Flexible Pressure Mapping Platform for Mobility Monitoring
S. Cruz
, D. Dias
, J. C. Viana
and L. A. Rocha
1, 2
Institute of Polymers and Composites/I3N, Polymer Engineering Department, University of Minho,
Campus Azurém, Guimarães, Portugal
Center ALGORITMI, University of Minho, Campus Azurém, Guimarães, Portugal
Keywords: Health Monitoring Device, Flexible Pressure Mapping Systems, Flexible PCB.
Abstract: The goal of the work presented here is the development, integration and testing of an innovative
technological approach to be the basis for a new product and service for markets associated with the
“Health” vector. Our research focuses on a Physiological computing approach, where a polymeric flexible
detection system, working as the sensing element is used as an input channel, and a computing system is
responsible for the physiological signals synthesis. The proposed solution provides a simpler, lower cost and
larger scale manufacturing production of polymer based sensors, along with an electronic interface and the
software design. The sensing platform consists in a flexible PCB (Printed Circuit Boards) manufactured
using conventional technology (defining the electrical connections and the capacitors dimensions) together
with two flexible polymeric membranes (TPU) printed with conductive ink (Plexcore®) for definition of the
electrodes. A Capacitance to Digital Converter (CDC) is used to measure the capacitance of the sensors, and
a graphical interface in MATLAB allows real-time visualization of data. Current results performed on the
pressure sensors indicate the feasibility of the approach.
Nowadays, humans are highly dependent on the use
of computers and therefore, they become highly
commonplace in work and leisure areas. The
availability of good information processing
capabilities and sensors is pushing for new
applications relating physical monitoring. For this
reason, the interest on Flexible Pressure Mapping
Systems (FPMS) for use on non-planar surfaces
grew tremendously (Engel J et al., 2006) (Kim &
Ho, 1998) in areas such as aerospace, automotive,
biomedical and robotics (Chiang, K. Lin, & Ju,
2007) (Cheng, Lin, & Yang, 2010) (Petriu, McMath,
Yeung, & Trif, 1992) (Pritchard, Mahfouz, Evans,
Eliza, & Haider, 2008) (Yeung, Petriu, McMath, &
Petriu, 1994).
Flexible Pressure Sensors (FPS) are transducers
that measure pressure distribution between two
contact surfaces, with the particularity of being
flexible. These sensors consist in a set of array of
sensors elements to force or pressure that are
embedded in a substrate. The force or pressure
sensors are connected to electronic devices
responsible for the signals reading sent by the
sensors (several times per second) and
communicates to the computer. Specialized software
enables reading of data in real time providing an
image in 2D or 3D graphics. Analysis tools acquire
the pressure peak, the center of pressure or force, the
output signal with respect to time and various
statistical parameters, thus visualizing the magnitude
and distribution of forces applied to the FPS.
Flexible polymer detection systems provide a
better contact area and therefore more accurate
readings thanks to its ability to fold/roll compared to
traditional hard circuits. In medical applications a
wide variety of configurations is needed and
therefore it requires the pressure sensors to be
flexible (bendable to a few degrees). Also, the
sensitive area should be as small as possible.
Depending on the spatial resolution required for the
intended application, the diameter for the sensitive
area of the sensor can range from 1 mm
to 1 cm
(Ashruf, 2002)). A high precision, reproducibility
and selectivity are other essential requirements for
the sensors. Flexible sensors have the capacity to
follow all the movements, capacity to stretch to
Cruz S., Dias D., Viana J. and Rocha L..
Flexible Pressure Mapping Platform for Mobility Monitoring Applications.
DOI: 10.5220/0004719500330039
In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014), pages 33-39
ISBN: 978-989-758-006-2
2014 SCITEPRESS (Science and Technology Publications, Lda.)
some degree (to measure correctly the applied
forces) and have low thickness (thick sensors tend to
provide erroneous readings (Ashruf, 2002)) which
makes them ideal candidates.
The use of Physiological Computing systems in
health care is necessary and presents significant
advantages. For instance, FPMS offers the
possibility of obtaining pressure readings measured
in the contact surface, revealing information which
is not readable to the naked eye. This technology is
interesting since it enables reading, recording,
processing and analysing the physiological physical
states of the user and continuously monitor mobility,
without the minimum disruption or loss of
information. Data acquisition and display plays an
important role in applications where for example, it
is useful and advantageous to make an evaluation of
a signal (measurement of pressure to prevent
pressure ulcers in patients in wheelchairs or hospital
beds) (Xsensor 2013).
In addition, an acquisition system can further
contribute for health monitoring situations where is
essential to follow patients during rehabilitation with
the best action/prevention, diagnosis and treatment.
This would also increase the autonomy of patients
during rehabilitation, during the postoperative
period; in medical facilities or in home environment,
thereby reducing the length of hospital stay. The
costs of surgery, treatment and rehabilitation are
high, not to mention the collateral consequences at
the social and psychological level. It is of clinical
interest, as well as financial interest, to eliminate the
gap between physiological data handling and
human-computer interaction, to reach the most
reliable diagnose for better action/prevention
treatment, to increase the comfort of patients and
eliminate some of the costs. Another element that
deserves consideration is that these systems allow to
perform intervention studies that assess the progress
of treatment and rehabilitation, as well as the
effectiveness of new treatments.
The objective of this study is to develop a
physiological computing approach based on a
flexible pressure mapping system for physiological
sensing and software for Physiological data handling
(see Figure 1). It will combine textile, polymers,
electronics, and psychological studies to develop a
support surface system able to acquire record and
evaluate body pressure.
The importance of real time physiological data
signal reading for prognostic for health monitoring
devices is underlined in this paper. Experimental
results on a prototype system are presented.
The paper is divided in six sections. After a brief
introduction, the system overview, the details of the
manufacturing process, the electronic interface, the
software design and experimental results are
presented. At the end, some conclusions are drawn.
The flexible pressure mapping system being
developed has the form of a carpet with four equal
sensitive areas as shown in Figure 2. The sensing
zone is about 12x8 cm
. The capacitive sensors
(between 24 and 32 sensors) are placed in these
regions. The capacitive readout electronics are
placed next to the carpet. Each sensor element
consists on a flexible Printed Circuit Board (PCB)
for definition of the electrical connections and
capacitor dielectric dimensions and two flexible
membranes with printed electrodes. It is flexible and
easy to handle. In addition, it will be integrated in
complex atmospheres, allowing the monitoring of
the balance during physical therapy sessions,
indicating the regions of the foot that are exerting
Figure 1: Block diagram of the physiological computing
Three main steps are necessary to complete the
proposed fabrication process: the fabrication of the
flexible membranes with the conductive ink, the
fabrication of the flexible PCB and an assembly
3.1 Flexible Membranes with
Conductive Ink
TPU is a material that offers the elasticity of rubber,
but with improved mechanical properties: good
flexibility, strength and durability, excellent wear
properties and elastic memory. Hence, polyester
based Thermoplastic polyurethane (TPU),
AVALON 65 AB grade, from Huntsman, was
selected for the flexible substrate.
Due to their excellent properties (in terms of
conductivity, inkjet printability and flexibility),
conductive inks became an emerging technology,
penetrating the sensors market and enabling new
applications. Conductive inks are a growing market,
representing a $2.86 billion market in 2012 and
forecasted to rise to $3.36 billion in 2018 (Zervos,
2012). In this work, a conductive ink, Plexcore® OC
RG-1100 grade (a Sulfonated polythiophene ink)
from Sigma Aldrich was selected for the fabrication
of the electrodes.
Figure 2: Sensorial platform scheme (the dimensions are
in cm).
Printing of patterns with conductive inks on
polymers surface enables the realization of “active
polymeric materials”. A high-definition printer
(Xennia Carnelian) was used for printing and
Figure 3: Inkjet printed flexible substrates (with
conductive inks electrodes on a TPU substrate). The upper
membrane is the mirror image of the lower membrane
(Top on the rigth and bottom on the left).
definition of the electrodes of the capacitive sensors.
Inkjet Printing technology is a simple, low cost
and large scale manufacturing production technique
making it a very interesting approach due to both
simplicity and cost of the solution. The conductive
ink was printed in two TPU flexible membranes (see
Figure 3); a substrate for the superior electrodes and
another for the inferior electrodes.
3.2 Flexible PCB’s
The flexible pressure sensors results from the
combination of flexible PCB technology and flexible
membranes with conductive inks. The flexible
PCB’s consists in a flexible substrate of polyimide
(PI), 125 µm thick with copper on both sides (35
µm). The copper is subsequently machined to define
the conductive lines, and then the substrate is open
in certain regions to define the dielectric (air) of the
capacitor. The platform has 24 capacitive pressure
sensors. Multiple geometries were draw with
increasing electrode size in the initial prototype.
This approach allows testing the several
configurations, and selecting later the one that best
suits the application. Figure 4, shows an image of
the flexible PI substrate and respective conductive
The sizes were selected to have a rest
capacitance between 5-10 pF (5 pF for the smaller
dimensions and 10 pF for the larger). The sensors
are expected to have high sensitivity due to the low
Young Modulus (as compared to silicon based
pressure sensors) of the TPU. No mechanical
reliability issues are expected since TPU has
excellent mechanical properties.
Since the readout electronics requires an
excitation AC signal, all the capacitors are
connected to the bus excitation. Figure 4 (bottom
side) shows the bottom of the flexible substrate. In
Figure 4: The top side (on the left) and bottom side (on the
right) of the flexible polyimide substrate and the
respective dielectric areas. The PCB size is 124 x 95 mm
this case, the capacitors have an independent link
which will be multiplexed for sequential reading of
the capacitors. Noteworthy, is the ring around the
dielectrics for electrical connection of the printed
conductive electrodes. These rings enable the
electrical connections to the capacitive electrodes.
3.3 Prototype Sensors Assembly
Finally, the flexible TPU flexible membranes were
bonded with ELECOLIT 414 (a polyester-based,
electrically conductive adhesive) in parts of the
substrate to the flexible substrate of polyimide in
order to manufacture the capacitive sensors (see
Figure 5).
Figure 5: Sensor elements manufacturing process in
which: a) the geometry of copper conductors and the
capacitor dielectric are defined using a PCB flexible
process; b) the electrodes of the capacitors are inkjet
printed using conductive inks and c) the flexible substrates
with electrodes are glued to the printed circuit board.
The reading circuit of the capacitive sensors has
been designed considering the sensors and its
manufacturing technology. Regarding the
electronics, the capacitor sensor reading is
performed using a 12-bit Capacitance to Digital
Converter (CDC) AD7150 for each sensory zone
allowing direct interface with the capacitive sensor.
The converter consists of a second-order sigma-delta
(Σ-Δ) modulator.
This low-power IC, 100 μA, has 2 input
channels, with a conversion time of approximately
10 ms and 1fF resolution, for a dynamic range of 4
pF. The use of two multiplexers enables sequential
reading of the 24 sensors comprising each sensory
area with the two multiplexers connected to the two
input channels of the converter. A microcontroller
feeds and controls the multiplexers. When testing a
particular sensor, the sensor is connected between
the excitation bus and the modulator input of the
CDC. The excitation signal is applied during the
Figure 6: Capacitive sensors reading system block
conversion and the modulator continuously acquires
the load through the sensor. The digital filter
processes the output of the modulator and the data
can be read by the I
C serial interface established
with the microcontroller. The microcontroller allows
the reading of the corresponding digital capacity
value for each sensor, as well as configures the
CDC. The CDC is connected to a computer, where
the obtained values are recorded by a graphical
interface implemented in MATLAB. This software
provides real time readings and allows data
The software for data acquisition and display is
implemented in the MATLAB graphical
environment. At the current stage of this work, it is a
very simple program providing:
- Serial communication with the microcontroller and
setting of the converter parameters such as the
conversion mode, the converter sensitivity, the input
dynamic range and others;
- Reading of the sensors capacity througth CDC;
- Real time values display, on a 2D graph, of the
capacity variation as a function of the pressure for
each of the sensors in each sensory area;
- Recording data on file for further detailed analysis.
The software allows testing all sensorial
platform, however, at this time one sensory area of
the prototype was tested, as depicted in figure 7 .
Figure 7: MATLAB graphical environment: Real-time
prototype sensors acquisition.
The assembled prototype (Figure 8) was placed
together with the readout electronic PCB (Figure 9)
inside a pressure chamber (Figure 10) and positive
pressures were applied (ranging between 0kPa-
100kPa). The pressure reading inside the pressure
chamber was preformed through a reference
pressure sensor (TECSIS P3297). Results are shown
in Figure 11.
The assembled prototype sensor flexibility
(bendable to a few degrees) is demonstrated in
Figure 8. The sensing platform has the ability to
bend, and it can be stretched to some degree (to
measure correctly the forces applied).
Figure 8: Assembled Prototype Sensors.
The sensors response to loads was also
performed. The test consisted on placing weights on
top of the sensors. For this purpose an acrylic plate
with an area of 5.10 cm x 7 cm was used, as an
interface surface between the load and the sensors,
to ensure an equal distribution of the weight on all
sensors. The capacity variation was measured for a
weight range between 0Kg-19.2Kg. The sensor
output was acquired at a sampling rate of 100 Hz
and 1000 samples for each sensor. Several sensors
were tested, and the average response of 5 sensors
with circular geometry and different areas is present
in Figure 12.
Figure 9: Assembly of the reading system.
Figure 10: Capacitive sensor test setup.
Figure 11 shows the static response of five sensors
with circular geometry with diferent areas. The
sensores capacity increases with the increasing
pressure, however, contrary to what would be
expected, higher sensor area (with 0.7 cm radius)
had the lowest capacity variations (1.68pF) when
compared to the smaller sensor areas (with 0.5 cm
radius) with a capacity variation of 2.955pF. This
unexpected behaviour is due to cross-coupling
between the sensors, i.e., when one of the sensors is
actuated, the surrounding sensors are also changing
(with much less variation). Since the smaller sensors
are the ones that have higher cross-coupling (these
sensors are the ones with longer electrical
connections and therefore with larger coupling
capacitances to the neighbouring sensors), they end
up showing a higher variation than the larger
sensors. This same behaviour was observed on the
loading tests (Figure 12) and this problem is already
being addressed in a future redesign of the system
(sensors plus electronics circuits).
Figure 12 depict the variation of sensor
capacitation with load. It can be observed that the
sensors capacitance increases with the load, but once
again, the sensor with higher area didn’t present
more capacitance variations. The 0.7 cm radius
sensor area showed the lowest capacity variation
(1.88pF). The largest variation was seen on sensors
whose area is between the maximum area and the
minimum area, with the sensor electrodes radius -
0.65cm the range of higher capacity (3.015pF).
Figure 11: Pressure sensors static response.
Results for the rectangular sensors were not possible
due to high parallel capacitances that increase the
total sensor capacitance and the system is not
capable of handling these high values (it saturates).
The actual electronic readout and assembly process
(glue used for contact) increases the parallel
capacitance increasing all sensors in general, but,
since rectangular sensors have larger areas, they
show higher parallel capacity influence.
Regarding the system response time, tests results
have demonstrated that the developed reading
system is capable to follow the expected physical
movements (average response time of 0.5 seconds)
during therapy sessions. This acquisition time (0.5s)
is insufficient for some type of exercises (especially
if fast movements are involved), but for simple
equilibrium exercises it might be sufficient. The
acquisition time is expected to be improved in the
next prototype.
The developed software has shown the ability to
read, record, process and analyse the physiological
physical states of the user and continuously monitor
mobility, without the minimum disruption or loss of
information. Overall, the measured response is in
according to what would be expected (increasing
pressure results in a capacitance increase).
This study had some limitations: at this phase,
the capacitive results are higher than it was predict,
which implies improvements in the electronic
readout circuit in order to eliminate the parallel
parasitic capacitance. In the next phase, the
multiplexing strategy will be improved, in order to
reduce the cross-coupling influence. The process
control will be essential to achieve high
reproducibility and desirable sensor specifications.
Nevertheless, pressure is not the most
representative and the most appropriate for testing
validation. In the future, the experimental testing
will be performed with the sensor platform in
contact with the human body and its performance
will be compared to the existing pressure mapping
platforms (Novel 2013)(PPS
2013) (Xsensor 2013).
Figure 12: Sensor response to loads.
This article described the design, fabrication and
experimental results of a flexible pressure mapping
system and its readout electronics interface and
software. Development of a better and efficient
sensor, capable of being integrated into complex
atmospheres, overcoming some of the difficulties
found in Physical Rehabilitation, and able to
measure balance during physical therapy is essential.
At this phase, the obtained results are very
promising and encouraging. The solution presented
here enables a high density of capacitive flexible
sensors with a simple and inexpensive process.
Nevertheless, there are some issues that must be
improved in the next prototype.
These devices can help the ubiquitous
management of the state of health of a patient, thus
enabling a better performance/prevention, diagnosis
and treatment. The developed sensors can help
humans to benefit from the interactions between
physiological data and computer. In the near future,
with the development of science and technology, we
will the development of new technologies with the
ability to make the connection between humans and
This work was co-financed by FEDER, Operational
Programme Competitiveness Factors through project
TICE-Healthy – QREN SI&IDT Mobilizing
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