A New Era in Stress Monitoring: A Review of Embedded Devices and Tools for Detecting Stress in the Workplace

Abstract

Detection of stress and the development of innovative platforms for stress monitoring have attracted significant attention in recent years due to the growing awareness of the harmful effects of stress on mental and physical health. Stress is a widespread issue affecting individuals and often goes unnoticed as a health concern. It can lead to various negative physiological conditions, including anxiety, depression, cardiovascular diseases and cognitive impairments. The aim of this paper is to provide an overview of studies focusing on embedded devices for non-invasive stress detection, primarily in the form of a modified computer mouse or keyboard. This study not only fills a critical gap in the literature but also provides valuable insights into the design and implementation of hardware-based stress-detection methods. By focusing on embedded devices, specifically computer peripherals, this research highlights the potential for integrating stress monitoring into everyday workplace tools, thereby offering practical solutions for improving occupational health and well-being.

Full text

Citation: Kafková, J.; Kuchár, P.;
Pirník, R.; Skuba, M.; Tichý, T.; Brož, J.
A New Era in Stress Monitoring: A
Review of Embedded Devices and
Tools for Detecting Stress in the
Workplace. Electronics 2024,13, 3899.
https://doi.org/10.3390/
electronics13193899
Academic Editor: Andrea Bonci
Received: 29 July 2024
Revised: 19 September 2024
Accepted: 19 September 2024
Published: 2 October 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
electronics
Review
A New Era in Stress Monitoring: A Review of Embedded
Devices and Tools for Detecting Stress in the Workplace
Júlia Kafková 1,* , Pavol Kuchár 1, Rastislav Pirník 1, Michal Skuba 1, Tomáš Tichý 2and Jiˇrí Brož 2
1Faculty of Electrical Engineering and Information Technology, University of Zilina,
010 26 Zilina, Slovakia
2Faculty of Transportation Sciences, Czech Technical University in Prague, 110 00 Prague, Czech Republic;
tomas.tichy@cvut.cz (T.T.); jiri.broz@cvut.cz (J.B.)
*Correspondence: julia.kafkova@feit.uniza.sk
Abstract: Detection of stress and the development of innovative platforms for stress monitoring
have attracted significant attention in recent years due to the growing awareness of the harmful
effects of stress on mental and physical health. Stress is a widespread issue affecting individuals and
often goes unnoticed as a health concern. It can lead to various negative physiological conditions,
including anxiety, depression, cardiovascular diseases and cognitive impairments. The aim of this
paper is to provide an overview of studies focusing on embedded devices for non-invasive stress
detection, primarily in the form of a modified computer mouse or keyboard. This study not only
fills a critical gap in the literature but also provides valuable insights into the design and implemen-
tation of hardware-based stress-detection methods. By focusing on embedded devices, specifically
computer peripherals, this research highlights the potential for integrating stress monitoring into
everyday workplace tools, thereby offering practical solutions for improving occupational health
and well-being.
Keywords: computer peripherals; resource-limited embedded systems; machine learning; artificial
intelligence
1. Introduction
Stress detection has gained significant attention recently due to the growing recogni-
tion of its adverse effects on mental and physical health [
1
,
2
]. Stress affects everyone and is
often a subtle health issue that can lead to severe conditions like depression, cardiovascular
disease, structural changes and cognitive impairment [
3
]. Early and continuous monitoring
of stress is essential for effective management and maintaining inner peace.
Historically, stress detection relied on subjective questionnaires, which can be unre-
liable [
4
]. Additionally, physiological markers such as heart rate variability and cortisol
levels have been used [
5
], but these methods often lack real-time data and accuracy. In-
novative stress detection utilizes recent advancements in technology, such as wearables,
biosensors and machine learning, offering new opportunities for monitoring stress [
6
–
8
].
These devices can track a wide range of physiological and behavioral indicators, providing
a more comprehensive view of an individual’s stress levels.
The focus of this paper is on stress detection in the workplace, which is critical for
several reasons. Chronic stress can lead to serious physical and mental health issues,
including burnout and anxiety [
9
]. Early identification of stress enables employers to
implement proactive measures to support their employees’ well-being and prevent long-
term adverse effects. Elevated or prolonged stress can affect cognitive function, concen-
tration and decision-making skills. By effectively monitoring and managing workplace
stress, employers can enhance employee performance and productivity. Employees ex-
periencing lower stress levels are generally more focused, creative and efficient in their
Electronics 2024,13, 3899. https://doi.org/10.3390/electronics13193899 https://www.mdpi.com/journal/electronics

Electronics 2024,13, 3899 2 of 33
roles. Addressing stress in the work environment is vital for maintaining a healthy and
high-performing workforce.
Stress detection and the development of new stress-monitoring platforms hold im-
mense potential for transforming stress management. By incorporating advanced sensor
technology and machine learning, these devices can provide a more precise and personal-
ized approach to managing stress, ultimately aiding individuals in leading healthier and
more fulfilling lives. However, achieving this potential will require collaboration among
psychologists, physicians and engineers.
The workplace is a major source of stress due to the numerous demands and pressures
faced by employees. Stress and health risks in the workplace can be categorized into two
main areas: those related to the nature of the work itself and those linked to the social and
organizational environment. Internal work-related factors include long working hours,
excessive workloads, tight deadlines, challenging or complex tasks, insufficient breaks,
lack of variety and poor physical working conditions (e.g., cramped spaces, uncomfortable
temperatures and inadequate lighting) [10].
Ambiguous work tasks or conflicting responsibilities frequently contribute to stress,
as does the challenge of supervising others. Career-advancement opportunities can act as a
valuable buffer against stress, while issues such as lack of promotions, insufficient training
and job instability tend to exacerbate stress levels. Furthermore, interpersonal relationships
at work and the prevailing organizational culture significantly influence whether stress is
heightened or mitigated.
Managers who are critical, demanding, or unsupportive can increase stress levels,
whereas positive social dynamics and effective teamwork help reduce stress. Cultures
that promote unpaid overtime tend to elevate stress, while those that encourage employee
involvement in decision-making, maintain transparency in organizational matters and
provide adequate equipment and recreational facilities help alleviate stress. Additionally,
organizational changes, particularly those implemented without sufficient consultation,
are significant stressors. Examples include mergers, relocations, restructuring, downsizing,
adoption of individual contracts and layoffs [11].
Evaluating stress in the workplace is crucial across a variety of fields, including health-
care, aviation, finance, information technology, industry and transport [
12
–
14
]. Stress-
detection technologies offer significant benefits by improving operational efficiency, safety
and employee well-being. In healthcare, these tools help manage high-pressure situations
and enhance patient care [
15
]. In aviation, they aid in maintaining flight safety by identify-
ing stress early among pilots and air traffic controllers [
16
,
17
]. The finance sector benefits
from stress monitoring by stabilizing performance and preventing costly errors. In informa-
tion technology, these technologies support effective problem-solving and reduce system
failures. In industry, stress detection improves productivity and safety in manufacturing,
construction and logistics [
18
]. Lastly, in the transport sector, it ensures efficiency and
reliability by monitoring stress levels among drivers and other personnel [
19
]. Overall,
stress-detection technologies foster healthier work environments and enhance performance
across these diverse domains.
The aim of this study is to analyze the current state of innovation in embedded
stress-monitoring methods, identify key trends and evaluate their potential impact on
occupational health. By examining data from 2014 to 2024, this research highlights the
changing focus in stress-detection research and development, offering insights into emerg-
ing priorities and themes in the field.
The major contributions of this study are stated as follows:
•
We present a review that contributes to this dynamic and growing field by providing
a comprehensive synthesis, critically analyzing the state of the art and aiming to
identify trends, challenges and emerging research areas in the use of PC peripherals
for stress detection.
•
We thoroughly examine the advantages of using PC peripherals for stress detection,
their contributions and their limitations in stress-monitoring systems.

Electronics 2024,13, 3899 3 of 33
•
With this contribution, we aim to guide future research and developments in the use
of PC mice and keyboards for stress detection.
The structure of the rest of this paper is as follows: Section 2presents a brief back-
ground about stress itself. Sections 3and 4provide explanations regarding the available
biosignals for stress detection and the mechanisms behind stress. Sections 5and 5.2
present a comprehensive comparison of the latest relevant studies. Section 6engages in
a detailed discussion about the findings. Section 7concludes the research and suggests
future directions.
Paper Selection Analysis
In order to systematically identify relevant published papers in this domain, literature
research was performed from 1992 up to and including 2024. To acquire as many papers
as possible, Web of Science, Scopus and Google Scholar were searched. The following
keywords were chosen: computer mouse, sensors, stress detection, deep learning. Existing
patents were not included. This review covers the field of biomedical engineering, artificial
intelligence and sensors. A total of 73 papers were analyzed in this review. A graph
showing the number of analyzed articles per year is shown in Figure 1.
Figure 1. Graph showing the number of analyzed articles published over time.
As we can see, interest in this area has steadily increased each year since 1990. This
review provides essential information on similar research in stress detection using embed-
ded devices, computer mice and keyboards, alongside artificial intelligence techniques,
highlighting recent advancements, methodologies and the effectiveness of these approaches
in identifying and mitigating stress in real-time.
2. Physiological Stress
From a physiological perspective, stress is defined as a state of threatened homeostasis
resulting from the action of external or internal adverse forces, known as stressors [
20
]. If
stress mechanisms are activated unnecessarily and for prolonged periods, health risks can
arise [
21
]. The action of stressors disrupts balance, swiftly mobilizing a range of physio-
logical and behavioral responses as an adaptive reaction to stress. Attention heightens,
and brain functions concentrate on, the perceived threat. These responses to stressors are
typically transient and aim to maximize an individual’s chances of survival, including [
20
]:
• An acceleration of cardiac output and an increase in blood pressure;
• Acceleration of breathing;
• Acceleration of catabolism;

Electronics 2024,13, 3899 4 of 33
•
Redirection of blood flow, with a temporary increase in perfusion to endangered areas
and the excited brain, heart and muscles.
An adaptive stress response can become maladaptive under chronic stimulation,
leading to potentially harmful consequences. Neurochemical and physiological research
has clarified how stress is regulated by two neuroendocrine systems [
22
]: the hypothalamic–
pituitary–adrenal (HPA) axis and the sympathetic-adrenomedullary (SAM) system of the
autonomic nervous system.
The HPA axis, shown in Figure 2, plays a crucial role in the organism’s adaptation
to stressful situations. Research has demonstrated a link between disorders induced by
stressful stimuli (especially long-term) and depression, often due to HPA axis dysfunction.
The HPA axis is vital for maintaining body homeostasis and managing the body’s response
to stress.
Figure 2. HPA axis.
Stress triggers the release of corticotropin-releasing hormone (CRH) from the hypotha-
lamus. This signal is then sent to the anterior lobe of the pituitary gland, prompting the
secretion of adrenocorticotropic hormone (ACTH). ACTH subsequently stimulates the
adrenal cortex to release cortisol into the bloodstream. Elevated cortisol levels inhibit the
secretion of CRH and ACTH through a negative feedback loop.
The importance of the HPA axis primarily lies in the action of cortisol. Cortisol
is released during stressful situations as a defense mechanism, reducing inflammatory
responses, stimulating gluconeogenesis and protecting the body against excessive immune
reactions. The HPA axis is also activated in non-stressful situations, such as regulating
circadian rhythms, with the highest cortisol levels observed in humans in the morning [
23
].
When a stressor is perceived, the brain processes this information and initiates the re-
lease of key hormones. Glucocorticoids are released via the HPA axis, while catecholamines,
including adrenaline and noradrenaline, are released through the SAM axis. These hor-
mones work together to elevate blood glucose levels by stimulating the liver to release
glucose, which supports the “fight or flight” response. This response also involves in-
creased cardiovascular output and the redirection of blood from the skin and gut to the
skeletal muscles. Concurrently, the brain activates the ANS, triggering a rapid release of
catecholamines, which enhances cardiac output and blood pressure, and further mobilizes
glucose. At the same time, the HPA axis releases adrenal glucocorticoids—cortisol in

Electronics 2024,13, 3899 5 of 33
humans and fish, and corticosterone in rodents. Elevated glucocorticoid levels improve
the organism’s ability to resist and adapt to stress, although the exact mechanisms of these
effects are not yet fully understood. Glucocorticoids cooperate with adrenaline to increase
blood glucose, ensuring the energy needed to effectively manage the stress response. The
brain’s central awareness and response to stress, anxiety and fear depend on extensive
neural circuits, including the amygdala, thalamus, hypothalamus, neocortex, limbic cortex
and brainstem nuclei like the locus coeruleus [22].
Stress can have a devastating impact on both physical and emotional health. Numerous
studies indicate that work stress is the primary source of stress for adults and has been
steadily increasing over the past few decades. Excessive or chronic exposure to stressors
can disrupt various fundamental physiological functions, including growth, metabolism,
immune competence, reproduction, behavior and personality development [
20
]. It is linked
to higher rates of heart attacks, addiction, hypertension, depression, obesity, anxiety and
other disorders. Stress is a highly individual phenomenon, varying based on each person’s
vulnerability and resilience [22].
The impact of stress extends beyond the work environment and plays a crucial role
in various mental disorders, including phobias, depression and bipolar disorder. Stress
and anxiety can exacerbate schizophrenia, making it more challenging for individuals with
this condition to manage daily life. Additionally, stress-inducing lifestyle changes can
significantly burden mental health [22].
Stress is a non-specific reaction of the organism to any demand. It is important to
recognize that an individual’s ability to respond to stressors is influenced by a combination
of developmental, genetic and environmental factors. These factors affect the effectiveness
of adaptive responses and can partially predict susceptibility to chronic stress [20].
3. Generation of Biosignals
Physiological structures that indicate stress play a role in generating biosignals in
humans [
24
]. To maximize the effectiveness of new platforms, it is essential to investigate
the generation of biosignals [25], which serves as the primary focus of the section.
The human cell membrane is a thin, semi-permeable layer that envelops the cells of
the human body. It is primarily composed of a phospholipid bilayer, within which various
proteins are embedded. This dynamic mosaic structure is held together by hydrophobic
interactions between phospholipid molecules. Due to the hydrophilic and hydrophobic
orientation of these molecules, non-polar substances can enter the cell, while ions and polar
molecules (e.g., water) cannot pass through the membrane unaided. The main function of
the cell membrane is to regulate the exchange of chemical substances. Phospholipids allow
access to non-polar hydrophobic molecules (e.g., hydrocarbons). However, the transfer of
ions and polar molecules requires the assistance of integral proteins, such as ion channels
and pumps or specific transport carriers. These proteins, known as ion channels, facilitate
the transfer of certain polar molecules or ions. These channels enable the diffusion of
ions from areas of higher concentration to lower concentration, driven by a specific ion
concentration gradient, making this process passive in nature.
Conversely, another group of proteins, known as ion pumps, use energy to transport
ions across a membrane against a potential and/or concentration gradient. The activity
of these pumps and channels maintains the differences in ion concentrations between the
intracellular and extracellular environments, thereby determining the cell’s electrochemical
properties. Consequently, a potential difference exists across the cell membrane in its
resting state, unless disturbed. This phenomenon is the primary factor behind the creation
of biosignals [25].
Bioelectric potentials (biopotentials) arise from the electrochemical activity of exci-
tatory cells in nerve, muscle, or glandular tissues [
26
]. External or internal stimulation
can cause excitatory cells, such as neurons, to alter their resting potential. This results
in a sudden change in the permeability of ions like K
+
and Na
+
. The shift in membrane
permeability from a resting state to an excited state and back again generates an electrical

Electronics 2024,13, 3899 6 of 33
phenomenon known as an action potential. The cell acts as an electrical source, generating
a current that propagates throughout the human body (acting as a conductor).
An action potential occurs when Na
+
ions suddenly pass through Na channels due
to a sufficient stimulus that overcomes the threshold potential. The influx of Na
+
ions
reduces the polarized resting potential to a range of +30 to +40 mV, a phenomenon known
as depolarization. At this point, potassium channels open, and the cell begins to repolarize
towards its equilibrium potential due to the efflux of positive K
+
ions. Because Kchannels
remain open for a relatively long period, there can sometimes be an increase in the polarized
resting potential, known as hyperpolarization. A typical nerve action potential, illustrating
the different phases, is shown in Figure 3.
Figure 3. Schematic action potential.
The cell’s ability to respond to a new stimulus and generate another action potential
immediately after one is limited by a specific time interval known as the refractory period.
This period is divided into two phases. The absolute refractory period occurs during the
initial phase of the action potential, when it is entirely impossible to initiate another action
potential, regardless of the stimulus intensity. Following this is the relative refractory
period, during which another action potential can be triggered, but only by a stimulus that
exceeds the threshold intensity.
An initial depolarization in one area of a neuron’s membrane can trigger depolarization
in an adjacent membrane area, provided the initial depolarization serves as an adequate
stimulus. This process causes depolarization to propagate along the entire length of the
cell membrane in a wave-like manner. Subsequently, depolarization that begins at the
axon hillock travels along the axon to its terminus, where it transmits the action potential
through a synaptic connection to a neighboring neuron or effector cell.
4. Biosignals for Stress Detection
Stress initiates a series of physiological reactions that can be monitored through various
biosignals (Figure 4). Each signal captures different aspects of the body’s autonomic nervous
system, which controls stress responses. These signals, originating from physiological
processes, offer real-time insights into how the body reacts to stress.
For stress detection, Sharma et al. recommend obtaining the following physiological
signals [27]:
• Electroencephalogram (EEG);
• Electromyogram (EMG);
• Blood Volume Pulse (BVP);
• Heart Rate Variability (HRV);
• Galvanic Skin Response (GSR).

Electronics 2024,13, 3899 7 of 33
Figure 4. Common physical and physiological indicators for stress detection.
Other indicators of stress include various physiological signals and parameters, such
as [25,28]:
•
Electrocardiogram (EKG)—This records the electrical activity of the heart and iscrucial
for monitoring and diagnosing heart diseases [
29
,
30
]. Since the body acts as a volume
conductor, the ECG can be captured through electrodes placed on the body surface.
The ECG signal typically includes a P wave, QRS complex, T wave, as well as PR and
ST segments [
31
]. These waves result from the propagation of the summation vector
of action potentials within the heart structures relative to the electrodes.
•
Recording of the electrical activity of the eyes—Eye monitoring is used to track eye
movements and gaze patterns, typically employing cameras or electrodes for elec-
trooculogram (EOG) measurement [
32
]. An electrooculogram measures the potential
generated by eye movements. The human eye functions as a dipole, with the cornea at
a positive potential relative to the retina, creating a potential difference between them.
This corneal-retinal potential, ranging from 0.4 to 1.0 mV, varies with eye movement.
Electrodes placed near the eyes record these potentials: the electrode closer to the
cornea will detect a positive potential, while the one nearer to the retina will detect a
negative potential. The potential difference between these electrodes, reflecting eye
movement, is known as the EOG.
•
Photoplethysmography (PPG)—This optical measurement technique detects changes
in blood volume within the microvascular tissue bed. It has extensive clinical appli-
cations and is utilized in various commercially available medical devices, including
pulse oximeters, vascular diagnostic tools and digital blood pressure monitors [33].
•
Determination of blood oxygenation level—Non-invasive monitoring of blood oxygen
saturation.
• EDA (Electrodermal Activity)—Monitoring of skin conductivity [34,35].
•
Measuring body temperature—Elevated body temperature, as measured by an armpit
thermometer, can be associated with psychological stress [36].
5. Comprehensive Overview of Related Studies and Their Key Contributions
This section provides an in-depth review of key studies related to the detection of stress
using physiological signals (Section 5.1) and neural network-based techniques (
Section 5.2
).
By analyzing the methodologies, hardware platforms and algorithms presented in the
literature, we aim to highlight the significant advancements and contributions made by
researchers in the field. The section also offers a comparative analysis of various ap-
proaches, focusing on their performance, accuracy and applicability in real-world scenar-
ios, providing a comprehensive understanding of current trends and challenges in stress
detection research.

Electronics 2024,13, 3899 8 of 33
5.1. Hardware Platforms for Stress Detection
Stress is a major health concern affecting both individuals and workplaces [
37
]. While
traditional methods like questionnaires offer insights, they rely on personal reports, which
can be unreliable. To address this, researchers have turned to physiological measurements
using wearable sensors.
Wearable devices, such as smartwatches, can continuously track physical signs of
stress [
38
,
39
]. This technology offers a more objective way to understand stress levels and
can help people detect their stress effectively. In high-pressure jobs, these devices can
even be crucial for maintaining performance and well-being. Building on this approach,
researchers are exploring innovative integrations of stress-detection technology into every-
day objects, such as computer peripherals [
40
–
43
]. This section presents the evolution of
stress-detection technologies, highlighting the transition from traditional wearable sensors
to the incorporation of stress-detection features into common devices like computer mice
or keyboard.
An examination of various methods and devices used for stress detection reveals
their technological foundations, applications and potential for future development. By
understanding these tools, we can help address stress-management issues and improve
the quality of life and productivity for individuals across different fields. A significant
advancement in stress detection is the development of multi-sensor platforms, such as those
by Rescio et al., which combine wearable and ambient sensors for remote and unobtrusive
monitoring [
44
]. Mishra et al. further demonstrated the effectiveness of affordable heart
rate monitors with advanced data processing for practical stress assessment [
45
]. The
integration of stress-detection technology into everyday devices is exemplified by Valenti
et al.’s ring probe, which combines PPG and GSR sensors for comprehensive physiolog-
ical monitoring [
46
]. Lin et al. showcased the potential of embedding stress-detection
technology into PC mice, achieving high accuracy in stress monitoring during regular
use [
47
]. Androutsou et al. [
48
,
49
] enhanced this approach by incorporating a multi-sensor
system into a standard optical mouse for occupational stress detection, while Chigira et al.
introduced a mouse with PPG sensing surfaces for continuous data collection [
50
]. How-
ever, Freihaut et al. highlighted challenges in linking mouse movements to stress levels,
indicating that while the technology holds promise, further research is needed to validate
these connections [
51
]. Sun et al. [
52
] explored the use of common mouse operations to
measure stress levels by capturing muscle stiffness during arm and hand movements. Vea
et al. [
53
] focused on detecting emotional states like confusion and frustration in novice
programmers by analyzing keyboard and mouse interactions. Silva et al. [
54
] introduced
cost-effective methods to measure typing and mouse grip pressure using force-sensitive
resistors. Tran et al. [
37
] developed a wireless PPG mouse for real-time monitoring of
heart rate and variability during mouse use. Belk et al. [
55
,
56
] created “CogniMouse”,
a sensor-equipped mouse designed to assess stress levels in older adults at work, using
grip force, heart rate and other physiological indicators through a probabilistic classifica-
tion algorithm. Kaklauskas et al. [
57
] developed a biometric mouse system that assesses
emotional states and productivity through real-time physiological, psychological and be-
havioral data, offering personalized stress-management recommendations. Leone et al. [
58
]
proposed a framework for monitoring mental load in the workplace using wearable devices
to measure heart rate, skin conductance and eye movements. Rescio et al. [
59
] developed
an unsupervised learning framework for stress detection using wearable devices. Overall,
these innovations reflect a significant leap in integrating stress-detection capabilities into
everyday objects, offering new avenues for managing stress and improving well-being and
productivity. All of these mentioned studies are detailed in the following text.
Rescio et al. [
44
] developed and tested a multi-sensor platform comprising a wearable
system and an ambient sensor. The wearable system was designed to be minimally intrusive
and comfortable for the user, while providing real-time data access and high accuracy for
reliable stress detection. The platform incorporated an inexpensive and readily available
RGB camera to assess eye blinking. This camera has proven effective for stress detection

Electronics 2024,13, 3899 9 of 33
and can be easily used in the workplace. Additionally, non-wearable sensors were included
to measure specific parameters or characteristics for remote stress evaluation without
physical contact. These sensors can be categorized into physical and visual measurements.
The wearable system is designed to be minimally intrusive, focusing on detecting
heart rate and EDA. It consists of a shoulder strap equipped with an electronic device.
To reduce physical discomfort and enhance user experience, the authors selected opti-
mal body locations, such as the shoulder and earlobe, for obtaining physiological data.
They emphasized the strategic placement of sensors rather than the design of the straps,
significantly improving the system’s wearability. The platform functions as a support
system for optimal worker management and well-being. Its performance was evaluated
under controlled laboratory conditions to determine one or two levels of stress. Three deep
learning algorithms were tested, with a 1D-convolutional neural network achieving the
highest detection accuracy. Specifically, the accuracy values were approximately 96.88% for
one stress level and 95.88% for two stress levels.
In another study, Mishra et al. [
45
] explored whether accessible and affordable wear-
able sensors could be used for stress monitoring and if commonly available devices could
accurately detect stress. They demonstrated that a widely available heart rate-monitoring
device (the commercial Polar H7 chest sensor) could measure stress in both controlled and
free-living conditions.
The authors compared different data-processing methods and their impact on the
accuracy of stress detection using a commodity sensor. They noted that some typical
preprocessing steps used in previous studies were less effective for commodity devices.
They provided recommendations on data-processing procedures for stress detection that
are also applicable to custom-built sensors. The authors introduced a novel two-layer
method for stress detection that accounts for both previous and current stress levels. They
found that this approach significantly improves stress detection. In a laboratory study
using only a consumer heart rate monitor, they achieved an F1 score of 0.87 for identifying
stress periods. They also distinguished between three types of stress-inducing tasks with a
maximum F1 score of 0.82. Adding a GSR sensor to the heart rate monitor increased the F1
score from 0.87 to 0.94. When using only heart rate data and GSR, the F1 scores were 0.66
and 0.72, respectively. The authors recommend future research focus on identifying not
just “stressful” versus “non-stressful” periods, but also the specific type of stress a person
is experiencing to enable early adaptive interventions.
Valenti et al. [
46
] have developed and implemented a wearable device that simul-
taneously collects data from PPG and GSR sensors, both positioned on the finger. Their
prototype, designed as a ring probe, also measures SpO
2
levels. This helps in evaluat-
ing physiological stress and blood oxygen saturation. By placing the sensors in a single
location, the device’s overall footprint is minimized, enhancing user comfort during mea-
surements. The device allows for the assessment of cardiovascular status, the analysis of
sympathetic nervous system activity (via HRV and GSR) and the detection of low oxygen
levels, which could pose health risks. To validate the ring probe’s functionality, multi-
parametric data were collected from healthy subjects during periods of rest, stress tasks
and breath-holding exercises.
Testing with the new ring-shaped probe confirmed the device’s functionality and its
capability for multi-parametric data acquisition to assess various physiological responses.
Lower heart rate and GSR values during relaxation indicated parasympathetic dominance,
while increases after exercise showed sympathetic activation. Stress induced consistent
trends across all subjects, but breath-holding produced varied responses. SpO
2
monitoring
revealed protocol-induced changes in oxygen saturation with individual differences. Over-
all, the results highlighted the potential of this synchronous biosignal acquisition device,
which offers convenience, cost-effectiveness and high-quality signal collection.
The system’s versatility makes it suitable for home monitoring, fitness and clinical
applications, particularly for diagnosing cardiovascular disease. However, it has limitations,
including insufficient detection of motion artifacts and potential inaccuracies in peak

Electronics 2024,13, 3899 10 of 33
detection and filtering. Laboratory results demonstrate that the system can effectively
monitor physiological states, assess emotional fluctuations and measure oxygen saturation.
Lin et al. [
47
] developed a user-friendly module for stable and accurate pulse detection,
integrated into a PC mouse. Given that most people use a PC mouse daily, whether at work
or home, their design incorporates multi-channel sensors to address issues like palm drift
or misalignment. To evaluate the performance of the proposed mixed-signal algorithm,
experiments were conducted in four phases involving six movements: rest, slow movement
(both horizontal and vertical), fast movement (both horizontal and vertical) and a search
phase. The accuracy of pulse rate detection was assessed by comparing PPG signals from
the proposed device with reference ECG signals. During rest, the mixed signal achieved a
sensitivity of 98.50% and a false detection rate (FDR) of 0.15%, indicating high accuracy.
Even the least sensitive channel (channel 4) maintained a sensitivity of 93.05% and an FDR
of 1.77%. The weighted mean method outperformed the median method in terms of higher
sensitivity and lower FDR. Motion artifacts during slow and fast movements affected signal
quality, but the weighted average method mitigated these effects more effectively than
the median method. In the search phase, sensitivity decreased due to intermittent sensor
contact during mouse movements and clicks. However, the proposed method improved
signal clarity and reduced noise effects.
The study also addressed eye safety concerns when the palm was not in contact with
the mouse by implementing an LED power-saving mode to reduce modulation frequency.
Limitations include the need for continuous palm contact during measurement and the
necessity to test the device across different demographic groups and conditions.
The study demonstrates that the proposed multi-sensor module and weighted average
method effectively enhance the usability of detected PPG signals, thereby increasing the like-
lihood of successful signal detection from the palm. Due to its simplicity, user-friendliness,
and cost-effectiveness, this device could be a valuable tool for collecting physiological sig-
nals and aiding early disease detection, particularly in low- and middle-income countries,
thus addressing healthcare access barriers.
Androutsou et al. [
48
,
49
] introduced a user-friendly system for detecting occupational
stress in office employees. This system uses a multisensor setup embedded in a computer
mouse to analyze physiological signals and identify stress. To validate the system, the
researchers designed and implemented an experimental protocol that simulates the stressful
office work environment. According to the authors, no similar system combining a non-
invasive, user-friendly design with wireless data transmission has been documented in the
literature. They also highlight that validation procedures for comparable stress-detection
devices have not been adequately detailed. The experimental protocols aim to replicate
real workplace stressors.
The smart computer mouse developed by the researchers includes a PPG PulseSensor,
a Grove—GSR sensor, and a development board with a microcontroller and a Wi-Fi module.
All components are integrated into a commercially available wired optical mouse.
The system’s objective was to determine users’ stress levels by analyzing physiological
parameters derived from signal processing. The GSR module includes a printed circuit
board with two disk-shaped electrodes, which measure changes in skin resistance. These
changes can be correlated with emotional variations.
During the experiment, PPG and GSR signals from all participants were recorded,
resulting in 32 datasets. Analysis of these datasets provided physiological BPM and skin
conductance (SC) values, allowing the investigation of occupational stress detection. It was
observed that both BPM and SC values increase with stress. However, there were inter- and
intra-subject variations in response to stress-inducing tasks. The physiological response’s
impact depends on the intensity of the stimulus and individual perception. Some subjects
remained continuously alert during stress, while others exhibited brief reactions.
Statistical analysis of the calculated physiological parameters was conducted. The null
hypothesis indicated no statistically significant difference between measurements taken
during controlled and stressful phases for both tasks. However, SC values showed highly

Electronics 2024,13, 3899 11 of 33
significant differences between control and stressful phases for both tasks. BPM values
also showed significant differences between phases of the Mental Arithmetic Task, but
not for the Stroop Color Word Task. The latter required longer and more abrupt mouse
movements, and the use of a Kalman filter to remove motion artifacts may have affected
the extraction of useful information from rapid or subtle changes in signals.
The system’s effectiveness for automatic stress detection in an office environment was
validated using stress-inducing tasks that mimic common workplace stressors. To address
motion and noise artifacts, a Kalman filter and a moving average filter were employed,
with the Kalman filter effectively preprocessing the PPG signal. Participants reported
higher stress levels and decreased performance during stressful tasks. Statistical analysis
showed significant differences between the different periods of the experimental protocol.
The proposed system aims to offer a non-invasive, easily customizable and user-friendly
solution for monitoring and automatically detecting the stress levels of office workers.
Chigira et al. [
50
] assessed that a conventional PPG sensor, being relatively small,
requires the user to place their hand or fingertip precisely on the sensor point. To address
this limitation, they introduced a mouse with PPG surfaces, allowing unobtrusive PPG
sensing during regular use [
60
]. Up to 90% of the light from the diffusion plate can pass
through the detection plate, and the light that reaches the fingertip is reflected back to the
detection plate.
The study collected PPG waveform data from three different sensors—left, right and
spot—each capturing 15 s of data from each subject. The results showed consistent patterns
across all participants, indicating reliable data collection. Correlation analysis between
surface sensor data and point sensor data revealed high correlation coefficients.
Specifically, the correlation coefficients were 0.97 for the left sensor and 0.99 for the
right sensor. These high coefficients indicate a strong agreement between the data collected
by the surface sensors and the point sensor, suggesting that the surface sensors capture
physiological signals with similar accuracy. Overall, the study findings indicate that surface
sensors are reliable for capturing PPG waveform data.
Freihaut et al. [
51
] explored the use of computer mouse movements to identify stress.
They emphasized the potential advantages of this approach, which is completely non-
invasive and unobtrusive compared to traditional methods. Participants were recruited
through WisoPanel, an online panel representative of the German population. Invita-
tions were sent to all 14,343 panel members, with 1941 (15.65%) opening the study link
and 1091 completing it for a reward of 1 euro (retention rate: 56.21%). After excluding
97 participants due to careless responses or technical issues, the final sample included
994 participants (mean age = 54.4; standard deviation = 13.3; 515 female; 479 male). Par-
ticipants were required to use a physical computer mouse, have a display resolution of
at least
950 × 600
, and use a modern web browser, with measures in place to filter out
noncompliant users. The median duration of the study was 21 min.
The experiment used a between-subjects design with two phases. In the baseline phase,
all participants practiced four mouse tasks to establish a baseline level. In the application
phase, they were randomly assigned to high-stress or low-stress conditions and worked
on the same tasks. The experiment was implemented as a web application. Stress was
manipulated by presenting either a difficult (high-stress) or easy (low-stress) counting task
before each mouse task. The high-stress counting task involved identifying squares among
complex geometric shapes and distractors, making the task challenging yet manageable.
In the high-stress condition, participants were told the tasks were a performance test
measuring intelligence, adding a social evaluation element. In the low-stress condition,
participants were informed that the tasks aimed to improve computer skills. Both groups
received feedback and were instructed to perform the tasks as quickly and accurately as
possible. The framing of instructions was identical for both conditions.
The four mouse tasks were designed to capture various mouse actions: point and click,
drag and release, press the scroll bar and follow the circle. These tasks remained consistent
across both baseline and stress conditions to avoid confounding effects. Mouse-usage

Electronics 2024,13, 3899 12 of 33
data were collected on the client side, recording mouse events at varying sampling rates.
Participants’ stress levels were measured after each task using the Self-Assessment Manikin
and the German Multidimensional Mood Questionnaire, which assessed emotional states
and stress levels to ensure the manipulation’s effectiveness.
Preparing the mouse data for analysis involved several steps for each task. First, all
relevant data points were extracted for each specific task. Next, artifacts in the data, identi-
fied by consecutive points with the same timestamp or the same x and y coordinates, were
removed. Visual inspection was then conducted to identify potential tracking problems.
Participants who exhibited tracking issues or whose task duration exceeded three times the
median were excluded from the analysis. Finally, mouse-movement data were interpolated
into uniform 15 ms intervals to ensure consistent temporal resolution. These steps ensured
reliable data preparation for subsequent analysis and allowed for a detailed investigation
of mouse behavior across various tasks and experimental conditions.
In this study, the first step was to predict stress conditions (high vs. low) for each
mouse task using features derived from mouse usage. Five-fold cross-validation assessed
the prediction accuracy, which was statistically validated against the null distribution
via permutation tests. The analysis standardized input functions and employed three
common algorithms: logistic regression, support vector machine classification and Random
Forest with default hyperparameters. Models were tested with and without baseline data,
resulting in six models for each task.
Regression analysis then examined the relationship between mouse usage and sub-
jective ratings of valence (qualitative emotional response) and emotional arousal. Despite
these efforts, no significant correlations were found, suggesting that stress does not clearly
affect mouse behavior as measured by self-reported affective states. An alternative ap-
proach involved generating images from raw mouse data to predict stress states and
affective ratings using a convolutional neural network. However, the results were similar
to those obtained using traditional features, with classification accuracy around chance
level. While some models showed promising results, the variability between tasks and
methodologies highlights the complexity of linking mouse usage to stress in a controlled
experimental setting.
The study also outlined several challenges with this method. Changes in mouse move-
ments can be influenced by various factors, not just stress. The authors acknowledge that
this research is still in its early stages and that further studies are needed to identify specific
patterns of mouse movements truly indicative of stress. Moreover, even if a link between
mouse movements and stress is established, the accuracy of this method needs thorough
evaluation. Can tracking mouse movements reliably distinguish between different stress
levels? Is it sensitive enough to detect subtle stress differences? These questions require
further investigation.
Computer mouse tracking is a simple and cost-effective method for collecting con-
tinuous behavioral data, with potential applications in various areas of psychological
science. This study aimed to assess its ability to measure individual stress levels, but no
clear correlation was found between stress and mouse usage. This suggests that using the
computer mouse as a general tool for measuring stress may not be feasible. However, these
results underscore the need for theoretical advances in understanding how stress affects
sensorimotor behavior.
Sun et al. [
52
] investigated how common computer mouse operations can be used
to measure stress levels. Their study highlights that muscle stiffness during arm and
hand movements can be captured through typical mouse usage, employing a physiological
model known as the Mass-Spring-Damper system. From this model, they derived two stress
metrics based on mouse movements and developed a computational method for accurately
estimating these parameters. A controlled study was conducted with 49 participants,
collecting data on mouse activity, ECG readings and subjective stress ratings under both
calm and stress-induced conditions.

Electronics 2024,13, 3899 13 of 33
The findings suggest that stress measures derived from mouse movements are highly
reliable, surpassing traditional physiological methods in strength. Moreover, the study
shows that stress detection is feasible, achieving 70% accuracy in identifying stress states
using just 10 samples of mouse movements under controlled conditions. However, the
model requires 100–200 training movements to establish a single-parameter model for
each user.
To determine which mouse operations to analyse, the researchers noted that a few
repetitive tasks account for most mouse interactions. These include pointing and clicking to
perform actions (e.g., launching applications or sending messages), dragging and dropping
to move or rearrange objects, and steering the cursor through constrained paths, such as
navigating drop-down menus or highlighting text. These interactions were abstracted into
three primary operations: point-and-click, drag-and-drop and steering. In the experiment,
two conditioning tasks were used—one to induce stress and another to alleviate it.
The study aimed to evaluate individual-level stress classification by integrating multi-
ple data types: subjective stress ratings (SSR), electrocardiogram data and mouse activity
data. SSRs were recorded at various stages using an 11-point Likert scale, where partici-
pants rated their stress levels from 0 (no stress) to 10 (extreme stress). Continuous ECG
data were captured using a 3-lead ECG setup, and analyzed with the Kubios HRV tool
to extract heart rate variability indicators, while mouse activity was monitored through a
high-resolution mouse and analyzed using a MSD model to compute stress-related metrics.
The study also investigated the effectiveness of a individual-level stress-classification
approach. The process involved training a classifier with a subset of data points from each
subject and testing it on the remaining unseen samples. A simple model-based classifier
was employed, characterized by a staircase structure representing stress behavior based on
target distance and size. The model used a fixed step magnitude for all subjects and varied
step slopes for different tasks.
To classify stress, the model required only the distance of mouse movements, thus
minimizing privacy concerns. A one-dimensional classifier was used to determine the best
threshold for classification. This model was evaluated by measuring classification accuracy
with varying numbers of samples for training and testing. The results indicated that the
model with a maximum-accuracy threshold significantly improved classification accuracy
compared to baseline models, reaching up to 71% accuracy with 30 samples. However,
accuracy declined with more samples due to insufficient data for model training. Overall,
the study demonstrated that around 10 mouse movements under a fixed stress state could
achieve approximately 70% accuracy in stress classification.
The study by Vea et al. [
53
] aims to advance the development of formal models for
recognizing the emotional states of novice programmers using common, low-cost, non-
intrusive computer peripherals. The models or patterns identified for detecting negative
emotional states may help computer scientists create computational systems that provide
automatic feedback to educators and students. The researchers utilized a customized
mouse-key logger and a webcam.
They then extracted relevant keystroke and mouse dynamic features, resulting in a
CSV file with these features recorded at 15 s intervals. This file, referred to as the incomplete
dataset, initially lacked affective labels. Video logs were similarly divided into 15 s segments
matching those in the incomplete dataset. The researchers developed various affective
models to detect confusion, frustration and boredom by training tree classifiers such as J48,
Decision Tree and Random Forest using RapidMiner. They evaluated different feature sets,
including keystroke verbosity features, keystroke time duration and latency, all keystroke
features combined, mouse features alone and a combination of all keystroke and mouse
features. Feature selection was based on the Gini index, and batch-X-validation was used
to validate the models. The depth of the trees in each classifier was also explored to identify
the model with the highest performance.
The study found that notable features for detecting negative affect include typing
errors (e.g., backspace and delete key presses), idle time, typing variance, key events,

Electronics 2024,13, 3899 14 of 33
mouse-movement distance and key press durations. Boredom was linked to minimal
keyboard activity and slight mouse movement, while frustration also involved reduced
keyboard use but with additional hand gestures. Confusion showed relationships with
both keyboard and mouse behaviors. Feature stability varied over time: typing errors were
significant in the early and middle stages, while idle time became more influential towards
the end. Idle time was a major indicator of boredom, typing errors indicated confusion
and a combination of both was needed for frustration. Combining mouse features, such
as distance traveled along the x-axis, with keystroke dynamics enhanced the accuracy of
detecting student affect compared to using either type of feature alone.
Silva’s study [
54
] introduces two cost-effective methods for measuring typing and
mouse grip pressure using readily available devices, incorporating force-sensitive resistors
(FSRs) on keyboards and mice to detect pressure variations. To evaluate these designs, a
user study was conducted where participants performed typical tasks, such as typing and
answering multiple-choice questionnaires. Binary classifiers were trained to distinguish
between stress and neutral states based on keystroke dynamics, mouse movements and
pressure data. The results showed that combining keystroke and mouse dynamics with
pressure features improved classification accuracy. In the study, software recorded typing
pressure, mouse pressure, keystrokes and mouse events in the background. Two types
of features were extracted from the keyboard data: keystroke dynamics and pressure
measurements. Pressure data were only collected during keydown events, excluding
inactive periods. The analysis revealed that the bottom-left sensor, located near the Z key,
was the most sensitive due to its proximity to 60% of the alphabetical keys, so the analysis
focused on data from this sensor. Similarly, mouse data provided two feature sets: mouse
dynamics and pressure measurements, focusing on six key dynamics—travel distance,
direction change, overall speed, moving speed, dwell duration and moving duration. The
keyboard analysis involved 188 samples from four control and four experimental sessions
per participant, where a random classifier would have achieved 50% accuracy. Mouse
data, collected from 87 sessions, resulted in 174 samples. Trajectory features outperformed
pressure features with accuracies of 70% and 61%, respectively. Combining both types
into a single model boosted classification accuracy to 73%, a 3% improvement over using
trajectory features alone.
Tran et al. [
37
] developed a device that transmits PPG signals to a PC via a wired
connection. However, the use of infrared light instead of the typical red light resulted in
weak output signals. To address this issue, the authors propose an alternative: a wireless
PPG mouse. This device integrates a compact PPG sensor and Bluetooth module within a
standard mouse, enabling real-time monitoring of PPG waveforms, heart rate and heart
rate variability during normal mouse use. Detecting peaks and minimum points is crucial
for analyzing PPG signals to estimate heart rate, heart rate variability, mental stress and
blood pressure. The Adaptive Threshold Algorithm (ADT) algorithm is commonly used
for peak detection, especially when baseline drift from respiration is present. However,
ADT’s reliance on two adaptive thresholds can lead to missed peak-valley pairs, affecting
its accuracy. ADT sometimes fails to detect peak-valley pairs when the valley drops sharply.
In contrast, the proposed Robust Peak Detection (RPD) algorithm consistently identifies
these pairs. The RPD algorithm also performs well under challenging conditions, accurately
detecting premature and delayed peaks while reducing error peaks. It corrected 103 peaks
in the noisy 3rd reference dataset, compared to 22 by ADT and none by Local Maximum and
Minimum Detection algorithm (LCM). Additionally, RPD achieved a false detection rate of
just 2.3%, significantly lower than the 13.4% for LCM and 11.7% for ADT, demonstrating
its superior performance in PPG peak detection using the PPG mouse.
Belk et al. [
55
,
56
] have focused on the seamless identification of psychological stress
among older adults who remain active in the workplace by using sensors embedded in
a computer mouse. They developed a custom mouse, known as CogniMouse, equipped
with sensors to measure heart rate, skin conductance, temperature and grip force in real
time. This device is part of the CogniWin project, which aims to assist and motivate

Electronics 2024,13, 3899 15 of 33
older adults to remain active in their jobs. The CogniMouse integrates a classification
algorithm based on probabilistic theory, designed to continuously assess the likelihood
that a user is experiencing stress. This Bayesian-based approach, utilizing conditional
probability distributions, is chosen for its flexibility in accommodating new variables. The
classification inputs include: grip force, heart rate, skin conductance, hand temperature
variations, hand trembling indicators from mouse motion and acceleration, and click stream
frequency. CogniMouse is supported by two applications. The first is a background worker
that parses and distributes incoming data messages to relevant applications. The second
provides a visual interface for easy data verification. CogniMouse not only measures and
analyzes physiological data but also delivers personalized feedback to users and caregivers,
including insights into emotional states, task-related difficulties, frustration levels and signs
of sleepiness.
Kaklauskas et al. [
57
] made significant advancements using the web-based biometric
computer mouse advisory system. This system provides a thorough analysis of a user’s
emotional state and work productivity using three primary biometric techniques: phys-
iological, psychological and behavioral. By integrating these methods, the system can
evaluate eleven different states of being, including stress, work productivity, mood and
interest, as well as seven specific emotions, such as self-control, happiness, anger, fear,
sadness, surprise and anxiety, all within a practical time frame. To enhance accuracy and re-
liability, the system incorporates additional data from the Biometric Finger, which measures
blood pressure and pulse rates, thus allowing for a more detailed physiological assessment.
A standout feature of the system is its ability to generate personalized stress-management
recommendations. Based on real-time biometric data and the user’s needs, the system
produces of potential recommendations derived from Maslow’s Pyramid Tables, which are
derived by survey data and global best practices. It then selects the most suitable recommen-
dations for each user. These tables provide recommendations based on Maslow’s hierarchy
of needs, covering physiological, safety, social, esteem and self-actualization needs, to help
users enhance work efficiency and reduce stress. The Model-base Management System
and Model Base analyse correlations between user emotions, mood, productivity and
biometric data, generating customized recommendations to boost both work productivity
and emotional well-being. Moreover, the system offers real-time assessments of productiv-
ity and emotional state, providing users with immediate insights into their performance
and well-being. The article includes a case study, and various scenarios used to test and
validate the system, demonstrating its validity, efficiency and practical utility. The system is
designed to analyse and improve user emotions and productivity through interconnected
subsystems. It begins with e-Self-Assessment, where users complete a questionnaire to
evaluate their mood, productivity, stress levels and emotions on a ten-point scale. This
self-reported data are then compared against biometric parameters.
Leone et al. [
58
] proposed a workplace framework to monitor mental load and im-
prove well-being using wearable devices that track heart rate, skin conductance and eye
movements. The system uses two wearable devices: J!NS MEME glasses and the E4 wrist-
band from Empatica. These devices send data to a computer or smartphone for processing,
aiming to automate stress detection. The experimental part involved a series of LEGO
brick-based tasks designed to simulate manufacturing activities like assembly and manual
handling. Data collection started with a 2 min baseline period, followed by five tasks to
induce cognitive load. Tasks included assembling LEGO robots and airplanes or filling and
carrying a toolbox. After each task, participants had a 2 min recovery period, and errors
were counted to help assess stress levels. A moderator recorded the task’s start and end
times and marked errors, as frequent mistakes may indicate higher stress. The collected
EDA and EOG signals were filtered to isolate relevant components. For classification, a
SSVM was used, testing various kernels like Linear, Polynomial, Gaussian radial basis
function (RBF) and Sigmoid. In this initial study, a two-class classification distinguished
between stress and no-stress states. The results showed that the Gaussian RBF kernel
allowed the SVM to recognize stress with 93.6% accuracy and a classification accuracy

Electronics 2024,13, 3899 16 of 33
of 92.7%. However, the study’s limitation was the small number of participants. Future
research aims to expand the participant pool, explore real-time stress detection and assess
multiclass classification to detect varying stress levels.
Rescio et al. [
59
], along with another group, proposed a stress assessment system based
on an unsupervised learning technique to monitor workers’ mental load, aiming to improve
well-being in the workplace. The system uses minimally invasive wearable devices that
combine HR, EDA and EOG measurements to automate stress detection. To evaluate the
framework’s performance, traditional mental stress tests and simulations of manufacturing
activities were conducted. The data collection was carried out under controlled, simulated
conditions, involving 7 female and 4 male volunteers. During the experiment, participants
wore the wearable devices and performed five tasks, each separated by a two-minute
rest period. The tasks, which included manufacturing simulations using LEGO bricks
and a toolbox, were designed to induce stress, with detailed procedures available in prior
studies. In addition, a mental arithmetic test, a common method for inducing stress, was
conducted. At the end of the experiments, participants were asked to assess the cognitive
load caused by each task. The mental arithmetic tests and complex LEGO assembly tasks,
performed without instructions, were unanimously rated as the most stressful. More than
90 stress events and 130 rest events were identified and labeled for performance evaluation.
During the data collection, data from the wristband and chest strap devices were stored
on integrated memory, while data from the glasses were transmitted to an embedded
PC. All data were synchronized and analyzed offline using MathWorks MATLAB. An
unsupervised k-means clustering approach was employed, yielding good results in terms
of specificity, sensitivity and accuracy (around 80%), without the need for a training phase,
which is often time-consuming and less accurate.
Comparative Analysis of Hardware Platforms for Stress Detection
The field of stress detection has advanced with the development of different hardware
platforms, using various technologies and sensors to provide continuous and reliable stress
monitoring. These systems range from wearable sensors to devices integrated into everyday
objects, offering multiple ways to track physiological and behavioral signs of stress. As
shown in Table 1, the various hardware platforms used for stress detection exhibit distinct
capabilities in terms of sensing, data transmission and sampling rates. These features play
a crucial role in determining the overall accuracy and efficiency of stress-detection systems.
• Multisensory Platform
In the study by Rescio et al. [
44
], the authors proposed a system that integrates both
an ambient and a wearable device within a multisensory platform [
61
]. The wearable
component is a sensorized backstrap integrated with a ShimmerGSR unit, a portable sensor
system. The ShimmerGSR unit is attached to the backstrap and uses Bluetooth for real-
time data transmission, making it convenient for wireless communication with external
systems. It features a range of sensing capabilities, including the measurement of GSR,
PPG and various motion parameters such as angular rate, orientation and acceleration.
GSR is measured using Ag/AgCl electrodes placed on the shoulder, which offer stable
signal acquisition compared to other electrode types, while heart rate is assessed via
a PPG probe connected to the earlobe. Vital parameters were sampled at a frequency
of 10 Hz and transmitted via Bluetooth to an embedded PC, where a stress-detection
algorithm was executed. The Bluetooth protocol, known for its low battery consumption,
enables continuous monitoring over an entire work shift (approximately 8 h). Additionally,
Bluetooth allows data to be transferred to a nearby cellphone for further transmission
to a server, facilitating subsequent processing on the embedded PC. Complementing the
wearable device, the system also includes an ambient device, which is a consumer-grade
RGB camera capable of capturing images with a minimum resolution of 320 × 240 pixels.
The RGB camera is used to perform facial recognition to estimate stress based on visual
cues, such as facial expressions. The integration of both physiological signals and visual

Electronics 2024,13, 3899 17 of 33
data provides a multimodal approach to stress detection, making the system well-suited
for research requiring comprehensive data collection.
• Polar H7 and Amulet Platform
The system presented in study by Mishra et al. [
45
] employs a more streamlined
approach by utilizing commercially available hardware. The primary sensing device is the
Polar H7 heart rate monitor, a chest-worn device capable of measuring both heart rate and
R-R intervals. The Amulet wearable platform is used as a data hub to collect and store the
data transmitted from the Polar H7. The Amulet is an open-source platform designed for
energy- and memory-efficient sensing applications and includes various onboard sensors
such as a three-axis accelerometer, light sensor and ambient air temperature sensor. Amulet
system focuses on physiological and activity data, making it more portable and user-
friendly. Data from the Polar H7 are transmitted via BLE at 1 Hz to the Amulet wearable
platform, which acts as the data hub for collection and storage.
• Compact Ring-Based Sensor System
In the paper by Valenti et al. [
46
], the authors proposed a compact and lightweight
system for stress monitoring, consisting of two main components: a ring-shaped sensor
probe and a microcontroller-based processing unit. The system integrates both GSR and
PPG sensors, which are used to monitor physiological signals related to stress. This
configuration optimizes sensor contact with the skin, standardizing the pressure across
different users. The GSR sensor in the system is composed of silver-chloride (Ag/AgCl)
electrodes. These electrodes are connected to the Mikroe-2860 GSR-Click sensor, which
utilizes a volt-amperometric method for skin conductance detection, a reliable technique
for assessing electrodermal activity, commonly associated with stress responses. The PPG
sensor is based on the MAX30102 chip, which operates in reflectance mode. This mode
involves placing the photodetector and light-emitting diodes on the same side of the sensor,
a design that reduces the dependency of the PPG signal on tissue volume. To manage
the sensors and process the acquired data, the authors use an STM32F446RE Nucleo-64
development board, a microcontroller that operates in continuous sampling mode. A key
aspect of the system is its low-power design, with an estimated total power consumption
of approximately 205 mW. This energy efficiency makes the system suitable for long-term
monitoring applications, where low power consumption is critical for extended wear and
continuous data acquisition. The GSR data are transmitted directly to the 12-bit analog-to-
digital converter of the microcontroller, while the PPG sensor communicates with the board
via the Inter-Integrated Circuit (I2C) protocol, allowing for efficient and high-frequency data
acquisition. The system supports a sampling rate of up to 1.6 kHz, but for the purposes
of the study, a rate of 800 Hz was selected, which is sufficient for analyzing heart rate
variability with a 17-bit ADC resolution
• Integrated PPG Sensor System Embedded in a PC Mouse
The system developed by Lin et al. [
47
] provides a hardware solution designed for
precise pulse rate detection across a range of conditions. The system integrates a NUC120
microcontroller, a Bluetooth module and four PPG sensors from uPI Semiconductor, which
operate at a wavelength of 850 nm. These sensors, equipped with an infrared LED and a
photodiode, capture physiological signals. To enhance signal quality, the system incorpo-
rates band-pass filters and a PGA117 amplifier with programmable gain, which allows for
real-time adjustments of signal amplification via the Serial Peripheral Interface (SPI). The
Inter-Integrated Circuit is utilized to control the PPG sensors, while the SPI manages the
PGA switching to adjust the gain across different PPG channels. The filtered and amplified
PPG signals are then input into a built-in 12-bit analog-to-digital converter with a sampling
rate of 200 Hz. The resulting output is transmitted to a personal computer via the Bluetooth
module, where the waveform and evaluated pulse rate are displayed. The data are stored
and can be analyzed using an application program. A notable feature of their system is
its Cortex-M0 processor, which is embedded in a PC mouse. This processor controls the

Electronics 2024,13, 3899 18 of 33
PPG sensors and handles signal processing within the mouse’s compact form factor. The
PPG sensor module is mounted on the top surface of the mouse, with sensors numbered 1
through 4 positioned to capture signals from different channels. This innovative integration
provides a unique approach to wearable monitoring.
• Multisensor System in a Computer Mouse
The system developed by Androutsou et al. [
48
,
49
] represents an approach to stress
detection by embedding a multisensor setup within a commercially available wired optical
mouse. This system integrates a PPG PulseSensor (Pulse Sensor by World Famous Electronics
LLC, Brooklyn, NY, USA), a Grove-GSR sensor (by Seeed), and a Particle Photon development
board with a 120 MHz ARM Cortex M3 microcontroller and an onboard Broadcom Wi-Fi
chip. The PPG PulseSensor includes a reverse-mounted green LED and an ambient light
sensor (Broadcom APDS-9008) to measure blood pulse by detecting light reflected from the
skin. The signal is filtered, amplified and adjusted for processing by the microcontroller. The
Grove-GSR sensor uses disc-shaped electrodes to detect changes in skin resistance due to
sweat, which affect the sensor’s output. The Particle Photon board processes these signals
with its 12-bit ADC and facilitates wireless data transmission via its Wi-Fi module, allowing
for remote data collection and updates without needing to disassemble the mouse or install
additional software. It operates within a supply voltage range of 3.6–5.5 V, drawing power
from the mouse’s USB connection. The system’s efficient integration of sensors and compact
design makes it a user-friendly solution for stress monitoring.
• PPG Sensors in a Wired Optical Mouse
The study by Chigira et al. [
50
] presents a design featuring wide, thin sensing surfaces
that are optimized for integration into everyday devices. It enhances a conventional
PPG sensor by employing two thin optical plates, a diffusion plate and a detection plate,
separated by a narrow air gap. These high-refractive-index acrylic plates act as a waveguide,
directing light from an IR LED through the user’s fingertip to a photodetector. Up to 90%
of the light from the diffusion plate passes through the detection plate, with light reflected
from the fingertip returning to the detection plate. The PPG sensors are installed on both
sides of a standard wired mouse, where a single IR LED and photodetector with a peak
wavelength of 940 nm are positioned at the plates’ light entrance. The LEDs are powered by
a 100 mA direct current. The captured signals are amplified, filtered with a 10 Hz low-pass
filter, digitized and transmitted to a PC via a USB connection.
• ECG and mouse activity
For data collection and processing, Sun et al. [
52
] obtained three distinct stress mea-
sures from each participant: subjective stress ratings, continuous electrocardiogram data
and mouse activity data. Continuous ECG data were captured through a 3-lead ECG
setup, with electrodes placed on the chest. These data were analyzed using the Kubios
HRV tool to extract heart rate variability indicators, which are established measures of
emotional response, particularly in relation to stress. Any incorrectly detected heartbeats
were corrected, and missing beats were added. Mouse activity data were gathered using
a high-resolution gaming mouse with a spatial resolution of 5700 counts per inch. The
system recorded raw mouse input events at a sub-pixel level using C++ and Microsoft
Windows GDI+. To simulate the resolution of a regular mouse, the data were decimated to
400 CPI, which did not affect detection accuracy.
• Mouse-Key Logger and Webcam
The study by Vea et al. [
53
] used a mouse-key logger to capture mouse movements,
clicks, scrolls and keystroke events, along with a webcam to record the students’ facial
expressions and body movements.
• Pressure-Sensitive Keyboard and Mouse Using Force-Sensitive Resistors
Due to the absence of pressure-sensitive keyboards and mice on the market, Silva
et al. [
54
] proposed a low-cost, simple design that can measure pressure using off-the-shelf

Electronics 2024,13, 3899 19 of 33
keyboards and mice. The experimental setup incorporates FSRs to detect typing pressure.
These FSRs are arranged in a voltage-divider configuration, with four sensors placed at
the keyboard’s underside corners. The sensors are connected to a microcontroller with
data transmitted via an HC-06 Bluetooth module. A voltage divider was used to adjust the
microcontroller’s 5 V output to 3.3 V, suitable for the HC-06’s input. The design utilizes a
Dell KB212-B keyboard, chosen for its flat underside and convenient placement of its feet
near the corners, making it ideal for sensor routing. In addition to the keyboard, the study
explored measuring mouse grip pressure with FSRs. Early tests showed variability in grip
patterns (palm, claw, tip), so the researchers opted for a vertical mouse (Anker Ergonomic)
to encourage consistent grip pressure. After attaching FSRs and protecting them with duct
tape, the final pressure-sensitive mouse was completed, with FSRs sampling at a rate of
100 Hz for both keyboard and mouse measurements.
• Wireless PPG Mouse Using Bluetooth Module
Tran et al. [
37
] developed a wireless PPG mouse by integrating a PPG sensor and a
Bluetooth module into a standard USB 2.0 PC mouse (Samsung Co., Ltd., Suwon, Republic
of Korea). The system features a red LED (APT1608SRCPRV) that provides a bright,
wide-angle illumination with low power consumption of 20 mA and a peak wavelength
of 660 nm. The accompanying photodetector (PDB-C160SM) has a large active area of
2.9 mm × 2.6 mm
and a 120° viewing angle, allowing it to capture most of the reflected
light from the LED. The proposed system includes a passive high-pass filter to eliminate
baseline drift, a microcontroller with a 10-bit A/D converter for digitizing the PPG signal
and a Bluetooth module for data transmission. With a total power consumption of 280 mW
and the PC mouse consuming approximately 300 mW, the system operates well within
the USB 2.0 standard’s maximum power limit of 2500 mW, ensuring it does not interfere
with normal PC operations. The PPG sensor is affixed to this window with silicon glue,
while the Bluetooth board is mounted on the upper part of the mouse. The PPG system
shares the PCB mouse’s main power source via a wired connection. As the user operates
the mouse, their thumb makes contact with the PPG sensor, allowing continuous, real-time
monitoring of PPG signals. This non-invasive method enhances user comfort by avoiding
the need for additional wearable devices. The system’s sampling rate is set at 100 Hz to
ensure accurate measurement of temporal parameters.
• CogniMouse
Belk et al. [
56
] developed the CogniMouse Architecture, a human interface device
that connects to any computer via USB. This device is compatible with all major operating
systems, thanks to a custom HID protocol that enables the transmission of 64 bytes of
sensor data per packet. The design features a transparent GSR sensor embedded in the
mouse button area to detect changes in the user’s skin response. The prototype uses a
commercial Microsoft Comfort Mouse 4500, which ensures user acceptance and familiarity.
The current focus is on creating a classifier to assess user hesitation based on GSR sensor
data and mouse motion patterns. A parser module processes the raw data from the sensors,
which are then analyzed by the classifier algorithm.
• Biometric Computer Mouse and Finger System
The core of Kaklauskas et al.’s [
57
] system includes a Biometric Computer Mouse that
measures various physical metrics such as hand temperature, skin conductance, touch
intensity and heart rate. These biometric data are used to assess the user’s emotional state
and productivity. Additionally, the system captures mouse events, such as movements,
clicks and idle times, storing this information in CSV format. It analyzes features like
mouse speed, acceleration and tremble to evaluate work productivity and emotional well-
being. The Biometric Finger subsystem complements this by providing further data on
skin humidity, electrogalvanic skin conductance, skin temperature and heart rate. These
additional data ensure a more comprehensive analysis.
• J!NS MEME ES_R Glasses and Empatica E4 Wristband

Electronics 2024,13, 3899 20 of 33
In the study by Leone et al. [
58
], two wearable devices, J!NS MEME ES_R glasses and
the Empatica E4 wristband, send data to an embedded PC or smartphone for recording and
processing. The J!NS MEME glasses continuously acquire real-time data using three-point
electrooculography sensors placed on the nose pads, which measure vertical and horizontal
EOG signals. Although the glasses also include an accelerometer and gyroscope to track
head movements, this study focuses on the EOG sensor, which detects eye-blinks, a key
factor in stress monitoring. The data are transmitted via a low-energy Bluetooth connection,
and the rechargeable battery lasts about 16 h in streaming mode. The sample rate for this
study is 50 Hz. The Empatica E4 wristband continuously collects data from four sensors:
a PPG sensor to measure heart rate, an EDA sensor for tracking sympathetic nervous
system arousal, an infrared thermopile for skin temperature and a tri-axial accelerometer
to evaluate wrist movements. For this study, only the HR and EDA data were used due to
their relevance in stress analysis. The sample rate is 1 Hz for HR and 4 Hz for EDA. Like
the glasses, the wristband uses low-energy Bluetooth for data transmission and is paired
with a mobile app for real-time data visualization and storage.
• J!NS MEME ES_R Glasses, Empatica E4 Wristband, and Bioharness 3.0 Chest Strap
In a study by Rescio et al. [
59
], an algorithmic framework was developed using three
minimally invasive commercial wearable devices. Bioharness 3.0 Chest Strap—Produced by
Zephyr, measures HR with built-in electrode sensors and an integrated electronic module.
It can operate in streaming mode for real-time data viewing via low-energy Bluetooth or
in recording mode using internal memory. Although the E4 wristband also measures HR,
it was found to be less accurate compared to the Bioharness 3.0. As a result, Rescio et al.
chose to use all three devices in their study to ensure better accuracy in data collection.
Table 1. Comparative overview of hardware platforms for stress detection.
Authors Hardware
Components
Sensing
Capabilities
Data Transmission
Sampling Rate Key Features
Rescio et al.
(2024) [44]
ShimmerGSR unit
integrated into a
sensorized
backstrap,
Bluetooth module,
RGB camera
GSR, PPG, heart
rate, motion
parameters
(angular rate,
orientation,
acceleration), facial
recognition
Bluetooth 10 Hz
Multisensory
platform,
combining
wearable and
ambient devices,
continuous
monitoring over
8 h, low battery
consumption
Mishra et al.
(2020) [45]
Polar H7 heart rate
monitor, Amulet
platform (with
3-axis
accelerometer,
light sensor,
ambient
temperature
sensor)
Heart rate, R-R
intervals, activity
data (motion,
ambient light,
temperature)
Bluetooth Low
Energy (BLE) 1 Hz
Commercially
available
hardware, energy-
and
memory-efficient
sensing, portable
and user-friendly
Valenti et al.
(2023) [46]
Ring-shaped GSR
and PPG sensors,
Mikroe-2860
GSR-Click sensor,
STM32F446RE
Nucleo-64
development
board
GSR (Ag/AgCl
electrodes), PPG
(MAX30102 chip in
reflectance mode)
Direct ADC, I2C
protocol 800 Hz
Compact,
lightweight,
low-power design
(205 mW),
optimized for skin
contact,
continuous
sampling,
high-frequency
data acquisition

Electronics 2024,13, 3899 21 of 33
Table 1. Cont.
Authors Hardware
Components
Sensing
Capabilities
Data Transmission
Sampling Rate Key Features
Lin et al.
(2017) [47]
Four PPG sensors
(uPI
Semiconductor),
NUC120
microcontroller,
PGA117 amplifier,
Bluetooth module
PPG, pulse rate Bluetooth 200 Hz
Embedded PPG
sensors in a PC
mouse, real-time
signal
amplification
adjustment
Androutsou et al.
(2023, 2021, 2022)
[40,48,49]
PulseSensor (PPG),
Grove-GSR sensor,
Particle Photon
development
board (ARM
Cortex M3, Wi-Fi
chip)
PPG, GSR Wi-Fi -
Integrated sensors
in a commercial
optical mouse,
remote data
collection, compact
and user-friendly
design
Chigira et al. (2012,
2011) [50,60]
Two optical plates,
IR LED,
photodetector, USB
connection
PPG (940 nm IR
LED) USB -
Thin optical plates
used in a wired
optical mouse,
high-refractive-
index acrylic plate,
digitized via USB
Freihaut et al.
(2021) [51]
ECG with 3-lead
electrodes,
high-resolution
gaming mouse
ECG (heart rate
variability), mouse
activity data
USB -
Combination of
ECG and
high-resolution
gaming mouse for
detailed stress
detection based on
HRV and mouse
activity
Sun et al. (2014)
[52]
3-lead ECG,
high-resolution
gaming mouse
ECG, mouse
acvtivity USB -
Combines ECG
and
high-resolution
gaming mouse for
stress detection,
tracks mouse
events and ECG
data
Vea et al.
(2017) [53]
Mouse-key logger,
webcam
Mouse movements,
clicks, scrolls,
keystrokes, facial
expressions
- -
Combines
mouse-key logger
and webcam for
stress detection
based on user
interaction
Silva et al. (2021)
[54]
Dell KB212-B
keyboard, Anker
Ergonomic mouse,
FSRs, HC-06
Bluetooth module
Typing and grip
pressure Bluetooth 100 Hz
Measures typing
and grip pressure
with FSRs, simple
design, vertical
mouse encourages
consistent grip
pressure

Electronics 2024,13, 3899 22 of 33
Table 1. Cont.
Authors Hardware
Components
Sensing
Capabilities
Data Transmission
Sampling Rate Key Features
Tran et al. (2020)
[37]
PPG sensor (red
LED,
photodetector),
Bluetooth module,
USB PC mouse
PPG (660 nm red
LED) Bluetooth 100 Hz
Wireless PPG
sensor integrated
in a PC mouse, low
power
consumption,
continuous
real-time
monitoring
Belk et al. (2016)
[55]
Microsoft Comfort
Mouse 4500, GSR
sensor
GSR, mouse
motion patterns Bluetooth -
Measures skin
response, mouse
motion, assesses
hesitation and
stress
Kaklauskas et al.
(2011) [57]
Biometric
computer mouse,
Biometric finger
system
Hand temperature,
skin conductance,
touch intensity,
heart rate, skin
humidity
- -
Comprehensive
physiological data,
multiple biometric
sensors for
detailed emotional
and productivity
analysis
Leone et al. (2020)
[58]
J!NS MEME ES_R
glasses, wristband
Empatica E4
3-point
EOG)sensors (J!NS
MEME),
Accelerometer and
Gyroscope (J!NS
MEME), PPG
sensor (Empatica),
EDA sensor
(Empatica),
Infrared
thermopile
(Empatica),
Tri-axial
accelerometer
(Empatica)
BLE
50 Hz (J!NS
MEME) 1 Hz (HR)
and 4 Hz (EDA)
(Empatica)
Eye-blink
detection and verti-
cal/horizontal
EOG tracking
(J!NS MEME), HR
and EDA data for
stress analysis
(Empatica), 16 h
battery life (J!NS
MEME), mbile app
for real-time data
visualization
(Empatica)
Rescio et al. (2020)
[59]
J!NS MEME ES_R
Glasses, Empatica
E4 Wristband,
Bioharness 3.0
Chest Strap
EOG, EDA, HR
Low-energy Blue-
tooth/Internal
memory
50 Hz (EOG), 4 Hz
(EDA)
J!NS MEME ESR:
Measures vertical
and horizontal eye
movements, 16 h
battery life;
Empatica E4:
Assesses
sympathetic
nervous system
arousal, includes
mobile app for
real-time
visualization and
storage, onboard
memory;
Bioharness 3.0:
Measures heart
rate with built-in
sensors, operates
in streaming or
recording mode

Electronics 2024,13, 3899 23 of 33
This comparative analysis highlights a wide range of hardware platforms devel-
oped for stress detection and monitoring, each utilizing distinct technologies and sen-
sor configurations.
Rescio et al.’s [
44
] multisensory platform combines physiological data from GSR, PPG
and motion sensors with facial expression analysis, while Valenti et al.’s [
46
] compact ring-
based sensor focuses on physiological data with a high sampling rate. Mishra et al.’s [
45
]
Polar H7 and Amulet system prioritize portability and low energy consumption for heart
rate and activity tracking. Lin et al. [
47
] integrate PPG sensors into a PC mouse for
non-intrusive pulse monitoring, whereas Androutsou et al. [
40
,
48
,
49
] combine PPG and
GSR sensors into a computer mouse for remote data collection. Chigira et al. [
50
,
60
] and
Freihaut et al. [
51
] offer more basic but cost-effective PPG-based stress detection using
a standard mouse. Sun et al.’s [
52
] system merges ECG and mouse activity data for a
detailed physiological and behavioral analysis, while Vea et al. [
53
] add facial recognition
and body movements to capture stress markers. Silva et al. [
54
] use force-sensitive resistors
to measure typing and grip pressure in their low-cost design. Tran et al. [
37
] offer a wireless
PPG mouse for continuous monitoring and Belk et al.’s [
55
] CogniMouse measures skin
response to track user stress. Kaklauskas et al. [
57
] provide a biometric mouse and finger
system that captures multiple physiological markers like heart rate, temperature and touch
intensity. Leone et al. [
58
] focused on developing a framework for monitoring mental
load and improving workplace well-being using wearable devices to track heart rate,
skin conductance and eye movements for automated stress detection. Finally, Rescio [
59
]
focused in another study on developing an unsupervised learning-based system to monitor
workplace stress using wearable devices that measure heart rate, electrodermal activity
and eye movements.
These platforms present a balance of multimodal, sensor-rich systems and simpler,
cost-effective devices, each with varying levels of data complexity, user comfort and
integration challenges. Their applications depend on specific research goals and practical
use cases.
5.2. Advanced Techniques in Neural Network-Based Stress Detection
In an era of rapid technological progress and increased mental health awareness,
developing effective stress-detection methods is crucial. Neural networks, particularly
deep learning models, have emerged as powerful tools for continuous and precise stress
monitoring. This section delves into various approaches leveraging neural networks for
stress detection, emphasizing the contributions of several key studies. Li et al. explored
the use of deep learning techniques, including one-dimensional convolutional neural net-
works (CNNs) and multilayer perceptron (MLP) networks, for analyzing physiological
signals, achieving notable accuracy in stress and emotion classification [
62
]. Similarly,
Song et al. proposed a stress-classification model based on a Deep Belief Network (DBN),
which bridged supervised and unsupervised learning. Their model demonstrated supe-
rior performance compared to traditional classifiers, indicating its effectiveness for stress
detection. Zanetti et al. extended the concept of Network Physiology, utilizing consumer-
grade wearable devices and sophisticated data-synchronization techniques to differentiate
between levels of psychological stress [
21
]. Han et al. applied Random Forest to opti-
mize symptom combinations for improving stress-classification performance [
63
], while
Pepa et al. developed a method based on keystroke and mouse dynamics to infer stress
levels in real-world settings [
64
]. Additionally, Gil-Martin et al. proposed a deep learn-
ing architecture using CNNs to analyze physiological signals, achieving high accuracy in
stress
detection [65]
. Meanwhile, Lawanont et al. employed unsupervised learning tech-
niques, such as k-means and hierarchical clustering, to analyze stress levels in a workplace
setting [66]
. They utilized various sensors to collect data related to behavior and the work
environment, providing insights into stress-related behaviors. Akhonda et al. [
67
] used
a three-layer Back Propagation Neural Network to detect stress by analyzing EEG alpha
waves and other physiological signals during computer use. Lv et al. [
68
] employed the

Electronics 2024,13, 3899 24 of 33
Common Spatial Pattern (CSP) algorithm and an SVM model to develop an accurate EOG-
based eye gesture recognition system, enhancing detection capabilities for both saccadic
movements and complex gestures.
Collectively, these studies highlight the effectiveness and versatility of neural networks
in advancing stress-detection technologies and their potential for practical applications in
stress monitoring. All of these mentioned studies are detailed in the following text.
Li et al. proposed the use of deep learning techniques, specifically a one-dimensional
(1D) CNN and a multilayer perceptron neural network, for stress detection and emotion
classification [
62
]. They transformed physiological signals into vectors and inputted them
directly into the neural network. The models were trained and tested using a dataset from
Schmidt et al. [
69
]. A deep convolutional neural network typically includes filtering layers,
activation functions, pooling layers and fully connected layers.
For their study, Li et al. utilized two datasets collected from sensors placed on the
participants’ bodies. The first dataset was acquired with sensors positioned on the chest,
monitoring ECG, EDA, electromyogram, skin temperature, respiration rate and a tri-axial
accelerometer. In the second dataset, sensors embedded in a wrist-worn device captured the
signals. This device included an accelerometer and measured pulse blood volume and EDA.
The results for three-class emotion classification and binary stress detection indicated that
deep neural networks consistently outperformed traditional machine learning algorithms.
Specifically, the deep 1D convolutional neural network, applied to data from chest-worn
sensors, achieved an accuracy of 99.55% for all physiological signals and 97.48% when
excluding accelerometer-derived signals for emotion classification. Similarly, the deep
multilayer perceptron neural network, applied to wrist-worn sensors, achieved accuracy
rates of 98.38% and 93.64% under the same conditions.
For binary stress detection, the deep 1D convolutional neural network, using chest-
worn sensors, attained accuracy rates of 99.80% and 99.14% for all physiological signals
and accelerometer-excluded signals, respectively. The deep multilayer perceptron neural
network, using wrist-worn sensors, achieved accuracy rates of 99.65% and 97.62% under
similar conditions. These findings demonstrate that both deep neural networks significantly
outperformed traditional machine learning methods, highlighting their effectiveness for
stress detection and emotion classification.
In a separate study, Zanetti et al. aimed to develop a model to distinguish between
three different levels of psychological stress, conducting measurements on 17 partici-
pants [
21
]. Their novel approach employs a stress-detection method known as Network
Physiology, developed by Bashan et al. [
70
]. Bashan conceptualized each organ system as a
node in a complex network of dynamic physiological interactions. Zanetti extends this idea
by analyzing the interactions among multiple systems and examining the relationships
between their output signals. They use information theory to quantify these physiological
interactions and differentiate between various levels of psychological stress.
Physiological signals were acquired using non-invasive, consumer-grade wearable
devices. A sensor trio from Smartex measured ECG and respiratory signals at sampling
rates of 250 Hz and 25 Hz, respectively, with respiratory waveforms recorded by a piezore-
sistive sensor positioned on the rib cage. An Empatica2 bracelet captured BVP signals at
64 Hz. EEG signals were collected using a 14-channel wireless head-mounted device, the
Emotiv EPOC PLUS, with sensors placed according to the international 10–20 system and a
sampling rate of 256 Hz for each channel.
Zanetti et al. emphasize the importance of accurate electronic clock generation to
prevent time shifts and desynchronization in recorded data, which can complicate the
analysis of signal interactions critical for network physiology. To address this issue, they
developed a synchronization method utilizing variables from all available devices. The
process involves:
1. Identifying the primary motion directions for each device,
2. Securing the devices with industrial Velcro fasteners to ensure stability,

Electronics 2024,13, 3899 25 of 33
3.
Creating a non-uniform acceleration pattern by moving the fixed support (with
sensors) along a sinusoidal path,
4.
Synchronizing the collected, low-pass filtered and accelerated signals with a refer-
ence signal.
The last two steps mentioned are conducted both at the start and end of the signal
recording to account for any factors that might affect the timeline. Synchronization is
achieved through linear time warping relative to the reference signal.
To differentiate among three psychological stress states, recordings from ECG, BVP,
EEG and respiration were collected using wearable devices. Information-theoretic mea-
surements were utilized to train various classification algorithms. The best performance
was achieved by Logistic Regression (LR) and Random Forest (RF) classifiers, with an accu-
racy of 84.6%. RF, when using only cardiac and respiratory signals, achieved an accuracy
of 76.5%.
The authors demonstrated the efficacy of applying the Network Physiology approach
with signals from low-invasive, consumer-grade wearable devices to identify different
levels of psychological stress. Their findings are promising and suggest that using inter-
subject models with these parameters is feasible.
In related work, Lawanont et al. explored stress levels in the workplace [
66
]. They
developed a monitoring device for subjects and applied unsupervised learning to the data.
By using the Perceived Stress Scale (PSS) [
71
], they identified relationships between data
clusters and stress levels, with the PSS assessing how stressful individuals perceive various
life situations to be.
Their system gathered various attributes related to behavior and the work environ-
ment [
66
]. These attributes contributed to clustering results and can be used to provide
employees with insights into their stress-related behaviors. The authors also developed
a data-acquisition device using Arduino, Raspberry Pi and multiple sensors. A force
measurement sensor integrated into the seat cushion tracked how frequently the subject
shifted between sitting and standing positions, while a sensor embedded in the mouse pad
recorded the force of mouse movements. By analyzing the force data from these sensors,
they linked it to individual stress levels. Additionally, humidity, temperature and ambient
light sensors collected data on the working environment’s attributes.
Instead of using PSS-based classification, the study employed unsupervised learning
techniques, specifically k-means clustering and hierarchical clustering. The experiment
involved seven participants who worked continuously at their workstation for five hours
without breaks. Initially, participants completed a PSS questionnaire. The researchers
compared the clustering results with the PSS scores of the subjects, using the number of
instances from each subject within each cluster to represent stress levels. Each cluster was
associated with high or low stress based on the number of instances and their PSS values,
thereby determining the stress levels in the work environment.
Song et al. developed a stress-classification model based on a Deep Belief Network
(DBN) [
72
]. DBN is a sophisticated machine learning method that bridges supervised
and unsupervised learning and is widely used in medical fields due to its effectiveness.
They utilized data from the Korea National Health and Nutrition Examination Survey
(2013–2015
) and employed two approaches. First, they assessed stress by comparing
physical activity and lifestyle data (such as sleep duration, blood pressure, body mass index,
alcohol and cigarette use) among individuals under 19 and over 80, relative to their stress
levels. Second, they applied a DBN classification model to this data, performing statistical
analysis to identify significant variables for stress detection. The results showed that the
DBN model achieved an accuracy of 66.23%, outperforming other classification models like
Support Vector Machine, Naive Bayes and Random Forest, proving its effectiveness for
stress detection.
Gil-Martin et al. proposed a deep learning architecture using CNNs for stress detec-
tion [
65
]. Their architecture includes three convolutional layers for extracting features from
inertial and physiological signals. They investigated several signal-processing techniques,

Electronics 2024,13, 3899 26 of 33
such as Fourier transform, third root transformation and Constant Q Transform (CQT),
to prepare the data for the deep learning model. Using the WESAD dataset [
69
], they
examined different classification tasks: two classes (stress vs. no stress), three classes (stress,
baseline and fun) and five classes (stress, baseline, fun, meditation and recovery).
Signals from wearable devices, sampled at different frequencies and containing in-
formation in various frequency ranges, were segmented into 60 s windows with a 0.25 s
overlap. Classification was performed at the window level, with preprocessing including
fast Fourier transform and averaging the spectra of subwindows. The input to the CNN
consisted of the frequency range and spectrum coefficients. They tested two preprocessing
options: calculating the third root of the spectra before averaging and applying CQT after
Fourier transformation. The CNN, featuring three convolutional layers, two max-pooling
layers and three fully connected layers with dropout, learned relevant features from the
signal spectra. Accuracy and F1 scores were used for evaluation, with the third root
transformation significantly improving the results.
The low-frequency components were emphasized, revealing a more distinct harmonic
structure, though the Constant Q Transform (CQT) did not offer additional improvements.
Comparing these results to previous studies, accuracy improved from 93.1% to 96.6% in
classifying stressed versus non-stressed states. Similarly, the accuracy for distinguishing
stress and enjoyment from a baseline increased from 80.3% to 85.1%. The authors suggested
exploring the use of physiological signal measurement devices on other body areas to avoid
potential interference, such as during professional flights, thereby enhancing practical
stress monitoring.
Han et al. utilized Random Forest to determine the optimal combination of symp-
toms for improving classifier performance [
63
]. They evaluated four different classifiers to
achieve the best results. Given the absence of a universally accepted definition of stress
and standardized databases, their study examined work-related stress from multiple per-
spectives. The first perspective combined psychological and psychosocial stress factors to
simulate stressful work conditions. In another approach, they aimed to identify three levels
of stress (none, moderate and high). Data were collected using a wearable device that mea-
sured ECG and respiratory signals, providing continuous stress level monitoring. Random
Forest was used to identify the best feature combinations to enhance
classifier performance.
The study involved 39 healthy participants who underwent the Montreal Imaging
Stress Task (MIST), designed to assess the effects of psychological stress on physiology
and brain activity. The data, segmented into one-minute intervals, included ECG and
respiratory signals. Random Forest ranked the significance of these symptoms to select the
most relevant ones for training and testing classifiers.
Feature selection improved classification accuracy from 78% to 84% when using the
Support Vector Machine (SVM) classifier. The SVM classifier achieved 94% accuracy in dis-
tinguishing between resting and stressed conditions. It also outperformed other classifiers,
such as Linear Discriminant Analysis (LDA), Adaboost and K-Nearest Neighbors (KNN),
with an accuracy of 84% in classifying three stress levels. Combining ECG and respiratory
signal features enhanced the classification model’s performance. The authors integrate
both psychological and psychosocial stress factors to simulate realistic office conditions.
Pepa et al. address stress classification by developing a method based on keystroke
and mouse dynamics (K&MD) [
64
]. Their approach uses real data collected in unsupervised
settings that resemble traditional office or remote work environments. They infer stress lev-
els from PC tasks performed on a custom web application, utilizing various K&MD features
for detection and validate their algorithm’s robustness through inter-subject analysis.
The study, conducted during the COVID-19 pandemic, involved 62 participants aged
18 and older, primarily working remotely. Participants used their own devices to complete
four progressively challenging tasks designed to induce cognitive load and anxiety. These
tasks included text writing, the Tower of Hanoi puzzle, the Simon Speaks game and the
Four Quadrant Test. Data collected included self-rated stress levels, keyboard data and
mouse data. Participants rated their stress on a scale from 1 to 10 after each task. The

Electronics 2024,13, 3899 27 of 33
keyboard data detailed each keystroke, including the character typed, event type, keystroke
duration and timestamp.
Features were extracted from the keyboard and mouse data using a 5 s scrolling
window, generating metrics such as maximum, minimum, mean, standard deviation and
point-to-point deviation. A total of 15 features were calculated. The data were categorized
into low (1–3), medium (4–7), or high (8–10) stress levels. After min-max normalization,
feature selection was conducted using Neighborhood Component Analysis (NCA). The RF
classifier performed the best and was further enhanced with Multiple Instance Learning
(MIL) to address inaccuracies in labeling. MIL involves learning from labeled ensembles of
multiple samples, with RF extending this approach.
Subject-independent 5-fold cross-validation tested both classifiers, with 80% of par-
ticipants used for training and 20% for testing. The dataset contained 429 points (120 low,
222 medium and 87 high stress). The mouse classifiers achieved 63% accuracy, while the
keyboard classifiers reached 76% accuracy.
Akhonda et al. [
67
] explored the relationship between EEG alpha wave amplitude and
concentration, noting that higher alpha wave activity is linked to increased concentration,
while stress is often inversely related. Alpha waves peak when the eyes are closed and are
lowest when open. The goal of the study was to analyze physical and mental performance
variations during prolonged computer use to determine stress levels. Using ECG, EMG,
EOG and EEG signals, the research aimed to accurately detect these changes. Physiological
signals were collected, leading to the extraction of 14 key features, with an expectation of
significant correlations between them due to their shared physiological origin. To enhance
stress detection and classification, a three-layer Back Propagation Neural Network was
employed. Data from 12 participants performing various computer tasks in an office-like
setting revealed that both intense eye work and mental stress were major contributors to
stress, though other factors such as mental state and lack of sleep may also have an impact.
The neural network was trained using data from 8 subjects, each contributing two datasets
(Resting State and Stress State), for a total of 16 datasets, with 14 features extracted from the
signals. The network was then tested on data from 4 subjects not involved in the training
process. Although the exact accuracy of the neural network is not provided as a percentage,
its effectiveness can be inferred from the test results. The network demonstrated its ability
to classify subjects into resting state (RS) and stress state (SS) with considerable precision.
For instance, in the case of Subject 3, the neural network correctly identified a resting
state at the start of the session and a stress state by the fourth hour. Similarly, when the
network was used to distinguish between stress due to eye strain (StES) and mental stress
(MSt), it was able to highlight which type of stress was more dominant for each subject.
For example, for Subject 1, the neural network output in the fourth hour indicated that
eye strain contributed more to stress (StES: 0.5023) than mental stress (MSt: 0.4105). This
consistency between the network’s predictions and the actual self-reports of stress levels
suggests that the neural network was highly effective at detecting and classifying stress
conditions, even though an explicit numerical measure of accuracy was not reported.
Lv et al. [
68
] presented a feature extraction method based on the CSP algorithm
to improve the accuracy of EOG-based saccadic detection. By using CSP to calculate
spatial information from eye-movement sources, the method effectively distinguished
predefined saccadic tasks, such as directional eye movements. Building on this, they
developed an 8-class eye gesture recognition system to detect complex gestures like vertical,
horizontal, diamond, “Z” shape and squares by analyzing consecutive saccadic signals. The
system achieved high accuracy, with 96.8% for saccadic signals and 95.0% for eye gestures.
The research aimed to create a precise EOG-based eye-movement-detection algorithm to
complement video-based methods, particularly for individuals with motor disabilities. The
CSP method enhanced the recognition of EOG signals by projecting them through a spatial
filter bank and using an SVM model for classification. Future work will focus on improving
saccadic detection through optimized sampling and noise reduction, and incorporating
additional features like micro-saccades.

Electronics 2024,13, 3899 28 of 33
Comparative Analysis of Advanced Techniques in Neural Network-Based
Stress Detection
Neural networks have become an effective tool for stress detection, enabling precise
analysis of complex physiological data to assess stress levels accurately. This section
provides a comparative analysis of various neural network techniques used in stress
detection. Table 2provides a comparative overview of the neural network techniques
used for stress detection, highlighting the differences in physiological data utilized and the
accuracy achieved by each method.
The compared studies demonstrate the growing potential of neural networks, espe-
cially deep learning models, to develop accurate, real-time stress-detection systems. These
techniques have been applied to a range of physiological signals, including ECG, EDA,
EEG and behavioral data such as keystroke dynamics.
Table 2. Comparative overview of neural network techniques for stress detection.
Authors Neural Network Technique Data Used Accuracy
Li et al. (2020) [62] 1D CNN, MLP
ECG, EDA, EMG, skin temp,
respiration, accelerometer
(chest/wrist)
CNN: 99.55% (emotion),
99.80% (stress); MLP: 98.38%
(emotion), 99.65% (stress)
Song et al. (2017) [72] DBN
Physical activity, lifestyle data
(sleep, BMI, etc.)
66.23% accuracy (DBN
outperformed SVM, Naive
Bayes and RF)
Zanetti et al. (2021) [21] Logistic Regression, RF ECG, BVP, EEG, respiration
RF: 76.5% accuracy
(cardiac/respiratory signals);
overall: 84.6%
Gil-Martin et al. (2022) [65] CNN Inertial and physiological
signals (WESAD dataset [69])
CNN: 96.6% (stress vs.
non-stress); 85.1% (stress, fun,
baseline)
Han et al. (2017) [63] RF ECG, respiration (MIST
stress task)
SVM with selected features:
94% accuracy (stress
detection); RF: 84% (three
stress levels)
Pepa et al. (2020) [64]RF with Multiple
Instance Learning
Keystroke and
mouse dynamics
Mouse: 63%; Keyboard:
76% accuracy
Akhonda et al. (2014) [67]Back Propagation
Neural Network
EEG, ECG, EMG, EOG (alpha
waves)
Accuracy not explicitly
provided as a percentage but
demonstrated through precise
classification of stress states
and corresponding neural
network outputs.
Lv et al. (2018) [68]SVM, CSP for
feature extraction
EOG signals (eye movements)
96.8% (saccadic detection);
95.0% (eye gestures)
The exploration of advanced neural network techniques for stress detection reveals
a rapidly evolving field with significant potential for enhancing mental health monitor-
ing. Neural networks, particularly deep learning models, have demonstrated remarkable
efficacy in analyzing physiological signals and inferring stress levels with high accuracy.
The studies reviewed provide a comprehensive overview of various approaches, each
contributing unique methodologies and insights into stress detection. Collectively, these
studies illustrate the diverse and innovative approaches being developed in the field of
neural network-based stress detection. The integration of deep learning models with physi-
ological data analysis holds promise for creating more effective, personalized and real-time
stress-monitoring systems. As the technology advances, future research will likely continue
to refine these methods, enhance their accuracy and expand their practical applications,
ultimately contributing to better mental health management and stress reduction.

Electronics 2024,13, 3899 29 of 33
6. Discussion
The study makes significant contributions to the field of stress detection by exploring
a diverse range of technologies and methods. By exploring the potential of integrating
stress-detection features into everyday objects like PC mice or keyboard, it opens up new
possibilities for continuous and unobtrusive stress monitoring. The research highlights
various innovative approaches and devices, demonstrating how these technologies can
enhance stress detection, leading to improved well-being for each employee, which in turn
increases their productivity.
Monitoring stress through PC peripherals, such as keyboards and mice, presents a
range of advantages and disadvantages. On the positive side, these peripherals are already
integrated into users’ daily routines, allowing for seamless and continuous stress monitor-
ing without requiring additional devices or altering existing workflows. This non-intrusive
approach ensures that stress assessment occurs in the background, thereby enhancing user
comfort and acceptance. Moreover, utilizing existing peripherals for stress monitoring
can be more cost-effective compared to developing separate, specialized wearable devices,
making the technology more accessible and scalable. Continuous data collection through
these devices can provide real-time feedback, potentially allowing users to manage stress
proactively and improve their overall well-being and productivity.
Implementing stress-monitoring systems using computer mice, keyboards (or even
smartphone keyboard [
73
]) can greatly benefit employers by fostering a more reliable
and productive workforce. These systems provide continuous, real-time assessments of
employee well-being, helping to identify stress levels that could impact performance and re-
liability. This proactive approach enables the implementation of timely interventions, such
as offering support or adjusting workloads, which can also enhance overall productivity
and job satisfaction. Furthermore, accurate tracking of stress levels also offers insights into
patterns and triggers affecting employee performance and decision-making, facilitating
better management of potentially risky situations and leading to improved problem-solving
and decision-making.
However, there are significant drawbacks to consider. The accuracy of stress detection
can be influenced by the sensitivity and placement of sensors within the peripherals, as well
as interference from other activities, potentially leading to unreliable stress assessments.
Additionally, the scope of monitoring may be limited to certain physiological signals or
behavioral patterns, potentially missing other crucial indicators of stress. Motion artifacts
from normal keyboard and mouse usage can complicate data interpretation, requiring
additional processing to ensure accuracy. Individual variability in typing patterns, mouse
usage and stress responses may also impact the effectiveness of the monitoring systems,
requiring customization or calibration for different users. Lastly, the effectiveness of stress
monitoring depends on the continuous use of these peripherals. Gaps in usage, such as
switching devices or taking breaks, can result in incomplete data and reduce the reliability
of stress assessments.
Overall, while stress monitoring using PC peripherals presents promising opportuni-
ties for improving employee well-being and productivity, it also comes with limitations
that need to be addressed to maximize its effectiveness and reliability.
7. Conclusions
In recent years, the importance of detecting and monitoring stress has become increas-
ingly evident due to its significant impact on both mental and physical health. Stress is
known to contribute to a range of issues, including anxiety, depression, cardiovascular
disease and cognitive impairment. As such, early detection and ongoing monitoring are
crucial for effective stress detection. Our review highlights the potential of innovative
devices to transform how we interact with technology by integrating advanced sensors and
tools for detecting stress.
Smart PC peripherals offer a range of functionalities beyond traditional input devices,
including the monitoring of physiological metrics such as stress levels, hand movements

Electronics 2024,13, 3899 30 of 33
and overall ergonomic impact. By leveraging technologies such as biosensors, machine
learning algorithms and real-time data analysis, these devices might provide valuable
insights into user well-being and performance.
The ability to monitor stress and other physiological signals through a smart PC
mouse opens new routes for improving workplace health and productivity. Real-time
feedback allows for timely interventions, potentially reducing the risk of stress-related
issues and enhancing overall job satisfaction. Furthermore, the integration of these devices
into everyday work routines offers a practical approach to ergonomics, helping users
optimize their working conditions and prevent strain or discomfort.
As the technology continues to evolve, future developments in smart PC perpiherals
could offer even more sophisticated features, including enhanced accuracy in physiological
measurements and more intuitive user interfaces. Continued research and innovation will
be crucial in refining these devices and expanding their applications, ultimately contributing
to healthier and more efficient work environments.
In summary, smart PC perpiherals represent a promising intersection of technol-
ogy and health monitoring, providing both immediate and long-term benefits for users.
By addressing the challenges of modern work environments and offering actionable in-
sights, these devices have the potential to significantly enhance both user experience
and productivity.
Author Contributions: Conceptualization, J.K. and P.K.; methodology, R.P., M.S. and T.T.; validation,
J.B., P.K. and R.P.; formal analysis, J.K. and M.S.; resources, T.T. and J.B.; writing—original draft
preparation, M.S. and J.K.; writing—review and editing, P.K. and R.P.; project administration, J.K.;
funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by VEGA 1/0241/22 Mobile robotic systems for support during
crisis situations.
Data Availability Statement: Not applicable.
Acknowledgments: We are grateful for the financial support provided by the project VEGA 1/0241/22
Mobile robotic systems for support during crisis situations. This work was also supported by “Nein-
vazívne monitorovanie stresu ˇcloveka za pomoci UI” through Nadácia Tatra banky under grant
2024digVS003.
Conflicts of Interest: The authors declare no conflicts of interest.
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