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Warning a distracted driver: smart phone applications, informative warnings and automated driving take-over requests

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While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature

While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.

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Date Created
2017

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Point of Care Detection of Iron Metabolism Parameters Through Colorimetric Sensing

Description

Abnormally low or high blood iron levels are common health conditions worldwide and can seriously affect an individual’s overall well-being. A low-cost point-of-care technology that measures blood iron markers with a goal of both preventing and treating iron-related disorders represents

Abnormally low or high blood iron levels are common health conditions worldwide and can seriously affect an individual’s overall well-being. A low-cost point-of-care technology that measures blood iron markers with a goal of both preventing and treating iron-related disorders represents a significant advancement in medical care delivery systems. Methods: A novel assay equipped with an accurate, storable, and robust dry sensor strip, as well as a smartphone mount and (iPhone) app is used to measure total iron in human serum. The sensor strip has a vertical flow design and is based on an optimized chemical reaction. The reaction strips iron ions from blood-transport proteins, reduces Fe(III) to Fe(II), and chelates Fe(II) with ferene, with the change indicated by a blue color on the strip. The smartphone mount is robust and controls the light source of the color reading App, which is calibrated to obtain output iron concentration results. The real serum samples are then used to assess iron concentrations from the new assay and validated through intra-laboratory and inter-laboratory experiments. The intra-laboratory validation uses an optimized iron detection assay with multi-well plate spectrophotometry. The inter-laboratory validation method is performed in a commercial testing facility (LabCorp). Results: The novel assay with the dry sensor strip and smartphone mount, and App is seen to be sensitive to iron detection with a dynamic range of 50 - 300 µg/dL, sensitivity of 0.00049 µg/dL, coefficient of variation (CV) of 10.5%, and an estimated detection limit of ~15 µg/dL These analytical specifications are useful for predicting iron deficiency and overloads. The optimized reference method has a sensitivity of 0.00093 µg/dL and CV of 2.2%. The correlation of serum iron concentrations (N=20) between the optimized reference method and the novel assay renders a slope of 0.95, and a regression coefficient of 0.98, suggesting that the new assay is accurate. Lastly, a spectrophotometric study of the iron detection reaction kinetics is seen to reveal the reaction order for iron and chelating agent. Conclusion: The new assay is able to provide accurate results in intra- and inter- laboratory validations and has promising features of both mobility and low-cost.

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Date Created
2020