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Smart Textile: Exploration of
Wireless Sensing Capabilities
Andrey Somov
CDISE
Skolkovo Institute of Science and Technology
Moscow, Russia
a.somov@skoltech.ru
Elias Torres Alonso, Monica F. Craciun,
Ana I. S. Neves, Anna Baldycheva
CEMPS
University of Exeter, UK
Exeter, UK
{m.f.craciun, a.neves, a.baldycheva}@exeter.ac.uk
Abstract—E-textile is a developing technology joining the
advantages of material science and information and
communication technologies. In this work, we present the
development and assessment of smart textile system containing
sensing, processing and wireless communication capabilities. We
demonstrate a wearable temperature sensing system based on
resistance temperature detection approach utilizing graphene
technology, which allows high flexibility and robustness of the
electronic textile. The developed sensing system demonstrates
experimental sensitivity as high as 80Ω/°C within the temperature
detection range from 24 °C to 35 °C, which is the highest reported
to date for wearable temperature sensors. In terms of wireless
communication, the system operates at 2.4 GHz supporting
Bluetooth low energy technology and securely transmits the
measured data for up to 10 m which is proved by received signal
strength and link quality indicators.
Keywords—e-textile; smart textile; wireless sensing, wearable
temperature sensor
I. INTRODUCTION
Internet of Things (IoT) is an emerging technology aiming
at seamless and unobtrusive communication between smart
objects and services [1] and their adjustment to changing
environmental or situation conditions. Wearable technology is a
good example of IoT: wearables or “things” are able to talk to
another local and remote “things” as well as associate the owner
with a wearable sensor and context [2]. E-textile is among the
pillar technologies for wearables and IoT, which focuses on a
number of applications, e.g. sport, medicine, elderly people
support [3].
Currently, there is a tremendous progress in terms of
fabrication of sensors able to be deployed on textile [4][5][6]. At
the same time there is a number of approaches reporting on
integration of electronics in textile: e-stripes and conductive
threads [7] and other methods [8]. However, there are just a few
works available in the literature reporting on the e-textile
systems able to sense, process the measured data and
communicate them to a remote user or operator. For example, in
our recent work, we presented processing of the thermistor
polycarbonate/α’-(BEDT-TTF)
2
I
x
Br
3-x
into polyester textile. It
enables engineering of e-textile which can detect relatively
small temperature changes [13]. For feasibility purpose we
implemented the associated ‘proof-of-concept’ by designing a
wireless sensor node with the proposed thermistor [9]. Hence,
there is still a lack of multidisciplinary research which could
result in the state-of-the-art e-textile systems.
In this work, we present the concept of exploring graphene-
coated textile fibres [10] as vital sign sensors, for example for
monitoring body temperature, fully integrated on textiles and
enriched with wireless sensing option [14]. This versatile
method can be performed with textile fibres of different
materials and shapes, and with different types of graphene. Sheet
resistance of graphene-coated textile fibres is very sensitive to
external stimuli, such as temperature, strain and stress, or contact
with certain chemicals, and can therefore be explored as
wearable sensing devices.
II. PLATFORM DESIGN
In this section we present the e-textile platform with sensing,
processing and wireless communication capabilities and
describe its interfacing with a smartphone.
A. Platform Overview
The proposed e-textile platform includes four units: sensing,
processing, wireless communication and power supply (see
Fig. 1).
Fig. 1. Block diagram of prototyped wireless sensing system.
Processing (MCU) and wireless communication (RF) are
realized using the CC2560 controller relying on Bluetooth Low
Energy 4.1 (BLE) technology compatible with IEEE802.15.4
standard. This feature allows for communicating at 2.4 GHz
between the proposed e-textile and an external wireless devices
supporting Bluetooth, e.g. a smartphone. This idea adds extra
value to the user: the measured data can be forwarded to a cloud
Power supply
MCU and RF
CC2560 SensorCPU RF
Power supply
Local
sensors
Cloud
Smartphone E-textile
This work
BLE
WiFi / 4G
978-1-5090-1012-7/17/$31.00 ©2017 IEEE
service for further powerful data processing, e.g. making
inference or prediction procedures. These procedures can not be
implemented on board of a low power micro controller. As a
power supply we use the button type Li battery CR-2477 (3 V,
1000 mAh) for experimenting and proof of concept reasons. We
do not investigate the power management strategy in details as
it is out of scope of this work. Instead we use the available on
board of CC2560 power management block. We present sensing
unit in details in Section II-B.
B. Sensor Design and Fabrication
The E-textile smart temperature sensor design is based on
the resistance temperature detection approach and consists of the
graphene-coated polypropylene (PP) fibres (see Fig. 2).
Utilising the graphene technology, we take an advantage of the
sharp change in sheet resistance of the graphene PP fibres upon
change of temperature and high robustness of graphene films to
bending and stretching, which was demonstrated in our previous
works [11][13].
Fig. 2. Vision of wireless communication wearable sensing devices based on
a system fabricated on fibers of textiles using graphene thin film technology.
The proposed sensors are prepared by coating the PP fibres
with single-layer graphene which is grown by chemical vapor
deposition with a cold-wall reaction chamber, using methane as
a carbon source and 0.025mm thick 99.999% pure copper foil.
The copper foils are annealed at 1035°C under H
2
(0.4 sccm)
for 10 minutes, the growth is performed under H
2
(2 sccm) and
CH
4
(35 sccm) for 5 minutes, and then the chamber is rapidly
cooled to room temperature for 10 minutes with a large Ar flow
(50 sccm). The thickness and quality of the graphene film is
first tested by transferring a CVD-grown graphene film onto a
SiO
2
/Si substrate and examined by optical microscopy and
Raman. This is followed by the transfer process, a thin
poly(methyl metracrylate) (PMMA 950K A4) film is spin-
coated on top of the graphene, and submitted to Ar plasma to
etch any extra graphene on the backside. The copper is then
etched in an aqueous solution of ammonium persulfate 0.1M,
the floating graphene and PMMA sheet is washed several times
with DI water. After this step, the graphene and PMMA sheet
are transferred to the fibers. Finally, the PMMA is dissolved
with acetone, leaving the graphene on the surface of the fibers.
To take the measurements we create metallic pads for
connecting the controller device CC2560 (see Fig. 1) to the
sensor. We use the controller’s 12-bit Analogue-to-Digital
Converter (ADC) for measuring temperature. In terms of
measurements we evaluate the sensor conductivity by
measuring the current flowing through the sensor.
III. RESULTS
In this section we present the results obtained during the
experimental work with the proposed e-textile platform.
Fig. 3. Wireless link assessment using RSSI.
Fig. 4. Wireless link assessment using LQI.
A. Assessment of Wireless Link
In this section, we experimentally evaluate the quality of
wireless link between the e-textile and smartphone. In the scope
of this experiment we consider three scenarios: (i) direct line of
sight communication, non direct line of sight when the
smartphone is locked, for instance, in a washroom cubicle (ii)
with holes of 2 mm diameter and (iii) 20 mm diameter on its
front door. The assessment of wireless link between two devices
is effectuated using Received Signal Strength Indicator (RSSI)
and Link Quality Indicator (LQI) metrics shown in Fig. 3 and
Fig. 4, respectively. This experiment demonstrates that the link
with low RSSI might have high LQI and vice versa. Wireless
link assessment demonstrates that the link quality does not
degrade drastically within 10 m distance between the e-textile
and smartphone supporting BLE technology (see Section II).
B. Sensor Assessment
The electric conductivity is measured by a two-probe
method using tungsten probes and a Keithley 237 source-
measure unit [10]. We calculate the sheet resistance by
subtracting the contact resistance and by taking into account the
aspect ratio of the graphene section on each sample. To estimate
the contact resistance we utilize a method commonly used for
graphene devices [11][12], based on measuring the two-probe
resistance in devices with different contact separation keeping
the width of the channel constant. Fig. 5 shows the change in
EPSRC UK grants EP/N035569/1, EP/G036101/1 and EP/M002438/1, and
the European Commission grant H2020-MSCA-IF-2015-704963.
temperature (black curve) and corresponding change in sheet
resistance (blue curve) of two graphene-coated polypropylene
fibres, over time. The temperature detection range depends on
the initial resistance of graphene film. For example, as
presented in Fig 5, the resistances at 25 °C for Fiber 1 and
Fiber 2 are ∼8670 Ω and ∼8750 Ω, respectively. An increase
in temperature results in reduction of resistance for both fibers
down to ∼8550Ω, while Fiber 2 reaches this minimum at 35 °C,
Fiber 1 demonstrates its minimal resistance at already 30 °C.
Overall, the fabricated textile sensors demonstrate an average
sensitivity as high as 80 Ω/°C.
Fig. 5. Experimental demonstration of sensing capabilities (sensitivity and
range of operation) of two sensors fabricated using the technology described in
Section II-B, but with different initial resistance (Fiber 1 and Fiber 2).
The effect of bending on the electrical resistance of the
graphene-coated PP
fibers was tested
for analyzing its
flexibility and suitability as a true multiple use smart textile
sensing system. The sheet resistance of graphene-coated PP
fibers were monitored during bendings of fibers down to 5mm.
The samples demonstrate stability upon bending for 1000 times
and, for approximately one third of the samples, the sheet
resistance showed little variation [new 9]. The limiting factor
for the graphene textile samples seems to be the textile fiber
itself, which is more fragile, and shreds upon bending, forming
breaks and discontinuities on the graphene sheet which lead to
debonding of graphene. However, utilizing a photo-sensitized
oxidation process on the fibers prior to the graphene deposition,
employing ultraviolet-ozone (UVO) treatment [10], we can
reduce the variation down to a bending radius of 2.4 mm
(approximate tensile strength of 2%). This is due to the fact that
the UVO treatment not only smoothes the textile fiber surface
and increases
the adhesion of graphene, but it also improves the
mechanical strength of the fibers [10].
IV. DISCUSSION
In this paper, we have presented an e-textile temperature
sensing system enriched with processing and wireless
communication capabilities aiming at the applications in the
medical and sport domains. For the first time, we demonstrate
a wearable temperature sensing system based on resistance
temperature detection approach and fabricated on textile fibers
using graphene technology. The developed sensing system
demonstrate sensitivity as high as 80Ω/°C, high robustness to
bending and stretching. In terms of wireless communication,
the system was tested in three scenarios including direct line of
site and non direct line of site conditions. In all cases the
designed e-textile system is able to securely transmit the
measured data for up to 10 m using BLE wireless
communication.
Our future work includes both vital signs sensing (body
temperature, heartbeat and breathing) and context aware sensing
based on the mutli-plexed sensing e-textile system using the
smartphone sensors. In particularly, it is also important to
understand the effects of multiple sensors on the operation of the
entire system, as well as to evaluate the error threshold. This idea
helps to add extra value to raw sensor data measured by the
sensors. The resultant data flow will be forwarded to a cloud
service for creating knowledge and inference procedures out of
measured data. It will be helpful for predicting health issues and
suggesting a proper training load at particular day time.
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