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    <ns1:title language="sr">Neuroergonomic assessment of mental workload in adaptive industrial human-robot collaboration: doctoral dissertation</ns1:title>
    <ns2:alt_title language="sr">Neuroergonomska procena mentalnog opterećenja u adaptivnoj industrijskoj saradnji čovek-robot : doctoral dissertation</ns2:alt_title>
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    <ns1:description language="sr">The fifth industrial revolution, known as Industry 5.0 (IR5.0), is on its way to of integratinghumans at the centre of production processes, resulting in creative industrial collaborativeworkplace designs. Collaborative robots, or cobots, enable enterprises to improve their socialsustainability while maintaining efficiency. The deployment of these devices pave the way forhuman-robot collaboration (HRC), which combines the benefits of automation, such asefficiency and precision, with the versatility and soft skills of humans. Psychophysiologicalmeasurements in HRC, through the deployment of non-invasive, compact, and versatilesensors, can be used to identify and analyse the human&apos;s physiological responses, like themental workload (MWL), to the robot engagement. In this sense, Neuroergonomics enables amore accurate estimation of the workload of the operator executing a task. MWL reflects theamount of mental effort required by an employee to complete a task.The PhD work presents a comparative analysis of three laboratory experimental scenarios: inthe first, the participant assembles a component without the intervention of the robot (StandardScenario); in the second scenario, the participant performs the same activity in collaborationwith the robot (Collaborative Scenario); in the third scenario, the participant gets fully guidedtask in collaboration with the robot (Collaborative Guided Scenario). EEG analysis was appliedto objectively and real-time assess mental workload. To decrease noise and artefacts, the datawere pre-processed using several procedures such as Independent Component Analysis (ICA).The mental workload was calculated using a formula that correlated the most intense wavesfrom the scalp during the analysis. The analysis was connected with questionnaires andobservational data to evaluate the effectiveness of the task completed by the participant in thethree 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 notalongside the robot in terms of mental workload, efficiency, and productivity of the task.</ns1:description>
    <ns1:description language="en">Peta industrijska revolucija, poznata kao Industrija 5.0 (IR5.0), je na putu da integriše ljude ucentar proizvodnih procesa, što rezultira kreativnim industrijskim kolaborativnomorganizacijom radnog mesta. Kolaborativni roboti ili koboti, omogućavaju kompanijama dapoboljšaju svoju društvenu održivost uz održavanje efikasnosti. Primena ove opreme otvaraput saradnji čoveka i robota (HRC), koja kombinuje prednosti automatizacije, kao što suefikasnost i preciznost, sa svestranošću i mekim veštinama ljudi. Psihofiziološka merenja usaradnji čoveka i robota, kroz primenu neinvazivnih, kompaktnih i raznovrsnih senzora, moguse koristiti za identifikaciju i analizu ljudskih fizioloških odgovora, poput mentalnogopterećenja (MWL), na zajednički rad sa robotom. U tom smislu, neuroergonomija omogućavatačniju procenu opterećenja operatera koji izvršava zadatak. MWL odražava količinumentalnog napora potrebnog od strane zaposlenog da završi zadatak.Doktorska disertacija predstavlja komparativnu analizu tri laboratorijska eksperimentalnascenarija: 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 saradnjisa robotom (vođen kolaborativni scenario). EEG analiza je primenjena za objektivnu procenumentalnog opterećenja u realnom vremenu. Da bi se smanjila buka i artefakti, podaci suprethodno obrađeni korišćenjem nekoliko procedura kao što je analiza nezavisnih komponenti(ICA). Mentalno opterećenje je izračunato korišćenjem formule koja je povezivalanajintenzivnije talase sa kože glave tokom analize. Analiza je povezana sa upitnicima iopservacionim podacima kako bi se procenila efikasnost zadatka koji je učesnik obavio u trirazličita scenarija, kao i uticaj kobota na radnu snagu.Cilj ove analize je da pokaže različite odgovore učesnika dok rade ili ne zajedno sa robotom usmislu mentalnog opterećenja, efikasnosti i produktivnosti zadatka.</ns1:description>
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        <ns3:lastname>Vukićević</ns3:lastname>
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        <ns3:firstname> Maria</ns3:firstname>
        <ns3:lastname>Chiara Leva</ns3:lastname>
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    <ns7:keyword language="sr" seq="1">Collaborative Robotics, Neuroergonomics, Industry 5.0, Human-RobotCollaboration, Mental Workload, Lean Manufacturing</ns7:keyword>
    <ns7:keyword language="sr" seq="1">Kolaborativna Robotika, Neuroergonomija, Industrija 5.0, Saradnja čovekrobot, Mentalno opterećenje, Lean proizvodnja</ns7:keyword>
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