@ARTICLE{Katerina2018-ch,
  title     = "Mouse behavioral patterns and keystroke dynamics in {End-User}
               Development: What can they tell us about users' behavioral
               attributes?",
  author    = "Katerina, Tzafilkou and Nicolaos, Protogeros",
  abstract  = "Studying human behavior is of particular interest within the
               field of Human-Computer Interaction (HCI) as it can provide
               insight into human performance. Prior HCI research suggests that
               mouse and keyboard monitoring may provide a more complete
               picture of user behavior under high cognitive loads like
               decision making and developing tasks. In this exploratory study
               we investigate the potential correlation between mouse
               behavioral patterns or keystroke dynamics and a set of End-User
               Development (EUD) behavioral attributes. We conduct a field test
               on 30 end-users interacting with a modern web-based EUD tool for
               the construction of simple web forms. Our findings reveal the
               existence of several significant correlations between end-users'
               behavioral attributes and mouse pattern metrics or keystroke
               dynamics during the development process. Mouse pattern metrics
               like random and straight movements, mouse hovers, etc., can be
               associated with perceived ease use, perceived usefulness,
               self-efficacy, willingness to learn or risk-perception.
               Similarly, some keystroke dynamics like key press speed and
               down-to-down time can be associated with perceived ease of use
               or self-efficacy. The findings of this work show a new
               interesting research direction and may motivate the EUD research
               community to study further the end-users' mouse and keyboard
               behavior in today's web-based EUD systems.",
  journal   = "Comput. Human Behav.",
  publisher = "Elsevier BV",
  volume    =  83,
  pages     = "288--305",
  month     =  jun,
  year      =  2018,
  language  = "en",
  url       = "https://doi.org/10.1016/j.chb.2018.02.012",
}

@INCOLLECTION{Cepeda2018-km,
  title     = "Mouse tracking measures and movement patterns with application
               for online surveys",
  booktitle = "Lecture Notes in Computer Science",
  author    = "Cepeda, Catia and Rodrigues, Joao and Dias, Maria Camila and
               Oliveira, Diogo and Rindlisbacher, Dina and Cheetham, Marcus and
               Gamboa, Hugo",
  publisher = "Springer International Publishing",
  pages     = "28--42",
  series    = "Lecture notes in computer science",
  year      =  2018,
  address   = "Cham",
  copyright = "https://dl.acm.org/doi/10.1007/978-3-319-99740-7_3",
  url       = "https://dl.acm.org/doi/10.1007/978-3-319-99740-7_3",
}

@INCOLLECTION{Kieslich2019-mt,
  title     = "Mouse-Tracking: A Practical Guide to Implementation and Analysis",
  booktitle = "A Handbook of Process Tracing Methods",
  author    = "Kieslich, Pascal J. and Henninger, Felix and Wulff, Dirk U. and Haslbeck, Jonas M. B. and Schulte-Mecklenbeck, Michael",
  publisher = "Routledge",
  edition   = "2nd Edition",
  pages     = "20",
  year      = 2019,
  url       = "https://www.taylorfrancis.com/chapters/edit/10.4324/9781315160559-9/mouse-tracking-pascal-kieslich-felix-henninger-dirk-wulff-jonas-haslbeck-michael-schulte-mecklenbeck",
}

@INPROCEEDINGS{Rhim2023-uz,
  title      = "Using geometric features of drag-and-drop trajectories to
                understand students' learning",
  booktitle  = "Proceedings of the 2023 {CHI} Conference on Human Factors in
                Computing Systems",
  author     = "Rhim, Jungwook and Kim, Jiwon and Gweon, Gahgene",
  publisher  = "ACM",
  volume     =  19,
  pages      = "1--14",
  month      =  apr,
  year       =  2023,
  address    = "New York, NY, USA",
  conference = "CHI '23: CHI Conference on Human Factors in Computing Systems",
  location   = "Hamburg Germany",
  url       = "https://dl.acm.org/doi/10.1145/3544548.3581143",
}

@ARTICLE{OReilly2011-dw,
  title     = "Can computer mice be used as low-cost devices for the
               acquisition of planar human movement velocity signals?",
  author    = "O'Reilly, Christian and Plamondon, R{\'e}jean",
  abstract  = "The main goal of this work is to determine whether a computer
               mouse can be used as a low-cost device for the acquisition of
               two-dimensional human movement velocity signals in the context
               of psychophysical studies and biomedical applications. A
               comprehensive overview of the related literature is presented,
               and the problem of characterizing mouse movement acquisition is
               analyzed and discussed. Then, the quality of velocity signals
               acquired with this kind of device is measured on horizontal
               oscillatory movements by comparing the mouse data to the signals
               acquired simultaneously by a video motion tracking system and a
               digitizing tablet. A synthesis of the information gathered in
               this work indicates that the computer mouse can be used for the
               reliable acquisition of biosignals in the context of human
               movement studies, particularly for many applications dealing
               with the velocity of the end effector of the upper limb. This
               paper concludes by discussing the possibilities and limitations
               of such use.",
  journal   = "Behav. Res. Methods",
  publisher = "Springer Science and Business Media LLC",
  volume    =  43,
  number    =  1,
  pages     = "229--238",
  month     =  mar,
  year      =  2011,
  language  = "en",
  url       = "https://link.springer.com/article/10.3758/s13428-010-0030-3#notes",
}


@ARTICLE{Seelye2015-yx,
  title     = "Computer mouse movement patterns: A potential marker of mild
               cognitive impairment",
  author    = "Seelye, Adriana and Hagler, Stuart and Mattek, Nora and
               Howieson, Diane B and Wild, Katherine and Dodge, Hiroko H and
               Kaye, Jeffrey A",
  abstract  = "INTRODUCTION: Subtle changes in cognitively demanding activities
               occur in MCI but are difficult to assess with conventional
               methods. In an exploratory study, we examined whether patterns
               of computer mouse movements obtained from routine home computer
               use discriminated between older adults with and without MCI.
               METHODS: Participants were 42 cognitively intact and 20 older
               adults with MCI enrolled in a longitudinal study of in-home
               monitoring technologies. Mouse pointer movement variables were
               computed during one week of routine home computer use using
               algorithms that identified and characterized mouse movements
               within each computer use session. RESULTS: MCI was associated
               with making significantly fewer total mouse moves (p<.01), and
               making mouse movements that were more variable, less efficient,
               and with longer pauses between movements (p<.05). Mouse movement
               measures were significantly associated with several cognitive
               domains (p's<.01-.05). DISCUSSION: Remotely monitored computer
               mouse movement patterns are a potential early marker of
               real-world cognitive changes in MCI.",
  journal   = "Alzheimers Dement. (Amst.)",
  publisher = "Wiley",
  volume    =  1,
  number    =  4,
  pages     = "472--480",
  month     =  dec,
  year      =  2015,
  keywords  = "aging; cognitive assessment; computer use; early detection of
               cognitive decline; ecological validity; everyday functioning;
               functional assessment; instrumental activities of daily living
               (IADLs); mild cognitive impairment; remote monitoring;
               technology",
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en",
  url = "https://alz-journals.onlinelibrary.wiley.com/doi/10.1016/j.dadm.2015.09.006"
}

@ARTICLE{Pariente-Martinez2016-te,
  title     = "Measuring the role of age in user performance during interaction
               with computers",
  author    = "Pariente-Martinez, Beatriz and Gonzalez-Rodriguez, Martin and
               Fernandez-Lanvin, Daniel and De Andres-Suarez, Javier",
  journal   = "Univers. Access Inf. Soc.",
  publisher = "Springer Science and Business Media LLC",
  volume    =  15,
  number    =  2,
  pages     = "237--247",
  month     =  jun,
  year      =  2016,
  language  = "en",
  url= "https://link.springer.com/article/10.1007/s10209-014-0388-6"
}

@ARTICLE{Monrose2000-oc,
  title     = "Keystroke dynamics as a biometric for authentication",
  author    = "Monrose, Fabian and Rubin, Aviel D",
  abstract  = "More than ever before the Internet is changing computing as we
               know it. Global access to information and resources is becoming
               an integral part of nearly every aspect of our lives.
               Unfortunately, with this global network access comes increased
               chances of malicious attack and intrusion. In an effort to
               confront the new threats unveiled by the networking revolution
               of the past few years reliable, rapid, and unintrusive means for
               automatically recognizing the identity of individuals are now
               being sought. In this paper we examine an emerging non-static
               biometric technique that aims to identify users based on
               analyzing habitual rhythm patterns in the way they type.",
  journal   = "Future Gener. Comput. Syst.",
  publisher = "Elsevier BV",
  volume    =  16,
  number    =  4,
  pages     = "351--359",
  month     =  feb,
  year      =  2000,
  copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  language  = "en"
}

@INPROCEEDINGS{Epp2011-rj,
  title           = "Identifying emotional states using keystroke dynamics",
  booktitle       = "Proceedings of the {SIGCHI} Conference on Human Factors in
                     Computing Systems",
  author          = "Epp, Clayton and Lippold, Michael and Mandryk, Regan L",
  abstract        = "The ability to recognize emotions is an important part of
                     building intelligent computers. Emotionally-aware systems
                     would have a rich context from which to make appropriate
                     decisions about how to interact with the user or adapt
                     their system response. There are two main problems with
                     current system approaches for identifying emotions that
                     limit their applicability: they can be invasive and can
                     require costly equipment. Our solution is to determine
                     user emotion by analyzing the rhythm of their typing
                     patterns on a standard keyboard. We conducted a field
                     study where we collected participants' keystrokes and
                     their emotional states via self-reports. From this data,
                     we extracted keystroke features, and created classifiers
                     for 15 emotional states. Our top results include 2-level
                     classifiers for confidence, hesitance, nervousness,
                     relaxation, sadness, and tiredness with accuracies ranging
                     from 77 to 88\%. In addition, we show promise for anger
                     and excitement, with accuracies of 84\%.",
  publisher       = "ACM",
  month           =  may,
  year            =  2011,
  address         = "New York, NY, USA",
  conference      = "CHI '11: CHI Conference on Human Factors in Computing
                     Systems",
  location        = "Vancouver BC Canada"
}

@ARTICLE{Khanna2010-gn,
  title     = "Recognising emotions from keyboard stroke pattern",
  author    = "Khanna, Preeti and Sasikumar, M",
  abstract  = "In day to day life, emotions are becoming an important tool
               which helps to take not only the decisions but also to enhance
               learning, creative thinking and to effectively correspond in the
               social interaction. Several studies have been conducted
               comprising of classical human human interaction and human
               computer interaction. They concluded that for intelligent
               interaction, emotions play an important role. By embedding the
               emotions in the interaction of human with machine, machine would
               be in a position to sense the mood of the user and change its
               interaction accordingly. Hence the system will be friendlier to
               the user and its responses will be more similar to human
               behaviour. In general, human beings make use of emotions through
               speech, facial expression and gestures for conveying the crucial
               information. This paper presents an attempt to recognize
               selected emotion categories from keyboard stroke pattern. The
               emotional categories considered for our analysis are neutral,
               positive and negative. We have used various classifiers like
               Simple Logistics, SMO, Multilayer Perceptron, Random Tree, J48
               and BF Tree, which is a part of WEKA tool, to analyse the
               selected features from keyboard stroke pattern.",
  journal   = "Int. J. Comput. Appl.",
  publisher = "Foundation of Computer Science",
  volume    =  11,
  number    =  9,
  pages     = "1--5",
  month     =  dec,
  year      =  2010
}

@ARTICLE{Nahin2014-yj,
  title     = "Identifying emotion by keystroke dynamics and text pattern
               analysis",
  author    = "Nahin, A F M Nazmul Haque and Alam, Jawad Mohammad and Mahmud,
               Hasan and Hasan, Kamrul",
  abstract  = "Emotion is a cognitive process and is one of the important
               characteristics of human beings that makes them different from
               machines. Traditionally, interactions between humans and
               machines like computers do not exhibit any emotional exchanges.
               If we could build any system that is intelligent enough to
               interact with humans that involves emotions, that is, it can
               detect user emotions and change its behaviour accordingly, then
               using machines could be more effective and friendly. Many
               approaches have been taken to detect user emotions. Affective
               computing is the field that detects user emotion in a particular
               moment. Our approach in this paper is to detect user emotions by
               analysing the keyboard typing patterns of the user and the type
               of texts (words, sentences) typed by them. This combined
               analysis gives us a promising result showing a substantial
               number of emotional states detected from user input. Several
               machine learning algorithms were used to analyse keystroke
               timing attributes and text pattern. We have chosen keystroke
               because it is the cheapest and most available medium to interact
               with computers. We have considered seven emotional classes for
               classifying the emotional states. For text pattern analysis, we
               have used vector space model with Jaccard similarity method to
               classify free-text input. Our combined approach showed above
               80\% accuracies in identifying emotions.",
  journal   = "Behav. Inf. Technol.",
  publisher = "Informa UK Limited",
  volume    =  33,
  number    =  9,
  pages     = "987--996",
  month     =  sep,
  year      =  2014,
  language  = "en"
}

@INPROCEEDINGS{Khan2008-is,
  title           = "Measuring personality from keyboard and mouse use",
  booktitle       = "Proceedings of the 15th European conference on Cognitive
                     ergonomics: the ergonomics of cool interaction",
  author          = "Khan, Iftikhar Ahmed and Brinkman, Willem-Paul and Fine,
                     Nick and Hierons, Robert M",
  abstract        = "Motivation -- To measure computer users' personality from
                     their use of keyboard and mouse. Research approach -- Two
                     explorative studies were conducted. In first study a
                     background application was executed on 20 participants'
                     computers to record keys pressed and mouse clicks on an
                     average of eight days. In a second Study 15 participants'
                     completed a programming task in an hour while a background
                     application recorded keys pressed and mouse clicks.
                     Participants were asked to complete the short form of
                     IPIP-NEO personality inventory afterwards. Pearson
                     correlation analysis was done between participants'
                     behaviour on keyboard, mouse events and personality
                     ratings. Findings/Design -- The results suggest that some
                     of the main traits and sub traits of personality can be
                     measured from keyboard and mouse use. Significant
                     correlations were found between personality main traits
                     and sub traits and the use of keyboard and mouse. The
                     maximum and minimum significant correlations were r (20) =
                     0.62, p <= 0.01 and r (20) = 0.40, p <= 0.05 respectively
                     in first study and were r (15) = 0.7, p < 0.01 and r (15)
                     = 0.51, p <= 0.05 respectively in the second study.
                     Personality trait activity level was found to be
                     significantly correlated with behavioural measure
                     'standard deviation of average time between events' in
                     both the studies with r (20) = 0.54, p <= 0.05 and r (15)
                     = 0.58, p <= 0.05 respectively Research
                     limitations/Implications -- The group of participants in
                     both studies was relatively small. There was about 40\%
                     overlap of participants in the studies. Originality/Value
                     -- Relatively little research has focussed on personality
                     and behaviour and specifically on the measurement of
                     personality from the user behaviour on keyboard and mouse.
                     Take away message -- It might be possible to measure
                     computer users' personality from their use of keyboard and
                     mouse.",
  publisher       = "ACM",
  month           =  jan,
  year            =  2008,
  address         = "New York, NY, USA",
  conference      = "ECCE08: European Conference on Cognitive Ergonomics",
  location        = "Funchal Portugal"
}

@ARTICLE{Vizer2009-sg,
  title     = "Automated stress detection using keystroke and linguistic
               features: An exploratory study",
  author    = "Vizer, Lisa M and Zhou, Lina and Sears, Andrew",
  abstract  = "Monitoring of cognitive and physical function is central to the
               care of people with or at risk for various health conditions,
               but existing solutions rely on intrusive methods that are
               inadequate for continuous tracking. Less intrusive techniques
               that facilitate more accurate and frequent monitoring of the
               status of cognitive or physical function become increasingly
               desirable as the population ages and lifespan increases. Since
               the number of seniors using computers continues to grow
               dramatically, a method that exploits normal daily computer
               interactions is attractive. This research explores the
               possibility of detecting cognitive and physical stress by
               monitoring keyboard interactions with the eventual goal of
               detecting acute or gradual changes in cognitive and physical
               function. Researchers have already attributed a certain amount
               of variability and ``drift'' in an individual's typing pattern
               to situational factors as well as stress, but this phenomenon
               has not been explored adequately. In an attempt to detect
               changes in typing associated with stress, this research analyzes
               keystroke and linguistic features of spontaneously generated
               text. Results show that it is possible to classify cognitive and
               physical stress conditions relative to non-stress conditions
               based on keystroke and linguistic features with accuracy rates
               comparable to those currently obtained using affective computing
               methods. The proposed approach is attractive because it requires
               no additional hardware, is unobtrusive, is adaptable to
               individual users, and is of very low cost. This research
               demonstrates the potential of exploiting continuous monitoring
               of keyboard interactions to support the early detection of
               changes in cognitive and physical function.",
  journal   = "Int. J. Hum. Comput. Stud.",
  publisher = "Elsevier BV",
  volume    =  67,
  number    =  10,
  pages     = "870--886",
  month     =  oct,
  year      =  2009,
  language  = "en"
}


@phdthesis{Dijkstra_2013, title={The diagnosis of self-efficacy using mouse and keyboard input}, author={Dijkstra, Maarten}, year={2013}, school={Utrecht University} } 
