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Experimental Evaluation of Auditory Human Interface for Radiation Awareness Based on Different Acoustic Features

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Human Interface and the Management of Information. Information Presentation and Visualization (HCII 2021)

Abstract

During the maintenance of a nuclear power plants (NPP), being aware of radioactive strength at one’s position is an important issue to ensure workers’ safety. For that purpose, a human interface for radiation awareness is useful. Auditory sensation is a reliable sensation widely used for different kinds of warning signals. Different acoustic features such as loudness, frequency and interval between beeps can give different acoustic stimuli which represent radioactive intensity information to the users. In this study, experiments were conducted to find out which acoustic feature perform the best in radiation awareness and avoidance of radiation in NPP field work. Three different kind of alarms were designed based on different acoustic features. In order to evaluate these alarms, a virtual experimental environment was developed where virtual radiation distribution was simulated. Participants were required to move around and do calculation tasks in the environment. Meanwhile, they were also required to avoid the high radioactive area and reduce their radiation exposure listening to the alarm as the human interface. Their traces were recorded and their total radiation exposure was calculated and compared among three kinds of alarms. The result showed that loudness was the best acoustic feature for radiation awareness and avoidance comparing with the two acoustic features. Besides, staying time in radioactive environment has strong influence on radiation exposure. Although human interface is effective in radiation awareness, reduction of staying time in radioactive environment is very important to keep workers’ safety.

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Correspondence to Dingming Xue .

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Xue, D., Shinma, D., Harazono, Y., Ishii, H., Shimoda, H. (2021). Experimental Evaluation of Auditory Human Interface for Radiation Awareness Based on Different Acoustic Features. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information Presentation and Visualization. HCII 2021. Lecture Notes in Computer Science(), vol 12765. Springer, Cham. https://doi.org/10.1007/978-3-030-78321-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-78321-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78320-4

  • Online ISBN: 978-3-030-78321-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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