The MIT AgeLab n-back

Reimer, Bryan; Gulash, Chuck; Foley, James; Arredondo, Stephen; Waldmann, Alexander

doi:10.1145/2667239.2667293

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…Therefore, glance data for the current study were manually coded based on video of the driver following the taxonomy and procedures outlined in Reimer et al ( 2013 , Appendix G). Software, now available as open source (Reimer, Gruevski, and Couglin 2014 ), allowed for rapid frame-by-fame review and coding. Each task period of interest was independently coded by two evaluators.…”

Section: Methodsmentioning

confidence: 99%

Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

et al. 2015

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One purpose of integrating voice interfaces into embedded vehicle systems is to reduce drivers’ visual and manual distractions with ‘infotainment’ technologies. However, there is scant research on actual benefits in production vehicles or how different interface designs affect attentional demands. Driving performance, visual engagement, and indices of workload (heart rate, skin conductance, subjective ratings) were assessed in 80 drivers randomly assigned to drive a 2013 Chevrolet Equinox or Volvo XC60. The Chevrolet MyLink system allowed completing tasks with one voice command, while the Volvo Sensus required multiple commands to navigate the menu structure. When calling a phone contact, both voice systems reduced visual demand relative to the visual–manual interfaces, with reductions for drivers in the Equinox being greater. The Equinox ‘one-shot’ voice command showed advantages during contact calling but had significantly higher error rates than Sensus during destination address entry. For both secondary tasks, neither voice interface entirely eliminated visual demand. Practitioner Summary: The findings reinforce the observation that most, if not all, automotive auditory–vocal interfaces are multi-modal interfaces in which the full range of potential demands (auditory, vocal, visual, manipulative, cognitive, tactile, etc.) need to be considered in developing optimal implementations and evaluating drivers’ interaction with the systems.Social Media: In-vehicle voice-interfaces can reduce visual demand but do not eliminate it and all types of demand need to be taken into account in a comprehensive evaluation.

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Section: Methodsmentioning

confidence: 99%

Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

et al. 2015

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“…Following procedures detailed in Mehler et al ( 4 ), two trained coders independently manually coded glances in each dataset and a third coder mediated any discrepancies. Open source software, which allowed for frame-by-frame review and annotation of driver eye movements, was used for this coding ( 15 ).…”

Section: Methodsmentioning

confidence: 99%

Assessing Driving Simulator Validity: A Comparison of Multi-Modal Smartphone Interactions across Simulated and Field Environments

McWilliams¹,

Ward

Mehler³

et al. 2018

Transportation Research Record: Journal of the Transportation R

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The use of a driving simulator as a tool to evaluate secondary task performance elicits the question of simulator validity. After upgrading an existing driving simulator from a medium-fidelity to a high-fidelity configuration with a new software environment, a study was run to benchmark this simulator against previously published highway-driving data. A primary goal was to assess relative and absolute validity in a simulated highway environment. Data from 72 participants who performed manual and voice-based contact dialing tasks with a center-stack-mounted smartphone in either the driving simulator or driving on the highway in one of two vehicles is considered. This analysis compared secondary task demand between the simulator and on-road vehicles by primarily considering driver off-road glance behavior. Mean total eyes-off-road time, mean single-glance duration, and the number of long off-road glances showed similar patterns relative to the manual versus voice-based tasks in the simulator and the two on-road vehicles. A driving performance metric, percentage change of standard deviation of velocity, showed differing results between the simulator and on-road vehicles. It is concluded that these data make a strong argument for relative validity, and in some cases absolute validity, for this simulator for studying glance behavior associated with in-vehicle devices under a highway-driving scenario.

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“…In the case of manual coding of video images, the timing of glance is labelled from the first video frame illustrating movement to a ‘new’ location of interest to the last video frame prior to movement to a ‘new’ location. Glance data for this study were manually coded using software, now available as open source (Reimer, Gruevski, and Coughlin 2014), that allowed for rapid frame-by-fame review and coding. Each task period of interest was independently coded by two evaluators.…”

Section: Methodsmentioning

confidence: 99%

Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems

2016

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There is limited research on trade-offs in demand between manual and voice interfaces of embedded and portable technologies. Mehler et al. identified differences in driving performance, visual engagement and workload between two contrasting embedded vehicle system designs (Chevrolet MyLink and Volvo Sensus). The current study extends this work by comparing these embedded systems with a smartphone (Samsung Galaxy S4). None of the voice interfaces eliminated visual demand. Relative to placing calls manually, both embedded voice interfaces resulted in less eyes-off-road time than the smartphone. Errors were most frequent when calling contacts using the smartphone. The smartphone and MyLink allowed addresses to be entered using compound voice commands resulting in shorter eyes-off-road time compared with the menu-based Sensus but with many more errors. Driving performance and physiological measures indicated increased demand when performing secondary tasks relative to ‘just driving’, but were not significantly different between the smartphone and embedded systems. Practitioner Summary: The findings show that embedded system and portable device voice interfaces place fewer visual demands on the driver than manual interfaces, but they also underscore how differences in system designs can significantly affect not only the demands placed on drivers, but also the successful completion of tasks.

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The MIT AgeLab n-back

Cited by 15 publications

References 31 publications

Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

Assessing Driving Simulator Validity: A Comparison of Multi-Modal Smartphone Interactions across Simulated and Field Environments

Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems

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