Emotion recognition and its application in software engineering

Kołakowska, Agata; Landowska, Agnieszka; Szwoch, Mariusz; Szwoch, Wioleta; Wróbel, Michał R.

doi:10.1109/hsi.2013.6577877

Cited by 68 publications

(32 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More recently, there have been efforts to detect time pressure with sentiment analysis and various sensors. Kołakowska et al [2013] introduced a multi-modal emotion recognition application, which combined physiological, video, and depth sensors, to train a classifier to be used with several software engineering methods. The motivation for the work in part came from future work of detecting stress induced by time pressure and an investigation of productivity and emotions.…”

Section: Metrics and Operationalizationmentioning

confidence: 99%

Time pressure in software engineering: A systematic review

Kuutila

Mäntylä

Farooq

et al. 2020

Information and Software Technology

View full text Add to dashboard Cite

Large project overruns and overtime work have been reported in the software industry. Experiments and case studies have investigated the relationship between time pressure and software quality and productivity. Our search strategy examined 5,332 papers and identified 88 papers as having relevant contributions related to time pressure in a software engineering. Our review investigated definitions, metrics, and causes of time pressure. Also, we map the papers to process phases and approaches. Last, we summarize the effects of time pressure on quality and productivity. The majority of the reported results support the outcome of reduced quality and increased productivity with time pressure.

show abstract

Section: Metrics and Operationalizationmentioning

confidence: 99%

Time pressure in software engineering: A systematic review

Kuutila

Mäntylä

Farooq

et al. 2020

Information and Software Technology

View full text Add to dashboard Cite

show abstract

“…Emotion recognition is a topic of broad and current interest, useful for many applications such as advertising [27] or psychological disorders understanding [35]. It is also a topic of importance for research in other areas e.g., video summarization [37] or face normalization (expression removal).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Temporal multimodal fusion for video emotion classification in the wild

Vielzeuf

Pateux

Jurie

2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

146

109

View full text Add to dashboard Cite

This paper addresses the question of emotion classification. The task consists in predicting emotion labels (taken among a set of possible labels) best describing the emotions contained in short video clips. Building on a standard framework -lying in describing videos by audio and visual features used by a supervised classifier to infer the labels -this paper investigates several novel directions. First of all, improved face descriptors based on 2D and 3D Convolutional Neural Networks are proposed. Second, the paper explores several fusion methods, temporal and multimodal, including a novel hierarchical method combining features and scores. In addition, we carefully reviewed the different stages of the pipeline and designed a CNN architecture adapted to the task; this is important as the size of the training set is small compared to the difficulty of the problem, making generalization difficult. The so-obtained model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of 58.8 %.

show abstract

“…The emotion monitoring stand is planned to be used in quantification of many observations from the human-computer interaction [25][26].…”

Section: Discussionmentioning

confidence: 99%

Emotion Monitoring – Verification of Physiological Characteristics Measurement Procedures

Landowska¹

2014

Metrology and Measurement Systems

Self Cite

View full text Add to dashboard Cite

This paper concerns measurement procedures on an emotion monitoring stand designed for tracking human emotions in the Human-Computer Interaction with physiological characteristics. The paper addresses the key problem of physiological measurements being disturbed by a motion typical for human-computer interaction such as keyboard typing or mouse movements. An original experiment is described, that aimed at practical evaluation of measurement procedures performed at the emotion monitoring stand constructed at GUT. Different locations of sensors were considered and evaluated for suitability and measurement precision in the HumanComputer Interaction monitoring. Alternative locations (ear lobes and forearms) for skin conductance, blood volume pulse and temperature sensors were proposed and verified. Alternative locations proved correlation with traditional locations as well as lower sensitiveness to movements like typing or mouse moving, therefore they can make a better solution for monitoring the Human-Computer Interaction.

show abstract

Emotion recognition and its application in software engineering

Cited by 68 publications

References 40 publications

Time pressure in software engineering: A systematic review

Time pressure in software engineering: A systematic review

Temporal multimodal fusion for video emotion classification in the wild

Emotion Monitoring – Verification of Physiological Characteristics Measurement Procedures

Contact Info

Product

Resources

About