Lean-Interaction: passive image manipulation in concurrent multitasking

D. Schott, Benjamin Hatscher, F. Joeres, Mareike Gabele, Steffi Hußlein, C. Hansen
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引用次数: 0

Abstract

Complex bi-manual tasks often benefit from supporting visual information and guidance. Controlling the system that provides this information is a secondary task that forces the user to perform concurrent multitasking, which in turn may affect the main task performance. Interactions based on natural behavior are a promising solution to this challenge. We investigated the performance of these interactions in a handsfree image manipulation task during a primary manual task with an upright stance. Essential tasks were extracted from the example of clinical workflow and turned into an abstract simulation to gain general insights into how different interaction techniques impact the user’s performance and workload. The interaction techniques we compared were full-body movements, facial expression, gesture and speech input. We found that leaning as an interaction technique facilitates significantly faster image manipulation at lower subjective workloads than facial expression. Our results pave the way towards efficient, natural, hands-free interaction in a challenging multitasking environment.
精益交互:并发多任务中的被动图像处理
复杂的双手动任务通常受益于支持视觉信息和指导。控制提供这些信息的系统是一个次要任务,它迫使用户执行并发多任务,这反过来可能影响主任务的性能。基于自然行为的交互是应对这一挑战的一个很有希望的解决方案。我们研究了这些交互的性能在免提图像处理任务期间的主要手动任务与直立姿态。从临床工作流程示例中提取基本任务,并将其转换为抽象模拟,以获得关于不同交互技术如何影响用户性能和工作量的一般见解。我们比较的交互技术是全身动作、面部表情、手势和语音输入。我们发现,学习作为一种交互技术,在较低的主观工作量下,比面部表情更能促进更快的图像处理。我们的研究结果为在具有挑战性的多任务环境中实现高效、自然、免提的交互铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.20
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