理解用户和任务的多模式协调措施

Siyuan Chen, J. Epps
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引用次数: 2

摘要

生理和行为测量允许计算设备通过了解用户的心理负荷来增强用户交互体验。当前的技术通常利用不同模式之间的互补信息来索引特定任务内的负载水平。在这项研究中,我们提出了一种新的方法,利用生理/行为变化事件之间的时间来衡量四种任务类型的低负荷和高负荷水平。一项用户研究收集了24名参与者的眼睛、语言和头部运动数据,结果表明,在高负荷水平和低负荷水平之间,所提出的测量方法存在显著差异,具有高效应量。研究还发现,自愿行动更有可能在任务期间得到协调。对多模态-多传感器接口设计的启示包括:(i)利用多模态的事件变化和交互是可行的,可以区分任务负载水平和负载类型;(ii)应该允许自愿行动有效地完成任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Coordination Measures to Understand Users and Tasks
Physiological and behavioral measures allow computing devices to augment user interaction experience by understanding their mental load. Current techniques often utilize complementary information between different modalities to index load level typically within a specific task. In this study, we propose a new approach utilizing the timing between physiology/behavior change events to index low and high load level of four task types. Findings from a user study where eye, speech, and head movement data were collected from 24 participants demonstrate that the proposed measures are significantly different between low and high load levels with high effect size. It was also found that voluntary actions are more likely to be coordinated during tasks. Implications for the design of multimodal-multisensor interfaces include (i) utilizing event change and interaction in multiple modalities is feasible to distinguish task load levels and load types and (ii) voluntary actions should be allowed for effective task completion.
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