Assessing Drivers’ Physiological Responses Using Consumer Grade
DevicesTimotej Gruden * , Kristina Stojmenova, Jaka Sodnik and
Grega Jakus
University of Ljubljana, Faculty of Electrical Engineering, Traška
Cesta 25, 1000 Ljubljana, Slovenia;
[email protected] (K.S.);
[email protected]
(J.S.);
[email protected] (G.J.) * Correspondence:
[email protected]; Tel.: +386-1-476-8951
Received: 30 October 2019; Accepted: 4 December 2019; Published: 7
December 2019
Abstract: The ability to measure drivers’ physiological responses
is important for understanding their state and behavior under
different driving conditions. Such measurements can be used in the
development of novel user interfaces, driver profiling, advanced
driver assistance systems, etc. In this paper, we present a user
study in which we performed an evaluation of two commercially
available wearable devices for assessment of drivers’ physiological
signals. Empatica’s E4 wristband measures blood volume pulse (BVP),
inter-beat interval (IBI), galvanic skin response (GSR),
temperature, and acceleration. Bittium’s Faros 360 is an
electrocardiographic (ECG) device that can record up to 3-channel
ECG signals. The aim of this study was to explore the use of such
devices in a dynamic driving environment and their ability to
differentiate between different levels of driving demand.
Twenty-two participants (eight female, 14 male) aged between 18 and
45 years old participated in the study. The experiment compared
three phases: Baseline (no driving), easy driving scenario, and
demanding driving scenario. Mean and median heart rate variability
(HRV), standard deviation of R–R intervals (SDNN), HRV variables
for shorter time frames (standard deviation of the average R–R
intervals over a shorter period—SDANN and mean value of the
standard deviations calculated over a shorter period—SDNN index),
HRV variables based on successive differences (root mean square of
successive differences—RMSSD and percentage of successive
differences, greater than 50 ms—pNN50), skin temperature, and GSR
were observed in each phase. The results showed that motion
artefacts due to driving affect the GSR recordings, which may limit
the use of wrist-based wearable devices in a driving environment.
In this case, due to the limitations of the photoplethysmography
(PPG) sensor, E4 only showed differences between non-driving and
driving phases but could not differentiate between different levels
of driving demand. On the other hand, the results obtained from the
ECG signals from Faros 360 showed statistically significant
differences also between the two levels of driving demand.
Keywords: physiological signals; heart rate variability (HRV); user
study; validation; driving environment
1. Introduction
The human factor is still one of the leading causes for road
traffic accidents. It has been reported that more years of life
were lost due to traffic accidents than due to most human diseases
[1]. Driving is a demanding process, mostly relying on the driver’s
visual and manual senses, and largely to their auditory and
cognitive capabilities. Vehicle manufactures have therefore been
constantly working on reducing the driver’s role and improving the
driving experience by adding a number of advanced driver-assistance
systems (ADAS) and including user-friendly in-vehicle information
systems (IVIS). Although the driver’s role is changing, people
spend more time in a vehicle compared to ever before,
Appl. Sci. 2019, 9, 5353; doi:10.3390/app9245353
www.mdpi.com/journal/applsci
Appl. Sci. 2019, 9, 5353 2 of 23
thus monitoring the driver’s state is somewhat necessary for
further research in current and higher levels of vehicle
autonomy.
The measurements of drivers’ physiological signals can be very
useful in the development of automated vehicles that tend to
imitate human driving, since driving algorithms can be improved
with the results of such measurements. Not only vehicles, driving
simulators can also use such algorithms to make the scenarios
(other vehicles in the scenarios) more realistic. Determining
different drivers’ state and behavior from the measured signals is
also beneficial to insurance companies for driver profiling and
cost calculation. Among other factors, stress levels, cognitive
demand, attention, arousal, and fatigue indicators have been used
for evaluation of usability of new ADAS and IVIS systems. These
indicators are mainly derived from drivers’ physiological signals
that represent cardiovascular, electrodermal, pupil, or brain
activity.
1.1. Physiological Responses for Assessment of Driver State
Among all studies that measure physiological signals of drivers,
driver drowsiness and fatigue are the most often researched topics.
Chronologically, the first studies on detection of physiological
signals for drowsiness detection were using video analysis with
image processing techniques [2,3]. Now, modern research often uses
electroencephalography (EEG) [4–7] or a combination of EEG with
other physiological signals [8,9]. Another common measure for
detecting drowsiness besides EEG, is cardiovascular activity.
Drowsiness has been assessed with different cardiovascular signals
including photoplethysmography (PPG) [9–12], blood volume pulse
(BVP) [11], and electrocardiogram (ECG) [8,12–14]. Some studies
report on using heart rate variability (HRV) [9,12,13],
electrooculography (EOG) [8,15,16], and galvanic skin response
(GSR) [9,11].
Another often studied topic regarding the driver’s physiological
signals is monitoring of individual driver’s state such as
situational awareness [17], emotional states [18–20], or driver
physical fitness [21–23]. Similar methods have also been used for
observing effects of use of different modalities when interacting
with in-vehicle information systems [24] and to assess drivers’
cognitive workload due to use of such systems [25,26] or
experienced stress due to demanding driving conditions [27]. By
observing EEG signals, physiological data can also be used for
prediction of drivers’ intentions [28,29].
Difficult driving conditions, e.g., interaction with reckless
drivers and bikers, impatient pedestrians crossing the road, and
operating the vehicle, e.g., gear shifting, can significantly
increase the load on drivers [30]. Different levels of driving
demand reflect in drivers’ mental and also physical load. What
makes the situation even worse is that the driving demand is
increasing every day [31]. The driver’s mental workload is usually
assessed by subjective reports, measures of task performance, or
physiological measures [32]. As Brookhius and de Waard suggest,
physical and mental workload have clear impact on physiological
signals [31], especially on heart rate, heart rate variability,
galvanic skin response, and blood pressure [33].
In summary, the list of common physiological measures for assessing
drivers consists of:
• Electroencephalography (EEG), • Electrocardiography (ECG), •
Photoplethysmography (PPG), • Heart rate (HR), • Galvanic skin
response (GSR), • Electromyography (EMG) and • Eye tracking (pupil
diameter—PD).
Combinations often include EEG and ECG or PPG and GSR, sometimes in
combination with eye tracking.
Appl. Sci. 2019, 9, 5353 3 of 23
1.2. Devices Used to Measure Driver’s Physiological Responses
There is a great variety of available devices for capturing
physiological signals. Most often higher reliability increases the
price of the device, and thus reduces the availability. Devices
used for signal capturing vary from study to study. Some even make
their own custom capturing devices. Among the presented studies,
Biopac MP-150 was often used as an overall acquisition system
[7,20,27], also FlexComp Infinity system [8,34], Nexus-10 from Mind
Media Schepersweg [11], Medac Systems/3 [25], and Geodesic EEG
System 300 [35] were mentioned. EEG was measured with NicoletOne
Ambulatory EEG [6], EMG was measured with Bagnoli-8™ [23], eye
movement with FaceLAB® [25]. Bittium’s Faros was also used in many
studies [36–38]. Based on the available research it can be
concluded that physiological signals can give important information
about the human’s state and prediction of human behavior or medical
state. It is also noticeable that with the introduction of wearable
devices, the research studies using such signals are no longer
limited to medical and laboratory environments, but can be used
also in dynamic environments such as vehicles. This can however
bring “noise” (e.g., motion artefacts, quantization noise,
electro-magnetic interferences from other devices, etc.) to the
measured signals and therefore, it is important to be aware of this
limitation when choosing a measuring device and later when
performing the analysis. Therefore, we assessed suitability,
accuracy, and robustness of two commonly used commercially
available devices in a simulated driving environment.
The E4 wristband can measure BVP, inter-beat interval (IBI), heart
rate (HR), electrodermal activity (EDA) or GSR, skin temperature
(ST), and motion with an accelerometer. A detailed presentation of
the device can be found in the next section.
In many studies, the raw data from the E4 device was used for
assessing stress levels [39–44]. Sevil et al. used all of the
available E4 output signals (GSR, ST, HR, BVP, and motion) to
detect various types of acute stress such as social, completion,
emotional and mental stress, and showed that using their algorithm,
they can detect stress with 87% accuracy [39]. Park et al. on the
other hand tried to estimate the relationship between EDA signals
and increased cognitive workload using the E4 [45]. Their results
showed that there is a somewhat linear relationship between these
data. Furthermore, it has also been used in studies of affective
computing in general [40,46], in medical researches for heart
arrhythmia and atrial fibrillation detection [47–49]. Studies
report also on using E4 data for assessing overall functional
health of people with dementia [50] and creating of personal
assistants for forgetful people [51]. Vandecasteele et al. even
tried to use the HR data for seizure detection; however, their
results showed that detection performance was considerably lower to
the usually used hospital and wearable Faros device [52].
Additionally, there seems to be a growing trend of using E4 data in
biofeedback applications [53–55].
When compared to other laboratory (stationary and wearable)
devices, E4 showed comparable results. Ollander et al. compared the
E4 device with stationary sensors for ECG and GSR [41]. Their
results showed incomplete IBI data, however mean values of HR and
standard deviation of HR showed good stress discrimination power.
For the GSR, their results showed that due to the placement, data
from the E4 yielded higher stress discrimination power than the
signal measured with the stationary sensors placed at the fingers.
When compared for emotion recognition to higher grade laboratory
equipment—Biopac MP150, it was found that E4 can provide comparable
results [40,56]. However, based on currently available data, no
research was found on E4 being used in a driving environment.
Faros is a family of ECG devices by Bittium. Faros 360 is able to
measure 3-channel ECG, body temperature, and accelerations. It is
presented in detail along with different models in the next
section.
Different versions of Faros are widely used in research studies for
detection of various medical conditions, which affect humans’
cardiovascular activity [57–60].
Since Faros 360 is certified as a medical device, it is also often
used as a reference for calibration or validation of new wearable
devices [61–63]. However, Faros has been used also for monitoring
different profiles of users’ states. For example, it has been used
for the assessment of mental stress when interacting with robots,
where, based on HRV data, it was found that human operators show
higher engagement levels when being successful at completing a task
[64]. It has also been used for
Appl. Sci. 2019, 9, 5353 4 of 23
monitoring effects of air pollution and noise due to traffic, and
the results showed that HRV and blood pressure (BP) drop when noise
increases [65].
Other studies have shown that the ECG signal from Faros can also be
used to calculate other variables such as BP [66], or to extract
core body temperature with respect to clothing and persons’
activity [67].
Since with wearable devices such as Faros, the experimental
conditions differ and are not always performed in a controlled
laboratory environment, the gathered data can be affected by motion
artefacts. Alikhani et al. found that motion can describe the high
frequencies in HRV up to 40% [68]. However, the noise of motion can
be eliminated by adding an additional accelerometer on each
electrode [69]. With this, it is possible to eliminate the baseline
wandering without affecting the ECG signal.
Contrary to E4, Faros has already been used in a driving
environment. Biondi et al. used HRV data gathered with Faros to
observe the effects of semi-automated driving in Tesla model S
[36]. The effects of different levels of automation on drivers’
state were studied also in a driving simulator environment by
Radlmayr, et al. [38] and Murase et al. [37]. Their findings were
however different, as Radlmayr et al. report on a decreased level
of arousal and sympathetic nervous system activity when switching
from automation level 2 to level 3, while Murase et al. did not
find any effects on the ECG channel for different levels of
autonomous driving [37].
1.3. Contribution
This research study explores two new and commercially available
devices, which have shown promising results for assessing
psychophysiological data in various medical research. The goal was
to evaluate the two devices and their usefulness also in a driving
environment, which is rather specific, and due to its dynamic
component, differs from other experimental environments used in
reported studies. Our motivation on evaluating these particular
devices was the fact that they are wearable and nonintrusive, and
therefore much easier to use in a simulated driving study and also
in a real environment. We try to answer to the following research
questions:
• Can E4 and Faros be used for assessment of physiological signals
in a driving environment? • If so, can E4 and Faros differentiate
between different levels of driving demand?
The used methodology and experimental environment are presented in
the Materials and Methods section and our findings are reported in
Results. The possible applications and found limitations of both
devices in a driving environment are discussed in the last section:
Discussion and Conclusions.
2. Materials and Methods
The presented study had a within-subject (repeated measures) design
and was conducted in a simulated driving environment. The drivers’
responses were assessed using two commercially available devices—E4
by Empatica [70] and Faros 360 by Bittium [71], whereas the driving
measurements were recorded using a Nervtech driving simulator
[72].
2.1. Empatica E4
E4 is a wearable device in the form of a wristband, equipped with a
number of sensors for assessment of electrodermal and
cardiovascular activity (Figure 1). The E4 wrist band can measure
BVP, inter-beat interval (IBI), heart rate (HR), electrodermal
activity (EDA) or GSR, skin temperature (ST), and motion with an
accelerometer.
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Figure 1. Empatica E4 with positions of its sensors.
One of the main features of the E4 device is a photoplethysmography
(PPG) sensor. Photoplethysmography sensors use an optical technique
to detect changes in blood volume in the microvascular bed of
tissue [73]. The PPG sensor in E4 consists of two red and two green
LEDs, which provide two different wavelength light sources, and two
sensors for detection of the reflected light, with a sampling
frequency fs = 64 Hz. The output is a blood volume pulse (BVP)
signal with an 8-bit resolution. Based on these measurements, a
proprietary algorithm from Empatica detects the heart beats (peaks)
in the signal and calculates the intervals between separate beats
(inter-beat intervals (IBI)) in milliseconds. Unlike the BVP
signal, which has a fixed sampling frequency, the IBI signal is
provided when a beat is detected. However, average heart rate (HR)
signal value is provided every second, as it is calculated from the
last known IBI sample.
E4 is equipped also with an electrodermal activity (EDA) or
galvanic skin response (GSR) sensor. With two 8-mm-wide
silver-plated copper electrodes, located at the end of the
wristband belt (see Figure 1), E4 captures skin conductance with a
sampling frequency fs = 4 Hz.
Additionally, E4 also features an optical infrared temperature
sensor that measures skin temperature assessed at the wrist, with a
sampling frequency fs = 4 Hz.
E4 is equipped also with a 3-axis accelerometer, which allows
monitoring of hand activity (movement). On every E4 device, the
X-axis is defined as a vector from the center of device towards the
USB connector, Y-axis is defined as a vector from the center of the
device towards the shorter end of the wrist belt, and Z-axis is
defined as a vector from the center of the device towards the
bottom of the device. The dynamic range of the accelerometer is ±2
g, the output is an 8-bit signed integer with the resolution of
1/64 g. The accelerometer’s sampling frequency (fs) is 32 Hz.
With the E4 device, data can be captured in two ways: In a
recording mode (retrieve after measurement) and in a streaming mode
(retrieved in real-time). E4 has an internal storage of up to 60 h
of measurements. It comes prepacked with a special USB dock station
and proprietary software for downloading data from the device and
uploading it to a cloud platform called Empatica connect [74]. When
using the recording mode, raw data can only be downloaded from this
cloud platform. In the streaming mode, E4 uses Bluetooth Low Energy
(BLE) to transfer data in real-time.
Besides the official software solutions that come with the device,
Empatica offers also iOS, Android and Windows development kits for
researchers that want to customize and upgrade this solution to
their needs. The development kits allow real-time data acquisition
with custom software that gives the ability of real-time
processing, feature extraction and on-demand visualization.
2.2. Bittium Faros 360
Faros by Bittium is a small electrocardiogram (ECG) measurement
device. It comes in three versions:
• Faros 90, which offers simple 1-channel ECG measurements,
Figure 1. Empatica E4 with positions of its sensors.
One of the main features of the E4 device is a photoplethysmography
(PPG) sensor. Photoplethysmography sensors use an optical technique
to detect changes in blood volume in the microvascular bed of
tissue [73]. The PPG sensor in E4 consists of two red and two green
LEDs, which provide two different wavelength light sources, and two
sensors for detection of the reflected light, with a sampling
frequency fs = 64 Hz. The output is a blood volume pulse (BVP)
signal with an 8-bit resolution. Based on these measurements, a
proprietary algorithm from Empatica detects the heart beats (peaks)
in the signal and calculates the intervals between separate beats
(inter-beat intervals (IBI)) in milliseconds. Unlike the BVP
signal, which has a fixed sampling frequency, the IBI signal is
provided when a beat is detected. However, average heart rate (HR)
signal value is provided every second, as it is calculated from the
last known IBI sample.
E4 is equipped also with an electrodermal activity (EDA) or
galvanic skin response (GSR) sensor. With two 8-mm-wide
silver-plated copper electrodes, located at the end of the
wristband belt (see Figure 1), E4 captures skin conductance with a
sampling frequency fs = 4 Hz.
Additionally, E4 also features an optical infrared temperature
sensor that measures skin temperature assessed at the wrist, with a
sampling frequency fs = 4 Hz.
E4 is equipped also with a 3-axis accelerometer, which allows
monitoring of hand activity (movement). On every E4 device, the
X-axis is defined as a vector from the center of device towards the
USB connector, Y-axis is defined as a vector from the center of the
device towards the shorter end of the wrist belt, and Z-axis is
defined as a vector from the center of the device towards the
bottom of the device. The dynamic range of the accelerometer is ±2
g, the output is an 8-bit signed integer with the resolution of
1/64 g. The accelerometer’s sampling frequency (fs) is 32 Hz.
With the E4 device, data can be captured in two ways: In a
recording mode (retrieve after measurement) and in a streaming mode
(retrieved in real-time). E4 has an internal storage of up to 60 h
of measurements. It comes prepacked with a special USB dock station
and proprietary software for downloading data from the device and
uploading it to a cloud platform called Empatica connect [74]. When
using the recording mode, raw data can only be downloaded from this
cloud platform. In the streaming mode, E4 uses Bluetooth Low Energy
(BLE) to transfer data in real-time.
Besides the official software solutions that come with the device,
Empatica offers also iOS, Android and Windows development kits for
researchers that want to customize and upgrade this solution to
their needs. The development kits allow real-time data acquisition
with custom software that gives the ability of real-time
processing, feature extraction and on-demand visualization.
2.2. Bittium Faros 360
Faros by Bittium is a small electrocardiogram (ECG) measurement
device. It comes in three versions:
• Faros 90, which offers simple 1-channel ECG measurements,
Appl. Sci. 2019, 9, 5353 6 of 23
• Faros 180, which offers 1-channel ECG measurements and is able to
stream data via Bluetooth, and • Faros 360, which enables
3-channels ECG measurements and is able to stream data via
Bluetooth.
Each version can be mounted to the participant’s body in three
ways: Fast-Fix, using a cable set, or using a stingray adapter.
Fast-Fix, which is a Bittium’s proprietary electrode, is designed
for quick mounting and is considered as the simplest and most
convenient way of using the device. Faros can be mounted also by
using cable sets (see Figure 2 left) with up to five electrodes for
3-channel measurements (or three electrodes for 1-channel). This
version is considered as the most accurate and reliable version of
Faros for measuring ECG because it enables best skin contact during
the measurement process. The third mounting option does not require
placing any electrodes on the participant’s body. It instead uses
an elastic textile belt with two electrodes and a mounting pad for
Faros (see Figure 2 right).
Appl. Sci. 2019, 9, x 6 of 23
• Faros 180, which offers 1-channel ECG measurements and is able to
stream data via Bluetooth, and
• Faros 360, which enables 3-channels ECG measurements and is able
to stream data via Bluetooth.
Each version can be mounted to the participant’s body in three
ways: Fast-Fix, using a cable set, or using a stingray adapter.
Fast-Fix, which is a Bittium’s proprietary electrode, is designed
for quick mounting and is considered as the simplest and most
convenient way of using the device. Faros can be mounted also by
using cable sets (see Figure 2 left) with up to five electrodes for
3-channel measurements (or three electrodes for 1-channel). This
version is considered as the most accurate and reliable version of
Faros for measuring ECG because it enables best skin contact during
the measurement process. The third mounting option does not require
placing any electrodes on the participant’s body. It instead uses
an elastic textile belt with two electrodes and a mounting pad for
Faros (see Figure 2 right).
Figure 2. Faros 360 with cable set for measuring with electrodes
(left) and Faros 360 with textile belt for electrode-free
measurements (right).
The most important feature of Faros is its ECG sensor. The sampling
frequency of the provided raw ECG data can be fs1 = 125 Hz, fs2 =
250 Hz, fs3 = 500 Hz, or fs4 = 1000 Hz depending on the user’s
needs. From the ECG signal, Faros automatically detects independent
heart beats as points R in a PQRST model of the ECG signal, where
P, Q, R, S, and T represent specific waves in the signal as
presented in Figure 3. It also derives heart rate variability
(HRV), sometimes also referred to as R–R or N–N interval, from the
time intervals between the consecutive R points, and presents it as
a single output in addition to raw ECG.
Figure 3. PQRST model and R–R interval.
Figure 2. Faros 360 with cable set for measuring with electrodes
(left) and Faros 360 with textile belt for electrode-free
measurements (right).
The most important feature of Faros is its ECG sensor. The sampling
frequency of the provided raw ECG data can be fs1 = 125 Hz, fs2 =
250 Hz, fs3 = 500 Hz, or fs4 = 1000 Hz depending on the user’s
needs. From the ECG signal, Faros automatically detects independent
heart beats as points R in a PQRST model of the ECG signal, where
P, Q, R, S, and T represent specific waves in the signal as
presented in Figure 3. It also derives heart rate variability
(HRV), sometimes also referred to as R–R or N–N interval, from the
time intervals between the consecutive R points, and presents it as
a single output in addition to raw ECG.
Faros is equipped also with a temperature sensor. According to the
manufacturer’s specifications [75], its characteristic is linear
only between 35 and 45 degrees Celsius when the sampling frequency
is fs = 5 Hz.
Faros features also an accelerometer with two possible sampling
frequencies: fs1 = 25 Hz and fs2 =
100 Hz. Its dynamic range can be set to ±2 g, ±4 g or ±16 g. Faros
360 can operate in one of two modes: Datalogger mode and online
mode. To use the datalogger
mode, Faros has to be connected to a computer, and the appropriate
measurement settings have to be set using the Faros Manager
software [71]. Raw data is stored on Faros’ internal storage in a
European data format (EDF) file. It can be viewed using the
provided EDF viewer or can easily be converted to acceleration
(ASC) or heart rate variability (SDF) file format. In online mode,
Faros periodically (five times in a second) sends data via
Bluetooth. After Faros is paired with a computer (or another
Bluetooth device), the computer sees Faros as an external serial
device accessible over the serial communication (COM) port. The
protocol itself and data packet specifications are provided in
Bittium’s document 810378 [75].
Appl. Sci. 2019, 9, 5353 7 of 23
Appl. Sci. 2019, 9, x 6 of 23
• Faros 180, which offers 1-channel ECG measurements and is able to
stream data via Bluetooth, and
• Faros 360, which enables 3-channels ECG measurements and is able
to stream data via Bluetooth.
Each version can be mounted to the participant’s body in three
ways: Fast-Fix, using a cable set, or using a stingray adapter.
Fast-Fix, which is a Bittium’s proprietary electrode, is designed
for quick mounting and is considered as the simplest and most
convenient way of using the device. Faros can be mounted also by
using cable sets (see Figure 2 left) with up to five electrodes for
3-channel measurements (or three electrodes for 1-channel). This
version is considered as the most accurate and reliable version of
Faros for measuring ECG because it enables best skin contact during
the measurement process. The third mounting option does not require
placing any electrodes on the participant’s body. It instead uses
an elastic textile belt with two electrodes and a mounting pad for
Faros (see Figure 2 right).
Figure 2. Faros 360 with cable set for measuring with electrodes
(left) and Faros 360 with textile belt for electrode-free
measurements (right).
The most important feature of Faros is its ECG sensor. The sampling
frequency of the provided raw ECG data can be fs1 = 125 Hz, fs2 =
250 Hz, fs3 = 500 Hz, or fs4 = 1000 Hz depending on the user’s
needs. From the ECG signal, Faros automatically detects independent
heart beats as points R in a PQRST model of the ECG signal, where
P, Q, R, S, and T represent specific waves in the signal as
presented in Figure 3. It also derives heart rate variability
(HRV), sometimes also referred to as R–R or N–N interval, from the
time intervals between the consecutive R points, and presents it as
a single output in addition to raw ECG.
Figure 3. PQRST model and R–R interval. Figure 3. PQRST model and
R–R interval.
2.3. Nervtech Driving Simulator Overview
To ensure a controllable and repetitive driving environment, the
study was performed in a driving simulator. We used Nervtech’s high
fidelity driving simulator [72], using a triple screen set-up
(Figure 4) with adjustable car seat, Fanatec ClubSport Wheel Base
V2 with dynamic feedback, Fanatech ClubSport Pedals V3 with dynamic
feedback, and Fanatec gear box. Total resolution of the three
Samsung Curved 48′ TVs is 5760 × 1080 px, using Nvidia GeForce GTX
1070 Ti graphic card. The on-screen cockpit included speedometer
and fuel gauge. Motion cues are provided by slightly moving the
cockpit up and down. The simulator uses OKTAL’s Scanner Studio,
which is managed by a supervising platform OktalControl by
Nervtech. Based on the definition of high physical and functional
fidelity as defined by Kinkade and Wheaton [76] and Hays [77], the
simulator used in this study qualifies as a high-fidelity driving
simulator.
Appl. Sci. 2019, 9, x 7 of 23
Faros is equipped also with a temperature sensor. According to the
manufacturer’s specifications [75], its characteristic is linear
only between 35 and 45 degrees Celsius when the sampling frequency
is fs = 5 Hz.
Faros features also an accelerometer with two possible sampling
frequencies: fs1 = 25 Hz and fs2 = 100 Hz. Its dynamic range can be
set to ±2 g, ±4 g or ±16 g.
Faros 360 can operate in one of two modes: Datalogger mode and
online mode. To use the datalogger mode, Faros has to be connected
to a computer, and the appropriate measurement settings have to be
set using the Faros Manager software [71]. Raw data is stored on
Faros’ internal storage in a European data format (EDF) file. It
can be viewed using the provided EDF viewer or can easily be
converted to acceleration (ASC) or heart rate variability (SDF)
file format. In online mode, Faros periodically (five times in a
second) sends data via Bluetooth. After Faros is paired with a
computer (or another Bluetooth device), the computer sees Faros as
an external serial device accessible over the serial communication
(COM) port. The protocol itself and data packet specifications are
provided in Bittium’s document 810378 [75].
2.3. Nervtech Driving Simulator Overview
To ensure a controllable and repetitive driving environment, the
study was performed in a driving simulator. We used Nervtech’s high
fidelity driving simulator [72], using a triple screen set- up
(Figure 4) with adjustable car seat, Fanatec ClubSport Wheel Base
V2 with dynamic feedback, Fanatech ClubSport Pedals V3 with dynamic
feedback, and Fanatec gear box. Total resolution of the three
Samsung Curved 48′ TVs is 5760 × 1080 px, using Nvidia GeForce GTX
1070 Ti graphic card. The on-screen cockpit included speedometer
and fuel gauge. Motion cues are provided by slightly moving the
cockpit up and down. The simulator uses OKTAL’s Scanner Studio,
which is managed by a supervising platform OktalControl by
Nervtech. Based on the definition of high physical and functional
fidelity as defined by Kinkade and Wheaton [76] and Hays [77], the
simulator used in this study qualifies as a high-fidelity driving
simulator.
Figure 4. Nervtech driving simulator with three Samsung Curved TV
48’.
Figure 4. Nervtech driving simulator with three Samsung Curved TV
48’.
Appl. Sci. 2019, 9, 5353 8 of 23
2.4. Technical Setup
E4 was placed on the participant’s left hand (Figure 5). For this
study, we used streaming mode to store data captured with E4 using
Windows Streaming Server, which requires a specific BLE USB dongle
to connect to the E4 wristband [78]. Once the Windows Streaming
Server and E4 are connected, users or software can connect to the
streaming server via the TCP socket. For this study, we created a
custom software that connects to the streaming server and stores
real-time data for further analysis.
From the three available versions for Faros, we used Faros 360,
which enables 3-channel ECG measurement and is able to stream data
via Bluetooth. For convenience of use, the stingray adapter was
used for mounting the device on the participants (Figure 5).
Following the recommendations by Laborde et al. we used the
sampling frequency fs = 500 Hz [79].
Appl. Sci. 2019, 9, x 8 of 23
2.4. Technical Setup
E4 was placed on the participant’s left hand (Figure 5). For this
study, we used streaming mode to store data captured with E4 using
Windows Streaming Server, which requires a specific BLE USB dongle
to connect to the E4 wristband [78]. Once the Windows Streaming
Server and E4 are connected, users or software can connect to the
streaming server via the TCP socket. For this study, we created a
custom software that connects to the streaming server and stores
real-time data for further analysis.
From the three available versions for Faros, we used Faros 360,
which enables 3-channel ECG measurement and is able to stream data
via Bluetooth. For convenience of use, the stingray adapter was
used for mounting the device on the participants (Figure 5).
Following the recommendations by Laborde et al. we used the
sampling frequency fs = 500 Hz [79].
Figure 5. Positioning of Empatica E4 and Bittium Faros 360. E4
should be placed on the wrist of a non- dominant hand in a way that
electrodes are on the line that goes between middle and ring
finger. Faros with the textile belt should be placed right below
the chest muscle [80].
2.5. User Study
2.5.1. Participants
Since the distribution of different factors that could affect the
measurement in different ways is usually unknown, the American
Association of Psychologists recommends using a slightly corrected
Cohen’s effect sizes. This study was aimed to detect large effect
sizes with 80% statistical power. Study [81] made some calculations
regarding statistical considerations and it turned out that for
statistical power of 80% at α = 0.05 in order to detect large
effect sizes, a sample size of at least 21 participants is
required.
Therefore, 22 drivers, aged from 18 to 45 participated in this
study, eight of them (36%) were female. There were no drivers with
any kind of heart diseases or other relevant health problems. A
requirement for every driver to participate was a valid driving
license.
All subjects gave their informed consent for inclusion before they
participated in the study. The study was conducted in accordance
with the Declaration of Helsinki, and the protocol was approved by
the head of the Laboratory of Information Technologies, Faculty of
Electrical Engineering, University of Ljubljana.
2.5.2. Tasks
This study had a within-subject design. It consisted of three
phases: Baseline, easy driving, and demanding driving. The duration
of each phase was 6–8 min to ensure at least a 5-min-long window of
data, as recommended for short-term recordings of physiological
signals by the European Society of Cardiology [79,82].
Figure 5. Positioning of Empatica E4 and Bittium Faros 360. E4
should be placed on the wrist of a non-dominant hand in a way that
electrodes are on the line that goes between middle and ring
finger. Faros with the textile belt should be placed right below
the chest muscle [80].
2.5. User Study
2.5.1. Participants
Since the distribution of different factors that could affect the
measurement in different ways is usually unknown, the American
Association of Psychologists recommends using a slightly corrected
Cohen’s effect sizes. This study was aimed to detect large effect
sizes with 80% statistical power. Study [81] made some calculations
regarding statistical considerations and it turned out that for
statistical power of 80% at α = 0.05 in order to detect large
effect sizes, a sample size of at least 21 participants is
required.
Therefore, 22 drivers, aged from 18 to 45 participated in this
study, eight of them (36%) were female. There were no drivers with
any kind of heart diseases or other relevant health problems. A
requirement for every driver to participate was a valid driving
license.
All subjects gave their informed consent for inclusion before they
participated in the study. The study was conducted in accordance
with the Declaration of Helsinki, and the protocol was approved by
the head of the Laboratory of Information Technologies, Faculty of
Electrical Engineering, University of Ljubljana.
2.5.2. Tasks
This study had a within-subject design. It consisted of three
phases: Baseline, easy driving, and demanding driving. The duration
of each phase was 6–8 min to ensure at least a 5-min-long
window
Appl. Sci. 2019, 9, 5353 9 of 23
of data, as recommended for short-term recordings of physiological
signals by the European Society of Cardiology [79,82].
Phase 1: Baseline
Physiological signals such as HR and HRV differ among people
therefore, we first had to collect baseline data for each
participant. Since there is not a standardized procedure for
baseline measurements, we followed the recommendations by Laborde
et al. [79]. To create similar environment with the following
driving tasks, the baseline measurements were also collected in a
sitting position. The test drivers were told to sit in a chair with
their legs straight (not crossed), keeping their knee angles at 90
degrees. They were asked to try to relax and stay calm, to lean
their head back, keep their eyes closed, and to ensure normal blood
flow to the wrists, place their arms on the supporters with hands
facing up (Figure 6a).
Appl. Sci. 2019, 9, x 9 of 23
Phase 1: Baseline
Physiological signals such as HR and HRV differ among people
therefore, we first had to collect baseline data for each
participant. Since there is not a standardized procedure for
baseline measurements, we followed the recommendations by Laborde
et al. [79]. To create similar environment with the following
driving tasks, the baseline measurements were also collected in a
sitting position. The test drivers were told to sit in a chair with
their legs straight (not crossed), keeping their knee angles at 90
degrees. They were asked to try to relax and stay calm, to lean
their head back, keep their eyes closed, and to ensure normal blood
flow to the wrists, place their arms on the supporters with hands
facing up (Figure 6a).
The 5-min measurement began with a short beep from Faros that
indicated a successful initialization of measurement system. During
this period, all light and noise sources were turned off and
remained off until the end of the study process in order to reduce
potential distractions that could affect the measurement
process.
Phase 2: Easy Driving
Phase 2 and 3 were performed in a driving simulator. The
participants’ task was safe driving in a very simple driving
scenario (Figure 6b). The participants were asked to drive on a
two-way one- lane six-kilometer country road with no other vehicles
or pedestrians. The speed limit was 90 km/h, and participants were
not asked to follow any pre-defined path.
Phase 3: Demanding Driving
In the third phase, the participants’ primary task remained safe
driving. However, in this task the participants had to follow a
specific route communicated with a navigation system displayed on a
built-in head-up display. In this phase, the driving scenario was
much more demanding as it featured six kilometers of two-lane
highway and two kilometers of city road with a lot of vehicles,
intersections and pedestrians.
While on the highway (Figure 6c), the participants faced many
critical situations, including a truck driving in the opposite
direction, work on the road, unsafe overtaking by another vehicle,
and traffic accident while driving in fog. The driver experienced
critical situations also on the city road such as pedestrians
crossing the road outside crosswalks, a bike forcing the driver’s
right of way, a child on the road running after a ball, etc.
Figure 6. Driver’s position during three phases of measurement: (a)
Baseline, (b) easy driving, (c) demanding driving [80]. Figure 6.
Driver’s position during three phases of measurement: (a) Baseline,
(b) easy driving, (c) demanding driving [80].
The 5-min measurement began with a short beep from Faros that
indicated a successful initialization of measurement system. During
this period, all light and noise sources were turned off and
remained off until the end of the study process in order to reduce
potential distractions that could affect the measurement
process.
Phase 2: Easy Driving
Phase 2 and 3 were performed in a driving simulator. The
participants’ task was safe driving in a very simple driving
scenario (Figure 6b). The participants were asked to drive on a
two-way one-lane six-kilometer country road with no other vehicles
or pedestrians. The speed limit was 90 km/h, and participants were
not asked to follow any pre-defined path.
Phase 3: Demanding Driving
In the third phase, the participants’ primary task remained safe
driving. However, in this task the participants had to follow a
specific route communicated with a navigation system displayed on a
built-in head-up display. In this phase, the driving scenario was
much more demanding as it featured
Appl. Sci. 2019, 9, 5353 10 of 23
six kilometers of two-lane highway and two kilometers of city road
with a lot of vehicles, intersections and pedestrians.
While on the highway (Figure 6c), the participants faced many
critical situations, including a truck driving in the opposite
direction, work on the road, unsafe overtaking by another vehicle,
and traffic accident while driving in fog. The driver experienced
critical situations also on the city road such as pedestrians
crossing the road outside crosswalks, a bike forcing the driver’s
right of way, a child on the road running after a ball, etc.
2.5.3. Variables
All output signals from both Faros 360 and E4 were recorded and
processed. Comparison between the two devices could be made with
HRV and temperature signals. Since only Faros provides raw ECG
signal, the evaluation of other ECG features than the ones already
extracted by the devices (HRV) is not feasible. The difference with
HRV is only in signal capturing methods which could provide
significantly different results. Faros measures ECG signal to
obtain R–R intervals (HRV) while E4 uses photoplethysmography (PPG)
to obtain BVP and IBI. Two temperature readings could also provide
significantly different results since measures take part at
different body parts with different thermal conductance. In
addition to the comparison, GSR signal from E4 was also analyzed to
provide the full picture of E4’s usability.
Independent variable in our tests was each phase’s driving
difficulty that could take one of three values: 1 = baseline
establishment, 2 = easy driving, 3 = demanding driving.
Dependent variables for both Faros and E4 were:
• Mean and median HRV, • HRV SDNN—standard deviation of the R–R
intervals (also known as normal-to-normal or N–N
intervals, Figure 3), which reflects cyclic components during the
measurement, • HRV SDANN—standard deviation of the average R–R
intervals over a shorter period (10 s), it
reflects changes due to longer cycles, • HRV SDNN index—mean of the
standard deviations, calculated over a shorter period (10 s),
it
reflects changes due to shorter cycles, • HRV RMSSD—root mean
square of successive differences, which reflects parasympathetic
nerve
system activity and is not affected by respiration process, • HRV
pNN50—the number of successive differences, greater than 50 ms,
derived by the number of
total N–N differences, should be highly correlated to RMSSD, •
Temperature mean and standard deviation.
Mean and standard deviation of recorded GSR signals were also
analyzed as dependent variables from E4.
2.5.4. Procedure
The study began with a demographic questionnaire and a consent form
for participants’ personal data processing. After that, Empatica E4
and Bittium Faros 360 were disinfected, turned on, and placed on
participants’ wrist and chest respectively. Exact position of both
devices is shown in Figure 5.
In [82], Task Force of the European Society of Cardiology set
standards for ECG and HRV measurements. According to them,
measurements of short events should take at least 5 min. Therefore,
the baseline and both tasks lasted at least 5 min and to make the
sample size equal, we only observed the last 5 min. Phase three was
usually longer than 7 min (it depended on participants’ driving),
so we only cut samples from the first half-minute to avoid having
artefacts from initial driver movements.
Before the driving, each participant was introduced to and shown
how to use the simulator. Participants were asked to follow the
traffic rules and try to drive as close as possible as they would
in real life.
Appl. Sci. 2019, 9, 5353 11 of 23
After completing all three phases, the drivers were asked to
subjectively assess their feelings regarding simulation sickness.
The values are equally distributed on a 10-point scale, where 1
indicated “I am perfectly fine”, and 10 meant “I have to throw
up”.
2.5.5. Statistical Analysis
The statistical analysis included Shapiro-Wilk normality test and
Maunchly’s test of sphericity for every dependent variable. Where
the distribution of the data was found normal, repeated measures
ANOVA (RMANOVA) and (when the null hypothesis was rejected)
Bonferroni post-hoc test. For normally distributed data with
violated assumption of sphericity, Greenhouse-Geisser correction
was applied.
For variables with non-normally distributed data we used Friedman’s
non-parametric test and (when the null hypothesis was rejected)
Dunn’s post-hoc test with Bonferroni correction.
3. Results
The results of HRV measurements are presented in four groups: Mean,
standard deviation, shorter time-frame variables and successive
differences, followed by skin temperature and GSR measurements. A
summary of the results is provided in Table 1.
Table 1. Results of each device (columns) and each dependent
variable (rows). ‘<’ indicates a statistically significant
increase in the corresponding dependent variable between the two
phases. X indicates that no significant differences among three
phases of driving were found. N/A indicates the measurements were
not applicable for the corresponding device.
E4 Faros 360
HRV SDNN baseline < easy driving baseline < demanding
driving
easy driving < demanding driving baseline < demanding
driving
HRV SDANN baseline < easy driving baseline < demanding
driving
easy driving < demanding driving baseline < demanding
driving
HRV SDNN Index X X
HRV RMSSD baseline < easy driving baseline < demanding
driving X
HRV pNN50 baseline < easy driving baseline < demanding
driving X
Mean skin temperature baseline < easy driving baseline <
demanding driving
baseline < easy driving baseline < demanding driving
easy driving < demanding driving
baseline < easy driving baseline < demanding driving
baseline < easy driving baseline < demanding driving
Mean and standard deviation of GSR baseline < demanding driving
N/A
It is important to mention that E4’s built-in algorithm for
calculating the HRV did often not provide any output. This resulted
in missing HRV samples from E4 at easy driving (58%) and demanding
driving (59%). It is important to notice that the missing values
may have influenced the results of HRV successive differences,
therefore they should be interpreted with caution. On the other
hand, Faros provided all HRV samples during every measurement
phase.
Appl. Sci. 2019, 9, 5353 12 of 23
3.1. Mean Heart Rate Variability (HRV)
RMANOVA test for mean values of HRV (Figure 7) did not reveal any
statistically significant differences among the data collected
during the three phases for E4 (F(1.166, 22.148) = 0.308, p =
0.619) or Faros (F(1.331, 27.941) = 2.330, p = 0.131). The same
test did not reveal any statistically significant differences also
in median HRV among the observed phases for E4 (F(1.219, 23.154) =
0.366, p = 0.593) or Faros (F(1.323, 27.774) = 2.332, p =
0.131).Appl. Sci. 2019, 9, x 12 of 23
Figure 7. Mean heart rate variability (HRV) for each participant in
each phase for E4 (left) and Faros 360 (right).
3.2. HRV SDNN
RMANOVA test for SDNN (Figure 8) showed statistically significant
differences among the data from different phases for E4 (F(2, 38) =
10.096, p < 0.001). Bonferroni post-hoc test revealed that the
HRV SDNN increased statistically significantly when comparing the
baseline and easy driving (p = 0.002) and between baseline and
demanding driving (p = 0.010). No statistically significant
differences were found between the easy driving and demanding
driving phase (p = 1.000).
Figure 8. HRV SDNN for each participant in each phase for E4 (left)
and Faros 360 (right).
RMANOVA test results for data captured with Faros 360 also showed
that there are some statistically significant differences among the
data from different phases (F(2, 42) = 6.967, p = 0.002).
Bonferroni post-hoc test showed that the HRV SDNN increased
statistically significantly when comparing the baseline and
demanding driving (p = 0.010) and between easy driving and
demanding driving (p = 0.027). The test did not show any
statistically significant differences between the baseline and easy
driving phase (p = 0.794).
3.3. HRV Variables for Shorter Timeframes
For further analysis, the data was divided into 10-s timeframes.
The analysis showed statistically significant differences among the
data from the observed phases for SDANN (Figure 9) but did not
reveal any differences in the SDNN Index, for data from the both
devices (Table 2).
Figure 7. Mean heart rate variability (HRV) for each participant in
each phase for E4 (left) and Faros 360 (right).
3.2. HRV SDNN
RMANOVA test for SDNN (Figure 8) showed statistically significant
differences among the data from different phases for E4 (F(2, 38) =
10.096, p < 0.001). Bonferroni post-hoc test revealed that the
HRV SDNN increased statistically significantly when comparing the
baseline and easy driving (p = 0.002) and between baseline and
demanding driving (p = 0.010). No statistically significant
differences were found between the easy driving and demanding
driving phase (p = 1.000).
Appl. Sci. 2019, 9, x 12 of 23
Figure 7. Mean heart rate variability (HRV) for each participant in
each phase for E4 (left) and Faros 360 (right).
3.2. HRV SDNN
RMANOVA test for SDNN (Figure 8) showed statistically significant
differences among the data from different phases for E4 (F(2, 38) =
10.096, p < 0.001). Bonferroni post-hoc test revealed that the
HRV SDNN increased statistically significantly when comparing the
baseline and easy driving (p = 0.002) and between baseline and
demanding driving (p = 0.010). No statistically significant
differences were found between the easy driving and demanding
driving phase (p = 1.000).
Figure 8. HRV SDNN for each participant in each phase for E4 (left)
and Faros 360 (right).
RMANOVA test results for data captured with Faros 360 also showed
that there are some statistically significant differences among the
data from different phases (F(2, 42) = 6.967, p = 0.002).
Bonferroni post-hoc test showed that the HRV SDNN increased
statistically significantly when comparing the baseline and
demanding driving (p = 0.010) and between easy driving and
demanding driving (p = 0.027). The test did not show any
statistically significant differences between the baseline and easy
driving phase (p = 0.794).
3.3. HRV Variables for Shorter Timeframes
For further analysis, the data was divided into 10-s timeframes.
The analysis showed statistically significant differences among the
data from the observed phases for SDANN (Figure 9) but did not
reveal any differences in the SDNN Index, for data from the both
devices (Table 2).
Figure 8. HRV SDNN for each participant in each phase for E4 (left)
and Faros 360 (right).
RMANOVA test results for data captured with Faros 360 also showed
that there are some statistically significant differences among the
data from different phases (F(2, 42) = 6.967, p = 0.002).
Bonferroni post-hoc test showed that the HRV SDNN increased
statistically significantly when
Appl. Sci. 2019, 9, 5353 13 of 23
comparing the baseline and demanding driving (p = 0.010) and
between easy driving and demanding driving (p = 0.027). The test
did not show any statistically significant differences between the
baseline and easy driving phase (p = 0.794).
3.3. HRV Variables for Shorter Timeframes
For further analysis, the data was divided into 10-s timeframes.
The analysis showed statistically significant differences among the
data from the observed phases for SDANN (Figure 9) but did not
reveal any differences in the SDNN Index, for data from the both
devices (Table 2).Appl. Sci. 2019, 9, x 13 of 23
Figure 9. HRV SDANN for 10-s timeframes for each participant in
each phase for E4 (left) and Faros 360 (right).
When observing the SDANN data measured with E4, Bonferroni post-hoc
test showed that it increased statistically significantly from the
baseline compared to the easy driving phase (p < 0.001) and
between the baseline and demanding driving phase (p < 0.001); no
statistically significant differences were found between the easy
driving and demanding driving phase (p = 1.000).
When observing the SDANN data measured with Faros 360, Bonferroni
post-hoc test showed that it increased statistically significantly
from the baseline compared to the demanding driving phase (p <
0.001) and between the easy driving and the demanding driving phase
(p = 0.016). The test showed no statistically significant
differences between baseline and easy driving (p = 0.086).
Table 2. Statistical tests’ results for HRV variables for shorter
timeframes.
SDANN SDNN Index
F(1.586, 33.303) = 3.923, p = 0.03811
Faros 360 RMANOVA:
F(1.398, 29.364) = 3.405, p = 0.062 1 Bonferroni post-hoc test
showed no statistically significant differences between phases,
p(1,2) = 0.166, p(2,3) = 0.641, p(1,3) = 0.091.
3.4. HRV Successive Differences
Statistically significant differences in the data from E4 data were
found among the phases in both RMSSD (F(2, 38) = 44.822, p <
0.001) and pNN50 (F(1.411, 26.816) = 57.508, p < 0.001) (Figure
10 and Figure 11). Bonferroni post-hoc test showed that the values
increased statistically significantly between the baseline and the
easy driving phase (p < 0.001) and between the baseline and the
demanding driving phase (p < 0.001), for both variables, RMSSD
and pNN50. The test showed no statistically significant differences
between easy driving and demanding driving (p = 0.964 (RMSSD), p =
0.530 (pNN50)).
In the data, captured with Faros 360, Friedman’s non-parametric
tests did not reveal any statistically significant differences in
RMSSD (χ2(2) = 3.545, p = 0.170) or pNN50 (χ2(2) = 0.364, p =
0.834).
Figure 9. HRV SDANN for 10-s timeframes for each participant in
each phase for E4 (left) and Faros 360 (right).
Table 2. Statistical tests’ results for HRV variables for shorter
timeframes.
SDANN SDNN Index
RMANOVA with Greenhouse-Geisser correction: F(1.586, 33.303) =
3.923, p = 0.0381 1
Faros 360 RMANOVA: F(2, 42) = 13.312, p < 0.001
RMANOVA with Greenhouse-Geisser correction: F(1.398, 29.364) =
3.405, p = 0.062
1 Bonferroni post-hoc test showed no statistically significant
differences between phases, p(1,2) = 0.166, p(2,3) = 0.641, p(1,3)
= 0.091.
When observing the SDANN data measured with E4, Bonferroni post-hoc
test showed that it increased statistically significantly from the
baseline compared to the easy driving phase (p < 0.001) and
between the baseline and demanding driving phase (p < 0.001); no
statistically significant differences were found between the easy
driving and demanding driving phase (p = 1.000).
When observing the SDANN data measured with Faros 360, Bonferroni
post-hoc test showed that it increased statistically significantly
from the baseline compared to the demanding driving phase (p <
0.001) and between the easy driving and the demanding driving phase
(p = 0.016). The test showed no statistically significant
differences between baseline and easy driving (p = 0.086).
3.4. HRV Successive Differences
Statistically significant differences in the data from E4 data were
found among the phases in both RMSSD (F(2, 38) = 44.822, p <
0.001) and pNN50 (F(1.411, 26.816) = 57.508, p < 0.001) (Figures
10 and 11). Bonferroni post-hoc test showed that the values
increased statistically significantly between the baseline and the
easy driving phase (p < 0.001) and between the baseline and the
demanding driving phase (p <
0.001), for both variables, RMSSD and pNN50. The test showed no
statistically significant differences between easy driving and
demanding driving (p = 0.964 (RMSSD), p = 0.530 (pNN50)).
Appl. Sci. 2019, 9, 5353 14 of 23
Appl. Sci. 2019, 9, x 14 of 23
Figure 10. HRV RMSSD for each participant in each phase for E4
(left) and Faros 360 (right).
Figure 11. HRV pNN50 for each participant in each phase for E4
(left) and Faros 360 (right).
3.5. Skin Temperature
During the experiment, the temperature was rising at what appears
to be an exponential curve (see Figure 12).
Statistically significant differences among phases (Table 3) were
found in mean and standard deviation of the skin temperature
readings (Figure 13, Figure 14) from the both devices.
Table 3. Statistical tests’ results for skin temperature.
Skin Temperature Mean Skin Temperature Standard Deviation
E4 RMANOVA: F(2, 42) = 29.313, p < 0.001
Friedman’s nonparametric test: χ2(2) = 9.818, p = 0.007
Faros 360 RMANOVA
RMANOVA with Greenhouse-Geisser correction:
F(1.207, 25.350) = 78.100, p < 0.001
Figure 10. HRV RMSSD for each participant in each phase for E4
(left) and Faros 360 (right).
Appl. Sci. 2019, 9, x 14 of 23
Figure 10. HRV RMSSD for each participant in each phase for E4
(left) and Faros 360 (right).
Figure 11. HRV pNN50 for each participant in each phase for E4
(left) and Faros 360 (right).
3.5. Skin Temperature
During the experiment, the temperature was rising at what appears
to be an exponential curve (see Figure 12).
Statistically significant differences among phases (Table 3) were
found in mean and standard deviation of the skin temperature
readings (Figure 13, Figure 14) from the both devices.
Table 3. Statistical tests’ results for skin temperature.
Skin Temperature Mean Skin Temperature Standard Deviation
E4 RMANOVA: F(2, 42) = 29.313, p < 0.001
Friedman’s nonparametric test: χ2(2) = 9.818, p = 0.007
Faros 360 RMANOVA
RMANOVA with Greenhouse-Geisser correction:
F(1.207, 25.350) = 78.100, p < 0.001
Figure 11. HRV pNN50 for each participant in each phase for E4
(left) and Faros 360 (right).
In the data, captured with Faros 360, Friedman’s non-parametric
tests did not reveal any statistically significant differences in
RMSSD (χ2(2) = 3.545, p = 0.170) or pNN50 (χ2(2) = 0.364, p =
0.834).
3.5. Skin Temperature
During the experiment, the temperature was rising at what appears
to be an exponential curve (see Figure 12).Appl. Sci. 2019, 9, x 15
of 23
Figure 12. Faros’ temperature readings from the beginning of the
baseline phase. Data for each participant is drawn with different
color. Straight lines interpolate the temperature between the end
of one phase and the beginning of the next one.
Figure 13. Mean skin temperature for each participant in each phase
for E4 (left) and Faros 360 (right).
Figure 14. Standard deviation of skin temperature for each
participant in each phase for E4 (left) and Faros 360
(right).
Bonferroni post-hoc test for mean skin temperature measured with E4
showed statistically significant increase between the baseline and
the easy driving phase (p < 0.001) and between the
Figure 12. Faros’ temperature readings from the beginning of the
baseline phase. Data for each participant is drawn with different
color. Straight lines interpolate the temperature between the end
of one phase and the beginning of the next one.
Appl. Sci. 2019, 9, 5353 15 of 23
Statistically significant differences among phases (Table 3) were
found in mean and standard deviation of the skin temperature
readings (Figure 13, Figure 14) from the both devices.
Table 3. Statistical tests’ results for skin temperature.
Skin Temperature Mean Skin Temperature Standard Deviation
E4 RMANOVA: F(2, 42) = 29.313, p < 0.001
Friedman’s nonparametric test: χ2(2) = 9.818, p = 0.007
Faros 360 RMANOVA with
RMANOVA with Greenhouse-Geisser correction:
Appl. Sci. 2019, 9, x 15 of 23
Figure 12. Faros’ temperature readings from the beginning of the
baseline phase. Data for each participant is drawn with different
color. Straight lines interpolate the temperature between the end
of one phase and the beginning of the next one.
Figure 13. Mean skin temperature for each participant in each phase
for E4 (left) and Faros 360 (right).
Figure 14. Standard deviation of skin temperature for each
participant in each phase for E4 (left) and Faros 360
(right).
Bonferroni post-hoc test for mean skin temperature measured with E4
showed statistically significant increase between the baseline and
the easy driving phase (p < 0.001) and between the
Figure 13. Mean skin temperature for each participant in each phase
for E4 (left) and Faros 360 (right).
Appl. Sci. 2019, 9, x 15 of 23
Figure 12. Faros’ temperature readings from the beginning of the
baseline phase. Data for each participant is drawn with different
color. Straight lines interpolate the temperature between the end
of one phase and the beginning of the next one.
Figure 13. Mean skin temperature for each participant in each phase
for E4 (left) and Faros 360 (right).
Figure 14. Standard deviation of skin temperature for each
participant in each phase for E4 (left) and Faros 360
(right).
Bonferroni post-hoc test for mean skin temperature measured with E4
showed statistically significant increase between the baseline and
the easy driving phase (p < 0.001) and between the
Figure 14. Standard deviation of skin temperature for each
participant in each phase for E4 (left) and Faros 360
(right).
Bonferroni post-hoc test for mean skin temperature measured with E4
showed statistically significant increase between the baseline and
the easy driving phase (p < 0.001) and between the baseline and
the demanding driving phase (p < 0.001). No statistically
significant differences were found between the easy and the
demanding driving phase (p = 0.770).
Bonferroni post-hoc test for mean skin temperature measure with
Faros 360 showed that it increased statistically significantly for
each consecutive phase (p < 0.001).
Dunn’s post-hoc test with Bonferroni correction for standard
deviation of the skin temperature measured with E4 showed it
increased statistically significantly between the baseline and the
easy driving phase (p = 0.020) and between the baseline and the
demanding driving phase (p = 0.020). The test did not show any
statistically significant differences between the easy driving and
the demanding driving phase (p = 1.000).
Appl. Sci. 2019, 9, 5353 16 of 23
Similarly, when analyzing the standard deviation of skin
temperature measured with Faros 360, Bonferroni post-hoc test
showed it increased statistically significantly from the baseline
compared to the easy driving phase (p < 0.001), and from the
baseline and compared to the demanding driving phase (p <
0.001). The test did not show any statistically significant
differences between the easy driving and the demanding driving
phase (p = 0.473).
3.6. E4’s GSR
Non-parametric Friedman test revealed that statistically
significant differences among the data from the observed phases for
mean GSR (χ2(2) = 8.273, p = 0.016) and standard deviation of GSR
(χ2(2) = 6.909, p = 0.032) (Figure 15). In both cases, Dunn’s
post-hoc test with Bonferroni correction revealed significant
increase in the values from the baseline compared to the demanding
driving phase (mean GSR: p = 0.013, standard deviation of GSR: p =
0.048), however there were no significant differences in these
values when comparing the baseline and the easy driving, and the
easy driving and the demanding driving phase (p > 0.1).
Appl. Sci. 2019, 9, x 16 of 23
baseline and the demanding driving phase (p < 0.001). No
statistically significant differences were found between the easy
and the demanding driving phase (p = 0.770).
Bonferroni post-hoc test for mean skin temperature measure with
Faros 360 showed that it increased statistically significantly for
each consecutive phase (p < 0.001).
Dunn’s post-hoc test with Bonferroni correction for standard
deviation of the skin temperature measured with E4 showed it
increased statistically significantly between the baseline and the
easy driving phase (p = 0.020) and between the baseline and the
demanding driving phase (p = 0.020). The test did not show any
statistically significant differences between the easy driving and
the demanding driving phase (p = 1.000).
Similarly, when analyzing the standard deviation of skin
temperature measured with Faros 360, Bonferroni post-hoc test
showed it increased statistically significantly from the baseline
compared to the easy driving phase (p < 0.001), and from the
baseline and compared to the demanding driving phase (p <
0.001). The test did not show any statistically significant
differences between the easy driving and the demanding driving
phase (p = 0.473).
3.6. E4’s GSR
Non-parametric Friedman test revealed that statistically
significant differences among the data from the observed phases for
mean GSR (χ2(2) = 8.273, p = 0.016) and standard deviation of GSR
(χ2(2) = 6.909, p = 0.032) (Figure 15). In both cases, Dunn’s
post-hoc test with Bonferroni correction revealed significant
increase in the values from the baseline compared to the demanding
driving phase (mean GSR: p = 0.013, standard deviation of GSR: p =
0.048), however there were no significant differences in these
values when comparing the baseline and the easy driving, and the
easy driving and the demanding driving phase (p > 0.1).
Figure 15. Mean and standard deviation of GSR for each participant
in each phase for E4 measurements.
3.7. Sickness
The majority of drivers (86%) rated their sickness level with a
score of equal to or less than 4 (out of 10). Only three
participants reported higher sickness levels, with scores of 6, 8
and 9 (Figure 16).
Figure 15. Mean and standard deviation of GSR for each participant
in each phase for E4 measurements.
3.7. Sickness
The majority of drivers (86%) rated their sickness level with a
score of equal to or less than 4 (out of 10). Only three
participants reported higher sickness levels, with scores of 6, 8
and 9 (Figure 16).Appl. Sci. 2019, 9, x 17 of 23
Figure 16. Drivers’ self-reports of sickness on a 10-point scale,
where 1 indicates “I am perfectly fine”, and 10 means “I have to
throw up”.
4. Discussion and Conclusions
In this paper, we compared two commonly used wearable devices for
measuring driver’s physiological signals—Empatica E4 and Bittium
Faros 360. We performed a user study with which we wanted to
evaluate the use of these devices in a dynamic driving environment.
Additionally, we explored if they can be used for differentiating
between different levels of driving difficulty.
4.1. Photoplethysmography (PPG) Limitations
E4 uses PPG, which is according to the literature less accurate
than ECG, used by Faros [79]. PPG can provide somewhat different
(delayed, malformed, etc.) results than ECG since the blood vessels
are elastic and the pulse transit times may vary. Furthermore, it
is considered that PPG can provide a valid representation of IBI
during rest, but not during activity [83]. This suggests additional
caution when assessing ECG data with wearable devices that use PPG
and not ECG sensors.
When examining the raw data from E4, multiple inter-beat interval
(IBI) samples were incomplete for most drivers in both of the
driving phases; furthermore, the collected data were completely
missing for two drivers. However, the IBI samples recordings were
complete for the baseline phase, suggesting the set-up was
performed correctly.
There are two potential reasons for incomplete IBI samples when
recording with E4. As the missing samples occurred mainly in the
dynamic driving environment, we can assume motion artefacts caused
by steering negatively influence the E4. Furthermore, using the
accelerometer and consequentially its internal algorithm for
obtaining IBI from blood volume pulse (BVP), the device might
detect intensive motion activity, which may have invalidated some
of the IBI samples. Since there is an entire sequence of missing
samples recorded, it is impossible to determine the differences
between successive samples. Therefore, analysis of variables based
on successive differences of such samples (i.e., RMSSD and pNN50)
cannot be used for making reliable conclusions.
4.2. HRV Analysis
The results of HRV analysis based on mean and median tests did not
show any statistically significant differences among the tested
phases. However, the SDNN and SDANN signals showed that it is
possible to differentiate between driving and non-driving
situations, and to differentiate between different levels of
driving demand.
When comparing the standard deviation (SDNN) of HRV, the analysis
showed that E4 can differentiate only between non-driving and
driving, but it cannot differentiate between different levels of
driving demand, where drivers had to move their hands in order to
steer the wheel. On the other hand, the analysis showed that with
the signals recorded with Faros 360 it was possible to
differentiate between the two different levels of driving
difficulty. We speculate that the different
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10
N um
be r
of d
ri ve
Reported sickness level
Figure 16. Drivers’ self-reports of sickness on a 10-point scale,
where 1 indicates “I am perfectly fine”, and 10 means “I have to
throw up”.
Appl. Sci. 2019, 9, 5353 17 of 23
4. Discussion and Conclusions
In this paper, we compared two commonly used wearable devices for
measuring driver’s physiological signals—Empatica E4 and Bittium
Faros 360. We performed a user study with which we wanted to
evaluate the use of these devices in a dynamic driving environment.
Additionally, we explored if they can be used for differentiating
between different levels of driving difficulty.
4.1. Photoplethysmography (PPG) Limitations
E4 uses PPG, which is according to the literature less accurate
than ECG, used by Faros [79]. PPG can provide somewhat different
(delayed, malformed, etc.) results than ECG since the blood vessels
are elastic and the pulse transit times may vary. Furthermore, it
is considered that PPG can provide a valid representation of IBI
during rest, but not during activity [83]. This suggests additional
caution when assessing ECG data with wearable devices that use PPG
and not ECG sensors.
When examining the raw data from E4, multiple inter-beat interval
(IBI) samples were incomplete for most drivers in both of the
driving phases; furthermore, the collected data were completely
missing for two drivers. However, the IBI samples recordings were
complete for the baseline phase, suggesting the set-up was
performed correctly.
There are two potential reasons for incomplete IBI samples when
recording with E4. As the missing samples occurred mainly in the
dynamic driving environment, we can assume motion artefacts caused
by steering negatively influence the E4. Furthermore, using the
accelerometer and consequentially its internal algorithm for
obtaining IBI from blood volume pulse (BVP), the device might
detect intensive motion activity, which may have invalidated some
of the IBI samples. Since there is an entire sequence of missing
samples recorded, it is impossible to determine the differences
between successive samples. Therefore, analysis of variables based
on successive differences of such samples (i.e., RMSSD and pNN50)
cannot be used for making reliable conclusions.
4.2. HRV Analysis
The results of HRV analysis based on mean and median tests did not
show any statistically significant differences among the tested
phases. However, the SDNN and SDANN signals showed that it is
possible to differentiate between driving and non-driving
situations, and to differentiate between different levels of
driving demand.
When comparing the standard deviation (SDNN) of HRV, the analysis
showed that E4 can differentiate only between non-driving and
driving, but it cannot differentiate between different levels of
driving demand, where drivers had to move their hands in order to
steer the wheel. On the other hand, the analysis showed that with
the signals recorded with Faros 360 it was possible to
differentiate between the two different levels of driving
difficulty. We speculate that the different results could be a
result of the PPG limitations discussed earlier. Another
possibility is that the driving demand did not differ
significantly, and the sensors could not detect any differences
because of the small differences in the driving demand.
The analysis of the HRV variables for shorter time frames (SDANN,
SDNN index) showed statistically significant differences only in
the values of SDANN, which suggests that changes in activities with
periods longer than 10 s can be detected. However, there were no
statistically significant differences in the SDNN index results,
suggesting that neither E4 nor Faros can detect changes in
different driving difficulties, based on recordings shorter than 10
s.
The analysis revealed statistically significant differences in
RMSSD and pNN50 between the baseline and driving phases only in the
data acquired with E4 but not in the data acquired with Faros
although the HRV signal was measured with both devices
simultaneously. We believe that the reason lies in E4’s
limitations. As described in the third paragraph of the “PPG
limitations” subsection, when there are missing samples in a
sequence, successive differences cannot be determined. Therefore,
we
Appl. Sci. 2019, 9, 5353 18 of 23
could perform analysis only on the signals measured with Faros,
which however did not provide statistically significant
results.
Based on these results, we can conclude that from the observed HRV
signals, only SDNN or SDANN can be used for differentiating between
the two observed levels of driving demand. However, we cannot rule
out the possibility that the differences could be captured also in
the other HRV variables, which could be investigated, for example,
by using longer trials for each condition. Furthermore, it is
better to use Faros than E4 as motion artefacts influence the
measurement process and collected data.
4.3. Skin Temperature Analysis
Direct comparison of skin temperature data between the two devices
is not possible, since they measure temperatures of different body
parts. As shown in Figure 12 the temperature measured with Faros
360 was constantly rising. This could be due to the fact that the
experiment lasted less than 20 min and it is possible that Faros
was still warming up. Consequently, with slower warming up, the
standard deviation significantly decreased in later phases. The
skin temperature sensor in Faros 360 would provide more reliable
data in studies that involve longer measurement periods (several
hours) or, alternatively, participants would have to wear the
measuring devices at least a few hours before the measurement
process. This phenomenon was not evident with the E4’s skin
temperature sensor.
4.4. Galvanic Skin Response (GSR) Analysis
The galvanic skin response (GSR) measurements with E4 revealed
statistically significant increased conductance between demanding
driving and baseline phases. However, there was a high standard
deviation and standard error in the collected GSR signals during
the driving phases, which can imply that the E4 may not have had
skin contact during the whole recordings, probably due to the hand
movements while steering.
4.5. Driver Sickness
Eighty-six percent of participants rated their feeling of sickness
with a score of lower than 4 out of 10, and 91% of participants
evaluated it with equal or lower than 6, suggesting that the
results were not influenced by simulation/motion sickness. The
measurements of the rest of the 9% of participants did not involve
any outliers in the observed dependent variables.
4.6. Conclusions
The results from this study suggest that devices with PPG sensors
such as E4 may be less reliable for assessing drivers’ signals
compared to an ECG sensor-based device such as Faros 360. The main
disadvantage is the sensitivity to motion artefacts, which can
influence the signal recording quality. This is also in line with
the placement of both devices, where Faros 360 is placed on the
torso, which is relatively still during driving, whereas the E4
device is placed on the wrist, which is involved in movement due to
steering the wheel. Therefore, in this particular case, we think
Faros 360 could provide more reliable results for assessing
drivers’ physiological signals.
Author Contributions: Conceptualization, T.G. and J.S.;
methodology, K.S., G.J. and T.G.; software, T.G.; validation, K.S.,
J.S. and G.J.; formal analysis, T.G. and G.J.; investigation, T.G.
and K.S.; resources, J.S.; data curation, T.G.; writing—original
draft preparation, T.G.; writing—review and editing, K.S. and G.J.;
visualization, T.G.; supervision, G.J. and J.S.; project
administration, T.G. and K.S.; funding acquisition, J.S.
Funding: This work was partly supported by the Slovenian Research
Agency within the research program ICT4QoL—Information and
Communications Technologies for Quality of Life, grant number
P2-0246, and the research project Neurophysiological and Cognitive
Profiling of Driving Skills, grant number L2-8178.
Conflicts of Interest: The authors declare no conflict of interest.
The funders had no role in the design of the study; in the
collection, analyses, or interpretation of data; in the writing of
the manuscript, or in the decision to publish the results.
Appl. Sci. 2019, 9, 5353 19 of 23
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