Energy Efficient Heart Rate Sensing using a Painted Electrode ECG

Energy Efficient Heart Rate Sensing using a Painted Electrode ECG
Energy Efficient Heart Rate Sensing using a Painted
Electrode ECG Wearable
Sammy Krachunov,
Christopher Beach and Alexander J. Casson
James Pope, Xenofon Fafoutis,
Robert J. Piechocki and Ian Craddock
School of Electrical and Electronic Engineering,
University of Manchester, UK
Email: [email protected]
[email protected]
[email protected]
Department of Electrical and Electronic Engineering,
University of Bristol, UK
Email: {james.pope, xenofon.fafoutis,
r.j.piechocki, ian.craddock}
Abstract—Many countries are facing burdens on their health
care systems due to ageing populations. A promising strategy
to address the problem is to allow selected people to remain
in their homes and be monitored using recent advances in
wearable devices, saving in-hospital resources. With respect to
heart monitoring, wearable devices to date have principally used
optical techniques by shining light through the skin. However,
these techniques are severely hampered by motion artifacts and
are limited to heart rate detection. Further, these optical devices
consume a large amount of power in order to receive a sufficient
signal, resulting in the need for frequent battery recharging. To
address these shortcomings we present a new wrist ECG wearable
that is similar to the clinical approach for heart monitoring. Our
device weighs less than 30 g, and is ultra low power, extending
the battery lifetime to over a month to make the device more
appropriate for in-home health care applications. The device uses
two electrodes activated by the user to measure the voltage across
the wrists. The electrodes are made from a flexible ink and can be
painted on to the device casing, making it adaptable for different
shapes and users. In this paper we show how the ECG sensor
can be integrated into an existing IoT wearable and compare the
device’s accuracy against other common commercial devices.
Index Terms—Body sensor networks, Low power sensors,
Electrocardiography, Heart rate, Heart rate variability.
More than 28% of people in the UK will be over 60 by
2033 [1], with an associated increase in old age and degenerative disorders. Keeping those with long term conditions
functioning in the community, out of hospital, is a strategic priority for the NHS and social care systems. Wearable
devices for monitoring a range of physiological parameters
are becoming widely known and available, and are having a
transformative impact on personalised and preventative health
and social care to enable this priority. To date by far the most
successful wearables have been ‘fitbit type’ ones for activity
monitoring, and significant research effort is being applied
to enabling other sensing modalities in wearable form with
sufficient accuracy, robustness, and ease of use for real-world
application by non-specialist users.
For heart monitoring Photoplethysmography (PPG) is a well
known noninvasive method based upon shining light into the
body and measuring the amount of reflection, which varies
with blood flow. It is easy to perform at peripheral sites such
as the wrist and the set up is also straight forward, and as
a result PPG sensors are finding substantial new applications
in wearable devices and current smartwatches for heart rate
monitoring. However, raw PPG signals are severely corrupted
by motion artifacts. These arise from a number of sources,
principally a relative movement between the skin and the PPG
light source/detector [2], and these obscure the heart related
information. They necessitate complex signal processing to
extract a reliable heart rate, for example [3], with debate
present on the accuracy of current methods [4], [5].
In addition, the PPG light source intrinsically must consume a large amount of power, of the order of 1 mW,
limiting the battery lifetime of a wearable device. Although
minimum light-on duty cycles [6] and compressive sensing
techniques [7] have been proposed to help overcome this, for
smartwatches and similar wearable devices battery lifetime
remains one of the primary concerns of end users [8]. The
need for frequent battery recharging is a major obstacle to
the wider take up and use of the technology, particularly by
vulnerable users, and significant improvements are required
for green Internet Of Things (IoT) devices.
For heart monitoring this can be achieved by using the
Electrocardiogram (ECG) as the sensing basis. The ECG is
the well known alternative method for monitoring the activity
of the heart and is used widely clinically. It operates by
placing small metal electrodes on the body to sense the microVolt sized electrical activity from the sino-atrial node and
heart muscle contractions that cause the heart pumping action.
Although lower power because the sensing element itself is
just an unpowered metal electrode, wearable ECG monitoring
is much more challenging than wearable PPG due to the
electrode contact required with the body, the difficulty in
maintaining this robustly over time, and the motion artifacts
that are introduced.
Moreover, the signal is normally recorded from the chest
with electrodes placed on either side of the heart. If electrodes
are placed on just one side of the heart the collected signals
reduce in amplitude and get increasingly small further away
from the heart, Fig. 1. Placing electrodes on just one arm,
Single wrist (5x zoom)
Painted electrode
Upper arm (5x zoom)
Both wrists
500 µV
0.5 s
Fig. 1. The ECG signal showing each heart beat is large when the electrodes
are on either side of the heart, and get smaller if the electrodes are placed
only on one side of the heart. Note measurements are not simultaneous.
SPHERE board ECG board
the time domain ECG signal reaches a 0 dB Signal-to-Noise
Ratio at (approximately) the elbow, and so a single site watch
type measurement of the ECG is not possible. It is possible,
however, to place one electrode on one wrist, and then touch
a second electrode with the other hand. As the two sensing
connection points are on either side of the heart a high Signalto-Noise Ratio ECG can be collected [9], see Fig. 1.
This paper presents an ultra low power two electrode wrist
ECG wearable for collecting heart information in such a manner. The ECG front-end is combined with a previously reported
IoT data collection node to automatically integrate the new
sensing with established IoT and smart home demonstrators
in the UK, and highly optimised circuit design is used to
allow more than one month of battery lifetime. In addition, the
metal ECG electrodes are made using a flexible Silver/Silver
Chloride (Ag/AgCl) ink, which can be painted on to the
wearable housing, allowing electrode sizes and shapes to be
personalised to different users.
The remainder of this paper is organised as follows. Section II details our design in terms of the system architecture,
hardware and software required. Section III reports the performance of the system, comparing it to currently available
commercial devices for heart rate monitoring performance, and
demonstrating the energy usage in detail. Finally conclusions
are drawn in Section IV.
Standard ECG recordings require three electrodes (2 for
sensing a differential signal, 1 for rejecting common mode
interference signals (50 Hz mains noise)) and a conductive gel
between the electrode and the skin to minimise the impedance
of the connection to the body. Both factors make conventional
ECG approaches unsuitable for wrist based wearable monitors.
We combine a number of techniques, described below, to
overcome these issues. The final wearable ECG sensor node is
shown in Fig. 2. It is made up of three consistent parts, each
discussed in detail here.
Fig. 2. The wrist worn wearable sensor node. Top: Outer view showing a
painted electrode which is in contact with the wrist when worn. A similar
electrode is present on the other side of the case for touching with the other
hand. Bottom: Inside view showing the SPHERE PCB board and ECG frontend amplifier circuit.
A. ECG front-end
A number of techniques have been suggested previously for
allowing ECG recordings using only two electrodes, simplifying the equipment set up by requiring just two body contacts.
[10] used very high common mode rejection electronics, [11],
[12] a DC servo loop for preventing saturation, [13] an active
virtual ground, and [14] a common mode follower and a.c.
bias approach. We make use of the topology introduced in [10]
in 1980, as it requires only 3 active components, intrinsically
minimising power consumption as our primary objective. This
is achieved due to the very low number of active components
Our ECG amplifier circuit is shown in Fig. 3, which uses the
LPV542 op-amp due to its ultra low 1 µW power consumption
per device. Two high input impedance buffers are used as the
subject connection with partial positive feedback employed
to increase the input impedance, making the circuit suitable
for gel-free recordings (see Section II-C). The a.c. coupling
provided by capacitors C1 and C2 allows the user to be d.c.
biased via resistor network R1/R2/R3, ensuring the absolute
input voltage remains within the input range of the amplifiers,
without affecting the biasing of the subsequent circuit stages.
To reduce power no common mode feedback or feedforward
is provided [15]. Instead, to ensure the collected ECG signal
is within the input ranges of the front-end amplifiers the user
is driven to a fixed mid-supply voltage, with resistors sized
to ensure that the single point failure current into the user
is limited to below 18 µA, ensuring compliance with IEC
60601 safety standards. This arrangement means that more
mains interference (50/60 Hz) is collected by the circuit, but
as this is a known frequency which does not overlap with the
User connection
and safety
10 MΩ
Electrode 1
100 kΩ
High input
impedance buffers amplifier, gain 33
_ LPV542
100 nF
120 kΩ
120 kΩ
330 kΩ
10 kΩ
10 kΩ
_ LPV542
Electrode 2
100 kΩ
Second order low pass filter,
30 Hz cut-off
82 kΩ
To ADC on
SPHERE board
Biasing network
120 kΩ
1.8 V
C2: 100 nF
120 kΩ
100 nF
330 kΩ
82 kΩ
R2: 1 MΩ
R3: 1 MΩ
Fig. 3. Two electrode ECG front-end design to minimise power consumption.
wanted ECG components it is easily removed by hardware and
software filtering.
Beyond the above, resistor sizes, particularly for the biasing,
are chosen to minimise power consumption, at the cost of interference pick-up due to high impedance nodes being present,
and introducing more thermal noise. All resistors are carefully
sized to keep the final system noise to an acceptable level
(see Section III). The front-end circuit is completed with a
standard difference amplifier made with high precision (0.1%)
resistors to ensure that a high common mode rejection ratio is
maintained and a single op-amp second order low pass filter
is used for anti-aliasing prior to digitisation.
The complete front-end circuit requires only four active
components and approximately 8 µW from a 1.8 V supply
for ultra low power operation.
B. IoT back-end
For digitisation and wireless transmission the new ECG
front-end circuitry is connected to the latest version of the
SPHERE wearable [16] which is an ultra low power body
sensor node based upon the Texas Instruments CC2650 chip.
The device incorporates wireless power transfer, two months
of battery life, accelerometers for motion/gait analyses, and
integrates with smart home infrastructure [17]. This paper adds
the potential for heart sensing to this platform for the first time.
To minimise power we make use of the Sensor Controller
on the CC2650 which allows continuous digitization without
turning on the main processor core. ECG from the front-end
circuit is sampled at 128 Hz and stored in a buffer up to
6 seconds in length, meaning that the main microcontroller
has to wake up only once every 6 seconds in order to fetch
the acquired ECG data. This leads to significant reduction
in power consumption as usually the main microcontroller
wakes up for every ADC sample (or in some cases every
few samples if there is a FIFO buffer present). The estimated
power consumption of the CC2650 system while running the
ADC sampling algorithm at 128 Hz is approximately 200 µW.
Running the same task without the sensor controller would
easily exceed several milli-Watts in power consumption.
The SPHERE wearable provides the option of data collection in a connected or not connected state. In the connected
state the raw ECG data is transmitted over a Bluetooth Low
Energy (BLE) link, allowing all of the data to be saved and
analysed offline or potentially in real-time as part of the smart
home set up. A simple GUI has been created for this purpose,
and the data can also be analysed in M ATLAB or any similar
software. In this connected mode the power consumption for
the complete system is low enough to allow a month of data
collection and streaming (see Section III-C).
For even longer term operation, in the not connected state
the SPHERE CC2650 is programmed to run an adapted
version of the Pan-Tompkins algorithm [18] for heart beat
detection from the ECG. This allows the user’s heart rate to
be extracted on the wearable itself, with this data transmitted
making use of the Bluetooth advertising packets as described
in [19]. This allows the heart rate information to be wirelessly
transmitted without having to power up and connect the full
Bluetooth stack.
For the heart rate measurement algorithm to operate in realtime on the CC2650 the ECG signal is initially filtered using a
low pass filter with a cut-off of 25 Hz and a baseline removal
filter. A single point numerical derivative is then calculated and
the output squared to amplify the QRS complex, the part of
the ECG signal which has the highest rate of change and corresponds to each heart beat. This squared signal is integrated
with a moving average of approximately 200 ms and a peak
detection algorithm is used to identify each individual heart
beat. The difference between the Pan-Tompkins algorithm and
the algorithm implemented on the SPHERE wearable is that
we do not store any of the results in memory and so have
a fixed rather than two adaptive thresholds for heart beat
detection. This has some disadvantages in terms of accuracy
PPG reference
Fig. 4. Typical ECG recording set up with two ECG electrodes used, one on
the back side of the SPHERE wearable touching the wrist, and one on the
front for touching with the other hand. A PPG device is worn on the user’s
other wrist to provide a reference heart rate measurement.
C. Case and electrodes
The electronics are housed in an off-the-shelf Minitec plastic
housing [20] and held on to the wrist in the position of
a standard smartwatch using an elasticated strap. The total
assembly weighs 29 g, including the strap, for easy wearability.
A typical recording set up is shown in Fig. 4 where the
electrode (on the back face of the case) is in constant contact
with the skin and when a heart rate recording is wanted the
user touches the front facing electrode with a finger from the
other hand. This provides two points of contact, on either
side of the heart, allowing a high amplitude ECG trace to
be recorded (as shown in Fig. 1).
New in this system, the body contact electrodes are made
using a medical grade Ag/AgCl ink which is painted on to the
casing. Previously we have demonstrated Silver (Ag) coatings
painted on to plastics for the recording of bio-potentials
without requiring a conductive gel to be present [21]. We
now make use of a Silver/Silver Chloride paint available from
Creative Materials [22], which is not only bio-compatible
but marketed as medical grade, and allows superior sensing
performance with lower half-cell potential, less long term drift,
and reduced contact noise compared to using Silver [23].
This painted electrode approach allows flexibility for different sizes and shapes of electrodes to be used with different
people, and different cases for the wearable sensor node to
be used and tailored for individual preference. As just one
example, Fig. 5 shows a customised electrode shape which can
be used instead of a flat electrode painted on the case in order
to get better penetration through hair on the wrist. Similar to
electrodes for recording through hair on the head which have
fingers [21], the electrode in Fig. 5 has small bumps to push
aside the hair and make a better contact with the skin. It has
not been investigated in this work, but this structure can be
3D printed as in [21], and the sizes and shapes of the bumps
changed on a person-by-person basis to get the best trade-off
between long term comfort, body contact quality, and sensing
Fig. 5. Example Ag/AgCl back face electrode which can be used instead of
the flat painted on electrode in Fig. 2 if better penetration through hair on the
wrist is desired. Top: The electrode has small bumps to better pass through
hair. Bottom: Placement on the wrist.
0.15 R Peak
Amplitude (normalised)
but it is saves great deal of processing time and memory.
T wave
Time (s)
Fig. 6. Example ECG trace shows R peaks due to heart beats. These can be
identified in the time domain allowing extraction of heart rate and heart rate
variability measures using standard ECG processing.
A. Example ECG
An example of the raw ECG signal recorded using the new
ultra low power sensor node is shown in Fig. 6, prior to
software filtering. R peaks corresponding to each heart beat,
and T waves, a morphological feature of the ECG, are clearly
seen and marked on Fig. 6. The R peaks allow heart rate and
heart rate variability analyses to be performed, extracting the
wanted long term physiological measurements when the user
has their hand on the front facing electrode.
In addition, in Fig. 6 a large amount of background noise
is also seen. This is a deliberate design decision: the system
is optimised to allow R peak identification for heart rate and
heart rate variability measurements. It is not optimised for
measuring ECG morphology components such as P and T
waves, which even with low recording noise floors may not
have the same shapes when recorded on the wrist as when
(conventionally) recorded on the chest. This noise is easily
removed in the system back-end using standard ECG filtering
approaches as described below.
B. Heart rate measurements
For quantifying the performance a series of experiments
were carried out with the new ECG sensor worn on one wrist,
and a commercially available PPG device (Empatica E4 [24])
worn on the other wrist. Note that it is not possible to use
a second simultaneous ECG recording as the gold standard
comparison: as both ECG units drive the body (via a driven
right leg circuit or fixed voltage in our design) the total body
driving is different when both units are connected compared to
using only one unit, resulting in neither device recording the
same ECG signal as they would if only one unit was connected
at a time.
A total of 12 five minute ECG recordings were carried out
with 8 different participants, aged 22–33. Subjects were sat
stationary at a desk while ECG data was streamed in the connected state. For analysis three recordings were subsequently
discarded: two due to the PPG device recording a poor quality
signal allowing no comparison signal to be extracted; and
one due to the SPHERE board being incorrectly used such
that only very small amplitude R peaks were recorded. To
extract a heart rate from the SPHERE data the following ECG
processing procedure was applied:
1) The raw ECG data was low pass, high pass and notch
filtered using first order zero phase delay (filtfilt)
Butterworth filters.
2) The ECG baseline wander was removed using the Discrete Wavelet Transform as described in [25].
3) Candidate initial R peak locations were extracted using
the Pan-Tompkins algorithm [18]. Identified R peaks
with amplitudes less than 1.5 times the RMS of the
signal, or greater than 9 times the RMS, were discarded.
4) The ECG smoothed using an extended Kalman filter
based around the initial R peak locations as described
in [26].
5) Final R peak locations extracted by re-running the PanTompkins algorithm on the cleaned ECG data. R peaks
closer together than 0.4 s (150 beats per minute) were
6) A Kalman tracking filter with zero order hold state
model implemented to track the heart rate in the presence of both missing R peaks and additional R peaks
due to transient events.
From this set of R peak locations a single heart rate value
was extracted from every 10 s window of data, with 8 s overlap
between estimates. The Empatica E4 gives an estimated heart
rate value every 2 s and a single mean value for each 10 s
window was similarly calculated. Results are given in Table I
which quantifies the mean and standard deviation of the
heart rate estimation difference across all of the heart rate
calculations in a record:
Difference = abs(ECG HR − PPG HR).
Mean difference
Standard deviation
Table I shows that all of the extracted heart rates are within
a few beats per minute of the comparison PPG recording, with
an average difference of 4.56 beats per minute. Note that the
PPG gold standard device itself is a wearable unit, subject to
motion interference and similar, giving some uncertainly in the
actual underlying heart rate. We have estimated the difference
between the reported heart rate from the algorithmic output of
the Empatica E4 and the actual number of peaks in the PPG
trace to be a mean of 2.7 beats per minute across all records,
with a standard deviation of 2.1 beats per minute. The new
ECG sensor accuracy compares well with this.
When the SPHERE board misreports the heart rate it is
generally due to the presence of additional R peaks from
movements of the finger/wrist. These incorrect peaks can
be discarded by eye, particularly after the Kalman filtering,
but are still identified by the Pan-Tompkins in automated
processing. The tracking filter removes many of the remaining
erroneous peaks, but not all. We anticipate that by replacing the
current Kalman tracking filter with a particle filter [27], and
the Pan-Tompkins algorithm with a more robust alternative
(many methods, such as [28], have been reported), that the
heart rate extraction can be improved further.
C. System energy usage
The energy usage of the system was determined by using
the Texas Instruments INA226 Power Monitor. The system
was powered using a 3.7 V battery with 100 mAh capacity
and the monitor configured to take 16 bit current samples
at approximately 22247 samples/second. The ECG system
was set up to take 128 samples per second and transmit
approximately 40 BLE messages per second in the connected
state. Fig. 7 shows the results of the experiment for a typical
0.5 second period during transmission.
In Fig. 7 the average current is approximately 9 mA. Note
that this is the energy usage of the entire system, including the
ECG front-end and IoT back-end. In practical use we assume
that a heart rate measurement will be performed once an hour
each day, and otherwise the system will be placed in a low
power idle state. In the idle state, the SPHERE wearable device
has previously been shown to operate at 3.3 µA [17]. Including
the front-end idle current, the total idle current is 11.3 µA.
When performing a heart rate reading it takes between 20
and 30 seconds for sufficient heart beats to be present to allow
BLE transmissions
Current (mA)
Time (s)
Fig. 7. Example energy usage during ECG collection in the connected state.
accurate estimation and the heart rate detection algorithm to
stabilise and provide a measurement. The average current in
one hour, where a 30 s ECG measurement is taken, is thus
86 µA (30 seconds at 9 mA and 3570 seconds at 11.3 µA).
Based on these parameters the system can last for an estimated
48 days (100 mAh / 0.086 mA), a step change in battery
lifetime for a heart sensing wearable device weighing only
29 g including the battery and strap.
In this paper we proposed a new wearable heart monitoring
device with ultra low power consumption and customisable
electrodes via the use of a Silver/Silver Chloride ink painted
on to the device casing. The design employs few active
components to minimise power and trades more system noise
to further reduce power consumption while still producing
an acceptable signal. The ECG front-end is integrated with
an existing IoT back-end to be worn on the wrist and we
compared the performance of the device against other comparable commercial devices, showing its accuracy to within
a few beats per minute. Assuming moderate user activated
sampling periods, the system can last for over a month using
the supplied battery, and weighs less than 30 g including
the battery and strap. In future work we will further use the
painted ECG electrodes with the capacitive sensing inputs on
the CC2650 chip to allow a button-free, customisable to the
user, interface to the SPHERE board, and to allow the ECG
circuit to automatically turn on when it detects that the user
has touched the front facing electrode.
This work was performed under the SPHERE IRC funded
by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1.
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