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Toward Cognitive Load Inference for Attention Management in Ubiquitous Systems
IEEE Pervasive Computing ( IF 1.6 ) Pub Date : 2020-04-01 , DOI: 10.1109/mprv.2020.2968909
Veljko Pejovic 1 , Martin Gjoreski 2 , Christoph Anderson 3 , Klaus David 3 , Mitja Lustrek 2
Affiliation  

From not disturbing a focused programmer to entertaining a restless commuter waiting for a train, personal ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of their users’ cognitive engagement. Despite impressive advances in the inference of human movement, physical activity, routines, and other behavioral aspects, inferring cognitive load remains challenging due to the subtle manifestations of users’ mental engagements via vital signal reactions. These signals are traditionally captured with expensive, obtrusive, and purpose-built equipment, preventing seamless cognitive load inference for human–computer interaction adaptation. In this article, we present our achievements toward enabling large-scale unobtrusive cognitive load inference. Our approaches rely on mining sensor data collected by commodity wearable devices, and software-defined radio-based wireless radars. We also discuss further related research avenues, as well as ethical issues surrounding automatic cognitive load inference.

中文翻译:

面向泛在系统中注意力管理的认知负荷推断

从不打扰专注的程序员到娱乐等待火车的焦躁不安的通勤者,个人无处不在的计算设备可以极大地增强他们与人类的互动,如果这些设备只知道用户的认知参与。尽管在推断人类运动、身体活动、日常活动和其他行为方面取得了令人瞩目的进展,但由于用户通过重要信号反应进行的心理参与的微妙表现,推断认知负荷仍然具有挑战性。这些信号传统上是使用昂贵的、引人注目的和专用设备来捕获的,从而阻止了人机交互适应的无缝认知负载推断。在本文中,我们展示了我们在实现大规模非侵入式认知负荷推断方面的成就。我们的方法依赖于挖掘由商品可穿戴设备和软件定义的基于无线电的无线雷达收集的传感器数据。我们还讨论了进一步的相关研究途径,以及围绕自动认知负荷推断的伦理问题。
更新日期:2020-04-01
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