Analysis of Keystroke Dynamics for Fatigue Recognition

Ulinskas, Mindaugas; Woźniak, Marcin; Damaševičius, Robertas

doi:10.1007/978-3-319-62404-4_18

Cited by 13 publications

(8 citation statements)

References 26 publications

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“…Fourth, the analyses in this paper did not account for changes in the writing processes over the course of the task. However, it could be that participants’ typing and/or pausing behavior changed over time because of factors such as fatigue or stress, due to a learning effect, or because they adapted their strategies to changes in the task situation ( Rijlaarsdam and Van den Bergh, 1996 ; Vizer et al, 2009 ; Ulinskas et al, 2017 ; Xu, 2018 ). To take into account such fluctuations, future studies could treat the writing processes as time series in their analyses ( Allen et al, 2016 ; Pham, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Cognitive Writing Process Characteristics in Alzheimer’s Disease

et al. 2022

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In this article, we explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). To this end, we examined the extent to which overall writing process measures and pausing behavior during writing differed between 15 cognitively impaired patients and 15 age- and gender-matched healthy controls. Participants completed two typed picture description tasks that were registered with Inputlog, a keystroke logging program that captures keyboard activity during text production. The following variables were analyzed with mixed-effects models: time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceding specific word categories was analyzed. Results showed a main effect of group on all variables. In addition, for pause time between words a main effect of part-of-speech was found as well. Results indicate that writing process analysis can possibly serve as a supplementary tool for the screening and follow-up of AD.

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Section: Discussionmentioning

confidence: 99%

Cognitive Writing Process Characteristics in Alzheimer’s Disease

et al. 2022

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“…In another paper, Ulinskas et al [15] used an existing keystroke dynamics dataset that came from recordings from 53 people typing the same password, in order to recognize user fatigue. From the data, they extracted as features the keystroke durations and all the different forms of diagram latencies.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

2021

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Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers.

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“…In a study by Epp et al [8], besides various time intervals, the number of errors, the number of keystrokes, and the number of characters were also used as the original features, from which the feature subset was selected by the feature selection approach. With some statistical values (e.g., average, equation, skewness, autocorrelation, and moment) and information measurements (e.g., entropy) of the time interval as the features, Ulinskas et al [9] applied a feature selection approach to select a feature subset from them. Based on fuzzy logic, de ru and Eloff [10] divided the time interval into four categories, that is, very short, short, moderately short, and somewhat short, as the characteristics of keystrokes.…”

Section: Research Backgroundmentioning

confidence: 99%

Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process

Zhang

2021

Mathematical Problems in Engineering

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In this paper, we propose a new method of extracting the features of keystrokes. The Hawkes process based on exponential excitation kernel was used to model the sequence of keystrokes in fixed text, and the intensity function vector and adjacency matrix of the model obtained through training were regarded as the characteristics of the keystrokes. A visual analysis was carried out on the CMU keystroke raw data and the feature data extracted using the proposed method. We used one-class classifier to compare the classification effect of CMU keystroke raw data and the feature data extracted by the Hawkes process model and POHMM model. The experimental results show that the feature data extracted using the proposed method contains rich information to distinguish users. In addition, the feature data extracted using the proposed method has a slightly better classification performance than the original CMU keystroke data for some users who are not easy to distinguish.

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Analysis of Keystroke Dynamics for Fatigue Recognition

Cited by 13 publications

References 26 publications

Cognitive Writing Process Characteristics in Alzheimer’s Disease

Cognitive Writing Process Characteristics in Alzheimer’s Disease

Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes

Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process

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