Browsing by Author "Pandey, Shivam"

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    Buzz Buzz: Haptic Cuing of Road Conditions in Autonomous Cars for Drivers Engaged in Secondary Tasks
    (2021-04-27) Pandey, Shivam; Byrne, Michael
    Can drivers’ situation awareness during automated driving be maintained using haptic cues which provide information about road and traffic scenarios while the drivers are engaged in a secondary task and without disengaging them from the secondary task? Multiple Resource Theory predicts that using different sensory channels can improve multiple-task performance. Using haptics to provide information avoids the audio-visual channels likely occupied by the secondary task. Drivers played Fruit Ninja as the secondary task while seated in a driving simulator with a Level 4 autonomous system driving. A mixed design was used for the experiment with the presence of haptic cues and the presentation time of situation awareness questions as the between-subjects conditions. Five road and traffic scenarios comprised the within-subjects part of the design. Subjects who received haptic cues had a higher number of correct responses to the situation awareness questions and looked up at the simulator screen fewer times than those who were not provided cues. Subjects did not find the cues to be disruptive and gave good satisfaction scores to the haptic device. Additionally, subjects across all conditions seemed to have performed equally well in playing Fruit Ninja. It appears that haptic cuing can maintain drivers’ situation awareness during automated driving while drivers are engaged in a secondary task. Practical implications of these findings for implementing haptic cues in autonomous vehicles are also discussed.
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    Toward training surgeons with motion-based feedback: Initial validation of smoothness as a measure of motor learning
    (Sage, 2017) Pandey, Shivam; Byrne, Michael D.; Jantscher, William H.; O’Malley, Marcia K.; Agarwal, Priyanshu
    Surgery is a challenging domain for motor skill acquisition. A critical contributing factor in this difficulty is that feedback is often delayed from performance and qualitative in nature. Collection of highdensity motion information may offer a solution. Metrics derived from this motion capture, in particular indices of movement smoothness, have been shown to correlate with task outcomes in multiple domains, including endovascular surgery. The open question is whether providing feedback based on these metrics can be used to accelerate learning. In pursuit of that goal, we examined the relationship between a motion metric that is computationally simple to compute—spectral arc length—and performance on a simple but challenging motor task, mirror tracing. We were able to replicate previous results showing that movement smoothness measures are linked to overall performance, and now have performance thresholds to use in subsequent work on using these metrics for training.