背景:精神疲劳是一种常见且潜在的衰弱状态,可影响个体的健康和生活质量。在某些情况下,它的表现可以先于或掩盖其他严重的精神或生理状况的早期迹象。如今,检测和评估精神疲劳可能具有挑战性,因为它依赖于自我评估和评级问卷,受主观偏见的影响很大。引入更客观的,定量,和敏感的方法来表征精神疲劳可能是关键,以改善其管理和了解其与其他临床条件的联系。
目的:本文旨在研究在自然分型过程中使用击键生物识别技术进行精神疲劳检测的可行性。由于打字涉及受精神疲劳影响的多种运动和认知过程,我们的假设是,在击键动力学中捕获的信息可以提供一个有趣的手段来表征用户在现实世界中的精神疲劳。
方法:我们应用域转换技术来适应和转换TypeNet,一个最先进的深度神经网络,最初用于用户身份验证,生成针对疲劳检测任务优化的网络。所有实验均使用包含不同上下文和数据收集协议的3个击键数据库进行。
结果:我们的初步结果表明,疲劳与静止样品分类的曲线下面积表现在72.2%至80%之间,这与以前发表的关于日常警觉性和昼夜节律周期的模型一致。这证明了我们提出的系统通过自然打字模式表征精神疲劳波动的潜力。最后,我们研究了一种主动检测方法的性能,该方法利用按键生物特征模式的连续性来实时评估用户的疲劳。
结论:我们的结果表明,精神疲劳的精神运动模式在自然分型过程中表现出来,这可以通过自动分析用户与他们的设备的日常交互来量化。这些发现代表了朝着更客观的发展迈出的一步,可访问,和透明的解决方案来监控现实环境中的精神疲劳。
BACKGROUND: Mental fatigue is a common and potentially debilitating state that can affect individuals\' health and quality of life. In some cases, its manifestation can precede or mask early signs of other serious mental or physiological conditions. Detecting and assessing mental fatigue can be challenging nowadays as it relies on self-evaluation and rating questionnaires, which are highly influenced by subjective bias. Introducing more objective, quantitative, and sensitive methods to characterize mental fatigue could be critical to improve its management and the understanding of its connection to other clinical conditions.
OBJECTIVE: This paper aimed to study the feasibility of using keystroke biometrics for mental fatigue detection during natural typing. As typing involves multiple motor and cognitive processes that are affected by mental fatigue, our hypothesis was that the information captured in keystroke dynamics can offer an interesting mean to characterize users\' mental fatigue in a real-world setting.
METHODS: We apply domain transformation techniques to adapt and transform TypeNet, a state-of-the-art deep neural network, originally intended for user authentication, to generate a network optimized for the fatigue detection task. All experiments were conducted using 3 keystroke databases that comprise different contexts and data collection protocols.
RESULTS: Our preliminary results showed area under the curve performances ranging between 72.2% and 80% for fatigue versus rested sample classification, which is aligned with previously published models on daily alertness and circadian cycles. This demonstrates the potential of our proposed system to characterize mental fatigue fluctuations via natural typing patterns. Finally, we studied the performance of an active detection approach that leverages the continuous nature of keystroke biometric patterns for the assessment of users\' fatigue in real time.
CONCLUSIONS: Our results suggest that the psychomotor patterns that characterize mental fatigue manifest during natural typing, which can be quantified via automated analysis of users\' daily interaction with their device. These findings represent a step towards the development of a more objective, accessible, and transparent solution to monitor mental fatigue in a real-world environment.