User abilities in detecting vibrotactile signals on the feet under varying attention loads

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Abstract

The future of human space exploration will involve extravehicular activities (EVA) on foreign planetary surfaces (i.e. Mars), an activity that will have significantly different characteristics than the common exploration scenarios on Earth. The required use of a bulky, pressurized EVA suit perceptually disconnects human explorers from the hostile foreign environment, increasing the navigation workload and risk of collision associated with traversing through unfamiliar, rocky terrain. To assist the explorer in such tasks, multi-modal information presentation devices are being designed and evaluated. One application is to assist astronauts in ground obstacle avoidance via tactile channels of the feet. Before utilizing these signals as a form of information presentation, it is necessary to first characterize the tactile perception capabilities of the feet for selected vibration location and signal types, in particular during distracted attention states. The perception of tactile signals must be robust under various cognitive loads as the user will be involved in multiple tasks. The current study consisted of participants completing a vibrotactile detection study, with independent variables of attention state, vibration location and vibration signal type. Tactile cues were provided using haptic motor vibrations at six different locations on each foot for four different vibration levels (High, Low, Increasing and Decreasing), resulting in 24 unique vibrations per foot. Each treatment was repeated six times per attention state and vibrations were presented randomly within a time window of 2–7 s. After each trial, participants indicated the location and level of the vibration perceived. Accuracy of response was analyzed across conditions and results provide implications for the presentation of tactile information on the feet under varying attention states.

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APA

Gibson, A., Webb, A., & Stirling, L. (2016). User abilities in detecting vibrotactile signals on the feet under varying attention loads. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9743, pp. 322–331). Springer Verlag. https://doi.org/10.1007/978-3-319-39955-3_30

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