Abstract

Acute stress is a short-term cognitive burden. It impacts user engagement and performance during online activities such as meetings, classes, competitive exams, and more. This mental state can be predicted and analysed using Keystroke Dynamics (KD) attributes and the combination of advanced sensory features and Machine Learning (ML) techniques. The purpose of this study is to develop a benchmark dataset incorporating KD and sensory features through a web-based application to analyse acute stress in online settings. The dataset was collected from 103 participants across different demographic groups (e.g., age, gender, and qualification) in two sessions, each involving a minimum of three repetitions in a real-world environment. It includes timing and sensory features, such as gyroscope, accelerometer, and rotational information in various directions, recorded at 2 Hz. In the first session, participants were asked to complete four simple mental math tasks without any induced mental pressure. These samples were labelled as “Calm”. In the second session, the same participants were asked to perform three complex mental math tasks, designed to induce mental pressure, and these samples were labelled as “Stress”. This dataset provides KD patterns in both non-stress and stress conditions, enabling the design of a classification model to detect acute stress in real-time environments. The findings could be applied to implement more advanced online platforms for meetings, learning, and competitive exams.

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