Department of Biological Sciences, Wright State University, Dayton, OH 45435, USA.
Department of Leadership Studies in Education and Organizations, Wright State University, Dayton, OH 45435, USA.
Sensors (Basel). 2020 Aug 27;20(17):4833. doi: 10.3390/s20174833.
Automated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body's physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user's learning activities and cognitive load.
自动跟踪身体状况通过允许个人实时跟踪自己的身体活动和健康状况，引发了一场健康革命。这个概念开始被应用于跟踪认知负荷。众所周知，大脑的活动可以通过身体生理学的变化来测量，但是目前的实时测量方法往往是单一模式和侵入性的。因此，我们提出了可穿戴教育健身（EduFit）追踪器的概念。我们使用机器学习和生理数据来了解如何开发一种能够实时准确跟踪认知负荷的可穿戴设备。在一项初步研究中，我们发现体温、皮肤电导和心率能够以高精度区分（i）解决问题的活动（高认知负荷）、（ii）休闲活动（中等认知负荷）和（iii）白日梦（低认知负荷）在测试数据集。在第二项研究中，我们发现这些生理特征可以在测试数据集中准确地预测用户报告的精神焦点，即使使用相对较少的训练数据。我们解释了这些发现如何为可穿戴设备的开发和实施提供信息，以实时跟踪和记录用户的学习活动和认知负荷。
