Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Yamauchi, Takashi

doi:10.1109/acii.2013.72

Cited by 35 publications

(22 citation statements)

References 31 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found no works that investigate implicit interaction with mediated sensing, in any pose. Works that use mediated sensing to analyse human behaviour typically aim at understanding usage patterns or emotions, for example, by looking at the trajectory of the mouse [Yamauchi 2013] or how users press the buttons on a gamepad [Sykes and Brown 2003]. Therefore, we attribute the lack of studies of implicit user behaviour when interacting with foot-operated devices to the limited number of use cases for such input devices and the small populations to which they are targeted.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

The Feet in Human--Computer Interaction

et al. 2015

View full text Add to dashboard Cite

Foot-operated computer interfaces have been studied since the inception of Human-Computer Interaction. Thanks to the miniaturisation and decreasing cost of sensing technology, there is an increasing interest exploring this alternative input modality, but no comprehensive overview of its research landscape. In this survey, we review the literature on interfaces operated by the lower limbs. We investigate the characteristics of users and how they affect the design of such interfaces. Next, we describe and analyse foot-based research prototypes and commercial systems in how they capture input and provide feedback. We then analyse the interactions between users and systems from the perspective of the actions performed in these interactions. Finally, we discuss our findings and use them to identify open questions and directions for future research.

show abstract

Section: Discussion and Future Directionsmentioning

confidence: 99%

The Feet in Human--Computer Interaction

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Mouse movement features, such as the area under the curve or the deviation from the straight line between the start and end point, were found to be indicative of perceptual and numerical judgment ( Song and Nakayama, 2008 ; Chapman et al, 2010 ; Xiao and Yamauchi, 2014 , Yamauchi and Xiao, 2017 ), semantic categorization ( Dale et al, 2007 ), linguistic judgment ( Spivey et al, 2005 ; Farmer et al, 2007 ), and racial and gender judgment of morphed face pictures ( Freeman and Ambady, 2009 ; Freeman et al, 2010 ). Additionally, mouse movement has been found to be related to attitudinal ambivalence toward certain topics (e.g., abortion) ( Wojnowicz et al, 2009 ; Schneider et al, 2015 ), uncertainty in economic choices ( Calluso et al, 2015 ) as well as general emotional states, such as anxiety ( Yamauchi, 2013 ; Yamauchi and Xiao, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Augmented Go/No-Go Task: Mouse Cursor Motion Measures Improve ADHD Symptom Assessment in Healthy College Students

Leontyev¹,

Sun²,

Wolfe³

et al. 2018

Front. Psychol.

Self Cite

View full text Add to dashboard Cite

Attention deficit/hyperactivity disorder (ADHD) is frequently characterized as a disorder of executive function (EF). However, behavioral tests of EF, such as go/No-go tasks, often fail to grasp the deficiency in EF revealed by questionnaire-based measures. This inability is usually attributed to questionnaires and behavioral tasks assessing different constructs of EFs. We propose an additional explanation for this discrepancy. We hypothesize that this problem stems from the lack of dynamic assessment of decision-making (e.g., continuous monitoring of motor behavior such as velocity and acceleration in choice reaching) in classical versions of behavioral tasks. We test this hypothesis by introducing dynamic assessment in the form of mouse motion in a go/No-go task. Our results indicate that, among healthy college students, self-report measures of ADHD symptoms become strongly associated with performance in behavioral tasks when continuous assessment (e.g., acceleration in the mouse-cursor motion) is introduced.

show abstract

“…Mouse is another common input device and mouse features are also effective in emotion recognition whether used separately or in combination with keystroke features [4,84]. Depending on the types of mouse events, mouse features can be divided into three categories: click related features (M1~M6), movement related (M7~M19) features, and other (M20, M21) features [45,85]. The most used mouse features are summarized in Table Ⅳ.…”

Section: B: Mouse Featuresmentioning

confidence: 99%

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

Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Cited by 35 publications

References 31 publications

The Feet in Human--Computer Interaction

The Feet in Human--Computer Interaction

Augmented Go/No-Go Task: Mouse Cursor Motion Measures Improve ADHD Symptom Assessment in Healthy College Students

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

Contact Info

Product

Resources

About