Buono, Mario

Capece, Assunta

Lombardi, Ilaria

Monaco, Maria Grazia Lourdes

English

Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli"

Safety Management and Human Factors, Vol. 64, 2022, 221–228https://doi.org/10.54941/ahfe1002649Human-Machine Interaction Safety inManufacturing: A Scoping ReviewIlaria Lombardi1, Sonia Capece1, Maria Grazia Lourdes Monaco2,and Mario Buono11Department of Industrial Engineering, University of Campania Luigi Vanvitelli, 81031Aversa, Italy2Occupational Medicine Unit, University Hospital of Verona, 37134 Verona, ItalyABSTRACTAs a consequence of robotics integration in industrial production processes, physi-cal and mental stress levels due to human-machine interaction increased, potentiallyincreasing production errors and worker injuries. A systematic scoping review of theliterature published in the last 20 years was carried out via PubMed, Scopus, Web ofScience, and Embase to answer this question: ‘What is known from the current litera-ture about the effects of human-machine interaction in the workplace?’. The analysishighlighted that themain problemswere due to the lack of ergonomy in the work orga-nisation, especially regarding cognitive aspects, and the reduced attention thresholdassociated with the monotony and repetitiveness of the work tasks. Training and infor-mation were the essential aspects in managing the reduction of accidents, also usingnew technologies such as cross-reality.Keywords: Human-Machine interaction, Health and safety, Occupational risks, Manufacturing,Injures, Human factorsINTRODUCTIONIndustrialisation has led to the widespread use of tools and machines in dailylife and the workplace. Still, we have moved to user-centred design in thelast few decades and introduced ergonomics or Human Factors (Buono andCapece, 2016). Occupational health and safety have been improved by ada-pting machines and tools to human capabilities, limitations and anatomy.Today, work systems are increasingly built on a foundation of a system con-sisting of workers, tools, tasks, and work contexts (Flaspoler et al. 2009).Rapid advances in automation and digitisation mean that current workingconditions are subject to dynamic change, especially in industrial environ-ments (Körner et al., 2018), which are objectively exposed to risk given thehigh rates of occupational injuries and deaths that are regularly encountered(Song and Awolusi, 2020).Throughout EU-OSHA’s European Agency for Safety and Health at Workreports, including the third European Survey of Enterprises on New andEmerging Risks (ESENER-3), workplace health and safety risks are analy-sed, focusing on psychosocial risks related to work and new technologies© 2022. Published by AHFE Open Access. All rights reserved. 221222 Lombardi et al.linked to Industry 4.0 (Irastorza, 2019). In spring/summer 2019, a total of45,420 establishments - across all business sectors and employing at leastfive people - were surveyed in the 33 countries covered. Although on the onehand, psychosocial and ergonomic factors also related to human-machineinteraction resulted in critical issues, on the other hand mechanisation anddigitisation play an important role in mitigating these risks. In particular, theintroduction and use of digital technologies such as wearable devices and arti-ficial intelligence is an opportunity to support manufacturing processes andworkplace safety. Focusing on the possible impacts of automation systemswithin production lines, the need for ongoing training to keep skills up-to-date is apparent from 79% of the factories surveyed in the EU28. Technologytouched on other important points such as workstation set-up and workinghours (66%), as well as making it easier for the operator to sit for long periods(65%) and perform repetitive movements (60%) (Irastorza, 2019).The paper aimed to summarise available research to answer the followingquestion: ‘What is known from the current literature about the effects ofhuman-machine interaction in the workplace?’.METHODSReferences were screened by setting the database parameters to English,manufacturing, and study type (original articles published in the peer-reviewed journal). Studies published between January 1990 andMarch 2020were searched for in PubMed, Scopus, Web of Science, and Embase. Thepaper is based on a literature review focused onworkplace safety in themanu-facturing sector. The following keywords (MESH and non-MESH terms)were used: ‘Human-machine’ OR ‘Human-robot’ OR ‘Human-computer’AND ‘Interaction*’ OR ‘Interface*’ OR ‘Cooperation’ AND ‘Occupatio-nal’ OR ‘Work*’. Two reviewers independently screened the citations (titleand abstract) identified from all sources. Subsequently, full-text articles werereviewed to determine the final set of eligible studies. Disagreements wereresolved by discussion with the remaining authors. A data extraction formwas developed to determine which variables to extract. The following itemswere included: article identifiers (authors, year of publication); study iden-tifiers (sample size, design, country); the aim of the study; main results. Aqualitative description of the included studies can be found below. Tablesand diagrams are used to synthesise the main findings. The selection processwas carried out using some freeware (Zotero, Rayyan). PRISMA-ScR guide-lines on conducting systematic scoping reviews were followed (Tricco et al.2018).RESULTSA total of 8,122 papers were identified and, after removing duplicates, 1,834available articles were selected. According to the inclusion and exclusioncriteria, 11 articles were included in the final analysis (see Figure n.1).Human-Machine Interaction Safety in Manufacturing: A Scoping Review 223Figure 1: Flow chart diagram depicting the different phases of studies selectionprocess.DISCUSSION AND CONCLUSIONThe current research on safety in the workplace concerning human-machineinteraction shows how this is influenced by the characteristics and modalitiesof the interaction itself. The evaluation of the quality of the interaction withthe machine and the safety levels in the workplace can proceed accordingto two different approaches: the empirical approach that involves samplesof users who interact with the machine performing various activities; theanalytical approach that does not involve users as test subjects, because one or224 Lombardi et al.Table1.Summaryofarticlesselected.Author,yearCountrySettingSamplesizeStudyaimsMainfindingsAyazH.etal.,2012USALaboratory24volunteers(agerangedbetween24to55years)Todescribeanaccuratemeasureofmentalworkloadinhumanoperatorsasacriticalelementofmonitoringandadaptiveaidingsystemsdesignedtoimprovetheefficiencyandsafetyofhuman-machinesystemsduringcriticaltasks.ResultsindicatethatFunctionalNearInfrared(fNIR)measuresaresensitivetomentaltaskloadandpracticelevelandprovideevidenceofthefNIRdeploymentinthefieldforitsabilitytomonitorhemodynamicchangesassociatedwithrelativecognitiveworkloadchangesofoperators.BiondiFNetal.,2021CanadaLaboratory22volunteers(12men,10women)ToinvestigatetheeffectofcognitiveoverloadonassemblytaskperformanceandmuscleactivityWorkingunderahighcognitiveloadnotonlyresultsingreatermuscleactivity(greaterintegratedsEMG)butalsoaffectsassemblytaskcompletiontimes,whichmaydirectlyaffectmanufacturingcycletimes.DasL.etal.,2017IndiaLaboratoryNotspecifiedTodescribeanewframeworkforoperatorlearningandillustrateitusingexperimentalstudiesTheavailableinput(fromeye-tracking)andoutput(operatoractions)datawhensuitablyanalysedwithrespecttotheprocessstatecanaidininferringtheoperator’smentalmodelatanygiventime.Basedonthemodel,theoperator’scurrentknowledgecanbededucedandgapsidentified.Fernetal.,2013GermanyLaboratoryNotapplicableTodescribeacognitivemodeltoinferthemorelikelyuser’sstatesindata-intensivecontexts.EEGandECGinputvariablesarerepresentedinaprobabilisticmodelinwhichlinksaredefinedbasedontheliterature.Theoutcomeofthemodelisaprobabilityofbeinginepttoperforminasuitableway.Incaseofinaptitude,assistanceshouldbedeliveredtotheusertonormalisethecurrentuser’sstate.HerpersR.etal.,2013UKOnthefieldNotspecifiedTodevelopasafetyevaluationstrategythatappliesVRandmixedrealitytechnologiestoinvestigatetheusabilityofworkingmachines.Anevaluationofthestudydemonstratedthatevenexperiencedmetalworkersacceptedthesimulationandreactedasifthegiventaskwasreal.BehaviouraldataofconsideredsubjectsshowedcomparableprofilesandmostsubjectsratedtheVR-basedapproachasareasonablemeansforinvestigatingworksafetyproblems.KörnerU.etal.,2018GermanyLaboratory36workers5differentcompaniesToidentifypotentialstressorsassociatedwiththeintroductionanduseofmoderntechnologiesinthemanufacturingindustry.Theresultsshowthatstressorslinkedtohuman-machineinteractionaretechnicalproblems,poorusability,lowsituationawareness,andincreasedrequirementsonemployeesqualification.Continued.Human-Machine Interaction Safety in Manufacturing: A Scoping Review 225Table1.Continued.Author,yearCountrySettingSamplesizeStudyaimsMainfindingsLuCW.etal.,2012ChinaOnthefield281workers(70male,211female)Toinvestigatetheriskofmusculoskeletaldisordersbecauseofthelongworkhoursandtherepetitiveactivitiesinanunfittedworkstation.Theresultshowsthattheparticipantsreportedhighmusculoskeletaldisordersymptomsintheshoulder(59.8%),neck(49.5%),wrist(39.5%),andupperback(30.6%).And,toreducetheergonomicriskfactors,revisingtheheightoftheworkbenches,chairsandredesigningthetrucktodecreasethechanceofunsuitablepositionswererecommendedandtoreduceotherergonomicshazardsandsetagoodhuman-machineinterfaceandappropriatejobdesign.MedvedykY.etal.,2019UkraineLaboratory10volunteers(agerangedbetween16to25years)Tocreateasimpleandoptimalsystemformonitoringthequalityofcognitivetasksundertheinfluenceofexternalstressors(noise,interference,vibration,etc.)forapplicationinfieldconditions,verifyifthereliablemeasurementispossibleandtodeterminetherecommendationstothemeasuringsystemsufficientlysimpleandsufficientlyprecisetocarryoutsuchmeasurements.Thispreliminarystudyfocusesontheproblemofthestressfactorsinfluenceontheabilityofapersontoperformmentalwork.Withintheframeworkofthestudy,theanalysisofchangesinhumanphysiologicalparametersinstressconditionswasmonitoredandexperimentalstudiesoftheinfluenceofstressfactorsonsolvingcognitivetasksusingpupillometrywereconducted.SauerJ.etal.,2019SwitzerlandLaboratoryNotapplicableToinvestigatetheissueofsocialstressinhuman-machineinteraction.Thereviewshowsthatacloserlinkisneededbetweentheseparateliteratureonsocialstressandautomation.Finally,threemechanismsareproposedthatmaypredicthowsocialstressmayaffectperformance:‘blank-out’-mechanism,‘rumination’-mechanism,and‘increased-motivation’-mechanism.ShahabMA.etal.,2021IndiaLaboratory10workers486tasksToproposeaneye-tracking-basedapproachthatusestheoperator’sattentionallocationduringdifferentpre-specifiedtrainingscenariosalongwithprocessdata,alarminformation,andoperatoractions.Thesaliencemetric(quantifiestheextentofproactivemonitoringusingtime-basedinformationobtainedfromthetrendofprocessvariables.Theauthorsdemonstratetheapplicabilityofthesemetricstoassessingoperators’expertiseusingvariouscasestudies.Thequantificationofferedbythemetricsprecludessubjectivefactorsinthebenchmarkingofoperators,withtheconcomitantbenefitsofobjectivityandrepeatability.ShuggiIMetal.,2017USALaboratory36healthyvolunteers(18menand18women,agerangedbetween19to25years)ToexaminementalworkloadbyemployingtheNASATLXaswellasthechangesinmotorperformanceresultingfromthepracticeofanovelreachingtask.Bothgroup-andcluster-levelanalysesrevealedthat:allparticipantsimprovedtheirperformancethroughoutmotorpractice,andanincreaseinmentalworkloadwasassociatedwithareductionofthequalityofmotorperformancealongwithaslowerrateofmotorimprovement.226 Lombardi et al.more analysts carry out the evaluation of the machine and the work environ-ment with the help of theoretical models. The description and understandingof the user and the task are key elements of the evaluations; other impor-tant elements are mapping task demands and the user’s mental and physicalworkloads during interaction with the machine (Bligard and Osvalder, 2014;Greco et al. 2020).The 11 selected articles highlighted how competence and training influencethe operator in an industrial dimension in which more and more adva-nced technologies are incorporated. Mechanisation and digitalisation playan essential role in mitigating ergonomic risks, assessed from cognitive (suchas mental workload or usability) and physical (such as physical workload orbody posture) perspectives. Therefore, the risk of work-related problems isnot always entirely avoided by a good ergonomic design of work tools andworkspace. Still, factors related to work organisation, user training, and theuse of new technologies also come into play. In particular, the introductionand use of digital technologies such as wearable devices and artificial intel-ligence is an opportunity to support production processes and safety in theworkplace (Körner et al. 2018).The operator is subjected to a high cognitive load due to environmen-tal characteristics, task complexity or repetitiveness, and work organisation.Modern technologies reduce heavy physical labour, speed up work processes,and increase flexibility. On the other hand, data also indicate that technology-related stressors accompany contemporary industrial work. Technical pro-blems were described as frequent stressors; users reported that technicalproblems lead to interruptions, additional pressure for timelines, and mul-titasking, in addition to poor usability of the technical systems they workwith. Working with highly automated systems creates difficulty in buildingan adequate mental representation of machine use. Operators are often facedwith situations where they are unable to develop or maintain proper levelsof awareness, caused by factors such as high system complexity, inadequatevisualisation of the current state of the systems, lack of experience in usingthe systems, and inadequate training and workstation design relative to theoperator’s anthropometric data, often resulting in musculoskeletal disorders(Lu CW et al. 2012).In conclusion, the design of work activities and workstations shouldconsider the workers’ cognitive load and anthropometry throughout theirinvolvement in the risk assessment to improve safety in the workplace.More-over, it is essential to enhance the training of ‘Operator 4.0, advancing bothtechnique and preparation, improving risks awareness of using the machine,finally ensuring optimal health and safety conditions.REFERENCESAyaz, H., Shewokis, P. A., Bunce, S., Izzetoglu, K., Willems, B., and Onaral, B.(2012) “Optical brain monitoring for operator training and mental workloadassessment”. NeuroImage, 59(1), 36–47.Human-Machine Interaction Safety in Manufacturing: A Scoping Review 227Biondi, F. N., Cacanindin, A., Douglas, C., and Cort, J. (2021) “Overloadedand at Work: Investigating the Effect of Cognitive Workload on Assembly TaskPerformance”. Human factors, 63(5), 813–820.Bligård, L-O., Osvalder, A. (2014) “CCPE: Methodology for a Combined Evalu-ation of Cognitive and Physical Ergonomics in the Interaction between Humanand Machine”. Human Factors and Ergonomics in Manufacturing & ServiceIndustries, 24.Buono, M., Capece, S., (2016) “Design and Ergonomics innovative modelof asses-sment and validity for passengerseats in the Aerospace industry”. Ergonomia:sfide sociali e opportunità professionali dalla creativitÃ￿ alla praticaper aumen-tare l’impatto dell’ergonomia nella società, XI National Conference SIE 2016, pp.259.Das, L., Srinivasan, B., and Srinivasan, R. (2017) “Cognitive behaviour based fra-mework for operator learning: Knowledge and capability assessment through eyetracking”.Fernández, F., Lázaro, I., Gilabert, E., Arnaiz, A.,MuÃ±oz, F.M., and Castellanos L.,(2013) “A probabilistic model for cognitive-affective user state awareness”. 11thIEEE International Conference on Industrial Informatics (INDIN), pp. 762–767.Flaspoler, E., Hauke, A., Pappachan, P., Reinert, D., Bleyer, T., Henke, N., Kaluza,S., Schieder, A., Windel, A., Karwowski, W., Slaminen, S., Blaise, J., Claudon, L.,Ciccotelli, J., Eeckelaert, L., Verjans, M., Muylaert, K. and De Beeck, R., (2009)“The humanmachine interface as an emerging risk. EU-OSHA – European Agencyfor Safety and Health at Work”.Greco, A., Caterino, M., Fera, M., and Gerbino, S. (2020) “Digital Twin for Monito-ring Ergonomics during Manufacturing Production”, Applied Sciences 10, n. 21:7758.Herpers, R., Scherfgen, D., Vieth, M., Saitov, T., Felsner, S., Hofhammer, T., Schaefer,M., and Huelke, M. (2013) “Vr-Based Safety Evaluation of Automatically Con-trolled Machine Tools,” 2013 5th International Conference on Games and VirtualWorlds for Serious Applications (VS-GAMES), 2013, pp. 1–4.Irastorza X. (2019). Third European Survey of Enterprises on New and EmergingRisks (ESENER 3), EU-OSHA – European Agency for Safety and Health at Work.Körner, U., MÃ¼ller-Thur, K., Lunau, T., Dragano, N., Angerer, P., and Buchner, A.,(2018) “Perceived stress in human-machine interaction in modern manufacturingenvironments-Results of a qualitative interview study”.Lu„ C.W., Yao C.C., and Kuo, C.W. (2012) “The ergonomics approach for thin filmtransistor-liquid crystal display manufacturing process”. Work (Reading, Mass.),41 Suppl 1, 5627–5630.Medvedyk, T., Antoniuk, I., and Lebid, S. (2019) “Influence of stress factorson cognitive tasks performance”. 2019 IEEE 20th International Conference onComputational Problems of Electrical Engineering, CPEE 2019.Rayyan, (Last access 28 February 2022). The Rayyan’s website: www.rayyan.aiSauer, J., Schmutz, S., Sonderegger, A., andMesserli, N. (2019) “Social stress and per-formance in human-machine interaction: a neglected research field”. Ergonomics,62(11), 1377–1391.Shahab, M.A., Iqbal, M. U., Srinivasan, B., and Srinivasan, R., (2021) “Metrics forobjectively assessing operator training using eye gaze patterns”, Process Safetyand Environmental Protection, Vol. 156, Pages 508-520, ISSN 0957–5820.Shuggi, I. M., Oh, H., Shewokis, P. A., and Gentili, R. J., (2017) “Mental workloadand motor performance dynamics during practice of reaching movements undervarious levels of task difficulty”. Neuroscience, 360, 166–179.228 Lombardi et al.Song, S., Awolusi, I., (2020) “Industrial Safety Management Using Innovative andProactive Strategies”, in G.Moynihan (ed.), Concepts, Applications and EmergingOpportunities in Industrial Engineering, IntechOpen, London.Torres, Y., Nadeau, S., and Landau, K., (2021) “Classification and Quantificationof Human Error in Manufacturing: A Case Study in Complex Manual Assembly”Applied Sciences 11, no. 2: 749.Tricco, A.C., Lillie, E., Zarin, W., O’Brien, K.K., Colquhoun, H., Levac, D., Moher,D., Peters,M.D.J., Horsley, T.,Weeks, L., Hempel, S., Akl, E.A., Chang, C.,McGo-wan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M.G., Garritty, C., Lewin,S., Godfrey, C.M., Macdonald, M.T., Langlois, E.V., Soares-Weiser, K., Mori-arty, J., Clifford, T., TunÃ§alp, Ö., Straus, S.E., (2018) “PRISMA Extension forScoping Reviews (PRISMA-ScR): Checklist and Explanation”. Annals of internalmedicie, Volume 169 No.7, pp. 467–473. ISSN 0003-4819.Tricco, Andrea C, Lillie, Erin, Zarin, Wasifa et al. (2018) “Prisma Extension forScoping Reviews (PRISMA-ScR): Checklist and Explanation”. Annals of InternalMedicine, Volume 169 No.7, pp. 467–473.Zotero, (Last access 31 January 2022). The Zotero’s website: www.zotero.org

Human-Machine Interaction Safety in Manufacturing: A Scoping Review

https://iris.unicampania.it/bitstream/11591/476833/3/AHFE_2022_BUONO_MONACO_CAPECE_LOMBARDI_978-1-958651-40-7_28.pdf

Human-Machine Interaction Safety in Manufacturing: A Scoping Review

Abstract

Similar works

Full text

Available Versions

Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli"