International Journal of Human Factors Modelling and Simulation
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International Journal of Human Factors Modelling and Simulation (4 papers in press)
Simulation-based Evaluation of Patient Appointment Policies for a Primary Care Clinic with Unscheduled Visits: A Case Study by Waldemar Karwowski, Afrifah Bobbie Abstract: Appointment scheduling represents an effective strategy to improve the health delivery process. The main objective of this study was to develop a discrete-event simulation model to evaluate the impact of appointment scheduling policies on a primary care clinic with unscheduled, walk-in patient visits. A teaching hospital primary care clinic employing faculty and resident physicians was modeled and evaluated. The investigated clinic system included various types of patients, patient paths, and multiple clinic staff that served them under conditions of patient no-shows and unscheduled patient arrivals. The developed simulation model can be used to evaluate trade-offs in appointment scheduling policies in a primary care clinic that utilizes a multi-server queueing process with stochastic arrivals and multiple processes. Recommendations for setting the number of treated walk-in patients with modifications to the appointment scheduling decisions were also provided. Keywords: Appointment scheduling; discrete event simulation; primary care clinic; walk-ins; patient access; simulation; human factors.
Predicting Respirator Size and Fit from 2D Images by Eric Biagiotti, Medhat Korna, Daniel O. Rice, Daniel Barker Abstract: Individuals rely heavily on the efficacy of respirators when there is potential for chemical, biological, radiological, or nuclear threats (CBRN). The U.S. Department of Defense (DoD) requires personnel to undergo time-consuming fit tests for full face respirators, which are a critical component of the Personal Protective Equipment (PPE) ensemble. The quality of respirator fit directly contributes to its effectiveness. Leveraging the ubiquity and capabilities of mobile devices, machine learning, and advances in computerized 3D modeling, we seek to make this process simpler, faster, and more accurate. This paper introduces the Mask Analysis and Size Quantification (MASQ) framework: an extensible mobile-based semi-automated system that (1) combines 2D images of a subjects head, an existing 3D headform model generation approach, and an analytic model to recommend a size; and (2) establishes a platform for future research of the shape and anthropometric features of the human face and respirator sizing and fit effectiveness. Keywords: 3DMM; 3D morphable model; landmark detection; camera calibration; mobile devices; anthropometry; chemical; biological; radiological; or nuclear threats; CBRN; respirator; sizing; fit; machine learning; 2D images; Mask Analysis and Size Quantification; MASQ.
Evaluation of a modelling workflow to obtain subject specific spine geometry for sitting postures by Ilias Theodorakos, Xuguang Wang Abstract: A three steps modelling workflow is introduced to obtain personalized spine models at standing and sitting postures. Initially, a principal component analysis based model is employed to locate internal points in a standing posture, using the external trunk shape as input. Then, these estimated internal points are used to personalize a generic anatomical spine template at a standing posture, using the AnyBody Modeling System. The personalized spine model is repositioned into a sitting posture using anatomical landmarks (AL). The proposed workflow was verified with MRI scans obtained from nine participants in standing and sitting postures. The mean prediction error of the spine joint centres was 18.7 (3.6) mm for the standing and 13.1 (5.0) mm for the sitting posture when all AL were used. The suggested workflow can provide accurate spine models at sitting postures when enough information (AL) is available regarding the spine, for the model positioning. Keywords: Personalization; Spine; Subject-specific model; sitting biomechanics; sitting posture; Anybody Modeling System.
Enhanced Perception of Risk in a Driving Simulator by Francesco Walker, Andrea L. Hauslbauer, Daniel Preciado, Marieke H. Martens, Willem B. Verwey Abstract: One major concern with driving simulator studies is the lack of perceived risk for participants. This has led some authors to question the behavioural validity of simulator-based research. In this study, we investigated this concern by compensating for the possible perceived absence of risk with an anxiety-inducing risk factor: Participants were told that if they had a collision, they would receive a mild electric shock. We hypothesized that the addition of the new risk factor would increase participants sense of presence the feeling of truly being and belonging in the virtual environment. We also analysed their driving behaviour, physiology, anxiety, and workload. Overall, we observed very few differences between the threat and the control group: Both reported a strong sense of presence. This suggests that, even without the risk of physical harm, mid-level driving simulators already elicit a strong sense of presence and that the lack of physical crash risk is unlikely to affect study results. Keywords: simulated driving; driving simulator; sense of presence; ecological validity; behavioural validity; risk perception; human factors; shock paradigm; mid-level simulator; driving behaviour; presence; fear conditioning; virtual environments; virtual reality.