JMIR Publications

ABSTRACT

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. Today, detecting and assessing mental fatigue can be challenging as it relies on self-evaluation and rating questionnaires, which is 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:

In this paper we study the feasibility of employing 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 is 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 neuronal network, originally intended for user authentication, to generate a network optimized for the fatigue detection task. All experiments were conducted using three keystroke databases that comprise different contexts and data collection protocols.

Results:

Our preliminary results showed performances ranging between 72.2% and 80.0% 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 in real time of users’ fatigue.

Conclusions:

Our results suggest that the psychomotor patterns that characterize mental fatigue manifest during natural typing, which can be quantified via automate 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.