position.
Generally, fall detectors placed on the wrist give rise
to a large number of false alerts, and this would
generate significant and unnecessary costs.
Our goal is to develop a small, comfortable, and
user friendly device, as well as an automatic fall
detection algorithm that will help elderly people to
handle this problem.
2 METHOD AND MATERIALS
The fall detector that we have developed is a device
capable of automatically detecting various body falls
and sending an alarm to a remote terminal. The user
can manually generate the alarm signal in case of
necessity or, inversely, he can cancel an
automatically generated alarm in case of a false fall
detection.
2.1 Fall Detection System
The detection system is integrated in the case of a
wrist watch. A picture of our prototype is depicted in
Figure 1.
Figure 1: Wrist fall detection system.
The core of the wrist-located device consists of a
microprocessor (MSP430) and two MEMS sensors
arranged perpendicularly to allow the measurement
of acceleration along three axes with a range equal
to ±18g (ADXL321). The user interface is simple
and comprises a small LCD screen (Nokia 3310), a
vibrator which advises the user that a fall has been
detected and an alarm will soon be sent, and a push
button on the front panel to manually trigger the
alarm signal. Data can be transmitted from fixed
and/or mobile devices over short distances utilizing
a short-range communication technology (Bluetooth
protocol).
For experimental purposes, the acceleration signals
can also be stored in a flash memory card. The serial
RS232 port as well as three additional buttons are
also available for debugging and test.
The device is powered by a 3.7 volts rechargeable
Lithium-Cobalt-Polymer battery. The battery life of
the device varies from about 15 days to one month,
depending on the sampling frequency and the details
of the implemented data handling and storage
functionalities.
2.2 Fall Detection Algorithm
The three-axes acceleration signals are recorded and
stored in the flash memory, with a sampling
frequency of 910 Hz and 12-bit resolution. Although
the algorithm has not been implemented yet in real-
time, it has been tested offline using Matlab.
Initially, the device is slowly rotated a few times
in order to project the gravity vector on the three
axes in various configurations. The resulting
acceleration signals roughly define the surface of a
sphere in a three dimensional space and are used to
calibrate the sensors. We define the time-dependent
acceleration vector
(
)
T
zyx
aaa
~
,
~
,
~
=A
whose entries
are defined as
iii
caa
=
~
, for zyxi ,,= , where
i
a
represents the acceleration in the i-direction while
i
c
is the i-coordinate of the centre of the sphere
corresponding to a zero acceleration. Therefore, the
equation of the sphere is defined using Cartesian
coordinates as:
0
2
2
2
= rA
(1)
where the radius r corresponds to the gravity
acceleration and defines the sensor gain while the
centre (c
x
,c
y
,c
z
) defines the accelerometer offset.
Notice that one can use the Gauss-Newton method
to estimate (c
x
,c
y
,c
z
,r) by minimizing the left-hand
part of equation (1) and use those parameters to
calibrate the acceleration signals (see Figure 2).
The three-axes acceleration experienced at the
wrist of the person wearing the device can not be
directly linked to the specific posture of the body.
However, it has been observed that the distribution
WRIST-WORN FALL DETECTION DEVICE - Development and Preliminary Evaluation
369
of the acceleration norm
2
A experienced at the
wrist over a time window ΔT provides a particularly
reliable signature, allowing the identification of a
given event happening to the person wearing the
device.
-6
-3
0
3
6
ã
x
(g)
-6
-3
0
3
6
ã
y
(g)
0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
-6
-3
0
3
6
ã
z
(g)
Time (s)
Figure 2: Typical recording of a fall event. Each signal
corresponds to the acceleration in the x, y, or z direction.
We define S
t
as the signature at instant t that
represents the distribution of the acceleration norm
2
A over a time window ΔT. The signature S
t
can
be extracted via a simple amplitude threshold and
compared to a reference signature S
ref
. To obtain S
ref
,
a subset of all the fall signatures recorded in the
database is used to obtain statistics in the form of
means and variances. Specifically, a particular value
of each signature in the subset is identified so as to
carry out an alignment, for example on the
maximum. This particular value is used to align the
signatures, so as to obtain a group of aligned fall
signatures. On the basis of the various aligned
signatures, the reference signature S
ref
is obtained by
averaging the values of the aligned signatures.
In order to construct a similarity measure d
between S
t
and S
ref
, the squared value of the
acceleration due to gravity is subtracted to remove
its influence. For this application, d is calculated
according to the Mahalanobis distance definition.
The variances of the aligned signatures are placed on
the diagonal of a matrix θ that is used together with
S
ref
to estimate if the instantaneous signature S
t
arises from a fall:
)(
1
)(
),(
ref
S
t
S
T
ref
S
t
S
e
ref
S
t
Sd
=
θ
(2)
The influence of the least reliable samples is reduced
due to the use of the variances. One can see that the
value of d is in the interval [0,1] and therefore can
be considered to be a fall probability. A threshold
was used to discriminate between the two classes:
“fall” and “no fall”.
2.3 Experimental Setup
A preliminary validation has been performed with
three adult healthy adult volunteers to assess the
reliability of our system for fall detection tasks. The
study consists in recording the three-axial
accelerometer signals (wrist-located) during
controlled fall events and daily life activities. Each
case was repeated three times and stored in a
separate file. In our database we have documented
180 situations by recording acceleration signals and
video sequences. The latter are particularly useful to
understand the dynamics of the wrist movement.
To evaluate the sensitivity of the fall detection a
first subset consisting of six kinds of falls is created.
The kind of falls selected for this subset are: front,
back, left, right, falling backwards while sitting
down, and falling forward while standing up. Each
fall was intentional and a mattress was used to
protect volunteers from injuries.
A second subset was used to estimate the
specificity of the fall detector. It includes 14
common situations representing challenges for the
detection algorithm (slow/fast walking, walking
upstairs/downstairs, hit on a table with the fist,
sitting down, lying down, applauding…).
3 RESULTS
To generate the reference signature of a fall event
we have taken 8 recordings representing fall events
randomly selected from the 54 falls of the database
and we have computed the mean and the standard
deviation for each event. Figure 3 shows a typical
reference signature that we obtain with the signals of
our database. The mean and standard deviation
values of the reference signature are then used to
compute a normalized similarity measurement for
each recording of the database and a binary classifier
is used to separate falls from other events.
BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices
370
0 0.5 1 1.5
0
2
4
6
8
10
12
14
Time
(
s
)
Acceleration norm (g)
0 0.5 1 1.5
0
2
4
6
8
10
12
14
Time
(
s
)
Acceleration norm (g)
Figure 3: Reference signature in terms of mean and
standard deviation.
A binary classifier system is generally assessed
in terms of sensitivity and specificity. A useful
graphical tool to represent the sensitivity versus (1-
specificity) for a binary classifier system as its
discrimination threshold is varied is the receiving
operating characteristic (ROC). Figure 4 shows the
ROC curve obtained with the signals of the database.
0 2 4 6 8 10
60
65
70
75
80
85
90
95
100
1 - Specificity (%)
Sensitivity (%)
0 2 4 6 8 10
60
65
70
75
80
85
90
95
100
1 - Specificity (%)
Sensitivity (%)
Figure 4: The receiver operating characteristic shows the
relationship between the sensitivity and (1-specificity).
In order to become independent from the
recordings used to generate the reference signal, we
repeated the same procedure ten times: random
selection of 8 fall events, estimation of the mean and
the standard deviation, classification of the 172
recordings, and estimation of the ROC curve. The
circles in the plot of Figure 4 are thus a mean value
of the ten simulated results.
One can see that there is a tradeoff between
sensitivity and specificity: the larger the sensitivity,
the smaller the specificity. We can decide to favour
one characteristic over the other, but a good balance
is normally to privilege the upper-left corner of the
ROC curve. In the present case, this particular point
allows us to detect about 90% of the falls while
keeping the false alarm rate below 3%.
4 CONCLUSIONS
We have developed a small, light, and comfortable
fall detector device which is worn at the wrist like an
ordinary watch, removing the social stigma of
wearing a medical device. The real advantage of
fixing the fall detector at the wrist is the possibility
of wearing the device at night, when falls can also
occur. The device is thus easy to wear continuously
without any specific constraints. The major
drawback is the signal processing challenge of
estimating a fall from wrist acceleration data, due to
the strong accelerations experienced by the forearm
during daily-life activities.
The proposed algorithm to detect falls is based
on accelerometric signals and has the advantage to
be simple and robust showing promising results with
our present database. Results demonstrate high
sensitivity (90%) as well as high specificity (97%)
for the detection of intentional falls.
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