Engineering, 2013, 5, 132-136 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
Evaluating Pressu re Ul cer D ev el o pm ent in
Wheelchair-Bound Population Using Sitting Posture
Farve Daneshvar Fard, Sahar Moghimi, Reza Lotfi
Depart ment of Ele ctrical Engineering, Biomedical Engineering Research Center,
Ferdowsi University of Mashhad, Mashhad, Iran
Email: f.,,
Received February 2013
Pressure ulcers are a common complication among wheelchair-bound population. They are resulted from prolonged
exposure to high pressure, which restricts blood flow and leads to tissue necrosis. In this work, a continuous pressure
monitoring system is developed for pressure ulcer prevention. The system consists of 64 pressure sensors on a 40 × 50
cm2 sheet. Real time pressure data and corresponding maps are displayed on a computer simultaneously. Furthermore, a
posture detection procedure is proposed for sitting posture identification. Having information about the patient’s postur-
al history, caregivers are capable of a better decision about repo s itio ning and treating the patient.
Keywords: Interface Pressure Monitoring; Pressure Ulcer; Sitting Posture
1. Introduction
Sitting-acquired pressure ulcers are a common complica-
tion among wheelchair-bound population. It is reported
in literature that 36% to 50% of pressure sores are attri-
buted to sitting in a wheelchair [1]. Pressure ulcers are
resulted from prolonged exposure to high pressure,
which restricts blood flow and prevents blood from
bringing oxygen and nutrients to underlying tissues.
Hence, continuous measuring and monitoring of interface
pressure is the most useful approach for preventing pres-
sure ulcers, which are considered both a health and eco-
nomic problem as they cause excessive expenditures by
increasing the length of treatment up to several time s [ 2].
In spite of so many attempts for improving ulcer preven-
tion techniques, high incidence of pressure ulcers is ob-
served and more effective prevention methods are re-
quired [3-5].
The relationship between pressure intensity and dura-
tion is explored by Reswick and Roger [6]. High pres-
sures are tolerable for short times only and will lead to
tissue necrosis if they are unrelieved [7]. However, low
pressures are damaging if sustained for a lengthy period
of time [8]. Meffre, et al. [9] designed a particular type
of seat for wheelchair-bound patients using electro-pneu-
matic pressure sensors. These kinds of pressure sensors
are more expensive than capacitive and resistive sensors
and slower in data acquisition. Yip, et al. [10] presented
a flexible pressure monitoring system. The prototype
consists of 99 capacitive pressure sensors on a 17 × 22
cm2 sheet. Yang, et al. [11] designed and evaluated an
air-alternating wheelchair seat. They used resistive pres-
sure sensors for measuring interface pressure. Drennan
and Southard [12] presented a system consisting of pres-
sure sensitive pads. The system produces alarms if the
pressure intensity is more than the threshold adjusted by
the user.
In this paper, we present a system for continuous mon-
itoring of interface pressure. Furthermore, a procedure
for identifying different postures of sitting is proposed. A
wheelchair-bound patient may develop pressure ulcer if
he has no sensation in his buttock. Sore development
may happen faster if the patient’s trunk is tilted to one
side for a long period of time. In this work, we simulated
different sitting postures of a wheelchair-bound patient.
Having information about the patient’s postural history,
let caregivers decide better about repositioning and
treating the patient .
The remainder of this paper is structured as follows:
Section 2 describes the system design, including sensors
array setup as well as, circuit and software design. In
Section 3, we present the proposed procedure for sitting
posture identification. The proposed method is verified
by a particular statistical test. Conclusions are given in
Section 4.
Copyright © 2013 SciRes. ENG
2. Methodology
2.1. Hardware Design of the System
In this work pressure sensing is carried out by Interlink
Electronics force sensing resistors (FSR-part no. 400),
which exhibit a decrease in resistance with an increase in
the force applied to the active surface. By measuring the
resistance, the applied force can be extracted and hence
the corresponding pressure value can be calculated. To
have a more precise measurement of force, each sensor
was calibrated before it was used. An array of sensors is
required for sensing pressure over a large area. We used
64 pressure sensors to cover an area of 30 × 40 cm2. Each
row of the array consists of 8 pressure sensors as illu-
strated in Figure 1. All sensors are fixed on a Plexiglas
sheet of size 40 × 50 cm2. A PCB of the same size was
designed for wiring the sensors and is fixed under the
Plexiglas sheet.
Measuring the resistances of FSR sensors is carried
out by an Atmel ATMEGA16 microcontroller analog-to-
digital converter. The same microcontroller controls
multiplexers to select one resistive sensor at any time
according to a particular sequence. The entire array of
the resistive sensors is scanned every 320 ms with a
sampling rate of 3 Hz.
A simplified schematic of the resistive sensors array
and the related electronic circuitry is shown in Figure 2.
Current source, designed by LM324 operational amplifi-
er and 2N3906 transistor, sources a current of 100 μA to
the selected resistor and the ADC measures the corres-
ponding value of voltage proportional to the resistance
value. Each sensor is placed in a serial connection with a
Figure 1. Pressure sensors array.
Control from
To current
Figure 2. Schematic of resistive sensors array.
diode to prevent current flows into other sensors and as a
result creating undesirable routs. The designed PCB, il-
lustrated in Figure 3, interfaces the electronics and the
sensors sheet.
2.2. Software Design of the System
Digitized data of sensors are transmitted from the mi-
crocontroller to a computer via a USB interface using
FT232 chip. A GUI (Graphical User Interface) is devel-
oped in MATLAB to report pressure maps in real time,
retrieve previous maps and risks and set alarms. Post
processing of the obtained data is carried out in MAT-
LAB. Measured values of pressure of each sensor in the
array are saved in matrices at each sampling interval. In
the GUI, there is an option for the user to define two
thresholds for pressure intensity and duration. An alarm
can be created by the software if the pressure intensity of
one sensor is larger than the adjusted threshold and the
duration of that pressure is more than the time threshold.
This event is considered as a risky situation.
The GUI is designed in a way that we can see the last
risks and their occurrence times. This would also provide
useful information regard ing patient’s postural history. A
sample pressure map of a person sitting on the setup is
shown in Figure 4. The GUI stores the pressure in units
of mmHg for each sensor and MAT LAB post processing
is used to generate this pressure map.
3. Sitting Posture Identification
3.1. Experiment and Results
As it was mentioned before, developing sores may hap-
pen faster in a wheelchair-bound patient if he involunta-
Figure 3. PCB u sed for the electronics.
Figure 4. Pressure map of sitting volunteer.
Copyright © 2013 SciRes. ENG
rily leans to one side for a long period of time. In this
work, we simulated different sitting postures of a wheel-
chair-bound patient. We had healthy volunteers in the
experiment and we defined four different postures for
them. These defined postures were assumed to simulate
sitting postures of a wheelchair-bound patient.
In the first defined posture, our subject sat straight on
the designed pressure sensitive seat with bent knees. This
was assumed to simulate proper sitting of a patient in a
wheelchair. In the second and third postures, the subject
sat with legs crossed, right on left and left on right re-
spectively. This was supposed to simulate the postures
during which a patient leans to his left and right sides. In
the last defined posture, the subject sat with legs
stretched. Figure 5 presents produced pressure maps for
different sitt i ng pos tures of the volunt e er.
3.2. Proposed Method for Identifying Sitting
Statistical parameters are used for detecting different
sitting postures. Values of mean, standard deviation,
skewness and kurtosis were calculated for each of the
produced maps shown in Figure 5. We used pressure
map matrices to calculate these parameters. Skewness
and kurtosis coefficients, related to each matrix, were
calculated from the probabilistic distribution of pressure
values in the middle rows of the matrix. Fitted distribu-
tions corresponding to each of pressure maps of Figu r e 5
are shown in Figure 6. As demonstrated, the result of
distribution fitting for posture 1 and 4 are close to stan-
dard normal distribution and therefore the corresponding
skewness values will be close to zero. Fitted distributions
for postures 2 and 3 result in negative and positive
skewness coefficients, respectively.
Now, we can present a method for detecting different
sitting postures according to the calculated parameters.
Skewness with negative sign (not close to zero) is an
indicator of the second posture. Skewness with positive
sign (not close to zero) is related to the third posture. The
first and last defined postures are identified by skewness
values close to zero (negative or positive). In addition,
we can distinguish the first posture from the last one us-
ing mean values, since the mean pressure value of the
total array in first posture is larger than that of the last
3.3. Verifying the Proposed Method
One way analysis of variance (ANOVA) was used to
verify the proposed method for identifying sitting post-
ures. The sitting posture identification experiment, de-
scribed in section 3.1, was performed for 5 volunteers (3
times for each subject), resulting in 15 different tests for
each of the four defined postures. We calculated mean,
standard deviation, skewness and kurtosis parameters for
these fifteen pressure matrices.
Figure 7 to Figure 10 represent the obtained box plots
for each parameter using MATLAB. Each column is re-
lated to one of the four defined postures. As it can be
seen in Figure 7, there is no overlap between mean val-
ues of posture 1 and posture 4. Therefore the mean val-
(a) (b)
(c) (d)
Figure 5. Different sitting postures, (a) sitting straight with bent knees; (b) sitting straight with crossed legs, right on left; (c)
sitting straight with crossed legs, left on right; (d) sitting with stretch ed l eg s .
50 100 150 200 250
50 100 150 200 250
50 100 150 200 250
50 100 150 200 250
Copyright © 2013 SciRes. ENG
(a) (b)
(c) (d)
Figure 6. Fitted distributions for each posture of Figure 5 respectively.
Figure 7. Box plots for mean values.
Figure 8. Box plots for standard deviation values.
Figure 9. Box plots for skewness values.
Figure 10. Box plots for kurtosis values.
246810 12 14 16
0. 02
0. 04
0. 06
0. 08
0. 1
0. 12
Dat a
fit norm al di stri but i on
2 4 6810 1214 16
0. 02
0. 04
0. 06
0. 08
0. 1
0. 12
0. 14
0. 16
0. 18
0. 2
0. 22
Dat a
fit norm al di stri but i on
246810 12 14 16
Dat a
fit norm al di stri but i on
1 2 34
Posture1 Posture2 Pos tur e3
P os tur e4
12 3 4
P os ture 2
P os ture 3
P os ture 4
P os ture 1
1 2 34
P os tur e1
P os tur e2
P os tur e3
P os tur e4
1 2 34
P os ture 1
Posture2 P os tur e3
P os ture 4
Copyright © 2013 SciRes. ENG
ues can be used for distinguishing these two postures.
Figu r e 8 shows that the values of standard deviation vary
from posture 1 to posture 4. According to Figure 9
skewness values of posture 2 have negative signs, and
skewness values of posture 3 have positive signs while
those of postures 1 and 4 are close to zero (negative or
positive). So these three groups (posture 2, posture 3,
posture 1 and 4) can be distinguished by the skewness
coefficient. Finally, Figure 10 shows that kurtosis coef-
ficients of posture 2 and 3 are generally larger than those
of postures 1 and 4. This sounds reasonable, since fitted
normal distributions of postures 1 and 4 are similar to
standard normal distribution, while those of postures 2
and 3 generally have higher peaks.
4. Conclusions
A continuous-time pressure monitoring system is pre-
sented. Due to its useful information about patient’s
movement history, feasibility for simultaneous monitor-
ing of pressure and alarming options, it is proposed that
this system can be utilized for pressure ulcer prevention.
Sitting posture identification is possible using the pre-
sented system. A method for detecting different sitting
postures has been proposed and verified. It is suggested
that preventing pressure ulcers in wheelchair-bound pa-
tients can be performed using the sitting posture detec-
tion method.
Spatial resolution of the designed system can be im-
proved in future works by increasing the number of
pressure sensors. The presented pressure monitoring sys-
tem can be expanded to be used in mattresses of bedrid-
den patients.
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