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Browsing by Author "Sanghavi, Harsh"

Now showing 1 - 4 of 4
  • Exploring the Influence of Driver Affective State and Auditory Display Urgency on Takeover Performance in Semi-automated Vehicles: Experiment and Modelling
    Sanghavi, Harsh; Zhang, Yiqi; Jeon, Myounghoon (Academic Press – Elsevier, 2023-03)
    As semi-automated vehicles become more available to the general public, it is important to investigate human factors, including both the driver side and the interface side. Despite much research on semi-automated vehicles, little research has conducted considering both driver states and takeover request display design. The present study investigated the effects of drivers’ affective states and auditory display urgency on takeover response time and performance quality. Thirty-six participants experienced takeover scenarios in a semi-automated vehicle using a driving simulator, while playing an online game. For takeover quality, angry drivers drove faster, took longer to change lanes and had lower steering wheel angles than neutral drivers, which made riskier driving. However, there was no difference in eye glance behaviors. Higher frequency and more repetitions of the auditory displays led to faster takeover reaction times, but there was no time difference between angry and neutral drivers. Drivers’ response time to takeover displays from both affect groups was modelled using the QN-MHP framework, which resulted in a R2 of 0.505 with the empirical data collected. In sum, results suggest that drivers’ anger state influenced takeover quality, while display urgency influenced takeover response time. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as to the takeover display design.
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    Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review
    Jesso, Stephanie Tulk; Kelliher, Aisling; Sanghavi, Harsh; Martin, Thomas; Parker, Sarah H. (Frontiers, 2022-04-07)
    The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process.
  • Modeling the Effects of Perceived Intuitiveness and Urgency of Various Auditory Warnings on Driver Takeover Performance in Automated Vehicles
    Ko, Sangjin; Sanghavi, Harsh; Zhang, Yiqi; Jeon, Myounghoon (Elsevier, 2022-10)
    Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles.
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    Using a human factors-centric approach to development and testing of a face shield designed for health care workers: A COVID-19 case study for process and outcomes
    Kurtz, Camden E.; Peng, Yuhao; Jesso, Matthew; Sanghavi, Harsh; Kuehl, Damon R.; Parker, Sarah H. (Mosby-Elsevier, 2022-03-01)
    Background: Face shields are a critical piece of personal protective equipment and their comfort impacts compliant use and thus protectiveness. Optimal design criteria for face shield use in healthcare environments are limited. We attempt to identify factors affecting face shield usability and to test and optimize a face shield for comfort and function in health care settings. Methods: A broad range of workers in a large health care system were surveyed regarding face shield features and usability. Quantitative and qualitative analysis informed the development of iterative prototypes which were tested against existing shields. Iterative testing and redesign utilized expert insight and feedback from participant focus groups to inform subsequent prototype designs. Results: From 1,648 responses, 6 key elements were identified: ability to adjust tension, shifting load bearing from the temples, anti-fogging, ventilation, freedom of movement, and durability. Iterative prototypes received consistently excellent feedback based on use in the clinical environment, demonstrating incremental improvement. Conclusion: We defined elements of face shield design necessary for usability in health care and produced a highly functional face shield that satisfies frontline provider criteria and Emergency Use Authorization standards set by the Food and Drug Administration. Integrating human factors principles into rapid-cycle prototyping for personal protective equipment is feasible and valuable.