# Multimodal Estimation of Change Points of Physiological
Arousal in Drivers

###### Abstract

Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is
an important component of ensuring driving safety and an essential
prerequisite for automatic intervention systems in vehicles. These concerning
conditions are primarily connected to the driver’s low or high arousal levels.
In this study, we describe a framework for processing multimodal physiological
time-series from wearable sensors during driving and locating points of
prominent change in drivers’ physiological arousal state. These points of
change could potentially indicate events that require just-in-time
intervention. We apply time-series segmentation on heart rate and breathing
rate measurements and quantify their robustness in capturing change points in
electrodermal activity, treated as a reference index for arousal, as well as
on self-reported stress ratings, using three public datasets. Our experiments
demonstrate that physiological measures are veritable indicators of change
points of arousal and perform robustly across an extensive ablation study.
111Code and results available at https://github.com/usc-sail/ggs_driving

Index Terms—  Physiological Signals, Time-Series Segmentation, Physiological
Arousal, Jaccard Similarity

##  1 Introduction

Driving safety has become a popular field of research and a critical
investment in the automobile industry. Yet, and despite the technological
advances, millions of car accidents still occur every year, with thousands of
casualties in total, as reported by the World Health Organization [1]. Among
the most common causes of car accidents is unsurprisingly due to the driver’s
behavior. Driving under stress or fatigue, caused either by the workload of
driving itself or by external factors that affect a driver’s life, is a
determining factor in these incidents. The affected drivers are more prone to
risky behavior, such as getting easily distracted or expressing road rage [2,
3], rendering them a danger both to themselves and the nearby drivers. Even
when it may not lead to accidents, stress or fatigue could still pose an
impact on drivers’ mental health. Research has linked driving stress to risk
of depression, and decline of satisfaction [4]. Fatigued drivers also report
degraded sleep quality and general well-being [5].

Stress can be described as a psycho-physiological reaction to a variety of
factors that attempt to interfere and deregulate human experience in everyday
life. This reaction is intrinsically connected to the activity of the
autonomous nervous system, which can be tracked by modern physiological
sensors. Stress has been connected to sweat gland activity [6] that is
measured by electrodermal activity (EDA) sensors. Heart activity, breathing
rate, and blood pressure have also been found to correlate with stress events
[7]. Given the wide availability of sensors to monitor these physiological
signals, many signal processing systems have been proposed [8, 9] for stress
detection, with the overarching goal of providing just-in-time intervention.
One of the major challenges in this process is to determine the appropriate
moments for the system to trigger just-in-time intervention by effectively
detecting a prominent change in the driver’s stress or arousal level.
Detecting dynamic changes in stress while driving is an under-explored topic
in the literature.

Fig. 1: The proposed framework for multimodal change point detection in the
driving setting. Physiological signals are downsampled and timestamps of
change points are provided by GGS. HR stands for Heart Rate, BR for Breathing
Rate, and GT for the Ground Truth.

In this study, we develop a signal processing framework to explicitly detect
such change points in the driver’s physiology. Specifically, we apply a
multimodal segmentation algorithm to identify change points in measured
physiological time series and establish a correspondence to change points in
stress-related metrics. We show that pattern changes in physiological signals
could be indicative of stress-level changes in drivers and could thus suggest
plausible timestamps for stress monitoring, fatigue identification, and just-
in-time intervention. We test these premises on three widely used driving
behavior analysis benchmarks and perform an extensive ablation study to
investigate the robustness of the proposed method.

##  2 Biosensing of Driving

The automobile industry as well as researchers have developed diverse
protocols for sensing and detecting human states while driving. In most cases,
experimental data are collected by recruiting subjects to drive, either in the
real-world through some pre-defined routes [10], or through a driving
simulator [11], where the driving settings can be configured. In most cases,
both vehicle status and the driver’s physiological signals are of interest.
Vehicle usage data such as the utilization of pedals and the steering wheel,
or meta-parameters like the speed and acceleration of the vehicle can be
informative about the state and behavior of the driver [12]. For the task of
arousal estimation, physiological measures are still the most prominent
features and multiple signals are typically recorded simultaneously, as single
measures could include severe inter-subject variability [13].

Some of the most widely used sensor measures include electrocardiogram (ECG),
electromyogram (EMG), blood pressure, respiration, electrodermal activity
(EDA) and skin temperature. Heart rate (HR) and heart rate variability (HRV),
derived from the ECG data, are commonly used and have been shown to increase
and decrease, respectively, during stress events [14, 15]. HRV can be
extracted in either time or frequency domain using various linear or non-
linear methods [16]. Respiration is another indicator for unsafe driving
states, such as stress or fatigue [17]. Notably, changes in breathing rate
have been correlated to stress episodes [18]. EMG and temperature measures are
more prone to noise and artifacts [19] hence their applicability has been
limited. On the other hand, EDA is considered one of the most representative
measures due to its connection to the autonomous nervous system activity that
causes physiological arousal [6, 20]. EDA captures the exosomatic conductance
changes that occur as a result of sweating. Due to the variety of factors that
influence the activity of sweat glands e.g., environment temperature,
different processing is usually applied. Two signal components that correspond
to low-frequency trends and high-frequency oscillations are usually extracted:
Skin Conductance Level (SCL) and Skin Conductance Response (SCR). In our
study, we follow the literature [21] in considering EDA as a reference
(“gold”) standard for the drivers’ arousal state.

Dataset | Physiological Signals | # of Drives | Route Length | Sample Rate | LP cutoff | GT cutoff
---|---|---|---|---|---|---
DriveDB | ECG, Br. Rate, EDA | 24 |  $>$ 30 km | 15.5 Hz | 0.05 Hz | 0.01 Hz
HCI Lab Driving | ECG, HR, Skin Temperature, EDA | 10 | About 24 km | 1024 Hz | 0.05 Hz | 0.01 Hz
AffectiveROAD | Br. Rate, Movement, HR, Skin Temperature, EDA | 14 | About 31 km | 1-4 Hz | 0.05 Hz | 0.01 Hz
Table 1: Metadata, sampling rates, and downsampling parameters for the
datasets used in this study. LP stands for low-pass and GT for ground truth.
HR is heart rate, ECG is electrocardiogram, and EDA is electrodermal activity.

##  3 Change Points In Driving States

In this section, we introduce our framework to detect change points from
physiological signals recorded during driving. Our pipeline starts with
removing high-frequency noises from the physiological time series using low-
pass filters. Downsampling is then applied to the filtered time-series data to
ensure a common sampling rate for post-analysis. In the final step, we utilize
the Gaussian segmentation (GGS) algorithm to partition the time-series data
and apply time-series clustering on the derived segments.

###  3.1 Data Preprocessing

For our study, we restrict the scope to HR and respiration measurements, since
those are easily available, or can be extracted, in all the aforementioned
datasets and have been shown before to be effective indicators of human stress
[14, 18]. In cases where a dataset does not include respiration measurements,
we proceed to extract such information through the ECG-derived Respiration
algorithm, as proposed in [22]. From the respiration time-series we then
extract the breathing rate using [23]. The physiological measures are
originally sampled in various sampling rates, hence we perform downsampling.
In specific, we first apply a low-pass, 3rd order Butterworth filter to reduce
the high-frequency components and then decimate the filtered signals to a
common rate of 0.5 Hz. This way we focus on prominent changes in the signals,
while removing possible artifacts. In Table 1 we summarize the sampling
parameters used in each of the datasets.

As for the ground truth, we experimented with three different approaches to
account for the variability in the provided data. Electrodermal activity (EDA)
has been used as a gold standard [21] for affective state estimation, hence we
use it as a ground truth measure in all cases. We also evaluate our method on
subjective stress ratings whenever available. To enhance the integrity of our
work, we additionally consider a reference standard by averaging the time-
series of EDA and subjective ratings whenever possible. This strategy has been
followed in the literature [21] as a means to ground subjective stress
ratings. All ground truth measures are further filtered so as to eliminate any
rapid oscillations (Table 1).

###  3.2 Greedy Gaussian Segmentation

To segment the time-series data we used the greedy Gaussian segmentation (GGS)
algorithm, proposed by Hallac et al. [24]. GGS progressively segments a data
stream into parts, whose data points can be described as independent samples
of a Gaussian distribution. Given a set of breakpoints
$\mathbf{B}=(b_{1},b_{2},...,b_{K})$, the algorithm considers the distribution
(mean and co-variance) of the signal changes only at these breakpoints. More
specifically, given 2 break points $b_{i}$ and $b_{i+1}$ in $\mathbf{B}$, the
GGS estimate the empirical co-variance $S_{i}$ for this segment using the
following:

$\displaystyle
S^{(i)}=\frac{1}{b_{i}-b_{i-1}}\sum_{t=b_{i-1}}^{b_{i}-1}(x_{t}-\mu^{(i)})(x_{t}-\mu^{(i)})^{T}$
(1)

where $x_{t}$ is the data sample at $t^{th}$ time point, and $\mu^{(i)}$ and
$\Sigma^{(i)}$ are the mean and covariance of the segment, respectively. Using
the co-variance computed from the above equation, the GGS tended to estimate
$\mathbf{B}$ that maximizes the likelihood shown below:

$\displaystyle-\frac{1}{2}$
$\displaystyle\sum_{i=1}^{K+1}\Big{[}(b_{i}-b_{i-1})\log\det\Big{(}S^{(i)}+\frac{\lambda}{b_{i}-b_{i-1}}\mathbf{I}\Big{)}$
(2)
$\displaystyle-\lambda\textbf{Tr}\Big{(}S^{(i)}+\frac{\lambda}{b_{i}-b_{i-1}}\mathbf{I}\Big{)}^{-1}\Big{]}$

Here $\lambda$ is a regularization term that sets the importance of the
covariance. The problem of searching and deciding over multiple breakpoints is
solved with dynamic programming. We intentionally selected GGS as the
segmentation algorithm because it can effectively work in multi-modal
scenarios by considering multivariate distributions as shown in [25]. Our aim
is to detect such breakpoints in the physiological signals and assess their
robustness in estimating respective change points of the driver’s stress
levels.

###  3.3 Time-Series Clustering

Fig. 2: Example plot of a clustered EDA ground truth signal from HCI Lab
Driving Dataset. The breakpoints denoted by dashed, vertical lines are
proposed by the GGS algorithm on the respective heart and breathing rate
signals. The covering metric for this case is 0.88.

The primary goal of this study is to detect the most prominent points of
change in terms of stress levels but not identify any kind of variation in the
data. To quantify this notion, we applied temporal clustering to the ground
truth time-series, based on the breakpoints proposed by the GGS algorithm.
After running GGS, we regarded each segment as an independent time-series
sample and performed simple time-series k-means to cluster the segments into
$k$ sets. Subsequently, we use the clustering results to discard all
breakpoints lying between segments of the same cluster. A visualization of the
resulting representation for a sample EDA signal can be seen in Figure 2.

##  4 Experimental Setup

###  4.1 Dataset

Data sources for this kind of study are limited, both because of challenges in
the data collection process and privacy issues regarding the collected
measures. To the best of our knowledge, there is still no publicly available
dataset that contains multiple physiological signals, alongside data with
respect to driving (e.g., speed, pedal utilization), and also fine-grained
stress annotations. For our study, we used three public datasets that aligned
most with our goals.

DriveDB dataset [26], available through PhysioNet [27], is collected from 24
drives with a minimum of 50-min duration over a 32.2 km driving session. The
dataset provides detailed physiological measurements of ECG, EMG, EDA, and
breathing rate from the driver. Each driver follows a set of pre-selected
routes including both highway and city driving conditions. The objective of
the study is to detect stress levels from physiological responses during
driving, however the stress ratings are not released within the dataset. The
raw ECG data has a sampling rate of 496 Hz, and the HR, EDA and respiration
have a sampling rate of 15.5 Hz.

HCI Lab Driving database [10] was created to assess driver workload in a real-
world driving setting. The data are collected from 10 participants, 7 male,
and 3 female. Each participant drove a selected route covering around 23.6 km
with instructions given by the researcher. Each driving route has five
different road types: highway, freeway, tunnel, 30 km/h zone, and 50 km/h
zone. The corpus contains GPS, physiological data, and acceleration data under
real-world driving setups. The recorded physiological data contains
measurements of ECG, heart rate, skin temperature, and EDA. In particular,
heart rate, body temperature, and EDA data were sampled at 128Hz, while the
ECG data was collected at 1024 Hz. Here, stress annotations are also provided
through a video rating session, performed by the subject after the driving
session and range between 0 (no workload) to 128 (maximum workload), recorded
at 29 FPS on average. All data streams were extrapolated to 1024 Hz.

AffectiveROAD dataset [28] is a multimodal driving database of physiological
data and ambient environment data collected from 10 participants (five male
and five female). The final set contains 14 drives, and each drive followed a
set of pre-selected routes with a total length of about 31 km containing both
city conditions and highway conditions. The physiological signals were
collected using two Empatica E4 wristband sensors and a chest-wrap wearable
sensor called Zephyr Bioharness 3. These sensors offer non-obtrusive measures
of EDA, skin temperature, HR, triaxial acceleration, and breathing rate at
sampling rates of 1-4 Hz.

Dataset | EDA Ground Truth | SR Ground Truth | EDA$+$SR Ground Truth
---|---|---|---
HR | BR | Multimodal | HR | BR | Multimodal | HR | BR | Multimodal
DriveDB |  0.80$\pm$0.10 |  0.78$\pm$0.09 |  0.76$\pm$0.12 | – | – | – | – | – | –
HCI Lab Driving |  0.82$\pm$0.09 | – | – |  0.78$\pm$0.09 | – | – |  0.75$\pm$0.14 | – | –
AffectiveROAD |  0.67$\pm$0.10 |  0.70$\pm$0.09 |  0.42$\pm$0.16 |  0.81$\pm$0.15 |  0.82$\pm$0.10 |  0.61$\pm$0.21 |  0.94$\pm$0.04 |  0.91$\pm$0.06 |  0.89$\pm$0.05
Table 2: Average covering metric [29] results for each dataset and signal
(heart rate – HR, breathing rate – BR, HR+BR – Multimodal), tested on
Electrodermal Activity (EDA), Stress Rating (SR), and fused (SR + EDA) ground
truths. $\pm$ denotes 1 standard deviation.

###  4.2 Evaluation Protocol

There are multiple metrics to evaluate a point detection problem. In our study
that focuses on time-series clustering, we use the covering metric, as
described in [29]. Let $S=\\{b_{1},\dots,b_{n}\\}$ denote the set of locations
of $n$ breakpoints provided in a ground truth time-series of length T. $S$
implies a partition $G$ of the time-series into segments, where $A_{j}$ is the
segment from $b_{j-1}$ to $b_{j}-1$ for $j=2,\dots,n$. Similarly, the GGS
algorithm proposes a set of breakpoints $S^{\prime}$ to partition the ground
truth. The covering metric utilizes the Jaccard index
$J\left(A,A^{\prime}\right)$ between a ground truth and a proposed set, $A$,
$A^{\prime}$. Then, the covering metric of a partition $G$ by a partition
$G^{\prime}$ is defined as the normalized sum of the similarity scores
$J(A,A^{\prime})$ over all ground-truth segments, where for each segment we
consider the set that provides the maximum index out of all intervals that can
be derived from the proposed breakpoints:

$Cover\left(G,G^{\prime}\right)=\frac{1}{T}\sum_{A\in G}|A|\max_{A^{\prime}\in
G^{\prime}}J\left(A,A^{\prime}\right)$ (3)

As a simple baseline for our experiments, we consider the covering metric of
the entire time-series being the only available proposal.

###  4.3 Change Point Detection

As described in section 3, we first apply a low-pass, 3rd-order Butterworth
filter to reduce the high-frequency components with a cutting frequency of
0.05Hz (Table 1). We then re-sample each time-series data to a common rate of
0.5Hz. Ground truth time-series are further downsampled at a cutting frequency
of 0.01Hz to preserve only prominent arousal changes. The GGS algorithm is
then used to extract a set of break-points with $\lambda=15$, whereas the
maximum number of breakpoints is determined as a function of the time-series
length, at a rate of 15 break-points per hour.

Fig. 3: Ablation study in which we mask random sub-sequences from the raw signals, up to 1% of continuous data points. For fair comparison, we solely include HR proposals and EDA ground truth. Dataset | $\lambda=1$ | $\lambda=5$ | $\lambda=15$
---|---|---|---
DriveDB |  0.80 $\pm$ 0.11 | 0.81 $\pm$ 0.09 |  0.80 $\pm$ 0.10
HCI Lab Driving |  0.76 $\pm$ 0.10 |  0.76 $\pm$ 0.10 | 0.82 $\pm$ 0.09
AffectiveROAD |  0.65 $\pm$ 0.10 |  0.66 $\pm$ 0.12 | 0.67 $\pm$ 0.10
Table 3: Ablation on the $\lambda$ parameter of the segmentation algorithm.

##  5 Experimental Results

The experimental results of monitoring the heart and breathing rate
measurements for the 3 available datasets are summarized in Table 2. We
observe that the covering metric is satisfactorily high in nearly all cases,
indicating that changes in physiological measures can indeed point to
prominent changes in the arousal state. Considering EDA as ground truth, we
report a covering metric above 70% for both DriveDB and HCI Lab Driving. In
the first case, heart rate shows slightly better performance, however if we
consider both HR and breathing rate, performance drops and variance increases.
This behavior is consistent in all experiments and indicates that the relative
contribution of each measure varies between subjects.

Regarding AffectiveROAD, the monitored signals do not align as well as in the
previous cases, limiting the obtained covering metric to 67% for the heart
rate and 70% for the breathing rate. In contrast, when considering the
explicit stress annotations as ground truth, both signals are robust in
locating state changes, at a comparable accuracy. In the third version of our
experiments, where we considered a fused gold standard of reported ratings and
EDA, AffectiveROAD gets a substantial boost of 27% percentage points, compared
to EDA-only performance and 13% compared to the rating-only performance,
indicating that fusion of that kind efficiently grounds arousal estimations in
AffeciveROAD. We underscore that the performance of the multimodal model
doubles its score compared to those from the EDA-only ones.

Our base experiment includes a $\lambda$ parameter of 15 for the segmentation.
Ablative experiments, shown in Table 3, show that higher values of this
parameter generally provide better outcomes. As presented in equation 2, lower
values of $\lambda$ would reduce regularization and thus result in a larger
number of proposed breakpoints that are sensitive to noisy elements of the
signals. By using higher values of $\lambda$ we focus instead on more
prominent changes that are typically shared between the monitored signal and
the ground truth.

###  5.1 Missing Data Setting

We further investigate the robustness of our algorithm by simulating missing
data points in the monitored signals. Missing data occurs frequently in
physiological data collection [30], especially in dynamic settings like
driving, where the sensors might disconnect or produce motion artifacts. Our
simulation includes masking the raw monitored signals with random patches of
at most 1% the length of the respective signal. The missing points are then
imputed with the last available signal value. We present our results in Figure
3 for all 3 datasets. We observe that both the algorithm performance and its
variance remain stable even when 50% of the data are missing. Performance
gradually drops under 40% when all data are masked, giving us a rough baseline
for all derived scores. It should be noted, though, that robustness in certain
cases results from pre-configured upsampling of the raw signals within the
dataset.

###  5.2 Number of Clusters

Fig. 4: Ablation of varying the number of ground truth clusters. EDA is
considered the ground truth and HR is the monitored signal.

We additionally experimented with the number of assigned clusters in the
ground truth signal. This is a crucial parameter in determining which change
points will be selected as prominent. While our initial configuration
accounted for the distribution of low – medium – high arousal, other
configurations are possible, hence we list their performance in Figure 4. As
expected, performance gradually drops as the number of clusters increases, but
remains satisfactorily high, above 70% in most cases, even in the scenario of
5 arousal levels.

##  6 Conclusion

In this paper we examined to what extent multimodal physiological signals like
HR and breathing rate could be indicative of prominent changes in
physiological arousal during driving. To that end, we developed a framework to
filter the signals, identify trends of interest and determine breakpoints
using time-series segmentation. The proposed algorithm was applied against
both reference EDA and self-reported stress ratings, showing that breakpoint
proposals from the monitored signals highly align with points of arousal
changes. We hope these results support further research into robust
localization of unsafe driving states and enhance just-in-time intervention
systems. Future work will address current limitations including the number of
false-positive proposals and the efficacy of multimodal monitoring.

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