Does Keystroke Dynamics tell us about Emotions? A Systematic Literature Review and Dataset Construction

Maalej, Aicha; Kallel, Ilhem

doi:10.1109/ie49459.2020.9155004

Cited by 18 publications

(19 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used a systematic literature review method [49] to conduct this study. First, we identified six research questions (see Table Ⅰ) and used them as clues to generate literature search terms (See Fig.…”

Section: Methodsmentioning

confidence: 99%

“…For example, about two billion people all over the world now are smart phones users [41]. Adult smartphone users frequently type on touchscreen for communicating with others, surfing the Internet or posting on social media every day [49]. These interactions with computers and smartphones provide rich secondary data sources derived from hand motions for emotion recognition [4], making it a hot topic to study the users' emotional states [49] from KMT dynamics.…”

Section: Critical Analysismentioning

confidence: 99%

“…It can also be used for emotion recognition [4,41,42], which is the focus of this paper. There have been some review papers [43,44,[46][47][48][49] related to emotion recognition from KMT dynamics. Some [43,44,46,47] were published in 2013-2015, reflecting on some earlier research, while the recent paper [48] focused on advances and applications of KMT dynamics, and paper [49] only reviewed the research over the last decade.…”

Section: Introductionmentioning

confidence: 99%

“…There have been some review papers [43,44,[46][47][48][49] related to emotion recognition from KMT dynamics. Some [43,44,46,47] were published in 2013-2015, reflecting on some earlier research, while the recent paper [48] focused on advances and applications of KMT dynamics, and paper [49] only reviewed the research over the last decade. These studies summarized general information about emotion recognition procedures, features, datasets, and classification methods, but did not provide answers to further questions we concerned (See Table I).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

View full text Add to dashboard Cite

Emotion can be defined as a subject's organismic response to an external or internal stimulus event. The responses could be reflected in pattern changes of the subject's facial expression, gesture, gait, eye-movement, physiological signals, speech and voice, keystroke, and mouse dynamics, etc. This suggests that on the one hand emotions can be measured/recognized from the responses, and on the other hand they can be facilitated/regulated by external stimulus events, situation changes or internal motivation changes. It is well-known that emotion has a close relationship with both physical and mental health, usually affecting an individual's and a team's work performance, thus emotion recognition is an important prerequisite for emotion regulation towards better emotional states and work performance. The primary problem in emotion recognition is how to recognize a subject's emotional states easily and accurately. Currently, there are a body of good research on emotion recognition from facial expression, gesture, gait, eye-tracking, and other physiological signals such as speech and voice, but they are all intrusive and obtrusive to some extent. In contrast, keystroke, mouse and touchscreen (KMT) dynamics data can be collected non-intrusively and unobtrusively as secondary data responding to primary physical actions, thus, this paper aims to review the state-of-the-art research on emotion recognition from KMT dynamics and to identify key research challenges, opportunities and a future research roadmap for referencing. In addition, this paper answers the following six research questions (RQs): (1) what are the commonly used emotion elicitation methods and databases for emotion recognition? (2) which emotions could be recognized from KMT dynamics? (3) what key features are most appropriate for recognizing different specific emotions? (4) which classification methods are most effective for specific emotions? ( 5) what are the application trends of emotion recognition from KMT dynamics? (6) which application contexts are of greatest concern?

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Critical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Biometric methods based on keystroke dynamics may be applied in several areas, such as user authentication [ 3 , 4 , 5 , 6 ], emotion recognition [ 1 , 7 , 8 , 9 , 10 , 11 , 12 ], monitoring mood disorders [ 13 , 14 ] and so forth. The proposed solutions are usually based on hand-crafted features extracted on the basis of keystroke timing or frequency characteristics, for example, dwell time, flight time, typing speed, frequency of using selected keys and so forth.…”

Section: Related Workmentioning

confidence: 99%

Keystroke Dynamics Patterns While Writing Positive and Negative Opinions

Kołakowska

Landowska

2021

Sensors

View full text Add to dashboard Cite

This paper deals with analysis of behavioural patterns in human–computer interaction. In the study, keystroke dynamics were analysed while participants were writing positive and negative opinions. A semi-experiment with 50 participants was performed. The participants were asked to recall the most negative and positive learning experiences (subject and teacher) and write an opinion about it. Keystroke dynamics were captured and over 50 diverse features were calculated and checked against the ability to differentiate positive and negative opinions. Moreover, classification of opinions was performed providing accuracy slightly above the random guess level. The second classification approach used self-report labels of pleasure and arousal and showed more accurate results. The study confirmed that it was possible to recognize positive and negative opinions from the keystroke patterns with accuracy above the random guess; however, combination with other modalities might produce more accurate results.

show abstract