Advancements in Monitoring Physical Fatigue in Aviation:

A Comprehensive Analysis of State-of-the-Art ECG Sensor

Technologies

Andreea Daniela Florea

, Simona-Narcisa Arghir

, Alina Ioana Chira

Ana-María Sollars-Castellanos

, Oscar Sipele-Siale

and Alberto Calvo-Córdoba

INCAS, Bucharest, Romania

INDRA, Madrid, Spain

Keywords: ECG Sensor, Physical Fatigue, Human Factors, Aviation Safety, Monitoring Technology.

Abstract: Physical fatigue in aviation poses a critical challenge to flight safety, as it is characterized by causing reduced

performance and feelings of tiredness, which can be temporary or chronic in nature, necessitating effective

detection methods. Nowadays, due to very promising advances in non-obtrusive sensing technologies,

wearable electrocardiography (ECG) devices have become a reliable physiological instrument to analyze the

heart’s behavior, and ultimately physical fatigue levels. In this study, a literature review is conducted to

explore how detection of physical fatigue can be tackled in the aviation context through current advances in

ECG technologies, delving into commercial-off-the-shelf ECGs from conventional adhesive electrodes to

innovative textile-integrated alternatives. Our approach also involves a comprehensive analysis of the most

relevant metrics, such as SDNN (standard deviation of the N-N interval), SDSD (standard deviation of

successive differences), RMSSD (root mean square of successive N-N interval differences), pNN50

(percentage of successive N-N intervals differing by more than 50 milliseconds) and CV (coefficient of

variation), regarding physical fatigue prediction in the distinct scenario of airplane cockpits. This includes

detailing the latest updates and versions, along with addressing open challenges in deploying these sensors

effectively within the aviation context. Hence, the core focus is on the pivotal role of ECG sensors, the

technical requirements and methodologies needed in identifying physical fatigue to increase flight safety

during a mission. This paper contributes to providing insights into the effectiveness of ECG sensors, exploring

their integration into the cockpit and addressing challenges of incorporating effective computing and health

monitoring in military aviation settings.

1 INTRODUCTION

When experiencing physical fatigue, muscles and

central nervous system weaken, impacting force,

productivity, or performance (Gonzalez et al., 2017).

Fatigue can manifest as annoyance and reduced

capabilities, with effects ranging from transient to

chronic.

Specifically physical fatigue is categorized as

either active, resulting from intense activities causing

muscle soreness, or passive, stemming from

monotonous work leading to symptoms like

forgetfulness, drowsiness, and difficulty focusing

(Hooda et al., 2022).

Previous studies suggest that fatigue poses a

significant safety risk in civil and military aviation.

European aviation's fatigue risk management report

(Booth & Holmes, 2023) reveals alarming data: 25%

of pilots experienced five or more microsleeps, and

72.9% had inadequate sleep between assignments.

This publication was funded by the European Union under

the Grant Agreement 101103592. Its contents are the sole

responsibility of the EPIIC Consortium and do not

necessarily reflect the views of the European Union.

Florea, A., Arghir, S., Chira, A., Sollars-Castellanos, A., Sipele-Siale, O. and Calvo-Córdoba, A.

Advancements in Monitoring Physical Fatigue in Aviation: A Comprehensive Analysis of State-of-the-Art ECG Sensor Technologies.

DOI: 10.5220/0012950000004562

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Cognitive Aircraft Systems (ICCAS 2024), pages 35-42

ISBN: 978-989-758-724-5

Additionally, nearly one in five pilots extended

flight duties twice or more using the Commander's

Discretion in the past four weeks. These statistics

emphasize the pressing need for European airlines to

improve fatigue management to safeguard crew

welfare and flight safety.

Studies reveal that 72% of military pilots flew

while extremely drowsy, with 94% of USAF pilots

and navigators reporting significant fatigue affecting

their performance (Caldwell & Gilreath, 2002),

(Miller & Melfi, 2007). Modern fighter aircraft

cockpits impose considerable psychological and

physical stress, particularly during taxing activities

like combat or cruise, leading to heightened

weariness among pilots. Research indicates increased

fatigue in F-15 pilots during long-haul flights,

amplifying psychological stress (Ohrui et al., 2008).

These findings underscore the necessity of robust

fatigue (physical and mental) management strategies

to safeguard aviation mission safety and

effectiveness. In light of the aforementioned

considerations, the present research paper focuses on

the possibility of determining the installation of

physical fatigue by monitoring cardiac activity for

future applications for military aviation.

2 STATE-OF-THE-ART

Diverse methodologies quantify physical fatigue,

reflecting its complex manifestation and impact.

From traditional to advanced methods, various

techniques provide insights into the physiological

effects induced by fatigue. Multimodal approaches,

including exploring cerebral, cardiac, ocular,

electrodermal, respiratory, motor, glucose, and

thermal activities, are prevalent with wearable

sensors. These methods comprehensively assess

fatigue, capturing physiological responses to exertion

and stress.

Physical fatigue assessment often entails cardiac

activity monitoring with photoplethysmography

(PPG) sensors (Lohani et al., 2019). While PPG

sensors detect pulse wave peaks to determine

heartbeats, they are less precise than ECG sensors,

particularly in identifying the R peak. Irregularities in

pulse waveform morphology, like changes in peak

characteristics, signify compromised cardiovascular

function and heightened physiological stress linked to

physical fatigue. Electroencephalography (EEG)

captures shifts in wakefulness to sleep, with delta and

theta activities showing consistency during fatigue,

while alpha and beta activities decrease during

maximal muscular contraction (Ng & Raveendran,

2007). Additionally, electrodermal activity (EDA)

serves as an indicator of physiological arousal and

emotional states, detecting changes in skin resistance.

Increased skin conductance, decreased skin

conductance reactivity, and delayed skin conductance

reactions are commonly associated with physical

fatigue (Aeimpreeda et al., 2020). Electromyography

(EMG) sensors measure electrical impulses from

muscles, with EMG magnitude rising in the presence

of physical fatigue. Errors may arise due to

movement, electrode misplacement, and cross-talk

from neighboring muscles (Sueaseenak et al., 2017).

Respiratory monitoring using pulse oximeters or

pressure sensors shows increased rates and volumes

with fatigue (Daiana Da Costa et al., 2019). Lastly,

facial behavior analysis, including eye tracking,

reveals fatigue-related indicators such as blinking,

pupil size, and saccadic movement. Metrics like

PERCLOS and blink duration reflect fatigue levels (Ji

et al., 2004). Increased blink frequency is another sign

of tiredness. Saccadic velocity decrease has been

suggested as a biomarker of aviator fatigue (Göker,

2018).

Electrocardiography stands as a cornerstone in the

exploration of fatigue across various domains,

offering profound insights into the intricate

relationship between cardiac rhythm and the

autonomic nervous system. ECG, which records the

heart's electrical activity via repeated cycles, is the

gold standard test for determining cardiac activity.

Electrodes are applied to the skin to acquire the

electrical signal, which is later plotted as voltage

versus time. ECG measurements are performed to

evaluate heart function by using specific electrode

configurations, such as the typical bipolar limb leads

(I, II, III), chest leads (VV1–VV6), and amplified

unipolar limb leads (aaa LL, aaa FF, aaVVsRR)

(Meek & Morris, 2002). Heart rate (HR), the

instantaneous measure of heart electrical activity,

equates to the mean beats per minute (bpm) derived

directly from R-R intervals (Berntson et al., 1997).

Heart rate variability (HRV) denotes the variation in

durations between consecutive heartbeats (Lewis,

2005). Balanced sympathetic and parasympathetic

activities are requisite for relaxed states.

Parasympathetic processes induce increased HRV,

signifying relaxation, while sympathetic nervous

system (SNS) activity maintains readiness during

stress, resulting in reduced HRV and a higher heart

rate. HR analysis yields time, frequency, and

nonlinear parameters.

Heart rate data can be obtained over longer time

spans, up to 24 hours, or during shorter intervals,

ranging from 1 to 5 minutes. Frequency levels as well

ICCAS 2024 - International Conference on Cognitive Aircraft Systems

as time domain values are significantly impacted by

the recording duration (Shaffer & Ginsberg, 2017).

The categories for the recording periods are ultra

short term (less than 1-minute recordings), short term

(around 1–5 minutes recordings), long term (24 hours

or longer recordings) (Anna Persson, 2019).

The time domain analysis encompasses all

techniques that rely on the time R-R interval, which

is often referred to as the N-N (normal to normal)

interval. Some of the time domain parameters based

on R-R intervals are SDNN (standard deviation of the

N-N interval), SDSD (standard deviation of

successive differences), RMSSD (root mean square

of successive N-N interval differences), pNN50

(percentage of successive N-N intervals differing by

more than 50 milliseconds) and CV (coefficient of

variation).

Frequency domain analysis of ECG signals

involves assessing the Power Spectral Density (PSD)

to delineate energy distribution across specific

frequency bands within R-R intervals. These bands

are the following: Low Frequency (LF): 0.04-0.15

Hz; High Frequency (HF): 0.15-0.40 Hz; Very Low

Frequency (VLF): 0.003-0.04 Hz; Ultra Low

Frequency (ULF): <0.003 Hz (McCraty & Shaffer,

2015). The LF, HF, VLF, and ULF bands can

represent activity in the autonomic nervous system

and are associated with a number of physiological

events (McCraty & Shaffer, 2015) . These bands are

used to compute metrics such as ULF power, VLF

power, LF peak, LF power, HF peak, and HF power,

which offer insights into autonomic balance

(McCraty & Shaffer, 2015) . The LF/HF ratio serves

as an indicator of sympathetic and parasympathetic

balance, although its consistency as a fatigue

indicator across studies varies due to experimental

differences and external factors (Hu & Lodewijks,

2020).

When a straight line cannot be drawn to represent

the relationship between the variables, non-linear

parameters are employed. They measure a time series'

unpredictable nature, which represents the

complexity of the mechanisms controlling heart rate

variability (Shaffer et al., 2020). Commonly used

parameters are SD1, SD2, SD1/SD2, approximate

entropy, Shannon entropy, sample entropy.

After analyzing the time domain, frequency

domain, and non-linear metrics, it is clear due to the

fact that the flight scenarios are shorter, all measures

based on 24-hour recordings have to be rejected.

Metrics that can be obtained in 5 minutes or less,

specifically SDNN, pNN50, RMSSD, and HR Max –

HR Min, are desirable in a real-time processing

situation. The HRV features can be found in Table 1.

Various categories of Commercial off the shelf

(COTS) ECG sensors are available, catering to

different needs and preferences. Wearable chest-

based devices, such as the Zephyr BioHarness and

Equivital Ex eq02, offer continuous monitoring of

ECG signals and other physiological parameters,

suitable for various activities. Compact patch devices

like the VitalPatch Biosensor and Savvy provide

lightweight and portable solutions for long-term

monitoring, with discreet adherence to the skin.

Integrated garment systems like the Master Caution

System 2.0 offer comprehensive monitoring of

multiple parameters, ideal for clinical or research

settings. Traditional Holter monitors remain essential

in diagnostic settings, providing high-resolution ECG

Table 1: HRV features.

Measures Feature Unit Descri

tion

Time

domain

meanNN ms Mean of NN interval se

uence.

meanHR 1/min Mean of heart rate sequence.

SDNN ms Standard deviation of NN interval sequence.

RMSSD ms Root means square of successive differences in NN interval sequence.

NN50 % Percenta

e of NN50 in total intervals.

Frequency

domain

aLF ms2 Absolute

ower of LF band.

aHF ms2 Absolute power of HF band.

LF / HF - Ratio of aLF / aHF.

eakLF Hz Peak frequency for LF band.

eakHF Hz Peak fre

uenc

for HF band.

Nonlinear

domain

SD1 ms Standard deviations alon

the ma

or axis of the elli

se.

SD2 ms Standard deviations alon

the minor axis of the elli

se.

SD1 / SD2 - Ratio of SD1 to SD2.

Approximat

e entro

- Measures the predictability of a time series by quantifying the likelihood that

similar

atterns will continue in the data

Sample

Entro

- Quantifies the complexity of a time series by measuring the likelihood that

similar

atterns of data

oints

ersist within the series

Advancements in Monitoring Physical Fatigue in Aviation: A Comprehensive Analysis of State-of-the-Art ECG Sensor Technologies

Table 2: ECG Device.

Device name

Integration

wearable

technology

Sampling

frequency

[Hz]

Data transmission

protocol

Data

format

Battery

ZephyrTM Performance

System

Shirt, chest strap,

holder (direct)

1000 Hz

Bluetooth

ECHO radio

.csv

DaDISP

.zsf

Lithium

3-hour charging

cycle, up to 300

times.

Bittium Faros

Wearable Patch 1000 Hz

Bluetooth

USB

EDF 7 days battery

BITalino Patch 1000 Hz Bluetooth .csv

Li-Po battery, 8

hours

Equivital Ex eq02

Safe belt with dual

shoulder straps

256 Hz Bluetooth

.csv

Excel

raw

48h battery

duration

VitalPatch Patch 125 Hz

Bluetooth

Radiofrequency

- 120-168h battery

Movesense developer kit Chest strap

512 Hz

configurable

Bluetooth - Coin cell

Savvy Patch 125-500 Hz Bluetooth - 7 days battery

Master Caution System

2.0 by Healthwatch

Vest 200-1000 Hz

Bluetooth

WiFi

3G, 4G

USB

- 12h battery

Polar H10 Chest strap 1000 Hz Bluetooth -

400h, button

shape.

MP160 Starter Systems

with ECG100D (+

BioNomadix) by

BIOPAC

Traditional ECG

design (Einthoven

triangle)

Chest strap or shirt

(using

BioNomadix)

150 Hz

Ethernet

Radiofrequency

.acq

Power supply

72-90h (using

BioNomadix)

24h (Logger

battery)

recordings over an extended period. Biopac Systems'

modular data acquisition systems, including the

MP160 Starter Systems with ECG100D amplifier,

offer reliable and versatile solutions for research and

clinical applications. Wireless transmission systems

like BioNomadix provide flexibility and convenience

for remote monitoring and data collection. The

commercial products discussed in this section are

summarized in Table 2.

3 MATERIALS AND METHODS

3.1 Sensor Technology

In order to acquire cardiac signals, the Zephyr

BioHarness (Medtronic, Minneapolis, MN 55432-

5604 USA) sensor and Biopac MP160 System

(Biopac Systems, Inc., Goleta, CA 93117, USA) with

an ECG100D amplifier were used (see Figure 1).

The Zephyr BioHarness is a wireless chest-based

wearable device with multiparametric sensors for

ECG, respiration, estimated core body temperature,

accelerometry, time, and location. It offers three

modes of wear: as a patch directly on the chest,

secured within a chest strap, or integrated into a

compression shirt. Weighing 18 grams and measuring

28 mm in diameter by 7 mm, it's designed for physical

tasks. It records ECG at 1000 Hz, with a lithium

battery lasting up to 300 recharge cycles. It can store

data for 3.5 to 5 hours, communicates via Bluetooth

Low Energy, and allows data backup in formats such

as .csv, DaDISP, or .zsf files (Medtronic, 2018). The

signal is recorded with the help of the OmniSense

ICCAS 2024 - International Conference on Cognitive Aircraft Systems

Live software. After stopping the recording, the

signal is automatically sent to the OmniSense

Analysis software through which the heart rate can be

seen in the form of a graph and the signal can be

saved. Automated data processing enables direct

extraction of heart rate information (Medtronic,

2017).

Figure 1: Block diagram.

The Biopac MP160 System with ECG100D

Amplifier provides 16 channels for recording at

sampling rates up to 200 KHz, ensuring

comprehensive signal capture with a bandwidth of

0.05 Hz to 150 Hz. Weighing 1.154 kg and measuring

10 x 11 x 19 cm, it's powered via cable and connects

through Ethernet for data transmission.

AcqKnowledge software is used for the acquisition,

visualization and subsequent saving of the signals.

While not as wearable-friendly due to its size, its

extensive capabilities make it suitable for detailed

physiological studies. In contrast to the Zephyr

system, the Biopac allows direct access to raw ECG

signals for processing into measurements such as

heart rate or heart rate variability (Biopac, 2019).

The Zephyr BioHarness offers distinct advantages

over the Biopac MP160 system in terms of

portability, ease of use, and real-time monitoring

capabilities. Its lightweight and wearable design

make it ideal for studies involving dynamic physical

activities, whereas the Biopac system, while

powerful, is better suited for stationary laboratory

experiments.

Furthermore, the Zephyr BioHarness provides

immediate access to physiological data via Bluetooth

connectivity, enabling real-time analysis and

intervention, whereas data acquisition with the

Biopac system may require post-processing and

offline analysis. As a result of these advantages, the

Zephyr BioHarness will be utilized in future

experiments to assess physical fatigue due to its

suitability for real-time monitoring in dynamic

settings.

3.2 Experimental Procedure

The test started with participants receiving and

signing consent forms and GDPR documents,

ensuring their informed consent and compliance with

data protection regulations. A group of five male

subjects, characterized by diverse demographics and

physical fitness levels, participated in the study (age:

36± 12, height: 183 ± 7, weight: 93 ± 20).

The experiment consisted of two sequential

phases: initially, participants underwent a rested state

(RS) assessment (approximately 10 minutes) before

commencing the actual workout, in order to obtain the

individual's basal state of reference. During the initial

phase, participants were equipped with a Zephyr ECG

sensor positioned at chest level.

Subsequently, participants engaged in treadmill

exercises (approximately 20 minutes) designed to

induce physical fatigue (Figure 2).

Throughout the experiment, treadmill parameters

were systematically adjusted to escalate both incline

and speed, simulating strenuous physical activity.

The Zephyr ECG sensor continuously monitored

participants' cardiac activity during both phases,

providing real-time data on heart rate and related

metrics. This systematic data collection approach

enabled the analysis of physiological responses to

induced fatigue, with subsequent comparisons

between the rested and physically-fatigued states

(PFS).

Figure 2: Demo session - Treadmill exercises.

Advancements in Monitoring Physical Fatigue in Aviation: A Comprehensive Analysis of State-of-the-Art ECG Sensor Technologies

Figure 3: Parameters’ distribution over time for rested and fatigue states.

4 RESULTS

The Zephyr ECG sensor was selected for these

experiments due to its versatility, accuracy, and real-

time monitoring capabilities. Overall, the Zephyr

ECG sensor offered the necessary features and

functionality to effectively capture and analyze

participants' physiological responses to induced

fatigue, making it the preferred choice for this study.

The RR interval is a time-domain indicator of the

combined influence of the sympathetic nervous

system (SNS) and parasympathetic nervous system

(PNS). Therefore, this paper focuses on the

evaluation of parameters in the time domain in order

to observe changes with the onset of physical fatigue.

Time domain analysis is convenient when dealing

with real-time requirements (e.g., short duration

recordings). The recordings were taken during the rest

period and then at the beginning of the physical

exercise and until its completion. As mentioned earlier,

we chose to work with 5-minute windows. Hence, we

calculated the main indices: SDNN, RMSSD, SDSD,

NN50 and pNN50. The distribution of the values

obtained during the rest period and the running period

on the treadmill are shown in Figure 3.

There are no generally acknowledged standard

values for HRV indices that can be used for clinical

purposes due to the variability from person to person

influenced by age, sex, physical condition, etc.

Despite this, we can still identify whether physical

fatigue has been installed. That is because when

physical fatigue sets in, the parasympathetic activity

reduces, resulting in lower values for all the

parameters (Shaffer & Ginsberg, 2017). This is also

seen in the way the parameters are distributed in

Figure 3. We can assume that because of the fatigue

state, there are no considerable changes that occur in

the durations of successive RR intervals. This is

reflected in the low values of the median for NN50

and pNN50 parameters.

Additionally, we are able to state that HR levels

are rising, indicating the effort expended during

physical exercise. This occurs as a result of SNS

activity (fight or flight), which is dominant under

these circumstances.

5 DISCUSSION

Testing of the Zephyr and Biopac systems highlighted

Zephyr's advantage in real-time monitoring during

physical activity demonstrations, thanks to its

wireless design and Bluetooth connectivity that

allows immediate access to data. However, for

cockpit integration, both devices can provide valid

data but given considerations of space constraints and

interaction with the pilot's equipment the Zephyr still

manages to be a better fit.

Among the challenges encountered during the

experiments were limitations on the viability of

certain metrics due to their dependence on longer

recordings, as well as the need to derive metrics only

from the RR range. Some of the most widely used

measures are SDNN, SDSD, and RMSSD, according

to the literature (McCraty & Shaffer, 2015). This is

appropriate for our research goal, but there are certain

drawbacks. For example, we need to determine

whether these short-term metrics accurately capture

the physiological process we are studying and

whether they yield more accurate results than 24-hour

recordings, which could provide even more accurate

data.

It was decided to choose only male subjects based

on the dominance of this gender among pilots. Thus,

ICCAS 2024 - International Conference on Cognitive Aircraft Systems

the results are gender specific. A challenge

encountered was the need to modify the training

scenarios based on individual physical condition.

6 CONCLUSIONS

The selection of the Zephyr ECG sensor for these

experiments was driven by its exceptional suitability

for assessing physical fatigue. With its robust

capabilities in real-time monitoring and accurate

measurement of cardiac activity, including heart rate

and related metrics, the Zephyr emerged as the

optimal choice for capturing physiological responses

during treadmill exercises. Its wireless design and

comfortable chest-level positioning ensured seamless

integration into the experimental setup, allowing

participants to engage in physical activity freely.

While acknowledging the versatility of the Biopac

system for other types of data acquisition, the

Zephyr's physiological responses solidified its

position as the better option for this study.

Before and after fatigue, ECG data were examined

using linear (time domain) dynamics. The findings

indicated that following fatigue, the time-domain

indices (SDNN, RMSSD, SDSD, NN50, and pNN50)

decreased. The outcome confirms that assessing

physical fatigue levels with HRV is a feasible

approach (Shaffer & Ginsberg, 2017).

Examining pilots' physical fatigue is important for

aviation safety. Pilots face demanding schedules and

high-altitude environments, leading to fatigue. This

can impair cognitive function and decision-making

during flights. By understanding fatigue factors,

interventions can be implemented to mitigate risks.

In future works, more sensors will be included,

such as a photoplethysmograph, an electroencephalo-

graph and an electromyograph. These sensors add to

the real-time insights of physiological and cognitive

changes during flight. Incorporating them enhances

fatigue research, enabling targeted interventions and

improving aviation safety standards. Future research

will involve using flight simulators to induce fatigue

through prolonged or intensive flight simulations.

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