Cross-Subject Continuous Analytic Workload Profiling Using Stochastic Discrete Event Simulation

Operator functional state (OFS) in remotely piloted aircraft (RPA) simulations is modeled using electroencephalograph (EEG) physiological data and continuous analytic workload profiles (CAWPs). A framework is proposed that provides solutions to the limitations that stem from lengthy training data collection and labeling techniques associated with generating CAWPs for multiple operators/trials. The framework focuses on the creation of scalable machine learning models using two generalization methods: 1) the stochastic generation of CAWPs and 2) the use of cross-subject physiological training data to calibrate machine learning models. Cross-subject workload models are used to infer OFS on new subjects, reducing the need to collect truth data or train individualized workload models for unseen operators. Additionally, stochastic techniques are used to generate representative workload profiles using a limited number of training observations. Both methods are found to reduce data collection requirements at the cost of machine learning prediction quality. The costs in quality are considered acceptable due to drastic reductions in machine learning model calibration time for future operators

Objective-analytical Measures of Workload - the Third Pillar of Workload Triangulation?

The ability to assess operator workload is important for dynamically allocating tasks in a way that allows efficient and effective goal completion. For over fifty years, human factors professionals have relied upon self-reported measures of workload. However, these subjective-empirical measures have limited use for real-time applications because they are often collected only at the completion of the activity. In contrast, objective-empirical measurements of workload, such as physiological data, can be recorded continuously, and provide frequently-updated information over the course of a trial. Linking the low-sample-rate subjective-empirical measurement to the high-sample-rate objective-empirical measurements poses a significant challenge. While the series of objective-empirical measurements could be down–sampled or averaged over a longer time period to match the subjective-empirical sample rate, this process discards potentially relevant information, and may produce meaningless values for certain types of physiological data. This paper demonstrates the technique of using an objective-analytical measurement produced by mathematical models of workload to bridge the gap between subjective-empirical and objective-empirical measures. As a proof of concept, we predicted operator workload from physiological data using VACP, an objective-analytical measure, which was validated against NASA-TLX scores. Strong predictive results pave the way to use the objective-empirical measures in real-time augmentation (such as dynamic task allocation) to improve operator performance

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2015

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Proaktiv bed\uf6mning av kognitiva och mentala arbetsbelastningskrav och resurser i manuell montering - PreKo-modellen

Projektet ”Modell f\uf6r prediktiv bed\uf6mning av kognitiv belastning” (”PreKo”, dnr. 170018)\ua0genomf\uf6rdes 2018-2021 och syftade till att sammanst\ue4lla vetenskaplig och empirisk kunskap\ua0om kognitiv och mental belastning i sammanhanget taktad industriell montering av komplexa\ua0produkter. M\ue5let var att skapa underlag f\uf6r r\ue5dgivning kring utformning av s\ue5v\ue4l\ua0monteringsuppgifter och instruktioner som av monterbara komponenter.F\uf6religgande rapport ger \uf6versiktliga resultat av projektet ”Modell f\uf6r prediktiv bed\uf6mning av kognitiv\ua0belastning - PreKo”. Detaljerade resultat \ue5terges i separata vetenskapliga publikationer. Det praktiska\ua0slutresultatet av projektet \ue4r en fram\ue5tsyftande, holistisk och systemorienterad ”bed\uf6mningschecklista” som\ua0vi kallar PreKo-modellen. Denna modell st\uf6ttar olika roller i tillverkande f\uf6retag i att kartl\ue4gga m\uf6jliga\ua0kognitiva/mentala\ua0sund och lagom utmanande belastning, som fr\ue4mjar mont\uf6rernas trivsel och motivation i arbetet samt h\uf6g\ua0monteringskvalitet. Modellen och det tillh\uf6rande verktyget ”PRECO I” \ue4r t\ue4nkt att hj\ue4lpa f\uf6retag att\ua0analysera f\uf6ruts\ue4ttningarna f\uf6r mont\uf6rers v\ue4lm\ue5ende och prestation samt identifiera krav och resurser fr\ue5n\ua0ett flertal systemniv\ue5er (konstruktionen, monteringsstationen, produktionsfl\uf6det, inl\ue4rning och\ua0instruktioner, arbetslaget och \uf6vriga krav/resurser). F\uf6r att f\ue5 insikt i alla dessa aspekter, som ibland \ue4r\ua0organisatoriskt och geografiskt separerade p\ue5 vissa f\uf6retag, f\uf6respr\ue5kar modellen att ett flertal bed\uf6mare\ua0som representerar olika roller (erfaren mont\uf6r, oerfaren mont\uf6r, konstrukt\uf6r, produktionsberedare och\ua0ergonom/arbetsmilj\uf6ansvarig) f\uf6r att genomf\uf6ra bed\uf6mningen. Eftersom checklistan bygger p\ue5\ua0litteraturstudier och en intervjustudie s\ue5 syftar modellen i nul\ue4get inte till att kvantifiera och m\ue4takognitiv/mental belastning; i st\ue4llet b\uf6r modellen anv\ue4ndas som ett praktiskt orienterande scanningverktyg.Projektet utf\uf6rdes av ett k\ue4rnteam av forskare kopplade till Chalmers Tekniska H\uf6gskola i G\uf6teborg.\ua0Projektet omfattade en informell litteraturstudie; en intervjustudie med tre industriella f\uf6retag;\ua0medverkande i ett flertal forum d\ue4r f\uf6retagen och andra intressenter delgavs prelimin\ue4ra resultat l\uf6pande\ua0under projektet; samt ett antal konferenspresentationer och vetenskapliga publikationer.Covid 19-pandemin under v\ue5ren 2020 till h\uf6sten 2021 p\ue5verkade delvis projektets genomf\uf6rande, dock\ua0endast till lindrig grad. N\ue5gra planerade intervjustudier beh\uf6vde st\ue4llas in pga. utbrottet, som begr\ue4nsade\ua0m\uf6jligheten att kunna ta nya intervjupersoners tid i anspr\ue5k. Dock gjorde forskarlaget bed\uf6mningen att det\ua0insamlade antalet intervjuer (75 totalt, varav 50 med mont\uf6rer och resten med konstrukt\uf6rer och beredare)\ua0totalt sett \ue4r tillr\ue4ckligt f\uf6r att underst\uf6dja v\ue5ra resultat och slutsatser, med givna f\uf6rbeh\ue5ll om att studien \ue4r\ua0i huvudsak kvalitativ och att fokuset h\ue5lls p\ue5 kognitiv och mental belastning hos mont\uf6rer p\ue5 industriella\ua0monteringsf\uf6retag. Vissa personalrelaterade omst\ue4ndigheter och ett internt ekonomiskt sparbeting p\ue5\ua0Chalmers Tekniska H\uf6gskola under 2019-2021 p\ue5verkade ocks\ue5 delvis projektets bemanning och\ua0genomf\uf6rande, men anst\ue4llning s\ue4krades f\uf6r kritiska projektmedlemmar fram till projektets\ua0verksamhetsslut

Effects of Automation on Aircrew Workload and Situation Awareness in Tactical Airlift Missions

In tactical aviation, decision superiority brought upon by high situation awareness remains the arbiter of combat effectiveness. The advancement of sophisticated avionics and highly automated cockpits has allowed for the reduction of aircrew size, and in certain platforms, removal of the crew from the aircraft entirely. However, these developments have not reduced the complex and dynamic interaction between situation awareness and crew workload. While many predictive and experimental methods of evaluating workload exist, situation awareness can only be measured by conducting trials with human operators in a functional prototype. This thesis proposes an innovative methodology to predicatively determine situation awareness potential with discrete-event simulation software. This methodology measures situation awareness as both a function of task accomplishment and workload experienced. Utilizing two common but complex tactical scenarios, this method and existing workload measurement techniques can derive a direct comparison between a reduced-crew highly automated cockpit and a less automated legacy aircraft. Finally, conclusions regarding the effectiveness of replacing human operators with automation in tactical events can be made and tested in future experiments with actual aircraft and aircrews

Air Force Institute of Technology Research Report 2016

This Research Report presents the FY16 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)