Title
Neuroergonomic assessment of mental workload in adaptive industrial human-robot collaboration: doctoral dissertation
Creator
Caiazzo, Carlo, 1995-
CONOR:
99000841
Copyright date
2024
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Autorstvo 3.0 Srbija (CC BY 3.0)
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 29.11.2024.
Other responsibilities
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Kragujevcu
Faculty
Fakultet inženjerskih nauka
Alternative title
Neuroergonomska procena mentalnog opterećenja u adaptivnoj industrijskoj saradnji čovek-robot
Publisher
[C. Caiazzo]
Format
117 listova
Abstract (en)
The fifth industrial revolution, known as Industry 5.0 (IR5.0), is on its way to of integrating
humans at the centre of production processes, resulting in creative industrial collaborative
workplace designs. Collaborative robots, or cobots, enable enterprises to improve their social
sustainability while maintaining efficiency. The deployment of these devices pave the way for
human-robot collaboration (HRC), which combines the benefits of automation, such as
efficiency and precision, with the versatility and soft skills of humans. Psychophysiological
measurements in HRC, through the deployment of non-invasive, compact, and versatile
sensors, can be used to identify and analyse the human's physiological responses, like the
mental workload (MWL), to the robot engagement. In this sense, Neuroergonomics enables a
more accurate estimation of the workload of the operator executing a task. MWL reflects the
amount of mental effort required by an employee to complete a task.
The PhD work presents a comparative analysis of three laboratory experimental scenarios: in
the first, the participant assembles a component without the intervention of the robot (Standard
Scenario); in the second scenario, the participant performs the same activity in collaboration
with the robot (Collaborative Scenario); in the third scenario, the participant gets fully guided
task in collaboration with the robot (Collaborative Guided Scenario). EEG analysis was applied
to objectively and real-time assess mental workload. To decrease noise and artefacts, the data
were pre-processed using several procedures such as Independent Component Analysis (ICA).
The mental workload was calculated using a formula that correlated the most intense waves
from the scalp during the analysis. The analysis was connected with questionnaires and
observational data to evaluate the effectiveness of the task completed by the participant in the
three different scenarios, as well as the impact of the cobot on the workforce.
The goal of this analysis is to show the different responses of participants while working or not
alongside the robot in terms of mental workload, efficiency, and productivity of the task.
Abstract ()
Peta industrijska revolucija, poznata kao Industrija 5.0 (IR5.0), je na putu da integriše ljude u
centar proizvodnih procesa, što rezultira kreativnim industrijskim kolaborativnom
organizacijom radnog mesta. Kolaborativni roboti ili koboti, omogućavaju kompanijama da
poboljšaju svoju društvenu održivost uz održavanje efikasnosti. Primena ove opreme otvara
put saradnji čoveka i robota (HRC), koja kombinuje prednosti automatizacije, kao što su
efikasnost i preciznost, sa svestranošću i mekim veštinama ljudi. Psihofiziološka merenja u
saradnji čoveka i robota, kroz primenu neinvazivnih, kompaktnih i raznovrsnih senzora, mogu
se koristiti za identifikaciju i analizu ljudskih fizioloških odgovora, poput mentalnog
opterećenja (MWL), na zajednički rad sa robotom. U tom smislu, neuroergonomija omogućava
tačniju procenu opterećenja operatera koji izvršava zadatak. MWL odražava količinu
mentalnog napora potrebnog od strane zaposlenog da završi zadatak.
Doktorska disertacija predstavlja komparativnu analizu tri laboratorijska eksperimentalna
scenarija: u prvom, učesnik sprovodi proces montaže komponente bez intervencije robota
(standardni scenario); u drugom scenariju, učesnik obavlja istu aktivnost u saradnji sa robotom
(kolaborativni scenario); u trećem scenariju, učesnik dobija potpuno vođen zadatak u saradnji
sa robotom (vođen kolaborativni scenario). EEG analiza je primenjena za objektivnu procenu
mentalnog opterećenja u realnom vremenu. Da bi se smanjila buka i artefakti, podaci su
prethodno obrađeni korišćenjem nekoliko procedura kao što je analiza nezavisnih komponenti
(ICA). Mentalno opterećenje je izračunato korišćenjem formule koja je povezivala
najintenzivnije talase sa kože glave tokom analize. Analiza je povezana sa upitnicima i
opservacionim podacima kako bi se procenila efikasnost zadatka koji je učesnik obavio u tri
različita scenarija, kao i uticaj kobota na radnu snagu.
Cilj ove analize je da pokaže različite odgovore učesnika dok rade ili ne zajedno sa robotom u
smislu mentalnog opterećenja, efikasnosti i produktivnosti zadatka.
Authors Key words
Collaborative Robotics, Neuroergonomics, Industry 5.0, Human-Robot
Collaboration, Mental Workload, Lean Manufacturing
Authors Key words
Kolaborativna Robotika, Neuroergonomija, Industrija 5.0, Saradnja čovekrobot, Mentalno opterećenje, Lean proizvodnja
Classification
007.52:331.101.1(043)
Type
Tekst
Abstract (en)
The fifth industrial revolution, known as Industry 5.0 (IR5.0), is on its way to of integrating
humans at the centre of production processes, resulting in creative industrial collaborative
workplace designs. Collaborative robots, or cobots, enable enterprises to improve their social
sustainability while maintaining efficiency. The deployment of these devices pave the way for
human-robot collaboration (HRC), which combines the benefits of automation, such as
efficiency and precision, with the versatility and soft skills of humans. Psychophysiological
measurements in HRC, through the deployment of non-invasive, compact, and versatile
sensors, can be used to identify and analyse the human's physiological responses, like the
mental workload (MWL), to the robot engagement. In this sense, Neuroergonomics enables a
more accurate estimation of the workload of the operator executing a task. MWL reflects the
amount of mental effort required by an employee to complete a task.
The PhD work presents a comparative analysis of three laboratory experimental scenarios: in
the first, the participant assembles a component without the intervention of the robot (Standard
Scenario); in the second scenario, the participant performs the same activity in collaboration
with the robot (Collaborative Scenario); in the third scenario, the participant gets fully guided
task in collaboration with the robot (Collaborative Guided Scenario). EEG analysis was applied
to objectively and real-time assess mental workload. To decrease noise and artefacts, the data
were pre-processed using several procedures such as Independent Component Analysis (ICA).
The mental workload was calculated using a formula that correlated the most intense waves
from the scalp during the analysis. The analysis was connected with questionnaires and
observational data to evaluate the effectiveness of the task completed by the participant in the
three different scenarios, as well as the impact of the cobot on the workforce.
The goal of this analysis is to show the different responses of participants while working or not
alongside the robot in terms of mental workload, efficiency, and productivity of the task.
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