Web-based Biometric Computer Mouse Advisory System to Analyze a User's Emotions and Work Productivity

Kaklauskas, Artūras; Zavadskas, Edmundas K.; Seniut, Mark; Dzemyda, Gintautas; Stankevič, Voitech; Šimkevičius, Č.; Stankevič, Tomaš; Paliskiene, Rasa; Matuliauskaitė, Agnė; Kildienė, Simona; Bartkiene, Lina; Ivanikovas, Sergėjus; Gribniak, Viktor

doi:10.1016/j.engappai.2011.04.006

Cited by 64 publications

(24 citation statements)

References 48 publications

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“…However, there is an ambiguity concerning the impact of stress on skin temperature. Some studies confirm that skin temperature rises in the presence of stress [36], while other studies find that skin temperature decreases under stress [1,10,37,38].…”

Section: Related Workmentioning

confidence: 99%

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Kyriakou

Resch

Sagl

et al. 2019

Sensors

161

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There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

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Section: Related Workmentioning

confidence: 99%

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Kyriakou

Resch

Sagl

et al. 2019

Sensors

161

View full text Add to dashboard Cite

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“…Mice (touch pads), keyboards and touch screen monitors are the common input devices. At least one of these devices is available and accessible on all computers and digital systems [48][49][50].…”

Section: Common Input Devicesmentioning

confidence: 99%

Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment

Bakhtiyari

Taghavi

Husain

2014

Neural Comput & Applic

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Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction (HCI) more effective. Affective computing is an innovative computational modeling and detecting user's emotions to optimize system responses in HCI. However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real-world applications such as natural language processing and electroencephalography signals. To accomplish a reliable, usable and high-performance system, this paper proposes an intelligent hybrid approach to recognize users' emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by artificial neural network and support vector machine (SVM) techniques. The result shows an increase in accuracy of 6 % (93.20 %) by SVM in comparison with the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modeling and emotional intelligence.

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“…However, it is reasonable to presume that A Y H does not only manifest through facial expressions, like changes in the lip corners and in the muscles near cheeks and eyes. Prior research (Kaklauskas et al, 2011) investigated other possible biometrical manifestations of this emotional construct, as the heart rate acceleration, the skin temperature, and blood pressure growth and the skin conductance change. So, results should be replicated with other types of autonomic measures.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Future-Oriented Happiness: Its Nature and Role in Consumer Decision-Making for New Products

Bettiga

Lamberti

2020

Front. Psychol.

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Cognitive evaluations only partially explain the consumer purchasing patterns, especially when consumers approach a product for the first time. In such an encounter, consumers anticipate the emotions they might experience as a result of their decision, as they cannot realistically evaluate product performances. The work investigates the nature and the influence of these future-oriented emotions, namely anticipated and anticipatory happiness, in the first encounter with new products. Through a first laboratory study, adopting both physiological (micro-facial expressions analysis) and self-reported measures, we confirm the distinction between anticipated and anticipatory happiness. We further show the differential impact of these two emotional constructs on the consumer decision-making process by grounding on the Theory of Planned Behavior (TPB). Through a second study, based on a questionnaire, we further investigate the role of anticipated happiness within the TPB. We show that anticipated happiness is a pervasive emotional construct that influences all stages of the intention formation process. We discuss how these findings enrich existing knowledge on the interplay between cognitive and affective components of the decision-making process for new products. Moreover, we offer a methodological contribution to the use of physiological methods to assess emotions.

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Web-based Biometric Computer Mouse Advisory System to Analyze a User's Emotions and Work Productivity

Cited by 64 publications

References 48 publications

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment

Future-Oriented Happiness: Its Nature and Role in Consumer Decision-Making for New Products

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